Information Technology and Intelligent Transportation Systems

In this paper, we studied the SSI frame, and proposed a new plan of the comprehensive quality Management System by means of the advantage of SSI frame, which needs less codes. In order to solve the problem which the system cannot be compatible with the different version of the browser, we imported the JQuery technology. In order to make the user interface of the system more beautiful and concise, we designed the user interface by means of the EsayUI technology.

As the hazardous chemical transportation traffic accident is getting more and more serious, this paper designs a kind of active safety warning system for hazardous chemical transportation vehicle. The content of this paper mainly includes the composition and principle, establishment of theoretical model of safety distance, design of avoidance control strategy and algorithm and so on. This paper firstly introduces the composition and function of the active safety warning system for hazardous chemical transportation vehicle, and claries its working principle. And then establishes a safety distance model according to the theory of vehicle dynamics, and another four models of key vehicle distance which include safety critical, target vehicle, dangerous critical and limit critical. According to the different dynamic models, the paper designs four control strategies including safety control, alarm control, deceleration control and brake control and the algorithm. In this paper, the active safety warning system for hazardous chemical transportation vehicle can help the driver avoid traffic accident in the course of transportation, detect the safety state of the tank, improve the safety performance of the vehicle at the greatest degree and realize the intelligent monitoring of the hazardous chemical transportation vehicle.

This research utilizes company carbon information disclosure results obtained by web crawler from network media as data sources, discusses the factors which influenced carbon disclosure for the SSE social responsibility index. 100 stocks of Shanghai social responsibility index are selected as research samples. The multivariate regression analysis model is applied to study influence factors of carbon disclosure of listed companies. The research results show that the carbon disclosure of the listed companies in China has positive correlation with enterprise size, involved industry, growth ability, enterprise property and whether listed abroad or not. The first three factors have obvious significance, while the carbon disclosure has negative correlation with corporate profitability and liabilities level.

Saliency has been widely applied to detecting the most attractive object in an image. This paper presents a novel method based on saliency map for object localization in an image. We regard saliency map computation as a preprocessing step, which is obtained using a discriminative regional feature integration approach. Then, the saliency map is processed to highlight more saliency contour and remove some subordinately salient points using region-grow segmentation algorithm. Finally, saliency object is located with efficient sub-window search algorithm (ESS) in the binarized saliency map. This approach is able to identify the exact location and roughly the size of saliency object in an image. The performance evaluation using MSRA-B dataset demonstrates our approach performs well in object localization in images.

In this paper, a novel power allocation algorithm to maximize the throughput under the bit error rate (BER) constraint and the total power constraint in cognitive radio is proposed. Although water filling (WF) algorithm is the optimal power allocation in theory, it ignores the fact that the allocated power in use is discrete, and the algorithm doesnt take the waste power into consideration. In the improved algorithm, the total power is asymmetrically quantized to apply to the practice and reduce the computation complexity before adopting the dynamic programming which is commonly used to solve the knapsack problem, so this improved algorithm is called as asymmetrically quantized dynamic programming (AQDP). Moreover, AQDP reused the residual power to maximize the throughput further. The simulation results show that AQDP algorithm has improved the transmit throughput of all CR users compared with the classical power allocation algorithms referred as WF algorithm in this paper.

The proposal of building the Silk Road Economic Belt aiming to strengthen cooperation among Asian and European nations will benefit all the people along this Economic Belt. To establish the Silk Road Economic Belt, comprehensive transport systems are a principal premise. Freight is an important part of comprehensive transport systems. Thus, in this study, a curve regression model is established for statistics analysis and forecast freight turnover quantity and freight volume of nine provinces in China, in the Silk Road Economic Belt. Detailed parameters of the model could be calculated by SPSS, including a significant and fitting test. The calculation results by SPSS show that this curve regression model has a good fitness to actual freight data and could be significant. Finally, the development trend of freight turnover quantity and freight volume of nine provinces in future from 2015 to 2020 is predicted using the model, according to the supposed GDP growth rate (7 %). The results of the actual case analysis prove the feasibility and efficiency of the curve regression model. All these research results can be used to guide the construction of the comprehensive transportation system in the Silk Road Economic Belt.

