Information Computing and Applications

This paper presents a non-strategic behavior model for the application layer multicast networks in which the natural selfishness of the peers is exploited in order to maximize the overall utility of the network. In the non-strategic solution concept, the decisions that a peer might make does not affect the actions of the other peers at all. In other words, it incorporates the non-strategic decisions in order to design the mechanism for the overlay network. We have modeled the application layer multicast network as a non-strategic competitive economy based on the theory of consumer-firm of microeconomics by leveraging the concept of Walrasian general equilibrium.

In order to enhance the measure efficiency in the research field of importance measure for dynamic fault tree which contains some structural information, a new method using this information is presented. By means of creating an object for every node, identifying the isomorphic nodes and computing only once for the same kind of isomorphic nodes, this method reduces the number of states and the computation time. This method also introduces the idea of perturbation and extends the BI importance factor, then gives a concept of relative importance factor which is obtained according to the type of independent module and has no relation with the dependency within the independent module. The importance factor can be computed by the combination of the relative importance factors. Finally this method is applied on a real high reliability computer system. The analysis shows that this method can provide a credible importance measure and greatly reduce the time expense if the system structure has some replicate components or backups.

Interference between the control rules is an important index of the static features of a fuzzy controller. The book named Principle and Application of Fuzzy Control gives the method to calculate it. A test was carried to check whether the method was coincident with the law of the interference intensity or not. In the test both the kind of the subset and the parameters of each subset were changed according to the law of the interference intensity. The test shows that it is not easy to put the method into use because of its intricacy, and the result couldn’t be coincident with the law of the interference intensity in some case. So the method needs to be bettered.

Data access latency is one of the important factors of system performance in federated data grid. However, different replication strategies resulted in different access latency. This paper proposes a model of replication strategy in federated data grid that is known as dynamic replication for federation (DRF). DRF uses the concept of a defined ‘network core area’ (NCA) as the designated search area of which a search will be focussed. However, the area known as NCA is not static and it is bound to change if data requested cannot be found. This paper will also highlight how NCA is defined and reallocated if a search fails. Results from the analysis show that the DRF proposed in this paper is superior to Optimal Downloading Replication Strategy (ODRS) in terms of wide area network bandwidth requirement and in the average access latency data where the results was 48% higher than for

ρ

j

=0.3, 0.4, 0.

Task scheduling is an integrated component of computing. One of the goals of P2P Grid task scheduling is to achieve high system throughput while matching application needs with the available computing resources. This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS). In this paper, we integrate Grid with P2P to propose, a novel approach for QoS guided task scheduling algorithm called QoS guided P2P Grid Metascheduler(QPGM) using Min Mean Computation algorithm, which gives improved performance on load balancing and generates an optimal schedule so as to complete the tasks in a minimum time and to utilize the resources efficiently. Simulation results demonstrate that QPGM task scheduling algorithm can get better effect for a large scale optimization problem.

Replication provide an effective way to enhance performance, high availability and fault tolerance in distributed systems. There are numbers of fault tolerant and failure recovery techniques based on replication. These recovery techniques such as Netarkivet’s data grid and fast disaster recovery mechanism for volume replication systems were implemented in two-replica distribution technique(TRDT) or primary-backup architecture. However, these techniques have its weaknesses as they inherit irrecoverable scenarios from TRDT such as double faults, both copies of a file are damaged or lost, missing of the content index in index server table and index server has generated checksum error in content index. In this paper we propose the failure recovery based on the Neighbor Replication Distribution technique (NRDT) to recover the irrecoverable scenarios and to improve the recovery performance. This technique considered neighbors have the replicated data, and thus, maximize the fault tolerant as well as reliability in failure recovery. Also, the technique outperform the TRDT in failure recovery by reducing the irrecoverable cases in TRDT. It also tolerates failures such as server failures, site failure or even network partitioning due to it has more the one back up or replica.

Divide and rule is an effective strategy to solve large and complex problems. We propose an approach to make agent can discover autonomously subgoals for task decomposition to accelerate reinforcement learning. We remove the state loops in the state trajectories to get the shortest distance of every state from the goal state, then these states in acyclic state trajectories are arranged in different layers according to the shortest distance of them from the goal state. So, to reach these state layers with different distance to the goal state can be used as the subgoals for agent reaching the goal state eventually. Compared with others, autonomy and robustness are the major advantages of our approach. The experiments on Grid-World problem show the applicability, effectiveness and robustness of our approach.

Coastal areas are an important part of Tangshan, which groundwater has been over exploited for a long time. The hazard of land subsidence has appeared and kept aggravating continuously. By studying the hydrogeology and engineering geology condition, a new understanding of deformation mechanism of land subsidence in Tangshan coastal areas has been found. According to the distribution of groundwater funnel(GF) and monitor data of groundwater, firstly, the mathematical model of developing tendency of GF is established and it is verified the results are similar with the real results. Then the conception of groundwater dynamics coefficient has been introduced and the development tendency model is established according to the depth value of GF center water above. Finally, combine the mathematical model and the rate of land subsidence, establish the numerical analysis model about land subsidence. The results conform to the truth of subsidence of coastal areas.

