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Über dieses Buch

This book constitutes the proceedings of the 9th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2014, held in Wuhan, China, in October 2014. The 109 revised full papers presented were carefully reviewed and selected from 204 submissions. The papers focus on four main topics, namely evolutionary computing, neural computing, DNA computing, and membrane computing.

Inhaltsverzeichnis

Frontmatter

Worst-Case Execution Time Test Generation for Solutions of the Knapsack Problem Using a Genetic Algorithm

Worst-case execution time test generation can be hard if tested programs use complex heuristics. This is especially true in the case of the knapsack problem, which is often called “the easiest NP-complete problem”. For randomly generated test data, the expected running time of some algorithms for this problem is linear. We present an approach for generation of tests against algorithms for the knapsack problem. This approach is based on genetic algorithms. It is evaluated on five algorithms, including one simple branch-and-bound algorithm, two algorithms by David Pisinger and their partial implementations. The results show that the presented approach performs statistically better than generation of random tests belonging to certain classes. Moreover, a class of tests that are especially hard for one of the algorithms was discovered by the genetic algorithm.

Maxim Buzdalov, Anatoly Shalyto

The Application of Spiking Neural P Systems for PLC Programming

In this paper, five basic logic relationships are built by spiking neural P systems (SN P systems, for short). A method that uses spiking neural P systems to build a PLC control system model is proposed and implemented through the PLC programming application of a typical water level control system. Example shows that PLC programming process has been simplified and readability of the program has been enhanced after the introduction of SN P systems. Therefore, the upgraded and maintenance of PLC program can be effectively improved.

Ke Chen, Jun Wang, Zhang Sun, Juan Luo, Tao Liu

Criteria for Nonsingular H −Matrices

In order to achieve further investigation, new extended criteria of nonsingular

H

 −matrix are introduced. In the recent paper, several simple criteria, as well as some necessary conditions for nonsingular

H

 −matrix, have been obtained. Inspired by these results, we partition the row and column index set of square matrix, construct a positive diagonal matrix according to the elements and row sum, column sum of the matrix as well, then obtain a set of criteria for the nonsingular

H

 −matrix, and extend the criteria of nonsingular

H

 −matrix. Finally, a few numerical examples are given to illustrate relative merits of the proposed criteria.

Qi Chen, Min Li

Molecular Dynamic Simulation for Heat Capacity of MgSiO 3 Perovskite

The interaction potential plays an important role in molecular dynamics (MD) simulations of

MgSiO

3

perovskite. A new set of potential parameters is developed by means of combining two fitting potential parameters of previous studies. The constant-pressure heating capacity of

MgSiO

3

are simulated by using the new set of potential parameters. It is shown that the heating capacity of

MgSiO

3

perovskite are close to the experimental data.

Qiong Chen

Molecular Model for Information Addressing and Transporting Based on DNA Nanotechnology

DNA-based information processing includes storage, addressing, transporting and error-correction, etc. Here we describe a feasible model that can extract and transport the information from specified sources to targeted destinations. This model is established based on the DNA nanotechnologies of hybridization, DNA strand displacement and nanoparticle aggregation reactions. By adding the extracting strands, the data strands of specified sources can be extracted, and then transported to the targets by the transporting strands. The result could be detected by observing the change of color through the naked eyes. Theoretical analysis indicates that this model could achieve the parallel information extracting and transporting from different specified sources to different targeted destination, which offers a realistic technology for the flexibility of managing information.

Zhihua Chen, Xiaoli Qiang, Kai Zhang

Real-Coded Genetic Algorithm with Oriented Search towards Promising Region for Parameter Optimization

In this paper, a novel real-coded genetic algorithm is presented to generate offspring towards a promising polygon field with

k

+1 vertexes, which represents a set of promising points in the entire population at a particular generation. A set of 13 test problems available in the global parameter optimization literature is used to test the performance of the proposed real-coded genetic algorithms. Simulations show the proposed approach is a significant evolutionary computing to efficiently solve parameter optimization problems.

Zhiqiang Chen, Yun Jiang, Xudong Chen

A Population-P-Systems-Inspired Membrane Algorithm for Multi-objective Optimization

This paper proposes a Population-P-Systems-inspired Membrane Algorithm (PPSMA) for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups to implement different functions and the communications among cells are performed at two levels in order to obtain well converged and distributed solution set. Moreover, differential evolution is employed as search operator in PPSMA. Twelve multi-objective benchmark problems are utilized to test algorithm performance. Experimental results show that PPSMA performs better than five compared algorithms.

Jixiang Cheng, Gexiang Zhang, Yanhui Qin

The Algorithms and Composition of a Travel System Based on Geographical Information

The purpose of GIS is trying to tell people the environment around them, and providing the needed information to them, and it is widely used in Location Based Service (LBS) applications, and LBS have little processing on the direction information. In this paper, a travel system based on GIS is presented, which has a relationship with the direction and attitude angle information, for more convenient communication in the crowd of the tourists. People can see the doodles which are created by the man who was in the same place before through our system. The algorithms of the system are discussed, one algorithm is used for matching the best data from the request of users, which is based on

k-Nearest Neighbor (KNN)

algorithm, and the other one is supported by the concept of spherical coordinate system. The simulation results of the algorithms are presented, and show that the algorithms are reasonable and acceptable in the environment of the system.

Wen Cheng, Zheng Zhang

The Modular Inversion in GF(p) by Self-assembling and Its Application to Elliptic Curve Diffie-Hellman Key Exchange

The study of tile self-assembly model shows the development of self-assembling systems for solving complex computational problems. In this paper, we show the method of performing modular inversion in

GF

(

p

) by self-assembling with Θ(

p

) computational tile types in Θ(

p

) steps. Then, we discuss how the self-assembling systems for computing modular inversion in

GF

(

p

) apply to elliptic curve Diffie-Hellman key exchange algorithm. The self-assembled architectures provide the feasibility of cryptanalysis for this algorithm.

Zhen Cheng, Yufang Huang

Using Support Vector Machine to Identify Imaging Biomarkers of Major Depressive Disorder and Anxious Depression

Comorbidity with anxiety disorders is a relatively common occurrence in major depressive disorder. However, there are no objective, neurological markers which can be used to identify depressive disorder with and without anxiety disorders. The aim of this study was to examine the diagnostic value of structural MRI to distinguish depressive patients with and without ss using support vector machine. In this paper, we applied voxel-based morphometry of gray matter volume (GMV), then choose discriminative features to classify different group using linear support vector machine (SVM) classifier. The experimental results showed that specific structural brain regions may be a potential biomarkers for disease diagnosis.

Minyue Chi, Shengwen Guo, Yuping Ning, Jie Li, Haochen Qi, Minjian Gao, Jiexin Wang, Xiaowei Hu, Yangbo Guo, Yuling Yang, Hongjun Peng, Kai Wu

An Adaptive Differential Evolution Algorithm with Automatic Population Resizing for Global Numerical Optimization

An adaptive population resizing algorithm is presented. To improve the performance of DE, an adaptive population size algorithm that makes a balance between exploration-exploitation properties is required. Although adjusting population size is important, many researchers have not focused on this topic. The proposed algorithm calculates the deviation of the dispersed individuals in every certain evaluation counters and executes adjusting the population size based on this information. Therefore, the proposed algorithm can adapt the population size by including or excluding some individuals depending on the progress. The performance evaluation results showed that the proposed algorithm was better than standard DE algorithm.

Tae Jong Choi, Chang Wook Ahn

An Improved Method of DNA Information Encryption

A scheme utilizing DNA technology for the purposes of DNA cryptography has been developed. To enhance the security of DNA cryptosystem, we used DNA digital encoding, complementary rules and biotechnological methods in this scheme. The experimental result showed that the scheme is feasible and the security analysis indicated that the scheme has a strong confidentiality.

Guangzhao Cui, Dong Han, Yan Wang, Yanfeng Wang, Zicheng Wang

An Encryption Scheme Based on DNA Microdots Technology

As a new research area of information security, DNA cryptography has achieved rapid advancements over recent years. It has been demonstrated that data hiding and encryption can be realized based on DNA digital encoding and microdots steganographic techniques. Here we present a more secure encryption scheme utilizing DNA microdots. It is noteworthy that the scheme achieves double concealing, which combines the properties of traditional cryptography and DNA microdots. The security of the scheme is analyzed from several aspects as well. Finally, the simulation proved a strong confidentiality of the encryption scheme.