The merging zone in urban expressway is easy to turn into a traffic bottleneck just because of the complex interaction among vehicles there. But with the development of connected vehicle technology, it seems that this problem can be alleviated. This paper designs a system that can harmonize the interaction among connected vehicles in the merging zone, so that they can cross this region in a smaller cost—less average travel time and average fuel consumption. The controller of the system receives the real-time data from vehicles periodically, and sends instructions of the computed solution back to them. At last, Netlogo is utilized to act as a simulation platform to evaluate the proposed system, and the results show that the coordination system of connected vehicles can reduce cost significantly in the merging zone.

A pattern recognition approach is proposed for the Chinese digital gesture. We shot a group of digital gesture videos by a monocular camera. Then, the video was converted into frame format and turned into the gray image. We selected the gray image as our own dataset. The dataset was divided into six gesture classes and other meaningless gestures. We use the neural network (NN) combining convolution and Max-Pooling (MPCNN) for classification of digital gestures. The MPCNN presents some differences on the data preprocessing, the activation function and the network structure. The accuracy and the robustness have been verified by the simulation experiments with the dataset. The result shows that the MPCNN classifies six gesture classes with 99.98 % accuracy using the Max-Pooling, the Relu activation function, and the binarization processing.

The auto fault diagnosis complexity coefficient should be applied for repairing and maintaining price model according to modern auto complex structure. Based on the information entropy theory, this paper proposed the fault diagnosis complexity evaluation for the auto engine fault as a example, five hierarchies structure and evaluate index system were built, then worked out the uniform complexity information entropy value of the auto engine fault diagnosis, which was consistent with the auto repair and maintenance industrial actual situation. The information entropy evaluation provided theoretical basis for the existence and generation of the fault diagnosis complexity coefficient.

This paper deals with the problem of insensitivity and low-effect in the detecting feature points in lip regions. We propose a new feature points extraction model based on Harris algorithm, combining ideas of color statistic and graph connectivity. To detect feature points, firstly, we build a proper possibility statistical module of color component, with a specifying color model in lip regions, then we realize feature points filter module under the thought of graph connectivity. Experiments show that our algorithm has a better accuracy and stability, especially a competitive advantage in lip regions feature extraction.

In the electronic warfare, the jamming resource optimal distribution is always an important research topic. Through the existing interference effect assessment criteria, jamming effectiveness evaluation index, general jamming resource allocation model is established. Through the study of the integration of interference target, the distribution model is divided into two categories, one-to-one and much-to-little. The one-to-one model is dealt by Hungarian algorithm and the much-to-little model is dealt with the combination of Hungarian algorithm and dynamic programming algorithm, which is called secondary optimal distribution method. Finally the radar jamming resource assignment example shows the method is effective.

Communication network plays an important part in the construction of the smart power distribution and utilization system, and it covers wide area and has a great number of scattered signals. The paper summed up the advantages and disadvantages of several common power wireless communication technologies, and proposed a novel power broadband wireless system. In order to verify its performance, we tested the coverage, transmission rate etc. Meanwhile, the paper also analyzed the communication requirements of the smart distribution and utilization system in detail. Compared with the requirements, the test results show that the novel power broadband wireless system has the characteristics of wide coverage, high transmission rate and short transmission delay, which provides an effective wireless communication solution for the power distribution and utilization services.

This paper put forth two novel approaches to effectively improve the performance of mispronunciations detection in English learners speech. On one hand, a distance measure called Kullback–Leibler Divergence (KLD) between Hidden Markov Models (HMMs) is introduced to optimize the probability space of a posteriori probability; On the other hand, back end processing of normalization based on the variants of speakers is introduced to improve the performance of the system. Experiments on a database of 6360 syllables pronounced by 50 speakers with varied pronunciation proficiency indicate the promising effects of these methods by decreasing the FRR from 58 to 44 % at 20 % FAR.