In this paper based on the traditional stochastic inventory control problem, namely, the Newsboy problem, considered the factor of inventory item which has an impact on the decision-making model, a new model is built up. While assuming the form of demand to meet the adding form, and considering the impact of the price on the demand rate and the impact of the demand rate on inventory item, we discuss a new subscription model, and give corresponding calculation methods to determine the optimal order quantity and optimal sales price. Model in this paper is an extension of existing models, while the known model is a special case of this model. At last an simple example is given.

To investigate the implementation of Grey Correlation Analysis in the research of the hospitalization fee, to analyze the influential factors of the hospitalization expenses, and to provide proof for medical expenses containing measurements.To analyzed the in patient hospital fee of the top ten diseases of internal medicine inpatient in a hospital by Grey correlation analysis The grey correlation analysis shows the correlative degree between each kind of expense and their rankings are as follows: pharmaceutical fee 0.938, bed fee 0.8411, laboratory test fee 0.8331, radiation diagnosis fee 0.7655, and examination fee 0.6108. It indicates that for the internal medical disease, the dominating factor in hospital expenditure is the pharmaceutical fee. It is the medicine expenses which should be cut mostly in order to control the excessive increase of hospital medical expenditure. In the study of hospital medical expenditure, the grey correlation analysis fully utilized information and data, and led to more reasonable conclusions.

The order-k Voronoi diagrams are difficult to construct because of their complicated structures. In traditional algorithm, production process was extremely complex. While discrete algorithm is only concerned with positions of generators, so it is effective for constructing Voronoi diagrams with complicated shapes of Voronoi polygons. It can be applied to order-

k

Voronoi diagram with any generators, and can get over most shortcomings of traditional algorithm. So it is more useful and effective. Model is constructed with discrete algorithm. And the application example shows that the algorithm is both simple and useful, and it is of high potential value in practice.

Wang et al. proposed a double verifiably encrypted signature (DVES) scheme which can be used to design the contract signing protocol systems. In this paper, we propose an efficient contract signing protocol based on the DVES scheme. A semi-trusted third party is involved in our protocol to ensure firness. Moreover, the new contract signing protocol satisfies the desirable properties: unforgeability, opacity, extractability, timeliness, effectiveness and fairness.

Pervasive environments become a reality and involve a large variety of networking smart devices. This ambient intelligence tends to complex interactions. Lots of researches have been done on intelligent and reactive architectures able to manage multiple events and act in the environment. In the Robotics domain, a decision process must be implemented in the robot brain or a collective intelligence to accomplish the multimodal interaction with humans in human environment. We present a semantic agents architecture giving the robot and other entities the ability to well understand what is happening and thus provide more robust processing. We will describe our agent memory. Intelligence and knowledge about objects in the environment is stored in two ontologies linked to a reasoner an inference engine. To share and exchange information, an event knowledge representation language is used by semantic agents. This pervasive architecture brings other advantages: cooperation, redundancy, adaptability, interoperability and platforms independent.

E-payment protocol allows two or more users to securely exchange e-cash and digital product among them over an open network. There are some problems in the E-payment applications of cross-domain and cross-organization scenarios because of certificate-based authentication and digital signature, like inconsistent public key certificates and a heavy certificate management burden. ID-based cryptography is adopted to solve those problems, but it suffers the key escrow issue. Certificateless cryptography has been introduced to mitigate those limitations. A certificateless signature and authenticated key exchange scheme (CL-SAKE for short) is proposed, and its security is proved in the extended random oracle model. As an application, an E-payment protocol based on the new CL-SAKE is then proposed, which achieves unforgeability and un-reusability of e-cash, customer anonymity and fair exchange.

Short digital signatures are essential to ensure the authenticity of messages in low-bandwidth communication channels and are used to reduce the communication complexity of any transmission. A new short signature scheme based on the bilinear pairing in the standard model is introduced. The proposed scheme has short public parameters and the size of the signature achieves 160 bits. In addition, under the

n

-Exponent Computational Diffie-Hellman Problem(

n

-CDH), the new scheme is provable security. To the best of authors knowledge, this is the first scheme whose signature size achieves 160 bits based on the bilinear pairing.

As the rapid growth of personal credit business, we have always been seeking to establish an effective risk assessment model to achieve low costs and better accuracy of decision-making. Over the past few years, the so-called combined algorithms have appeared in many fields, but they are always useless in the field of individual credit risk assessment. So we constructed a practical method based on combined algorithms, and we tested it empirically. The result shows that the application of the method can achieve better accuracy than the BP neural network.