Guangzhao Cui, Yan Wang, Dong Han, Yanfeng Wang, Zicheng Wang, Yanmin Wu

Simulation DNA Algorithm

Satisfiability problem (SAT) is one of classical combinational problems, which is proved to be a famous NP-complete problem [1]. SAT is widely used [2], [3]. At present, research on algorithm of SAT has made significant progress [4], [5]. Among these algorithms, some are non polynomial even though they can obtain exact solution of SAT; and heuristic algorithm cannot obtain exact solution of SAT, even though it is polynomial. Therefore, it is essential to find an exact algorithm of SAT which is more practical and can effectively the control calculation time.

Peng Dai, Kang Zhou, Zhiwei Wei, Di Hu, Chun Liu

Research on Improved Locally Linear Embedding Algorithm

Locally linear embedding algorithm (LLE) is needed to be improved, since there were redundant information in its low dimensional feature space and no category information of the samples embedded into the low dimensional [1,2]. In this paper, we introduce a local linear maximum dispersion matrix algorithm (FSLLE), which integrated the sample category information. On this basis, the algorithm of locally linear embedding was improved and reexamined from the perspective of uncorrelated statistics. A kind of uncorrelated statistical with maximum dispersion matrix algorithm of locally linear embedding (OFSLLE) is proposed, with the application of elimination of redundant information among base vectors. Our algorithm was verified using the face library, showing that the base vector using uncorrelated constraints can effectively improve the performance of the algorithm and improve the recognition rate.

Tuo Deng, Yanni Deng, Yuan Shi, Xiaoqiu Zhou

An Improved Evolutionary Approach for Association Rules Mining

This paper tackles the association rules mining problem with the evolutionary approach. All previous bio-inspired based association rules mining approaches generate non admissible rules. In this paper, we propose an efficient strategy called delete and decomposition strategy permits to avoid non admissible rules. If an item is appeared in the antecedent and the consequent parts of a given rule, this rule is composed on two admissible rules. Then, we delete such item to the antecedent part of the first rule and we delete the same item to the consequent part of the second rule. We also incorporate the suggested strategy into two evolutionary algorithms (genetic and mimetic algorithms), To demonstrate the suggested approach, several experiments have been carried out using both synthetic and reals instances. The results reveal that it has a compromise between the execution time and the quality of out- put rules. Indeed, the improved genetic algorithm is faster than the improved mimetic algorithm whereas the last one outperforms the genetic algorithm in terms of rules quality.

Youcef Djenouri, Ahcene Bendjoudi, Nadia Nouali-Taboudjemat, Zineb Habbas

Multiple Depots Incomplete Open Vehicle Routing Problem Based on Carbon Tax

Incomplete open vehicle routing problem is proposed and defined, and the multi-depot incomplete open vehicle routing problem with soft time windows based on carbon tax (MDIOVRPSTWCT) is expounded. The quantity of carbon emissions is calculated based on vehicles’ travel distance, furthermore, the MDIOVRPSTWCT is modelled, and the adaptive genetic algorithm, which can dynamically adjust the crossover and mutation probability, is applied to solve the model. After the strategy of multi-depot is reasonably adopted and modern transportation and distribution optimization technology is applied, the computed results of the case show the transportation & distribution companies’ number of vehicles, fuel consumption and vehicle’ carbon emissions are still greater declines even though the carbon tax, and the benefit’s growth and low carbon logistics are realized at the same time.

Fenghua Duan, Xiaonian He

Research on a Class of Nonlinear Matrix Equation

In this paper, the nonlinear matrix equation

$X^{r}+\sum\limits_{i=1}^{m}A_{i}^{\ast}X^{\delta_{i}}A_{i}$

=

Q

is discussed. We propose the Newton iteration method for obtaining the Hermite positive definite solution of this equation. And a numerical example is given to identify the efficiency of the results obtained.

Jiating Fang, Haifeng Sang, Qingchun Li, Bo Wang

On the Convergence of Levenberg-Marquardt Method for Solving Nonlinear Systems

Levenberg-Marquardt (L-M forshort) method is one of the most important methods for solving systems of nonlinear equations. In this paper, we consider the convergence under

$\lambda_{k}=\min(\|F_{k}\|,\|J_{k}^{T}F_{k}\|)$

of L-M method. We will show that if ∥ 

F

(

x

k

) ∥ provides a local error bound, which is weaker than the condition of nonsingularity for the system of nonlinear equations, the sequence generated by the L-M method converges to the point of the solution set quadratically. As well, numerical experiments are reported.

Minglei Fang, Feng Xu, Zhibin Zhu, Lihua Jiang, Xianya Geng

DNA-Chip-Based Information Hiding Scheme

In this paper we present DNA-IH, which is the first DNA-chip-based information hiding scheme. In our scheme, given an ordinary message

M

o

, a secret message

M

s

is incorporated into the microarray

C

which represents the signature of

M

o

. Only the intended recipient can retrieve the secret message

M

s

, while other members can only read the ordinary message

M

o

without knowing whether there exists any other secret message. Conventional information hiding process changes the statistical properties of the original data. The existence of secret messages embedded can be detected using statistical steganalysis schemes. In DNA-IH, for secret message and ordinary message, the corresponding DNA microarrays can be identical, thus statistical steganalysis is no longer able to detect whether or not a given DNA chip contains a secret message.

Xiwen Fang, Xuejia Lai

Locate Multiple Pareto Optima Using a Species-Based Multi-objective Genetic Algorithm

In many real-world multi-objective optimization problems (MOOPs), the decision maker may only concern partial, rather than all, Pareto optima. This requires the solution algorithm to search and locate multiple Pareto optimal solutions simultaneously with higher accuracy and faster speed. To address this requirement, a species-based multi-objective GA (speMOGA) is designed in this paper, where multiple sub-populations would be constructed to search for multiple nondomiated solutions in parallel via decomposing a MOOP into a set of subproblems using the

Tchebycheff

approach. Based on a series of benchmark test problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with two classical multi-objective GAs: MOEA/D and NSGA-II. The experimental results show the validity of the proposed algorithm on locating multiple Pareto optima.

Yaping Fu, Hongfeng Wang, Min Huang

Expert Self-tuning Using Fuzzy Reasoning for PID Controller

This paper introduces a expert PID system utilizing fuzzy inference mechanism by defining TDR (rules degree of trigging) and TDS (targets degree of satisfaction), whose inference rulers are brief. The rules can be trigged simultaneously and even in the case of the failure of reasoning, can also alternate the suboptimal parameters to overcome the general PID expert systems short coming that be fail to settle the optimal parameter. The article makes simulation on a typical plant to verify the effectiveness of this method.

Tao Geng, Yunfei Lv, Yang Liu

Research for Performance Evaluation of University Teachers

Performance evaluation of university teachers is the important part of human resource management in universities. In this paper, fuzzy soft sets theory is adopted to make a comprehensive evaluation on university teachers. In the process of evaluation, each expert has different personal evaluation sets of indexes, which is also allowed overlap among them. Then, fuzzy soft sets theory is used to fuse the evaluating information and obtain the results of comprehensive evaluation. And thus is the key parts for university administrators to improve the teaching staff management, promote teachers’ personal and group development, deepen the reform of educational system and enhance higher education and academic quality.

Haixia Gui

Enterprise Network Sustainability through Bio-inspired Scheme

Sustainability is best observed within bio-organisms, where they undergo spatial and temporal evolution in their gene expressions to sustain. with constantly changing environment. Maintaining sustainability of Enterprise Networks (EN) is a challenging problem, especially in evolving its infrastructure and data system over time. In this paper, we have proposed a custom-made bio-inspired scheme to provide sustainable operations within EN considering the quality-of-service (QoS) to manage the traffic outbursts at the network infrastructure and at data depositories. Our scheme involves simulating EN with various workloads, formulating EN sustainability problem as an optimization problem, and utilizing of a family of bio-inspired algorithms, such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS), to search the sustainability space. Our computational experiments illustrate the feasibility of our scheme on a mid-size EN with 40% reduction in the traffic flow at the backbone and with 13.9% increasing in the storage system throughput.

Sami J. Habib, Paulvanna N. Marimuthu, Tahani H. Hussain

An Edge Detection Algorithm Based on Morphological Gradient and the Otsu Method

An edge detection algorithm based on the morphology gradient is presented to extract the edge pixels of grey-level image and convert the edge image to bi-level image by means of the Otsu method. The experimental results show that the algorithm is more efficient for the images with Gaussian noise or with salt&pepper noise.

Lu Han, Aili Han

Method for Forecasting Medium and Long-Term Power Loads Based on the Chaotic CPSO-GM

To address the shortcomings of traditional grey forecasting model GM (1, 1) in terms of poor forecasting on fast-growing power load, this paper proposes a chaotic co-evolutionary PSO algorithm which has better efficiency than the particle swarm optimization algorithm. Combined with the GM (1, 1) model, a chaotic co-evolutionary particle swarm optimization algorithm has been used to solve the values of two parameters in GM (1, 1) model. In this way, we have designed a CCPSO algorithm-based grey model. Results of case simulation on the power consumption in 3 regions show that the CCPSO-GM model is optimal over the other 4 forecasting models, proving that the CCPSO-GM model has a wide applicable scope and high forecasting accuracy.