When measuring three dimensional information of workpiece and object by laser triangulation method, one of the key issues about precision detection is how to quickly and accurately extract the center coordinate position of projected laser stripe. In the detection process of medium and heavy plate flatness, we use the cross line laser as the light source, attain image information by CMOS camera and store them in the computer. With digital image processing technology, we extract the center coordinate position, and utilize space projection matrix to convert image coordinate into a three-dimensional coordinate, thus obtaining the flatness of medium and heavy plate. Therefore, it will help improve the detection accuracy of the medium and heavy plate flatness to accurately extract the center coordinate position of the cross line laser stripe. Based on the characteristic that the pixels sum in the eight neighboring field of cross line laser center are four, this paper determine the coordinate of the center. Compared with the least square method, it verifies the reliability of the algorithm presented in this paper. The results can reach sub-pixel level and the system has high speed with processing time about 0.1252 s,improved by 92.22 %.

In order to segment adhesion ore image well, the adhesion ore image separation method based on concave points matching has been proposed. Firstly, adhesion ore images of conveyor belt were obtained by high-speed digital camera, and those images were preprocessed. Secondly, improved Harris corner detection operator was used to detect the corner points, and match concave point pair was determined by corner detection operator coupled with the circular template. Thirdly, sector searching was carried out in simply connected region to match concave point pair and find out the best match concave point pair by rectangular limiting. Finally, division line was determined between concave point pair, and adhesion ore image was completely segmented. Experimental results show the method has high segmentation accuracy and good segmentation effect.

In the paper the three-dimensional visualization system based on FlightGear, MFC and Matlab (Simulink) was designed. For future war mode that man-aircraft mixed with UAV cooperative engagement and enhance the demand of virtual realization effect of 3D visualization, the three-dimensional visualization system by functional modules was constructed and integrated each module to visual simulation; The system unions time-sequence actuation mechanism, virtual reality technology, UAV model rendering software, FlightGear visual simulation flight software and UAV dynamics Simulink and makes the flight data and flight visual to be Time-Succession, visible, and brings high participant operation to operator, enables each simulation module to be renewable and exchangeable. During numbers of simulation experimentation, this system implements a series of 3D visual formation flight simulation, air combat demonstration, weather conditions and geographical environment, which has three-dimensional visualization effect.

In order to ensure the accuracy of star sensor in high dynamic conditions, the sectional processing scheme is proposed for space-variant motion blurs. The degradation of star image may be characterized by a space-variant point spread function (PSF), and the PSF of blurred star spot was formulated by its nonuniform velocity across the image plane. Then, the image is divided into several rectangular sections and each contains an interesting star spot. The center of section is the location of star predicted by the attitude measurement equation, and its size is determined by the velocity of star spot and exposure time. Further, the PSF in each section can be approximately space-invariant. The scheme is illustrated by performing attitude determination against a restored space-variant blurred star image.

In this paper, a numerical method is presented for solving nonlinear Volterra–Fredholm–Hammerstein integral equations. The proposed method takes full advantage of Nyström method and Sinc quadrature. Nonlinear integral equations is converted into nonlinear algebraic system equations. Error estimation is derived which is shown to has an exponential order of convergence. The accuracy and effectiveness of the proposed method are illustrated by some numerical experiments.

Solid phase micro extraction(SPME), which is solventless, flexible, inexpensive and sensitive, plays an important role in pretreatment. Stainless steel wire can overcome the breakable defects of traditional quartz fiber, so it has been widely used as SPME fiber matrix. In order to develop a sample preparation instrument which use less organic solvent, own high enrichment quality and automated, this paper studied and developed a gas purge syringe micro extraction system, which only use stainless steel needle as extraction phase. The stainless steel needle is etched by hydrofluoric acid, without any coating adsorbent material. It is an automated and open system which contains heating sample matrix, condensing extraction phase and inert gas purging. Under optimized parameters, extraction experiments were performed, for enriching volatile and semi-volatile target compounds from the sample of 15 PAHs standard mixture. GC-MS analysis results indicated that a high enrichment factor was obtained from the system. The results demonstrate that system is potential in determination of volatile and semi-volatile analyzes from various kinds of samples.

In this paper, a novel approach is proposed for text-independent pronunciation quality assessment of Chinese students. We call the proposed method as double-models pronunciation scoring algorithm, which separates recognition from assessment stage. It can solve low recognition performance of standard method and score mismatch of nonstandard one. Applying the combination of Maximum Likelihood Linear Regression and Maximum A Posteriori adaptation achieves good recognition results for speech of Chinese students. Adjustment of scoring features signifies further improvement in correlation between machine scores and human judgment. The experimental results showed the proposed double-models technique reached good outcome for text-independent pronunciation quality assessment of Chinese students.