QoS driven service selection as an important way to satisfy user’s constraint on quality and maintain the runtime performance of services, has received much attention. This paper proposes a method for evaluating the QoS trustworthiness which reflects the possibility that the service can perform as its estimated QoS. Besides this, trustworthiness QoS driven service selection is also proposed. The experiments shows the proposed approach can reflect the dependent relation between estimated QoS and the environmental context effectively, and can insure the accuracy of trustworthiness evaluation. Meantime, the proposed service selection approach can improve the actual runtime performance of the selected service.

Three-party password-based authenticated key exchange (3-party PAKE) protocols enable two communication parties, each shares a human-memorable password with a trusted server, to establish a common session key with the help of the trusted server. We propose a provably-secure 3-party PAKE protocol using bilinear pairings, and prove its security in the random oracle model. The proposed protocol requires four communication steps, which is more efficient than previous solutions in terms of communication complexity. In addition to the semantic security, we also present the authentication security to resist the undetectable on-line dictionary attacks.

Safety evaluation is one of the most effective countermeasures to improve the safety in enterprises. Bayesian network based on FTA is a new safety evaluation method. On the basis of FTA, combining with the advantages of Bayesian network, the new method relax the harsh conditions of FTA which make it used in a wider area. This method is applied to the safety evaluation of underground haulage system in one mining area of Kailuan Coal Group Corporation Ltd. Through the result of the analysis we point out the most important factors influencing the safety state. The corresponding countermeasures are put forward too. The evaluation result indicates that this method is easy and can be popularized.

Accurate evaluation of QoS (Quality of Services) is critical for web services in business applications. In this paper a multi-factor evaluation approach is propose to evaluate QoS of web service. This approach synthetically considers service providers, the context of customers, and historical statistics to evaluate the QoS. The experimental results demonstrate that our approach can effectively obtain accurate QoS evaluation for web services.

Coverage-based fault localization techniques assess the suspiciousness of program entities individually. However, the individual coverage information cannot reveal the execution paths and to some extent it simplifies the executions. In this paper, the control flow analysis is adopted to analyze the executions first. Second, the edge suspiciousness is used to calculate the failed executions distribution to different control flows. By comparing different failed executions distributions of blocks covered by the same failed execution path, we propose the bug proneness to quantify how each block contributes to the failure. Similarly, the bug free confidence is also proposed to represent the possibility of bug free for blocks covered by a passed execution path. At last, the weighted coverage information statistic is proceeded and the weighted coverage based fault localization technique is brought out. We conduct several experiments to compare our technique with an existing representative technique by using standard benchmarks and the results are promising.

Several conference proceedings have been infiltrated by fake submissions generated by the SCIgen computer program. Due to the fictional content the chapter “The Influence of Cacheable Models on E-Voting Technology” by “Chen-shin Chien and Jason Chien” has been retracted by the publisher. Measures are being taken to avoid similar breaches in the future

Searching in peer-to-peer is started by flooding technique. This technique produces huge redundant messages in each hop. These Redundant messages limit system scalability and cause unnecessary traffic in a network. To improve this searching technique and reduce redundant messages, this paper proposes a novel algorithm called HybridFlood. In HybridFlood algorithm, flooding scheme divided into two phases. At the first phase the algorithm follows flooding by limited number of hops. In the second phase, it chooses nosey nodes in each searching horizon. The nosey nodes are nodes, which have the most links to others. These nodes maintain the data index of all clients. The proposed algorithm extends the search efficiency by reducing redundant messages in each hop. Simulation results show that the proposed algorithm decreases 60% of redundant messages and saves up to 70% of searching traffic.

Service and resource discovery has become an integral part of modern network systems, in which area all researches are aiming at RESTful Web services and SOAP Web services separately. Basically, a RESTful Web service is a simple Web service implemented by using HTTP protocol and the principles of REST, and does not address features such as security. On the other hand, the SOAP Web service is more feature rich, at the cost of increased complexity, yet still lacks a true resource-centric model. As there are apparent differences between RESTful Web services and traditional SOAP Web services, it’s difficult to perform service comparison, matching and evaluation. Therefore, the gap between them needs to be eliminated, and we prefer to employ ontology, which is a formal representation of the knowledge by a set of concepts within a domain and the relationships between those concepts. In this paper, a service registry framework based on ontology model is proposed, which is called Concept Operation Ontology Model (COOM), to avoid the differences between RESTful Web services and traditional SOAP Web services, and illustrate the Web service matching mechanism based on this ontology model. The result shows that this service registry framework has unified the interface of RESTful Web services and SOAP Web services, and returned the best service alternatives with higher similarity when performing service matching.

In Cloud Computing, service availability and performance are two significant aspects to be dealt with. These two aspects can deteriorate or even stopping the services of Cloud Computing, if they are not taken into account. Users see that cloud computing delivers elastic computing services to users on the basis of their needs. This paper aims at improving operation of service of the Cloud Computing environment. Cloud service must be available some is the situations and powerful being by a response time reduced at a user’s request. To meet this aim, we propose, in this paper, two approaches which aim at returning a better availability of Datacenters without deteriorating the performances for the answers of the users. The first uses the principle of the messages of availability and the second uses the principle of reservation in advance.