Libo Hao, Aijia Ouyang, Libin Liu

Solving the Multiobjective Multiple Traveling Salesmen Problem Using Membrane Algorithm

The multiple traveling salesmen problem (mTSP) is a generalization of the classical traveling salesman problem (TSP). The mTSP is more appropriate for real-life applications than the TSP, however, the mTSP has not received the same amount of attention. Due to the high complexity of the mTSP, a more efficient algorithm proposed for mTSP must be based on a global search procedure. Membrane algorithms are a class of hybrid intelligence algorithms, which has been introduced recently as a global optimization technique. In this work, a new membrane algorithm for solving mTSP with different numbers of salesmen and problem sizes is described. The experiment results are compared with several multiobjective evolutionary strategies.

Juanjuan He

A Novel Method to Derive Ambulatory Blood Pressure from the Radial Pressure Waveform

In this paper, we develop a novel application of independent component analysis (ICA) based auto-regression forecasting model(ICA-ARF). The method can noninvasively, continuously and conveniently derive ambulatory blood pressure (ABP) from the radial artery pressure waveform (RAPWF). To eliminate the effect of correlative factors in measurement, the ICA method is used to decomposite the raw signal and extract the independent component affected by blood pressure (BP). Based on the spectrum density of independent component, an auto-regression forecasting model is set up to derive BP. Experimental results show an excellent correlation and agreement with the BP measured by Omron electronic BP monitor (Type: HEM-7012). If the ICA-ARF is used, the the error can be reduced from 7.4mmHg to 2.55mmHg.

Long He, Zhihua Chen, Zheng Zhang

Association Rule Mining Based on Bat Algorithm

In this paper, we propose a bat-based algorithm (BA) for association rule mining (ARM Bat). Our algorithm aims to maximize the fitness function to generate the best rules in the defined dataset starting from a specific minimum support and minimum confidence. The efficiency of our proposed algorithm is tested on several generic datasets with different number of transactions and items. The results are compared to FPgrowth algorithm results on the same datasets. ARM bat algorithm perform better than the FPgrowth algorithm in term of computation speed and memory usage,

Kamel Eddine Heraguemi, Nadjet Kamel, Habiba Drias

Adaptive Mutation Behavior for Quantum Particle Swarm Optimization

Quantum particle swarm optimization algorithm (QPSO) is a good optimization technique combines the ideas of quantum computing. Quantum particle swarm optimization algorithm has been successfully applied in many research and application areas. But traditional QPSO is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. The experiments on high dimensional function optimization showed that the improved algorithm have more powerful global exploration ability.

Zhehuang Huang

Bifurcation and Global Synchronization Analysis in Diffusively Coupled Calcium Oscillators

In this paper, bursting calcium behaviors of coupled biological oscillators are discussed. Point-cycle bursting of Hopf/Hopf type is obtained by numerical computations based on the fast-slow dynamic analysis. Furthermore, the synchronization behaviors of four coupled identical and unidentical cells are investigated by gap-junction, respectively. It is concluded that bigger synchronization threshold is demanded for the chain coupling than for the ring coupling. This work reveals that the coupling strength for unidentical cells to make them reach synchronization is far bigger than that for identical cells.

Yuhong Huo, Yi Zhou

News Sentiments Grading Using Multiple Dictionaries Based Lexical Approach

In sentiment analysis, traditionally the results are thought of on scale of three grades, positive, negative and neutral. Going beyond the traditional emphasis, an effective and result oriented methodology for step by step news sentiment grading into three, five and seven different categories based on scale and intensity of sentiment using multiple dictionaries based lexical approach is going to be detailed in this paper, so they can be well understood easily, with the domain not restricted to just one section or subjective part. Potential differences in addition to unveiling of the tricky and challenging parts of news sentiment processing and grading process are also part of the details. Implementation and experimentation using MPQA dataset at the end reveals the effectiveness of methodology in addition to basis and formalization.

Adeel Iqbal, Shoab Ahmad Khan

Uniform Solution to Partition Problem Using P Systems with Membrane Division

Cell-like P systems are a class of distributed and parallel computing models inspired from the structure and the functioning of living cells. Such systems with membrane division (corresponding to the mitosis behavior of living cells) can theoretically generate exponential working space in linear time, therefore providing a possible way to solve computational hard problems in feasible time by a space-time trade-off. In this work, we construct a family of P system with membrane division to solve Partition problems, and achieve a polynomial time solution (with respect to the size of the problems). Furthermore, we prove that the systems are constructed in a uniform manner and work in a confluent way.

Yun Jiang, Zhiqiang Chen

Relating the Languages of Transition P Systems and Evolution-Communication P Systems with Energy

In this work, we explored the relation of languages generated by Evolution-Communication P systems with energy (ECPe systems) and Transition P systems without dissolution (TP systems). In particular, we have shown that for every language generated by a non-cooperative TP system, there exists an ECPe system that can generate the same language. We also look into languages for some cooperative TP systems, specifically TP systems where every object required for a cooperative rule can also evolve independently. The languages of TP systems with such property can also be generated by ECPe systems.

Richelle Ann B. Juayong, Henry N. Adorna

Myo-Signal Based Intuitive Driving Interface: Development and Evaluation

This study proposes an electromyogram (EMG)-based driving interface, MyoDrive, which uses two wrist motions to accelerate, brake, and steer in a simple manner. The horizontal angle of the right wrist is used for steering, and the vertical angle of the left wrist is used for speed control. Each angle was estimated from the EMG signals with a simple regression method. Conventional wheel/pedal interface and the proposed interface, MyoDrive, were compared with three tasks-slalom, speed tracking, and emergency stop to investigate their intuitive controllability. The proposed interface showed better learnability and response than wheel/pedal interface, but was behind in steering and speed control performance. In the speed tracking task, as the target speed increased, the deviation of MyoDrive increased whereas the deviation of Wheel/Pedal decreased in the slow region. It meant that Wheel/Pedal controlled speed more precisely. However, MyoDrive showed faster response both in the transition state and emergency.

Jinuk Kim, Sungyoon Lee, Jaehyo Kim

Reversible Spiking Neural P Systems with Astrocytes

Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, we investigate the reversibility in SNPA systems as well as the computing power of reversible SNPA systems. It is proved that reversible SNPA systems are universal even if the forgetting rules and the feature of delay in spiking rules are not used, and each neuron contains only one spiking rule. The result suggests that the astrocytes do play a key role in achieving a desired computing power for reversible SNPA systems.

Yuan Kong, Xiaolong Shi, Jinbang Xu, Xinquan Huang

On String Languages Generated by Spiking Neural P Systems with Astrocytes

Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way spikes pass through the synapses between the neurons. In this work, we investigate the language generation power of SNPA systems. Specifically, we prove that SNPA systems without the forgetting rules can generate recursively enumerable languages and characterize regular languages. Furthermore, we give a finite language that can be generated by SNPA systems, but which cannot be generated by spiking neural P systems without astrocytes. These results show that astrocyte is a powerful ingredient for generating languages by spiking neural P systems.

Yuan Kong, Zheng Zhang, Yang Liu

A Hybrid Bat Based Feature Selection Approach for Intrusion Detection

Intrusion detection Systems (IDS) are used for detecting malicious and abnormal behaviors, but they suffer from many issues like high resource consumption, high false alarm rate and many others. In this paper, we present a new algorithm to improve intrusion detection and reduce resource consumption. The proposed HBA-SVM IDS combines a hybrid Bat meta-heuristic Algorithm with a support vector machine (SVM) classifier for simultaneous feature and optimal SVM parameters selection, to reduce data dimensionality and to improve IDS detection. To evaluate our system, we used the NSL-KDD dataset and compare against a standard SVM and a PSO-SVM algorithm. Compared to these algorithms experimental result show that our system reduces the number of features needed for intrusion detection by 62% and achieves higher detection rate and lower false alarm rate.

Mohamed Amine Laamari, Nadjet Kamel

An Ensemble Pattern Classification System Based on Multitree Genetic Programming for Improving Intension Pattern Recognition Using Brain Computer Interaction

Ensemble learning is one of the successful methods to construct a classification system. Many researchers have been interested in the method for improving the classification accuracy. In this paper, we proposed an ensemble classification system based on multitree genetic programming for intension pattern recognition using BCI. The multitree genetic programming mechanism is designed to increase the diversity of each ensemble classifier. Also, the proposed system uses an evaluation method based on boosting and performs the parallel learning and the interaction by multitree. Finally, the system is validated by the comparison experiments with existing algorithms.