Sparse angle projection CT image reconstruction in medical diagnosis and industrial non-destructive testing has important theoretical significance and practical application value. In the paper, L1 norm was introduced as the CT images of regular constraint and optimization reconstruction model, and the method to solve it was presented based on the Split Bregman algorithm. Shepp-Logan numerical simulation experiments show that the image reconstructed by the traditional algebraic reconstruction algorithm of ART for sparse projection CT is poor. The Split Bregman may solve L1 regularization constraint model of sparse projection of CT with less number of iterations, fast reconstruction and good reconstruction quality. For the splitting factor of the algorithm, in a numerical range, the greater the reconstruction quality is better.

In the age of information security, whether a program is threatening or not is a crucial problem to solve. In this paper, a novel threat program judgment model based on fuzzy set theory is proposed. In the model, we derive a new evaluation function from multi-factor determined fuzzy synthetic function. Using the function, the program threat is evaluated by the membership of programs with great threat. Furthermore, we realize the judgment model and the experiment data shows its feasibility and effectiveness.

Aimed at the problem of adaptive detection threshold in the digital channelized receiver, this paper introduces a threshold generating method based on sorting statistics, which utilizes the magnitude vector in small serial number after ranking output amplitude of sub-channels from small to large order as a noise sample to form the threshold, but sorting operation of this method takes up a large number of FPGA resources. When intercepting and receiving the sky signal, this paper puts forward a simplified and improved method based on characteristics of clean sky signal environment and pulse signal environment. The sliding adaptive threshold method takes the forepart signal of the same channel as a noise sample to form the threshold.

Wireless sensor network has the characteristics of large-scale, ad-hoc network and wireless communication, it is one of research hotspots. The gateway plays a very important role as a conversion device between different protocols in the network. A kind of gateway with Ethernet, WI-FI, GPRS three access modes has been designed and realized in this paper, it realized the intelligent management of wireless sensor network and the conversion between sensor network protocol to TCP/IP. In the practical application test of nearly one year, gateway worked normally and performed stably. It can meet the storage, medical, household and other different application requirements.

There is useful data directly related to the end products quality, which is implied in the process data of traditional Chinese medicine (hereinafter referred to as TCM) production. Rapid process data collection, open data management and scalable monitoring technology, have great significance to achieve the direct quality control. Aiming at the procedure of TCM, it directly collects process date based on OPC technology and meanwhile stores real time data in the SQL database, and then develops monitoring software by using programming language C (C#). The result shows that the minimum monitoring system based on C# can improve the data acquisition and storage rate, and realize data open and sharing, with advantages of small memory footprint, simplified structure, and highly expansibility.

In order to improve the energy efficiency of electric vehicles, a regenerative braking control strategy of electric truck was developed to improve energy recovery based on braking security. After the prerequisites, the restrictions of ECE regulations, battery and motor were completed to ensure the braking security, the regenerative braking force allocation strategy was designed. Then a co-simulation of Cruise and Matlab of this control strategy was executed in Japan1015 operating cycle to evaluate the strategy effects. Simulation results show that the strategy proposed in this paper can recover as much as 11.48 % braking energy in Japan1015 cycle under braking security requirements. So this regenerative braking control strategy can significantly improve the economic performance for electric vehicles.

Nowadays, the technology of Faster than Nyquist (FTN) signaling has become one of the most developed technologies. Traditionally, FTN transmission is investigated for a point-to-point AWGN link. The demand to transmit FTN signal in frequency-selective fading channels is still growing dramatically. Rayleigh fading channels are the foundation of all frequency-selective fading channel models. The FTN signal transmits based on Reed Solomon (RS) codes and LMMSE algorithm in Rayleigh fading channel is surveyed in this paper.

Aiming at the status quo that the support equipment cannot well meet the support needs in the information war, the concept and connotation of the adaptability were defined basing on the thought of system engineering. And then the intrinsic relationship among support ability, the battlefield environment, support equipment, and its performance was systematically analyzed. And then the evaluation model on the adaptability of the support equipment basing on the entropy weight and TOPSIS method was constructed. And the evaluation model example of the support equipment in 9 kinds of environments was developed as the validation example. And it proved feasible and rational. At last, an application example in the performance measurement of supply and demand coordination is demonstrated.