The aim of this paper is to introduce a new statistical approach for software fault localization. To this end, a novel weighted predicate tree,

P-network

, has been introduced. The main contribution of the paper is to consider the behavior of branch statements, namely predicates, together, in failing and passing executions and detect those predicates having different behavior as fault relevant predicates. In order to assess the difference in behaviors of predicates together a null hypothesis testing has been used. The predicates with higher different ratios in failing and passing runs are selected as the nodes of the

P-network

. By using a BFS method on

P-network

all faulty predicates could be found. After that, by ranking the faulty predicates we are able to find the most relevant faulty ones, which might help the debugger easily locate the bug. The experiments on Siemens test suite reveal promising results.

For its simplicity and applicability, fuzzy

c

-means clustering algorithm is widely used in image segmentation. However, fuzzy

c

-means clustering algorithm has some problems in image segmentation, such as sensitivity to noise, local convergence, etc. In order to overcome the fuzzy

c

-means clustering shortcomings, this paper replaces Euclidean distance with Mahalanobis distance in the fuzzy

c

-means clustering algorithm. Experimental results show that the proposed algorithm has a significant improvement on the effect and efficiency of segmentation comparing with the standard FCM clustering algorithm.

The next-generation of wireless systems represents a heterogeneous environment with different access networks technologies that differ in bandwidth, latency or cost. The most challenging problem is the seamless vertical mobility across heterogeneous networks. In order to improve the accuracy and efficiency of the vertical handover for such heterogeneous networks, this paper proposes a novel handover network decision mechanism with Q

O

S provision based on analytic hierarchy process (AHP) and grey relational analysis (GRA) methods. We have successfully simulated and tested our approach using the OPNET simulation tool. The simulation results revealed that the proposed algorithm can not only work efficiently for 3G (TD-SCDMA), WLAN (IEEE 802.11),4G system but also reduce the complexity of implementation significantly.

This paper presents a novel automatic Flickr photos annotation method by ranking user-supplied tags and expanding the top ranked user-supplied tags. Firstly, user-supplied tags are filtered to obtain initial tags by noisy tags pruning. Secondly, the initial tags are ranked using manifold-ranking algorithm. In manifold-ranking process, the photo to be annotated is divided into several regions, and then these regions are acted as queries to launch the manifold-ranking algorithm which ranks the initial tags according to their relevance to the queries. Next, using Flickr API, top ranked initial annotations are expanded by a weighted voting policy. Finally, we combine top ranked initial tags with expanding tags to construct final annotations. Experiments conducted on Flickr photos demonstrate the effectiveness of the proposed approach.

Network video makes communication much easier. But data security becomes a troublesome issue. Fully using the characteristic of chaotic systems, the embedded system gets designed to enhance the security of network video. The FPGA and uclinux are adopted as the platform. The Qi hyper chaotic, Logistic mapping, Baker mapping and Cat mapping algorithm is applied to video encryption. Combination of on-line and off-line encryption, random encryption and double encryption methods make the security of data stronger. The system has been tested to run a long time and proved to be stable with a high security. As a platform, the system implements the complex algorithms at a very low cost: a 87MHz processor and 10M memory. But it still has the potential to develop more complex algorithms and applications. So the system has a broad prospect both in the area of application and research.

This paper takes an investigation into building and fusing multiple phone recognizers in the phonotactic system for language recognition. The phone recognizers are built using both phonetic and acoustic diversification. The phonetic diversification is achieved by training multiple phone recognizers on speech corpus of different languages. While the acoustic diversification is implemented in several ways, including using different acoustic features, different phone modeling techniques and training paradigms. As some phone recognizers are highly correlated with each other, we propose a performance optimization (PO) criterion to select a set of complementary phone recognizers for fusion. Experimental results on the NIST 2007 Language Recognition Evaluation (LRE) 30-s test set show the effectiveness of the proposed approach.

The maximum nominal data rate specified in IEEE 802.11a is up to 54Mbps.However, in practice, we found that the data rate which users could get is much lower than that. In order to accurately estimate the ability of IEEE 802.11a to support data services, we improved the existing two-dimensional Markov chain models of the backoff window scheme, and analyzed the efficiency of the IEEE 802.11a standard for wireless LANs in non-saturation conditions from the perspective of framing efficiency and medium access control (MAC) layer transport efficiency in this paper. Specifically, we derived an analytical formula for the protocol actual throughput. The results showed that the throughput of non-saturation could exceed the saturated one. To a certain degree of non-saturation, the network throughput can be optimal. And under the condition of different frame length, the performance in RTS/CTS mode is different with that in basic access model which can be measured by a switching threshold we give in the last of the paper.