Jong-Hyun Lee, Jinung An, Chang Wook Ahn

Diagnosis of Osteoporosis by Quantification of Trabecular Microarchitectures from Hip Radiographs Using Artificial Neural Networks

The purpose of this study was to assess the diagnostic efficacy of an artificial neural network (ANN) in identifying postmenopausal women with low bone mineral density (BMD) by quantifying trabecular bone microarchitectures. The study included 53 post-menopausal women, who were classified as normal (n=17) and osteoporotic (n=36) according to T-scores. BMD was measured on the femoral neck by dual-energy X-ray absorptiometry. Morphological features were extracted to find optimum input variables by quantifying microarchitectures of trabecular bone. Principal component analysis was used to reduce the dimen-sionalities and improve classification accuracy. For the classification, a two-layered feed forward ANNs was designed using the Levenberg-Marquardt train-ing algorithm. The experimental results indicated the superior performance of the proposed approach for discriminating osteoporotic cases from normal.

Ju Hwan Lee, Yoo Na Hwang, Sung Yun Park, Sung Min Kim

A New Kind of Model of Laminar Cooling: By LS-SVM and Genetic Algorithm

In this paper, the conventional mechanism model of laminar flow cooling of hot-rolled strips is modified and optimized. And the new prediction model can express the laminar cooling process unambiguously, in which, the least squares support vector machines(LS-SVM) learning machine is introduced to promote the accuracy of the temperature-varying parameters and the genetic algorithm is proposed to identify the system of the temperature-varying parameters. The model can be used to calculate the temperature along the length direction and the thickness direction at the same time. The movement of the steel plate and the change of the valve can be obtained.

Shuanghong Li, Xi Li, Zhonghua Deng

The Application of Artificial Bee Colony Algorithm in Protein Structure Prediction

The biological function of the protein is folded by their spatial structure decisions, and therefore the process of protein folding is one of the most challenging problems in the field of bioinformatics. Although many heuristic algorithms have been proposed to solve the protein structure prediction (PSP) problem. The existing algorithms are far from perfect since PSP is an NP-problem. In this paper, we proposed artificial bee colony algorithm on 3D AB off-lattice model to PSP problem. In order to improve the global convergence ability and convergence speed of ABC algorithm, we adopt the new search strategy by combining the global solution into the search equation. Experimental results illustrate that the suggested algorithm is effective when the algorithm is applied to the Fibonacci sequences and four real protein sequences in the Protein Data Bank.

Yanzhang Li, Changjun Zhou, Xuedong Zheng

Find Dense Correspondence between High Resolution Non-rigid 3D Human Faces

It’s a very complex problem to achieve dense correspondence between high resolution 3D human faces. Solving the problem can contribute to a variety of computer vision tasks. This paper proposed an automatic method to find dense correspondence between different high resolution non-rigid 3D human faces. The main idea of this method is to use the correspondent facial feature points to generate Möbius transformations and using these Möbius transformations to achieve sparse correspondence between 3D faces. The texture and shape information of 3D face are used to locate the facial feature points. TPS (Thin-Plate Spline) transformation is used to represent the deformation of 3D faces, the TPS control points are selected from the sparse correspondence set. After performing TPS warping, for every vertex of the warped reference 3D face, we project them to every triangle face of the sample 3D face and use the closest projections to define the new mesh vertices of the sample 3D face. The sample 3D face with new mesh shares the same connectivity with the reference 3D face, thus the dense correspondence between the reference 3D face and the sample 3D face with new mesh is achieved. The experiments on BJUT-3D face databases show that our method achieves better performance than existing methods.

Jian Liu, Quan Zhang, Chaojing Tang

An Improved Particle Swarm Optimization and Its Application for Micro-grid Economic Operation Optimization

A particle swarm optimization algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. At the same time, the proposed algorithm is investigated in experiments which are based on the function optimization of micro-grid’s economic operation. Furthermore, the feasibility and effectiveness of the proposed algorithm are showed in the experimental results.

Tao Liu, Jun Wang, Zhang Sun, Juan Luo, Tingting He, Ke Chen

On the Theory of Nonlinear Matrix Equation X s  = Q + A H (I ⊗ X − C)− δ A

In this paper, the nonlinear matrix equation

X

s

 = 

Q

±

A

H

(

I

 ⊗ 

X

 − 

C

)

− 

δ

A

are discussed. We present some necessary and sufficient conditions for the existence of a definite positive solution for this equation, and some related properties of the definite positive solution such as boundary.

Wei Liu, Haifeng Sang, Qingchun Li, Chunwei Zhai

Design of Coal Mine Monitoring System Based on Internet of Things

In view of the current status of the coal mine safety monitoring system, this paper puts forward a design schemes of monitoring mining safety system based on internet of things. The environment parameters such as gas concentration, moisture/temperature, the dust density and equipment state,secure environment are perceived through wireless sensor network and existing ethernet in coal mine. This paper analyzes the network architecture of the system. The designing considerations and the hardware/software implementing schemes of coordinator node and sensor node are mainly introduced. The design schemes improves the coverage, flexibility and scalability of the existing monitoring system, reduces installation and maintenance costs, avoids the problem of blind zone. The mine safety mining and the major disaster prevention are effectively solved.

Xiangju Liu, Lina Liu

Optimal Tuning of PI Controller for Full-Order Flux Observer of Induction Motor Using the Immune Genetic Algorithm

This paper presents a new method to tune the parameters of the adaptation PI controller of full-order flux observer. The method employs an Immune Genetic Algorithm (IGA) based optimization routine that can be implemented off-line. A novel fitness function is designed to assess both the estimation accuracy and the noise sensitivity of the rotor speed estimation system when each antibodys parameters are employed. The diversity of population is guaranteed by the evaluating of the antibody similarities function. The Roulette-wheel selection is used to choose the parents and large mutation probability is adopted to prevent the evolution from prematurity. The simulation results verify that the IGA has better performance in convergence speed and computation efficiency compared to the traditional GA.

Hui Luo, Yunfei Lv, Quan Yin, Huajun Zhang

A Novel Membrane Algorithm to Finding Global Shipping Route

Shipping route problem is a vital problem for maximizing the profits in sea transportation, which can be essentially regarded as a multi-objective optimization problem. In this work, a model of shipping route optimization problem is established, and then an efficient approach called membrane algorithm is introduced to solve the problem, which is inspired from the behaviors of chemicals communicating among living cells. We test our method on a simulated data experiment. Experimental results show that our method can efficiently find the globally optimal route and performs superior to the genetic algorithm and ant colony algorithm.

Tao Luo, Yajun She, Zongyuan Yang

Firefly Algorithm Solving Equal-Task Multiple Traveling Salesman Problem

A kind of equal-task multiple traveling salesman problem (ET-mTSP) was proposed based on the mTSP and its corresponding mathematical model was constructed; Then, a series of discrete operations for firefly algorithm (FA) were conducted to solve this problem; Finally, the results and analysis of experiments showed that the improved algorithm was efficient and suitable for solving such ET-mTSP.

Jianhua Ma, Mingfu Li, Yuyan Zhang, Houming Zhou

Accelerated Simulation of P Systems on the GPU: A Survey

The acceleration of P system simulations is required increasingly, since they are at the core of model verification and validation processes. For this purpose, GPU computing is an alternative to more classic approaches in Parallel Computing. It provides a manycore platform with a level of high parallelism at a low cost. In this paper, we survey the developments of P systems simulators using the GPU, and analyze some performance considerations.

Miguel A. Martínez-del-Amor, Luis F. Macías-Ramos, Luis Valencia-Cabrera, Agustín Riscos-Núñez, Mario J. Pérez-Jiménez

Accepting H-Array Splicing Systems

In [3], Tom Head defined splicing systems motivated by the behaviour of DNA sequences. The splicing system makes use of a new operation, called splicing on strings of symbols. Paun et al. [7] extended the definition of Head and defined extended

H

systems which are computationally universal.

V. Masilamani, D. K. Sheena Christy, D. G. Thomas, Atulya K. Nagar, Robinson Thamburaj

Research on Magnetotactic Bacteria Optimization Algorithm Based on the Best Individual

An improved magnetotactic bacteria optimization algorithm (MBOA) is researched based on the best individual and the performance effect of parameter settings is studied in order to show which setting is more suitable for solving optimization problems. It is tested on four standard function problems and compared with DE, ABC. Experiment results show that MBOAs with different parameter settings are effective for solving most of the benchmark functions. And they do show different performance on a few benchmark functions.