Aiming at the problem that routing security for Geographic information cannot be guaranteed, a Secure Geographic based Routing Protocol of Aeronautical Ad hoc Networks (S-GRP) is proposed. Through identity-based public key cryptosystem, it makes authentication for neighbor node and establish a shared secret in the neighbor discovery phase, and then makes signcryption for data in the data for-warding phase. When in the next hop selection, it makes an integrated assessment for node location-speed factor and trust factor, selecting the optimal next hop. Security analysis and performance analysis for S-GPR show that the algorithm is effective and feasible.

In order to realize the timely management of available garbage recycling and utilization in daily life, so writing code based on Spring and MyBatis framework. Through the use of role-based access control principle, the system achieve different login roles display the different function of related modules, and gives part of the implementation code. The management of available garbage prompt the user, waste recycling stations, processing treatment station and ele-commerce company which make a well-connected cycle network. The system not only be beneficial to the service provider, but also reduce the number of garbage bin, strengthen the awareness of environmental protection, finally make high quality living environment. Web application framework based on Spring and MyBatis can solve problems such as low reusability of code, hard Extensible maintainability, poor performance and etc. through the analysis of experimental results.

Stable video has a significant effect on video based traffic parameters extraction system. This paper proposed a fast video stabilization method based on feature matching and histogram clustering. Firstly, the method extract ORB feature points on sub-images which are set in adjacent frames and then matches them based on Hamming Distance. Secondly, the motion consistency principle combined with histogram clustering is used to get correct matches which can be used to calculate the parameters of the affine transformation matrix. Finally, after filtering those parameters, stable frame can be obtained by warping the original frame using the affine transformation matrix. Experimental results show that the proposed method can effectively stabilize videos with low complexity in real time.

For the application of Time Domain Reflectometry (TDR) technology in landslide monitoring, a new mountain landslide slip amount calculation model based on TDR technology is put forward. This article describes the basic principles of TDR landslide monitoring and the theory of electromagnetic pulse propagation in a coaxial cable. Deduced calculation model of landslides slippage, conducted a laboratory simulation tests landslides, and discussed the landslides slippage effect on TDR waveform, get and verify specific calculation model by the test. The results showed that, TDR reflected voltage with the landslide slip amount is increasing, the different deformation point of the reflective voltage with the shift to a linear relationship. The two errors between calculation model slip and the actual slip are not more than 2 mm.

Image classifications the basis to solve visual tracking, image segmentation, scenes understanding and other complex visual tasks. Bag of words(Bow) model is initially applied in text classification area, introduced in image proceeding and recognition on account of its simple and effective. This paper follows the standard bag-of-words pipeline, but substitutes original SIFT descriptor with DSP-SIFT(domain-size pooling sift). Then, taking account of the truth that an DSP-SIFT was developed for gray images which limits its performance with regard to some colored objects, we present to add CN(color-name) descriptor to collect the color information to form visual words of image. The experimental results over fifteen scene categories and Caltech 101 datasets indicate that our method can achieve very promising performance.

Scene classification is an important task for computer vision, and Convolutional Neural Networks, a model of deep learning, is widely used for object classification. However, they rely on pooling and large fully connected layers to combine information from spatially disparate regions; these operations can throw away useful fine-grained information, and in natural scenes, there are many useless information which will increase computation cost. In this paper, mid-level discriminative patches are utilized to pre-process the full images. The proposed method which combines mid-level discriminative patches for preprocessing with CNN for feature extraction improved the efficiency of computation and are more suitable for classifying scenes. Firstly, full images are divided into discriminative parts. Then utilize these patches to go through CNN for feature extraction. Finally, a support vector machine will be used to classify the scenes. Experimental evaluations using MIT 67 indoor dataset performs well and proved that proposed method can be applied to scene classification.

The massive data puts forward higher requirements on the capacity of storage devices, but from a practical point of view, the increasement of capacity is far more behind the growth of data. Deduplication technique, for its high efficiency, few resource consumption and extensive application scope, comes to the fore among various data reduction techniques. The so-called data deduplication refers to find and eliminate redundant data among the storage system. For local storage system, the only one data object is needed to store to save limited storage space; for network system, not only storage space can be saved, but also transmission bandwidth can be reduced to increase the transmission rate. It is a compromise to achieve the purpose of efficient storage at cost of computational overhead. This article will introduce data deduplication techniques, describe basic principles and processes, summarize the main technique of the current study and provide recommendations for future development.