Computational models of the artificial intelligence such as soft set theory have several applications. Soft data reduction can be considered as a machine learning technique for features selection. In this paper, we present the applicability of soft set theoryfor feature selection of Traditional Malay musical instrument sounds. The modeling processes consist of three stages: feature extraction, data discretization and finally using the multi-soft sets approach for feature selection through dimensionality reduction in multi-valued domain. The result shows that the obtained features of proposed model are 35 out of 37 attributes.

To minimize the execution time (makespan) of a given task, an efficient task scheduling algorithm (ETSA) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. ETSA builds from eliminating transmission collisions and idle gaps between two successive data transmissions. Simulation results are presented to demonstrate the impacts of different network parameters on the number of rounds, makespan and energy consumption.

Uncovering the overlapping community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Here three fuzzy

c

-means methods, based on optimal prediction, diffusion distance and dissimilarity index, respectively, are test on two artificial networks, including the widely known ad hoc networks and a recently introduced LFR benchmarks with heterogeneous distributions of degree and community size. All of them have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems. Moreover, successful applications to real world networks confirm the capability of the methods.

The present paper places specific swarm based optimization methods that are the predator prey optimizer, the symbiotic algorithm, the cooperative co-evolutionary optimizer and the bees’ algorithm in color image segmentation framework to offer global pixels clustering. The Predator prey optimiser is mainly designed to create diversity through predators to permit better segmentation accuracy. The symbiotic one is proposed to allow finer search through a symbiotic interaction with varying parameters. The cooperative co-evolutionary optimizer which results in a good quality of image segmentation through interaction between three species where each of them evolves in an independent color space through a standard particle swarm optimizer and the bees algorithm which is proposed to offer the most accurate results based on a neighborhood search.

Considering the segmentation results of region growing depend on two key factors: seed selection and growing strategy, this paper proposed a method of adaptive seeded region growing based on edge detection, texture extraction and cloud model. Our work included two aspects. Firstly, we proposed a new method to extract region seeds automatically based on spectrum features, edge information and texture features. According to two conditions defined by us, region seeds could be extracted as accurately as possible. Secondly, we proposed an adaptive region growing strategy based on cloud model. Our strategy consisted of three major stages: expressing region by cloud model, calculating the qualitative region concept based on the backward cloud generator, and region growing based on cloud synthesis. The experiment results demonstrate seed extraction based on spectrum features, edge information and texture features has a good accuracy, and almost all of the extracted seeds are located at the homogeneous objects inner. The experiment results also demonstrate the adaptive region growing strategy based on cloud model makes regions grow not only in a simultaneous way but also with inner homogeneity.

The aim has been to achieve the highest degree of possible concurrency in the execution of distributed program modules. To achieve this, a new invocation reordering algorithm is offered in this paper. The algorithm attempts to increase the time interval between each remote call instruction and the very first instructions using the values effected by the remote call. In order to increase the time distance, the algorithm reshuffles invocations, when possible, such that local invocations move in between remote calls and the instructions applying the results of the calls. The evaluation results indicate that the proposed algorithm provides higher degree of concurrency compared with the existing instruction reordering algorithms.

In order to cope with the multidimensional non-linear optimization problems which involved a great number of discrete variables and continuous variables, a self-organizing learning algorithm (SOLA) was proposed in this paper, in which the parallel search strategy of genetic algorithm(GA) and the serial search strategy of simulated annealing (SA) were involved. Additionally, the learning principle of particle swarm optimization(PSO) and the tabu search strategy were adopted into the SOLA, wherein the integrated frame work was different from traditional optimization methods and the interactive learning strategy was involved in the process of random searching. SOLA was divided into two handling courses: self-learning and interdependent-learning. The local optimal solution would be achieved through self-learning in the process of local searching and the global optimal solution would be achieved via the interdependent learning based on the information sharing mechanism. The search strategies and controlled parameters of SOLA were adaptively fixed according to the feedback information from interactive learning with the environments thus SOLA is self-organizing and intelligent. Experiments for the multidimensional testbed functions showed that SOLA was far superior to traditional optimization methods at the robustness and the global search capability while the solution space ranged from low-dimensional space to the high-dimensional space.

Aiming at the disadvantage of cognitive map which expressed the concept causality in one direction now, Set Pair Cognitive Map Model is proposed which compromise three-dimensional measurement methods (positive, negative and uncertain) of Set Pair and the cognitive map. First, considering the time factor in the premise, Set Pair Cognitive Map Time Three Dimensional Model which is analysis dynamic model with time trend is given; then, further considering the spatial characteristics that impact on the concept, Set Pair Cognitive Map Space-Time Multi-Dimensional Model is given. Using Set Pair Situation Table, it can be obtained the development trend of causal relationship between concepts with multi-state; Finally, Analysis indicates that two basic theorems of Set Pair Cognitive Map Model-Concepts Equivalent transformation Theorem and Set Pair dynamic measurement equivalence theorems play an active role in guiding between the system conversion and identification of the new system.