Hongwei Mo, Lili Liu, Lifang Xu, Yanyan Zhao

Uniform Solution to Common Algorithmic Problem in the Minimally Parallel Mode

A P system is a novel computing model introduced by Păun in the area of membrane computing. It is known that the Common Algorithmic Problem (

CAP

) has a nice property that several other

NP

-complete problems can be reduced to it in linear time. The decision version of this problem is known to be efficiently solved with a family of recognizer P systems with active membranes with three electrical charges working in the maximally parallel way. We here work with a variant of a P system with active membranes that does not use polarizations and present a uniform solution to

CAP

in the minimally parallel mode.

Yunyun Niu, Ibrahim Venkat, Ahamad Tajudin Khader, K. G. Subramanian

A Novel Task Scheduling Scheme in Heterogeneous Computing Systems Using Chemical Reaction Optimization

The task scheduling problem is normally an NP-hard problem. A chemical reaction optimization (CRO) is a new meta-heuristic optimization method, which has demonstrated its capability in solving NP-hard optimization problems. In this paper, a novel CRO algorithm for task scheduling (NCROTS) is proposed on heterogeneous computing systems. Over the real-world problems with various characteristics and randomly generated graphs, the simulation results show that the proposed NCROTS algorithm significantly improves the schedule quality (makespan), compared with two existing solutions (GA and HEFT).

Guo Pan, Kenli Li, Yuming Xu, Keqin Li

Public Traffic Network Simulation System

Urban transportation problem is the bottleneck problem [1] of limiting urban economic development [2]. With the rapid development of the scale of the city, it is a hot issue in the urban traffic optimization problem [3] how to use reasonably, scientifically and efficiently the existing urban public traffic network system for passengers traveling to provide strong guarantee. With the rapid development of artificial intelligence algorithms [4], the application of artificial intelligence algorithms in public transport planning is one of the research directions in the field of public transport network optimization [5].

Wen Jin Rong, Kang Zhou, Peng Dai, Song Ping Li, Wei Tan

An Improved Golden Ball Algorithm for the Capacitated Vehicle Routing Problem

In this paper, we have presented an algorithm that has been improved from the original golden ball algorithm (GB) to solve the capacitated vehicle routing problem (CVRP). The problem objective is to construct a feasible set of vehicle routes that minimizes the total traveling distance and the total number of vehicles used. We have tested the improved GB (IGB) with 88 problem instances. The computational results indicate that IGB outperforms GB in all directions, and the best known solutions are obtained for 79 instances. Moreover, new best-known solutions for three instances are also found.

Kanjana Ruttanateerawichien, Werasak Kurutach, Tantikorn Pichpibul

A New Method for Singular Polynomial Systems

In this paper, we present a new algorithm to compute the singular solution of polynomial systems by using approximate left and right nullspaces of the Jacobian matrix. The theory and algorithm are illustrated by several examples.

Haifeng Sang, Panpan Liu, Qingchun Li, Yan Li

Accepting String Graph Splicing System

In this paper, we extend the idea of accepting splicing systems on strings which was introduced by Mitrana et al. to string graphs. The input string graph is accepted as soon as the permitting string graph is obtained provided that no forbidding string graph has been obtained so far, otherwise it is rejected. We study the computational power of the new variants of the accepting string graph splicing system and the interrelationships among them.

Meena Parvathy Sankar, N. G. David, D. G. Thomas

ABC Algorithm for VRP

Vehicle routing problem(VRP) has the same mathematical model with many combinatorial optimization problems, they are typical NP- hard problems in combinatorial optimization problem. In this paper, artificial bee colony (ABC) algorithm is applied to solve VRP. We propose a method to keep the colony diversity and improve the search efficiency of ABC. To validate the performance of ABC algorithm, we calculate the instance from standard VRP database. The results show that improved ABC can be used for VRP efficiently.

Kai Shao, Kang Zhou, Jiang Qiu, Juan Zhao

Joint Estimation of QTL Positions and Effects in Multiple-Interval Mapping for Count Trait

Compared with other quantitative traits, count trait express discrete variation generally with countable values. It is necessary to consider an effective model incorporating both multiple genetic factors and environment factor in count trait mapping. In this article, we apply a multivariate Poisson model to fit the count traits of individuals and consider the multiple-interval mapping for QTLs by providing the joint estimating of QTL positions and effects. Simulation studies are conducted to validate the proposed algorithm.

Xiaona Sheng, Weijun Ma, Ying Zhou

Based on Different Atom of Protein Backbone Predicting Protein Structural Class

In this paper, we studied the protein structure database including all-

α

,

all

 − 

β

,

α

 + 

β

and

α

/

β

structural classes based on the Hamilton-factor of the graph theory model and proposed a new method for predicting protein structural class with Hamilton-factor based on N-atom of protein backbone. By using this method to test the 135 single proteins shows that the overall accuracy rate reached 94.81%. It provided the new idea for protein structural class.

Xiaohong Shi, Qian Fan

On Directional Bias for Network Coverage

Random walks have been proposed as a simple method of efficiently searching, or disseminating information throughout, communication and sensor networks. In nature, animals (such as ants) tend to follow

correlated

random walks, i.e., random walks that are biased towards their current heading. In this paper, we investigate whether or not complementing random walks with directional bias can decrease the expected discovery and coverage times in networks. To do so, we use a macro-level model of a directionally biased random walk based on Markov chains. By focussing on regular, connected networks, the model allows us to efficiently calculate expected coverage times for different network sizes and biases. Our analysis shows that directional bias can significantly reduce the coverage time, but only when the bias is below a certain value which is dependent on the network size.

Graeme Smith, J. W. Sanders, Qin Li

Computational Efficiency and Universality of Timed P Systems with Membrane Creation

In this work, inspired from this biological motivation that in living cells, the execution time of different biological processes is difficult to know precisely and very sensitive to environmental factors that might be hard to control, the computational efficiency and universality of timed P systems with membrane creation are investigated. Specifically, we give a time-free solution to

SAT

problem by a family of P systems with membrane creation in the sense that the correctness of the solution is irrelevant to the times associated with the involved rules. We further show that time-free P systems with membrane creation are computationally universal.

Bosheng Song, Mario J. Pérez-Jiménez, Linqiang Pan

A Membrane-Inspired Evolutionary Algorithm Based on Artificial Bee Colony Algorithm

The paper presents a novel membrane-inspired evolutionary algorithm, named artificial bee colony algorithm based on P systems (ABCPS), which combines P systems and artificial bee colony algorithm (ABC). ABCPS uses the evolutionary rules of ABC, the one level membrane structure, and transformation or communication rules in P systems to design its algorithm. Experiments have been conducted on a set of 29 benchmark functions. The results demonstrate good performance of ABCPS in solving complex function optimization problems when compared with ABC.

Xiaoxiao Song, Jun Wang

The Intelligent Editing Based on Perception for Structuring Digital Ink

With the development of human-computer interface moving in the natural and efficient direction, people pay more and more attentions to the structural analysis and intelligent editing of digital Ink, also known as digital Handwriting. Firstly, we put forward a new method of the multi-level interactional structure analysis and understanding based on context awareness for handwriting, and then we have the intelligent edit operations. The result of experimental evaluation shows that the method has an effective effect for intelligent editing of handwriting.

Rui Su

Compactly and Weakly Compactly Strongly Exposed Properties in Musielak-Orlicz Sequence Spaces Equipped with the Orlicz Norm

In order to improve and generalize the discussion of exposed properties in Musielak-Orlicz function space,in the paper we consider the compactly and weakly compactly strongly exposed properties in Musielak-Orlicz sequence spaces equipped with the Orlicz Norm. First, some criteria for S-property and weakly S-property in Musielak-Orlicz sequence spaces equipped with the Luxemburg norm are given. Second, specific characterizations of compactly strongly exposed property and weakly compactly strongly exposed property in Musielak-Orlicz sequence spaces equipped with the Orlicz norm are given.

Lihuan Sun

Extended Extreme Learning Machine for Tensorial Signal Classification

Recently, there are several multilinear methods have been proposed for tensorial data dimensionality reduction (feature extraction). However, there are few new algorithms for tensorial signals classification. To solve this problem, in this paper, a novel classifier as a tensor extension of extreme learning machine for multi-dimensional data recognition is introduced. Due to the proposed solution can classify tensorial data directly without vectorizing them, the intrinsic structure information of the input data can be reserved. It is demonstrated that the new tensor based classifier can get better recognition performance with a faster learning speed.

Shuai Sun, Bin Zhou, Fan Zhang

A Report on Uncorrelated Multilinear PCA Plus Extreme Learning Machine to Deal with Tensorial Data

Subspace learning is an important direction in computer vision research. In this paper, a new method of tensor objects recognition based on uncorrelated multilinear principal component analysis (UMPCA) and extreme learning machine (ELM) is proposed. Because of mostly input data sets for pattern recognition are naturally multi-dimensional objects, UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. A subset of features is extracted and the classifier ELM with extremely fast learning speed is then applied to achieve better performance.