To improve the classification accuracy of foreign language mobile learning (m-learning) strategies applied by college students, an evaluation model based on principal component analysis (PCA) and support vector machine (SVM) is proposed. PCA was first employed to reduce the dimensionality of an evaluation system of foreign language m-learning strategies and the correlation between the indices in the system was eliminated. The first 5 principal components were extracted and a classification model based on SVM was established by taking the extracted principal components as its inputs. Gaussian radial basis function was adopted as the kernel function and the optimal SVM model was realized by adjusting the parameters C and g. The classification result was compared with those produced by a BP neural network model and a single SVM model. The simulation results prove that the PCA-SVM model has a simpler algorithm, faster calculating speed, higher classification accuracy and better generalization ability.

Most of the traditional machine learning algorithms are based on the vector, but in tensor space, Tensor learning is useful to overcome the over fitting problem in vector-based learning. In the meanwhile, tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful tensor training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object and fuzzy membership, this paper presents a novel fuzzy classifier for Image processing based on support vector data description (SVDD), termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database.

FTN has been proposed for several decades, and it is attractive for the reason that it can boost the symbol rate without changing the power spectral density. Actually, FTN has been used in some 5G standards. FTN signaling detection is an important element in FTN signaling scheme. In this paper, we shall introduce some FTN signaling detection algorithms that were proposed in literatures.

The Speaker change detection task usually contains two passes: potential change detection and verification. BIC criterion is often utilized to measure the dissimilarity. However, insufficient data may lead to modeling error, which cannot represent one speaker correctly. In this paper, we propose combining prosodic feature with LSP (Linear Spectrum Pair) feature to detect change points. Prosodic feature can contribute to eliminate false change points locally. The experiments show an improvement compared with the traditional speaker change point mechanism.

CPU/GPU heterogeneous computing has become a tendency in scientific and engineering computing. The level of heterogeneity in modern computing systems gradually rises, and CPU/GPU Heterogeneous system contains three levels of heterogeneity. Conventional parallel computation models cannot be used to estimate the running time under the CPU/GPU heterogeneous computing environment. In this paper, a new model named VLogGP is proposed, and the communication and memory access characteristics are both abstracted based on CPU/GPU heterogeneous system. We map the model to TH-1A platform, and measure all model parameters for this kind of platforms. The model can be used to study the behavior of parallel applications, estimate the execution time and guide the optimization of parallel programs.

Unmanned surveillance system is mainly composed of objects detection and classification tasks. Traditional methods which are mostly based on vision cannot obtain the velocity and distance information of the objects. So they suffer from high false alarm rate and miss alarm rate. In this paper, a new kind of unmanned surveillance system based on radar and vision fusion is designed and realized. The main two contributions of the paper are: (1) We first introduce the objects’ area which is calculated by fusing the radar and vision data as a robust feature for objects detection and classification; (2) We proposed a new approach to detect human of different postures away from other objects. Experiments show the effectiveness and robustness of the proposed system.

In the age of Big Data, input determines output. There is a large amount of data on the internet, but little knowledge. So researchers develop different kinds of methods to automatically extract knowledge from different data platforms. The traditional methods of supervised learning cost more time and labor, which are willing to be gradually replaced by the semi-supervised and unsupervised learning methods. In this paper we proposed a new semi-supervised method to complete this task, which costs just little, called TSVM (Transductive Support Vector Machine). In order to improve the accuracy and the intelligent level, we also add the Word Embeddings to the semi-supervised method. The AP (Affinity Propagation) algorithm makes a contribution to the word clustering automatically. Experimental results demonstrate a better performance to extract the attribute information in the military transportation domain from the Wikipedia compared with the traditional supervised leaning method.