A study on improving training efficiency of Artificial Neural Networks algorithm was carried out throughout many previous papers. This paper presents a new approach to improve the training efficiency of back propagation neural network algorithms. The proposed algorithm (GDM/AG) adaptively modifies the gradient based search direction by introducing the value of gain parameter in the activation function. It has been shown that this modification significantly enhance the computational efficiency of training process. The proposed algorithm is generic and can be implemented in almost all gradient based optimization processes. The robustness of the proposed algorithm is shown by comparing convergence rates and the effectiveness of gradient descent methods using the proposed method on heart disease data.

To identify the location and extent of structural damage, a new two-stage approach was developed, which combined the artificial neural networks (ANN) and genetic algorithms (GA). The changes in the dynamic characteristics of a structure as the input parameters of ANN were used for the interval estimation of damage element. Subsequently, the estimation interval is considered as a feasible region of GA to obtain the accurate estimate of damage location and damage extent. One advantage of the proposed approach is that it would decrease the size of ANN and form a small feasible region of GA. Another one is that only a few frequencies and associated modal shapes are needed to accurately assess the location and extent of damage. So it is suitable for damage detection of large and complex structure of civil engineering.

The purpose of this paper is to develop an efficient approach to improve medical diagnosis performance of breast cancer. First, the medical dataset of breast cancer is selected from UCI Machine Learning Repository. After that, the standardization and normalization of datasets are pre-processing procedure. Secondly, the proposed approach combines support vector machine with artificial immune system as the medical diagnosis classifier. The results of diagnosis are identified and the rates of classification accuracy are evaluated. A simple artificial immune algorithm with various affinity criteria is investigated for comparison. Furthermore, the grid-search with 10-fold cross-validation is applied to choose two parameters of

C

and

γ

for AIS-based machine learning classifier. Through grid-search technique, the proposed classifier could yield the best results.

There have been two problems in the implementation of a content-based image retrieval (CBIR) system in web. One is the absence of a standardized way to describe image content, the other is the disregard for the special needs of individual users To address these two problems, in this paper, a human-oriented CBIR system is presented which is implemented by applying MPEG-7 descriptors. In the new system, a multi-feature space is established and both homogeneous texture descriptor and color layout descriptor are used. Since there are difference in human perceptions of color and texture, in order to successfully retrieve an image which caters to the users, PGA (parallel genetic algorithm) is employed to adjust the weight of each feature space. The experimental evidence shows that the system is robust in general format by using MPEG-7 and it is capable of matching the user profile as well.

Chromosome encoded by the matrix of activities resource competition relation (ARCR) in Genetic algorithm (GA) is used to solve the resource constrained project scheduling problem (RCPSP). The relevant code length and code data structure etc. are studied. Decoding, the fitness computation, selection, crossover and mutation algorithms based on ARCR encoding method are proposed. Finally, the standard data collection download from PSPLIB is used to test the algorithm, the results show the algorithm is effective and feasible.

Aim at the problems occurring in a least square method model and a neural network model for flatness pattern recognition, a new approach of flatness pattern recognition based on the variable metric chaos optimization neural network is proposed to meet the demand of high-precision flatness control for cold strip mill. The model is shown to fit the actual data pricisely and to overcome several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum. A series of tests have been conducted based on the data of the actual flatness pattern. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously improved.

BP neural network and multiple linear regression model can be used for multi-factor analysis and forecasting, but the data of the multiple linear regression required to meet independence, normality and other conditions, while the data of the BP neural network do not need to. This article uses the same set of data to established BP neural network model and multiple linear regression model, then compare the ability of fitting and forecasting of the two kinds of models finding that BP neural network has a strong fitting ability and a stable ability of prediction, which can be further used and promoted in the anglicizing and forecasting of the continuous data factors.

The hierarchical model reflects different awareness from different levels. Combining the property of certainty-uncertainty of set pair and the hierarchy of granularity, a new concept is proposed in this paper, that is granularity pair, which is the definition of granularity in set pair, connects the granular computing and the set pair analysis, pairs with object sets and attribute sets, owns the properties of dynamics, limits and similarity. And a similar hierarchical algorithm is designed about the similarity. The example shows that the algorithm is feasible.

Through the research of MPI’s theory and features, the G-MPI parallel program design and running framework have been constructed. Afterwards the design and communication cost of GMRES (m) Algorithm has been studied, so one parallel numerical algorithm, with coarse granularity and low communication cost which is applied to solving the large elastic problems by using boundary element method, has been presented. Through the comparison with the result of the traditional parallel GMRES (m) in MPI, the new parallel algorithm in G-MPI has comparatively higher calculation accuracy and calculation efficiency.