Shuai Sun, Bin Zhou, Fan Zhang

Quantum Statistical Edge Detection Using Path Integral Monte Carlo Simulation

A novel statistical method using path integral Monte Carlo simulation based on quantum mechanics to detect edges of interested objects was proposed in this paper. Our method was inspired by essential characteristics of quantum, and based on the quantum particle movement evolved towards the edge position with high probability density in the gradient-based image potential field. The discussion about computational complexity and parameter settings demonstrated the feasibility and robustness of our method.

Yangguang Sun

Intelligent Model-Based Speed Controller Design for PMSM

An intelligent model-based speed controller of permanent magnet synchronous motors (PMSM) is proposed in this paper, which synthesizes the simplicity of PID controller and the adaptability of artificial neural network based controller. Small signal model of the speed loop as the foundation of the framework of the controller is analyzed. The gradient rule is utilized to update the weights of the neuron inputs, and auxiliary retrain process is used to improve the dynamic performance of the controller. A comprehensive simulation was done to verify the validation of the intelligent speed controller. Comparison with the conventional PID controller demonstrates the outstanding performance and robustness of the proposed controller. The feasibility of implementation of the proposed speed controller on physical platforms is also covered in this paper.

Lisi Tian, Yang Liu, Jin Zhao

Phase-Locked Loop (PLL) Type Sensorless Control of PMSM Using Neural Network Filter

This paper describes a Phase-Locked Loop (PLL) type sensorless control strategy of permanent-magnet synchronous machines with pulsating high frequency (HF) voltage signal injection. The HF model of the PMSM and the demodulation scheme are analyzed in detail. A neural network (NN) based adaptive filter replaces the common band-pass filter (BPF) to extract the desired frequency signal which includes the rotor position information. The proposed NN filter has an adjustable bandwidth which helps the filter to capture the desired signal even in the presence of high speed estimation errors. The recursive least square (RLS) algorithm is used to update the neuron weights. The simulation results verify the performance of the proposed NN adaptive filter in the sensorless control strategy.

Lisi Tian, Jin Zhao, Zhenfeng Peng

Facial Expression Recognition Based on Feature Block and Local Binary Pattern

Automatic facial expression recognition (FER) played more and more important role in recent years for its wide range of potential applications. So, as one of the challenging tasks in intelligent system, it still has many questions need to be deeply researched. Taking into account the importance of eyes and mouth for FER and the outstanding performance of local binary pattern (LBP) to extract local textures, a representation model for facial expressions based on feature blocks and LBP descriptor is proposed. The strategies of feature blocks obtaining and LBP feature extracting are analyzed in details and the recognition experiment is conducted. Experimental result shows that this algorithm has good performance.

Wencheng Wang, Faliang Chang

Multiple Positive Solutions of Nonlinear Fourth-Order Singular P-Laplacian Resonant Problems

Nonlinear fourth-order singular P-Laplacian resonant problems is a class of classical problems in mathematics and have lots of applications in engineering practice. In this paper, by using the variational method and Mountain pass lemma, multiple positive solutions of the nonlinear fourth-order singular resonant problems involving the p-Laplace operator were obtained.

Xuchao Wang, Libo Wang

A 3D DNA Self-assembly Model and Algorithm for Minimum Spanning Tree Problem

The minimum spanning tree (MST) problem has been widely studied for its wide applicationsin recent years. Because of its outstanding advantages, DNA self-assembly computing has been used to solve MST problem. A new paradigm, called a three dimensional (3D) DNA self-assembly model, is proposed for this type of problem in this paper. The results show that it is efficient in solving MST problem and the algorithm has a high-efficiency.

Zicheng Wang, Lizheng Bian, Yanfeng Wang, Guangzhao Cui

Multi-digit Logic Operation Using DNA Strand Displacement

DNA strand displacement which is an approach of dynamic nanotechnology has been widely used in constructing of molecular logic circuit, molecular automata and nanomedicine and so on. DNA strand displacement is enormous capable of implementation of logical calculation which plays a critical role in the acquirement of bio-computer. In our paper, the multi-digit full adder which is based on the reaction of DNA strand displacement is designed and has been verified by simulation of DSD (DNA strand displacement). The accuracy of simulation result further confirmed DNA strand displacement is a valid method for the research of logical bio-chemical circuit.

Zicheng Wang, Guihua Tian, Yan Wang, Yanfeng Wang, Guangzhao Cui

Existence of Multiple Positive Solutions of Nonlinear Singular Fourth-Order Problems Involving the P-Laplacian

Nonlinear fourth-order singular P-Laplacian resonant problems is a class of classical problems in mathematics and have lots of applications in engineering practice. In this paper, by using the variational method, the existence of multiple solutions for some nonlinear singular fourth-order p-Laplacian problems were obtained.

Ying Wei, Min Wang, Libo Wang

Real-Time Surgery Simulation Systems Based on Collision Detection

In this paper, we present a novel collision detection approach for surgery simulation. Each model is represented as a clustered hierarchy of sphere tree (CHST). GPU-based occlusion queries were used for fast collision culling between massive models. Furthermore, we are able to generate these hierarchies and perform collision queries using out-of-core techniques on all triangulated models. Experimental results show that our algorithm performs real-time collision detection between massive bones and implant models, consisting of millions of triangles at interactive rates on a commodity PC.

Chang Wen, Kai Xie

Fuzzy RBF Neural PID Control to Simulation Turntable Servo System

Simulation turntable servo system is highly nonlinear and uncertainty plants. Up to now, various kinds of nonlinear PID controllers have been designed in order to satisfactorily control this system and some of them applied in actual systems with different degrees. Given this background, the simulation experiments are carried out based on MATLAB/SIMULINK tools to evaluate the performances of four different turntable servo controllers, including the conventional PID, the fuzzy self-tuning PID, the neural network PID and the fuzzy RBF neural network PID controller. The simulation results show that the simulation turntable servo system with fuzzy RBF neural PID controller exhibits robustness and good adaptation capability.

Yanmin Wu, Guangzhao Cui, Xuncai Zhang

Three-Input Molecular Logic Based on Light-Control System

In biomolecular programming, the control of interaction among biomolecules is harnessed for computational purposessuch as proteins and nucleic acids. Recently, a new kind of biocompution system has drew considerable attention. Rational designing and engineering of biological computing systems can promote our ability to control biological processes. In this paper we construct a multi-input logic circuit based on photoreceptor protein, which aims to achieve spatial regulation of gene expression in the tissue level. It is a certain inspiration for applying to tissue engineering.

Yuhui Xia, Shanshan Liu, Xiangxiang Chen, Ming Song, Yafei Dong

The Reconstruction of 3D Human Face Based on the Post-Plastic Surgery

In medical area, the technology of 3D visualization has been widely used and the method of high-quality synthesizing of human face is of great importance. In this paper, we present a method to synthesize a high-quality human face. We employ three 2D digital color photos to obtain high-resolution textures and take advantages of 3D face model reconstructed from CT data for flexible deformation of finite elements. We get the accurately projective parameters from 3D face model to 2D photos via the iterative algorithm of DSM. We use multi-texture mapping of OpenGL to avoid splicing crack and accelerate the whole fusing process due to GPU-based parallel processing. Experimental results show that our method can synthesize a high-definition and realistic human face on standard PC hardware.

Kai Xie, Bo Liu, Ningjun Ruan, Jun Chen

PSO Application in Power System: Low-Frequency Oscillation Fault Analysis

Low-frequency oscillation is an important problem in modern power systems, the system monitoring and realtime analysis operation are the most basic and important part of the system stability. This paper introduces the main issues of the detection of power system low-frequency oscillation and the fundamental principles of particle swarm optimization (PSO) technique. Point out the lack of the ordinary signal analysis methods in low-frequency oscillations detection, and discuss the feasibility of using the PSO algorithm to detect low-frequency oscillation in power system.

Wei Xiong, Xiaoya Hu, Zhuguo Li, Lejiang Guo

Interference Avoidance Scheme for Relay-Based Device-to-Device Communication System in LTE Network

In this paper, we propose an interference avoidance scheme for relay-based D2D communication system. First reach a relay trigger condition of D2D communication through the analyze of outage probability, and then a relay selection algorithm that based on optimal capacity is proposed. Simulations demonstrate that, compared with traditional cellular communication, D2D communication with relay can make full use of the idle resource of the cell so as to increase the users’ capacity effectively and improve system performance as well.