In large-scale chemical processes involving a number of control loops, oscillations propagate to many units through control loops and physical connections between units. The propagation may result in plant-wide oscillation that is closely related to product quality, costs and accident risk. In order to detect the root cause of the plant-wide oscillation, this paper presents a new method that is based on interpretative structural model (ISM) that is established according to process topology. Firstly, a topological graph is obtained by considering process prior knowledge of the flowchart. Then according to the graph the adjacency matrix and reachability matrix are calculated. After that a multilayered structure of all control loops is obtained by establishing ISM. Thus the root causes of the oscillations are determined by propagation analysis. The superiority of this method is that the oscillation source can be effectively determined and clearly interpreted by the ISM. An application to a typical process from Eastman Chemical Company plant is provided to illustrate the methodology.

Finding key genes associated with diseases is an essential problem of disease diagnosis and treatment, and drug design. Bioinformatics takes advantage of computer technology to analyze biomedical data to help finding the information about these genes. Biomedical literatures, which consists of original experimental data and results, are attracting more attention from bio-informatics researchers because literature mining technology can extract knowledge more efficiently. This paper designs an algorithm to estimate the association degree between genes according to their co-citations in biomedical literatures from PubMed database, and to further predict the causative genes associated with a disease. The paper also uses hierarchical clustering algorithm to build a specific genes regulation network. Experiments on uterine cancer shows that the proposed algorithm can identify pathogenic genes of uterine cancer accurately and rapidly.

CP-ABE (Cipher-text Policy Attribute Based Encryption) can help providing reliable, fine-grained access control in untrusted cloud storage environment, since users can access to data files only if their attributes satisfy the access policies associated with the files. However, CP-ABE has two main drawbacks: its policies are not expressed using standard languages and it can’t support non-monotonic policies. So we extended CP-ABE to support XACML (eXtensible Access Control Markup Language) based policy transformation and to support logical NOT in policies through De Morgan’s Laws. And then we applied it to a secure overlay cloud storage system called FADE to deploy access control for Amazon S3 cloud storage service. The simulation results show that our proposal is practical and time efficient.

Fault diagnosis plays an important role in diverse engineering areas including industrial control loop systems, mechanical systems, etc. Bayesian methods are a class of data-driven fault diagnosis methods developed in recent years. However, one difficulty with Bayesian methods is that they do not deal with the case that there is uncertainty about the underlying mode in the historical data. For this problem, a new approach is proposed in this paper, through which the ambiguous modes are softly classified by combing historical data, current evidence and the prior knowledge under a Bayesian framework. In addition, weighted kernel density estimation instead of classic histogram method is used for likelihood estimation to enhance diagnosis. The proposed Bayesian approach is tested on the fault diagnosis of Tennessee Eastman (TE) process using benchmark data and the proposed approach performs better in comparison with typical previous methods.

Grammatical evolution (GE) is an important automatic programming technique developed on the basis of genetic algorithm and context-free grammar. Making changes with either its chromosome structure or decoding method, we will obtain a great many GE variants such as $$\pi $$πGE, model-based GE, etc. In the present paper, we will examine the performances, on some previous experimental results, of GE and $$\pi $$πGE with model techniques successfully applied in delineating relationships of production rules of context-free grammars. Research indicates modeling technology suits not only for GE constructions, but also for the analysis of GE performance.

Muscle fatigue often occurs in daily life and work. The assessment method of muscle fatigue based on the surface electromyography (sEMG) has been well reported in some comprehensive reviews. Recently, the time domain parameters, frequency domain parameters, discrete wavelet transform and non-linear methods have been widely used for the evaluation of muscle fatigue. In this paper, one fatigue indice was proposed: area ratio modified index (ARMI) was compared to seven assessment indices: root mean square (RMS), integrated electromyography (IEMG), mean power frequency (MPF), median frequency (MF), wavelet energy (WE), wavelet entropy (WEn) and Lemple–Ziv complexity (LZC). Ten healthy participants completed a fatigue experiment with isometric contraction of biceps brachii. It was showed that there was a significant positive rate of change of RMS, IEMG, WE and ARMI of sEMG, while a negative rate of change of MPF, MF and LZC of sEMG when fatigue occurred, and WEn of sEMG basically had no change. Meanwhile, the percentage deviation in ARMI was greater than the other seven indexes. It was proved that ARMI can be used as effective indicator and was more sensitive than the other seven indexes to evaluate the muscle fatigue.