In this paper, a family of seventh-order iterative methods for solving nonlinear equation is presented and analyzed. This family of seventh-order methods contains the Bi’s seventh-order methods and many other seventh-order iterative methods as special cases. In terms of computational cost, per iteration the new methods require three evaluations of the function and one evaluation of its first derivative. Therefore their efficiency index is 1.627. The convergence of this family of methods is analyzed to establish its seventh-order convergence.

Filter methods, introduced by Fletcher and Leyffer for nonlinear programming are characterized by the use of the dominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. This paper presents a way to implement a filter based approach to solve a nonlinear bilevel programming problem in a linear approximations framework. The approach presented is based on the trust region idea from nonlinear programming, combined with filter-SQP algorithm, smooth and active sets techniques. The restoration procedure introduced in our algorithm consists in computing a rational solution.

A class of semi-implicit methods is introduced for stochastic age-dependent population equations with Markovian switching. In general, most of stochastic age-dependent population equations do not have explicit solutions. Thus numerical approximation schemes are invaluable tools for exploring their properties. It is proved that the numerical approximation solutions converge to the exact solutions of the equations under the given conditions.

To trace the aggregate homotopy method for constrained sequential max-min problems, a new interior point algorithm is proposed, and its global convergence is established under some conditions. The residual control criteria, which ensures that the obtained iterative points are interior points, is given by the condition that ensures the

β

-cone neighborhood to be included in the interior of the feasible region. Hence, the algorithm avoids judging whether the iterative points are the interior points or not in every predictor step and corrector step of the Euler-Newton method so that the computation is reduced greatly.

To investigate the generalized Drazin invertible of a 2×2 operator matrix

M

, representations for the generalized Drazin inverse of

M

in terms of its individual blocks are presented under some conditions and some recent results are extended.

In this paper we analyse the uniformity of Chord P2P system and its improved versions defined in [1] - folded Chord. We are interested in the maximal and the minimal areas controlled by nodes in these systems. We recall known results for the classical Chord system and compare it with the new results for its modifications. It is known that the function

n

↦

n

− 2

is a threshold for a number of nodes in Chord controlling small areas (i.e. w.h.p. there exists one node controlling area of length ≤ 

n

− 2

and for every

ε

 > 0 there are no nodes controlling areas of size less than

n

− 2 − 

ε

). We show that the function

$n\mapsto \sqrt{2} n^{-3/2}$

is a similar threshold for 2-folded Chord. We also discuss the number of nodes controlling large areas and we find upper thresholds for all these P2P systems. All modifications of Chord are very soft and flexible and can be easily applied to P2P systems which use the classical Chord protocol.

To solve the nonlinear complementarity problem, a new aggregate homotopy method is considered. The homotopy equation is constructed based on the aggregate function which is the smooth approximation to the reformulation of the nonlinear complementarity problem. Under certain conditions, the existence and convergence of a smooth path defined by a new homotopy which leads to a solution of the original problem are proved. The results provide a theoretical basis to develop a new computational method for nonlinear complementarity problem.

According the World Health Organization, one third of the world’s population is infected with tuberculosis (TB), leading to between two and three million deaths each year. TB is now the second most common cause of death from infectious disease in the world after human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Tuberculosis is a leading cause of infectious mortality. Although anti-biotic treatment is available and there is vaccine, tuberculosis levels are rising in many areas of the world. Mathematical models have been used to study tuberculosis in the past and have influenced policy; the spread of HIV and the emergence of drug-resistant TB strains motivate the use of mathematical models today .In the recent year, the Biomathmetics has become the main important trend of research dirction which has applied to the epidemic models of disease mechanism, spreading, regulation, and stategy of disease preventing in the field of medical and public health. The papers will apply Lyapunov stability function V (x) to construct a dynamic mathematics models for tuberculosis and to meet the above-mentioned TB disease mechanism,spreading regulation, and stategy of disease preventing in the medical field. The theory of Lyapunov stability function is a general rule and method to examine and determine the stability characteristics of a dynamic system. There are two functions of Lyapunov theory: (a) the Lyapunov indirect method which solves the dynamics differential equations of the constructing system then determines its stability properties, and (b) the Lyapunov direct method which determine the system stability directly via constructing a Lyapunov Energy Function V(x) of the dynamic mathematic model for tuberculosis. Here we will analyse the complex dynamic mathematic model of tuberculosis epidemic and determine its stability property by using the popular Matlab/Simulink software and relative software packages.

In this paper, we investigate the geometric continuous connection between the adjacent cubic T-B spline curves, and the construction of transition curve between nonadjacent T-B spline curves. First, we calculate the expression of cubic T-B spline basis function and the expression of cubic T-B spline curve. Then based on the condition of smooth connection between adjacent cubic T-B spline curves, we construct the relations of control points between transition curve and nonadjacent T-B spline curves. Thus we get the geometric continuous connect conditions between transition curve and nonadjacent T-B spline curves.