Jianbin Xue, Supan Wei

Power Control Research Based on Interference Coordination for D2D Underlaying Cellular Networks

This paper considers a device-to-device (D2D) underlaying cellular network where an uplink cellular user communicates with the base station while multiple direct D2D links share the uplink spectrum. This paper proposes a random network model based on stochastic geometry and develops centralized power control algorithm. The goal of the proposed power control algorithm is two-fold: ensure the cellular users have sufficient coverage probability by limiting the interference created by underlaid D2D users, while also attempting to support as many D2D links as possible. This paper adopts the two level control strategy: Cellular link SINR(signal-to-interference-plus noise ratio) and sum rate maximization, to ensure the cellular communication and improve the coverage probability of D2D and cellular links. Numerical results show the gains of the proposed power control algorithm and accuracy of the analysis.

Jianbin Xue, Bing Wen, Supan Wei

The Molecular Beacons Self-assembly Model of the Dislocation Permutation Problem

Special “hairpin” structure of molecular beacon makes it with strong specificity, high sensitivity, fast reaction and fluorescence labeling and so on. In this paper, we choose to use a special structure design of self-assembled tile.Compared with DX tiles, TX tile, its special “hairpin” structure makes the ring and stem cadres can express different information, in order to design the assembly module can reduce the complexity, reduce the use of the module, lower assembled depth.The paper uses molecular beacons in self-assembled tile to try and resolve a derangement-wide issues to verify the feasibility of its design.

Jing Yang, Zhixiang Yin, Mingqiang Chen, Jianzhong Cui

Detecting Bots in Follower Markets

Along with the widely using of microblog, third party services such as follower markets sell bots to customers to build fake influence and reputation. However, the bots and the customers that have large numbers of followers usually post spam messages such as promoted messages, messages containing malicious links. In this paper, we propose an effective approach for bots detection based on interaction graph model and BP neural network. We build an interaction graph model based on user interaction and design robust interaction-based features. We conduct a comprehensive set of experiments to evaluate the proposed features using different machine learning classifiers. The results of our evaluation experiments show that BP neural network classifier using our proposed features can be effectively used to detect bots compared to other existing state-of-the-art approaches.

Wu Yang, Guozhong Dong, Wei Wang, Guowei Shen, Liangyi Gong, Miao Yu, Jiguang Lv, Yaxue Hu

Digital TV Program Recommendation System Based on Collaboration Filtering

In order to solve the problem of information overload brought by over abundant digital television (TV) program resources, this paper proposed a digital TV program recommendation system based on collaborative filtering (CF) which contains information inputting unit, system analysis unit and recommendation sending unit. The audience behavior analysis proposed in this paper was based on two forms: personalized audience behaviour analysis and group audience behaviour analysis, which can recommend interesting TV programs suited for the particular individual or group. The result of simulation proves the feasibility of algorithms.

Fulian Yin, Jianping Chai, Hanlei Wang, Ya Gao

A Variable Multidimensional Fuzzy Model and Its Application to Online Tidal Level Prediction

To represent the time-varying dynamics of nonlinear systems, a variable multidimensional fuzzy model is online constructed by the Delaunay triangulation method. Construction and adjustment of model is based on the learning of samples in a real-time updated sliding data window (SDW). The proposed fuzzy model is combined with harmonic method and the resulted modular model is implemented for real-time tidal level prediction where the harmonic method is used to represent the periodic changes of tidal level caused by celestial movement and the variable fuzzy model is used to give predictions caused by water temperature and air pressure. The measured tidal data at the port of Honolulu is implemented for the online tidal level prediction and the results demonstrate the feasibility and effectiveness of the proposed variable multidimensional fuzzy model and the modular prediction method.

Jianchuan Yin, Nini Wang

Research on Adaptive PI Controller of LCL-Filtered Voltage Source Rectifier with Neural-Network

In this paper, a neural-network-based adaptive PI current controller of voltage source rectifier (VSR) is proposed. Complex vector PI decoupling scheme can eliminate the voltage coupling completely, and the neural network algorithm can tune the parameters of current regulator online. The novel controller has a fast dynamic response, strong adaptability to those occasions such as the nonlinear model and variable load. A double loop control strategy with an outer dc link voltage control based on traditional PI controller and inner current control based on the novel controller is discussed in this paper. Simulation results verify the correctness and feasibility of the neural-network-based PI controller.

Quan Yin, Ronghui Lu, Qingyi Wang, Hui Luo, Hu Guo

A DNA Computing Model on Triple-Stranded for Minimum Spanning Tree Problem

Single-strand DNA can match with homologous double- stranded into a triple-stranded structure mediated by RecA protein.The paper provides a triple-stranded DNA computing model for minimum spanning tree problem. DNA fragments corresponding to edges are coded by double-stranded DNA, wrong hybridization does not take place and hairpin structure does not form. The single-strand DNA probe is bond with RecA protein, so the rate of wrong solution will reduce. And in this way, encoding complexity and the errors in computation will be decreased.

Zhixiang Yin, Xia Sun, Feng Xu, Xianwen Fang, Hui Xu

An Adaptive Unimodal and Hysteresis Thresholding Method

This paper addresses the unimodal and hysteresis thresholding, where a pair of low and high thresholds is under investigation targeted with the unimodal image histogram. The novel

bowstring

is introduced to make an accurate

priori

-measurement of the overall tendency of the histogram. The dual-threshold is further computed by adaptively searching two tangent points corresponding to the properly defined transitional characteristics over the whole histogram. The effectiveness of this proposed algorithm is evaluated using the Baddeley’s discrepancy.

Yin Yu, Zhen Li, Bing Liu, Xiangdong Liu

An Artificial Immune System Approach for Malware Detection

Artificial immune system(AIS) is an efficient solution for network security. In this paper, an artificial immune system approach for malware detection is proposed, which is referred to AISMD. In AISMD, the method to build the profile of benign executables in computer systems is given. Based on the built model of benign executable, the detectors are generated to detect malware. Experimental results show that AISMD is an efficient method to build the profile of benign executable and extract the characteristics of the executable, and has better detecting ability than that of the previous techniques.

Jinquan Zeng, Weiwen Tang

Time-Free Tissue P Systems for Solving the Hamilton Path Problem

Tissue P systems are distributed and parallel computing models inspired by the communication behavior of living cells in tissues. Theoretically, tissue P systems can generate exponential working space in linear time, which makes it feasible to solve computational hard problems in polynomial time. In traditional tissue P systems, the execution of each rule takes exactly one time unit, thus making the system work synchronously. However, the restriction does not correspond with the biological fact, since biochemical reactions may vary in unpredicted conditions. In this work, we define the timed tissue P systems by adding a time mapping to each rules to specify the execution time for them. Furthermore, a uniform and time-free solution to Hamilton Path Problems is obtained by a family of such systems, where the execution time of rules can change and the output produced is always correct.

abstract

environment.

Xiangxiang Zeng, Ningxiang Ding, Fei Xing, Xiangrong Liu

Face Classification via a Novel Two-Dimensional Extreme Learning Machine

Because of face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. In this work, by using a linear tensor projection, a new two-dimensional classifier based on extreme learning machine is introduced. Due to the proposed algorithm can classify matrix data directly without vectorizing them, the intrinsic structure information of the input data can be reserved. It is demonstrated that the proposed 2D-ELM achieves better recognition performance.

Fan Zhang, Lin Qi, Enqing Chen

Prediction Nucleic Acid Secondary Structure with Planar Pseudoknots and Genetic Algorithm

In recent years, DNA and RNA molecules have shown great potential as a design medium for the construction of nanostructures and the programmed assembly of molecular computing. In this paper, we propose a novel genetic algorithm to predict nucleic acid secondary structure with planar pseudoknots. In our algorithm, the free energy and the number of continues base pairs stacking are used as fitness function to evaluate the individuals. We have compared the results obtained by our algorithm with RNAStructure. As will be discussed, our algorithm can efficiently predict the planar pseudoknots with lower free energy and more continues base pairs stacking.

Kai Zhang, Xinquan Huang, Wei Hu, Jun Liu

Accelerating Genetic Algorithm for Solving Graph Coloring Problem Based on CUDA Architecture

Graph coloring problem (GCP) is a well-known NP-hard combinatorial optimization problem in graph theory. Solution for GCP often finds its applications to various engineering fields. So it is very important to find a feasible solution quickly. Recent years, Compute Unified Device Architecture (CUDA) show tremendous computational power by allowing parallel high performance computing. In this paper, we present a novel parallel genetic algorithm to solve the GCP based on CUDA. The initialization, crossover, mutation and selection operators are designed parallel in threads. Moreover, the performance of our algorithm is compared with the other graph coloring methods using standard DIMACS benchmarking graphs, and the comparison result shows that our algorithm is more competitive with computation time and graph instances size.