Qualified for the detection of cases of industrial products, the proposed non-contact detection system based on the MFC. CCD based imaging and product distribution geometry and boundary conditions, combined with digital image processing technology for industrial product testing, given the overall design concept detection system, complete MFC detection system based on PC software, use the button controls to achieve the start-stop control of the process, with the sound of an alarm signal is given substandard products, software for image preprocessing first, then the outline of the product for testing, with the realization of testing products based matching approach. Experimental results show that the system can be very good to complete product testing capabilities, the system is robust and real-time, accurate detection rate is higher than 998, the detection time of less than 10 ms, fully meet the requirements of industrial inspection.

Set Pair Analysis (SPA) theory is a relatively new approach to deal with uncertainty, and has been successfully applied to areas including decision making, data fusion, product design, etc. The set pair is defined as a pair that consists of two interrelated sets, SPA’s main idea is considering the relation of certainties and uncertainties of the set pair, then analyzing and processing the relation. In this study, we employ SPA theory in pattern recognition to enhance accuracy and speed. Class $$c_j(j=1,2,...,n)$$cj(j=1,2,...,n) is divided into several subclasses which are represented as binary connection number, the value of coefficient i is optimized by genetic algorithm. To validate the usefulness of our method, experiments were carried out and the results indicated that accuracy and speed may be improved significantly by using our method.

When collecting blasting signal, the blasting signal is inevitably mixed with a certain noise signal, and the noise signal will affect the analysis of the blasting seismic wave, and it is not conducive to formulate corresponding measures. The blasting signals are periodically and changeable, the previous denoising methods are not totally fit to blasting signals, sometimes the original signals are regarded as noise signal and are being removed. In this paper, a new algorithm combine the soft and hard threshold function is introduced and the Simulation experiments and results analysis show that it can not only effectively remove the noise signals, but also save the details of the blasting signals.

The level set method (LSM) has been widely utilized in image segmentation due to its intrinsic nature which sanctions to handle intricate shapes and topological changes facilely. The current work proposed an incipient level set algorithm, which uses histogram analysis in order to efficiently segmenting images. The computational intricacy of the proposed LSM is greatly reduced by utilizing the highly parallelizable lattice Boltzmann method (LBM). The incipient algorithm is efficacious and highly parallelizable. Recently, with the development of high dimensional astronomically an immense-scale images contrivance, the desideratum of expeditious and precise segmentation methods is incrementing. The present work suggested a histogram analysis based level set approach for image segmentation. Experimental results on real images demonstrated the performance of the proposed method. It is established that the proposed segmentation methods using Level set methods for image segmentation achieved 0.92 average similarity value and average 1.35 s to run the algorithm, which outperformed Li method for segmentation.

Wheel slip affects the accuracy of dead-reckoning based localization techniques as they introduce measurement errors in odometers. This investigation presents a new slip compensation scheme that uses neuro-fuzzy technique for self-calibration of odometer. The proposed self calibration procedure can be executed in robot navigating environment rather than having a separate test platform and neuro-fuzzy system employed can able to learn the dynamics of wheel slip from the data autonomously. The wheel slip data are generated using the standard data generated by the laser range finder with known landmarks and measurement data from the odometer. The proposed technique is implemented on a four wheel mobile robot navigating in a concrete terrain and the localization performance was evaluated with mean square error (MSE) of 0.0382 m for a 6.12 m run during training phase and 0.0442 m for a 4.20 m test run of the robot.

Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic.

The acoustic detection was novel method for natural gas pipeline leak. In order to improve accuracy and stability detection system, redundant structures of multiple sensor was necessary. The complex background noise and various working-condition adjective caused uncertainty, inadequacy and inconsistency of acoustic signal. In the process of multisource fusing identification, the high conflict among different sensor signal was inevitable. In this paper, the decision fusion model is built to identify natural gas pipeline leak. The decision fuse algorithm procedure includes signal preprocessing, feature extraction, basic relief assignment by BP neural network and decision fusion utilizing DSmT (Dezert–Smarandache Theory) and PCR5 rule. Experimental results show that the decision fusion model is effective and feasible. The information conflict of among different acoustic sensors is resolved perfectively. The fusion results for 150 group test samples indicate that the accuracy of leak detection reach 94.7 % under the given condition.