Kai Zhang, Ming Qiu, Lin Li, Xiaoming Liu

Parallel Genetic Algorithm with OpenCL for Traveling Salesman Problem

In the past few years, CUDA and OpenCL are developed in full use of the GPU, which is a significant topic in high performance computing. In this paper, we have proposed an implementation of the genetic algorithm for the traveling salesman problem on the parallel OpenCL architecture. Population initialization, fitness evaluation, selection, crossover and mutation operators are implemented on the GPU by using individual to thread mapping. Moreover we have evaluated our algorithm using a set of benchmark instances from the TSPLIB library. The comparison results shows that GPU computations provide better performance than traditional CPU implementation.

Kai Zhang, Siman Yang, Li li, Ming Qiu

Multiobjective Genetic Algorithm for Optimized DNA Sequences for DNA Self-assembly

DNA sequence design is a very important task for DNA self-assembly technologies, including DNA computing, complex 3D nanostructures and nano-devices design. These experimental DNA molecules must satisfy several combinatorial, thermodynamic and secondary structure criteria, which aim to avoid undesired hybridizations and make the molecular experiment more re-liable and stable. In this paper, the DNA sequence design problem is formulated as a multi-objective optimization problem and solving it using multi-objective genetic algorithm. Moreover, the performance of our algorithm is compared with the other sequence design methods, and the comparison result shows that our algorithm is able to generate higher quality DNA sequences than previously published studies which consider same criteria.

Kai Zhang, Jiaren Yi, Jun Liu, Wei Hu

Sufficient Conditions for Nonsingular H −matrices

For many applications of numerical analysis, control theory, economic mathematics and so on, it is very useful to know whether a matrix is a nonsingular matrix or not. In this paper, by using the theory of diagonally dominant matrices, we discuss some criteria for nonsingular matrices according to partition for the index set of diagonal dominance. A set of sufficient conditions for nonsingular matrices are given. These results are illustrated by using a numerical example.

Lijun Zhang, Min Li

PBIL Algorithm for Signal Timing Optimization of Isolated Intersection

In the research of Webster delay model, Genetic Algorithm (GA), Ant colony algorithm (ACO) and Particle Swarm Optimization algorithm (PSO) have been used to solve the signal timing problem. However, the performances of these algorithms depend heavily on determination of the operators and the choice of related parameters. In this paper, an improved PBIL algorithm is proposed to solve the signal timing problem of an isolated intersection. The experimental results show that the algorithm can get rational signal timing effectively with more insensitive to the parameters setting.

Qingbin Zhang, Wenlei Dong, Xianfang Xing

Application to Logic Circuits Using Combinatorial Displacement of DNA Strands

The toehold and branch migration domain of traditional DNA strand displacement are covalently connected, such a structure cannot be changed during the execution of the circuit, so to some extent it limits the construction of DNA circuits. To solve this problem, we use combinatorial displacement of DNA strands technology where toehold and branch migration domains are located in different strand, these two domains must be firstly linked by hybridization of linking domains that can occur strand displacement reaction, this paper is to design an Inhibit and a XOR based on this principle which is theoretically possible.

Xuncai Zhang, Weiwei Zhang, Yanfeng Wang, Guangzhao Cui

Microscan Imager Logging Data Compression Using improved Huffman Algorithm

The conflict between ever-increasing volumes of microscan imager logging data and limited cable transmission bandwidth intensifying day by day. In this paper, an improved lossless data compression algorithm is proposed. Specifically, according to the characteristics of the micro resistivity imaging logging data, it is proved that Hex character encoding has better compressibility than decimal character encoding. Then, it analyzed that traditional quaternary Huffman algorithm does not be fully applicable to microscan imager logging data. Lastly, it employed improved quaternary Huffman algorithm for logging data compression so as to enhance the data compression ratio. The experiment comparsions show that ,compared to the convention quaternary algorithm and the improved compressed Huffman encoding, Both elapsed time and compression ratio are a great improvement.

Yanhui Zhang, He Zhang, Jun Shi, Xiaozheng Yin

Decision-Making Strategies for Multi-Objective Community Detection in Complex Networks

Multi-objective optimization algorithms have demonstrated their effectiveness and efficiency in detecting community structure in complex networks, by which a set of trade-off partitions of networks are obtained instead of a single partition. The large number of partitions lead to a challenging problem for decision makers: how to obtain an ideal partition from the set of trade-off partitions of networks. In this paper, we present two decision-making strategies for obtaining the ideal partition, one is based on the knee points and the other is based on the majority voting. Experimental results on random networks and real-world networks illustrate that the presented two strategies are very competitive for obtaining an ideal partition of networks.

Yu Zhang, Xingyi Zhang, Jin Tang, Bin Luo

A New Graphical Representation of Protein Sequences Based on Dual-Vector Model

Graphical representation of protein sequences is an important method of sequences analysis. We propose a new graphical representation of protein sequences with Virtual DNA Codon and DV-Curve. With virtual DNA codon, we successfully retranslate a protein sequence to a virtual DNA sequence. And then we use DV-Curve to visualize the virtual DNA sequence. The new graphical representation avoids degeneracy and loss of information. It also has good visualization even sequences are long, and avoids the difficulty of observing in multi-dimensional graph.

Zhujin Zhang, Xiangxiang Zeng, Zhihua Chen, Eric Ke Wang

New Fuzzy Probability Spaces and Fuzzy Random Variables Based on Gradual Numbers

In this paper, we deal with special generalization of probability measures and random variables by considering their values in the set of gradual numbers. Firstly, the concept of gradual probability measures is introduced and some of its properties are discussed. And then, the concept of gradual random variables is introduced and weak law of large numbers for gradual random variables on a gradual probability space is obtained.

Caili Zhou, Peng Wang

Protein Structure Prediction Based on Improved Multiple Populations and GA-PSO

Predicted amino acid sequence of the protein through its spatial structure can be attributed to a multivariable multi extreme global optimization problem. Based on AB off lattice model, a novel hybrid algorithm-MPGPSO which brings together the idea of multiple populations with the improved genetic algorithm and particle swarm optimization algorithm is presented in this paper for searching for the ground state structure of protein. The new algorithm taking advantages of the idea of best of best to enhance the algorithm’s search capability. Experimental results are effective when it is applied to predict the best 3D structure of real protein sequences.

Changjun Zhou, Tianyun Hu, Shihua Zhou

A Multi-population Discrete Firefly Algorithm to Solve TSP

In this paper, the Firefly algorithm (FA) is improved and a multi-population discrete firefly algorithm is presented combined with k-opt algorithm to solve the traveling salesman problem (TSP). The proposed algorithm is tested on some instances and the performance of the proposed algorithm is compared with the other discrete firefly algorithm for TSP. The results of the tests show that the proposed algorithm performs better in terms of convergence rate and solution quality.

Lingyun Zhou, Lixin Ding, Xiaoli Qiang

Study and Application of DNA Cellular Automata Self-assembly

Because DNA self-assembly technique has its unique molecular function and control of nanomaterials synthesis, DNA computing model based on the self-assembly owns great prospects for development. At present, humans have been widely used image to record and express the objective world, and image also becomes a kind of extremely important multimedia information storage and transmission format. In order to enhance the security of image, people have started to use DNA self-assembly to study DNA cryptography-based image encryption. In this paper, design of image encryption-oriented DNA self-assembly is introduced. The paper gives the self-assembly definition of elementary XOR-DNA cellular automata and elementary T-shaped XOR-DNA cellular automata. Combined with the characteristics of image encryption, the design of image encryption self-assembly is given by the definition of elementary T-shaped DNA cellular automata.

Shihua Zhou, Bin Wang, Xuedong Zheng, Changjun Zhou

Fixed Point in Fuzzy Metric Spaces

In this paper, we propose a new definition of generalized fuzzy contractive mapping,which is a generalization of the fuzzy contractive mapping in the sense of Yonghong Shen. and prove some convergence theorems by different contractive conditions in fuzzy metric space. Our results improve and extend the corresponding results in [5,6,8].

Yinying Zhou

Shortest Path Problem with k-Intermediate Vertex Constraints

In urban transit network inquiry system, the optimal scheme of bus line having excessive transfers requires us to consider limiting the number of transfers when to compute the optimal scheme of bus line [1], which can come down to shortest path problem limiting the number of intermediate vertex. And support system of QoS (Quality-of-Service) is playing a more and more important role in communication network. QoS routing problem [2] is to choose transmission path meeting the requirements of QoS service to hop counts, bandwidth, latency, packet loss rate etc. and ensuring effective use of global network resources, which can come down to multi-constrained shortest path problem limiting the number of intermediate vertex. Shortest path problem limiting the number of intermediate vertex is shortest path problem with

k

-intermediate vertex constraints.

Chun Liu, Kang Zhou

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