The finite element model for reinforced-concrete frame beam to resist progressive collapse was established by ADINA program, which geometrical and material non-linearity considered. Axial forces are often present in reinforced- concrete beam at the ultimate load as a result of the boundary conditions and geometry of deformation of the beam segments. So the ultimate load-carrying capacity of reinforced-concrete frame beam was increased because of the arching and cable action (compressive and tensile membrane action). The influence of the beam to resist progressive collapse has been discussed such as steel ratio, span-depth ratio. It shows that the finite element model presented is feasible and can be applied to further research on engineering practice for reinforced-concrete frame structure to resist progressive collapse.

Based on the mathematical morphological image analysis theory, the research has established a kind of description method about the character parameters of linkage curves which is not associated with the graphic’s location, scale and rotation. The research has summarized the internal relationships between the types of linkage curves and their corresponding shape spectrums, and proposed a new idea that the narrow degree of a linkage curve graphic determines its position of the shape spectrum’s crest. Furthermore, for two linkage curves, their similarity function value mainly depends on the distance between their shape spectrums’ crests. According to that, the linkage curve regional electronic atlas that is divided into several characteristic regions of linkage curves is constructed. This kind of electronic atlas can bring some advantages. For instance, it has a higher inquiry and recognition speed of similar linkage curves, and can reduce the data redundancy and so on.

The interval implicit representations of parametric surfaces have wide applications in Computer Graphics, Geometric Modelling, and others. Based on the properties of interval algebraic surfaces, barycentric coordinates and Bernstein basis functions, this paper presents an algorithm to compute the interval implicit representation of a rational Bézier surface by solving an optimal problem with a quadratic object function and linear constraints. This problem is equivalent to finding the two bounding surfaces of the interval implicit surface. An example is provided to demonstrate the algorithm.

In view of the widespread serious phenomenon of safety loans among domestic coal mines, this paper puts forward a calculation method of indirect accident loss in the hope of raising the awareness of coal mines on safety input thorough analysis. This paper studies the validity of the calculation method based on the fatalities caused by coal mine accidents and puts forward the method of indirect accident loss through studying the number of death and severe injuries, and therefore builds a corresponding optimization model of Safety input. Value of life, created by a miner through his whole life can be determined as much as 5million Yuan with reference to foreign standard of compensation in coal mine accidents. Approximate solution, figured out through the model, can help the enterprise examine the reasonable safety input and find the method to calculate the economic benefits under a certain amount of input. This paper analyzes the validation of the model with a case study of a mining group with different input parameters on coal mine safety standards and a reasonable level of ultimate benefit.

In order to avoid or reduce the mined-out area of instability, we should monitor these hazards in situation so as to achieve the purpose of disaster prevention and mitigation, using the monitoring results predicted and timely protective measures. Due to rock with nonlinear dynamic instability disasters and time series data, the theory of grey system was suitable for disaster of identification. So the method on the modeling of grey prediction model was researched in this paper, the grey system theory was put into application in mined-out area with instability of rock, the identification model was established with the characteristics of rock acoustic emission. The data sequence of acoustic emission monitoring was forecasted by the established model of mine in field. Forecasting results in the actual situation had shown relatively close to the grey prediction model, It was indicated that the grey prediction model was feasible.

With increasing demands and changing environment on software systems, a major challenge for those systems is to evolve themselves to adapt to these variations, especially during their running, where dynamic evolution of software architectures has been a key issue of software dynamic evolution research. Most current research in this direction focuses on describing dynamic evolution process of software architectures, and lack consistency analysis of dynamic evolution of software architectures. In this paper, we propose to represent software architectures with constraint hypergraphs, model dynamic evolution of software architectures with constraint hypergraph grammars, and discuss the consistency condition and corresponding consistency decision method of dynamic evolution of software architectures. Our approach provides a formal theoretical basis and a user-friendly graphical representation for consistency analysis of dynamic evolution of software architectures.

A research in mining least association rules is still outstanding and thus requiring more attentions. Until now; only few algorithms and techniques are developed to mine the significant least association rules. In addition, mining such rules always suffered from the high computational costs, complicated and required dedicated measurement. Therefore, this paper proposed a scalable model called Critical Least Association Rule (CLAR) to discover the significant and critical least association rules. Experiments with a real and UCI datasets show that the CLAR can generate the critical least association rules, up to 1.5 times faster and less 100% complexity than benchmarked FP-Growth.

Based on the TM/ETM imagines in 2004, administration district map and related social and economic statistical data in Beijing, China, based on RS/GIS, the land use information for Beijing suburbs is extracted and consolidated, and by employing the theories and methodologies of spatial structure and landscape pattern, the land use situations in Beijing suburb are analyzed quantitatively. The result shows, (1) the diversity is not high and the influences differ in various areas caused by human activities and natural factors. (2) The combination type of land use is simple, forest land and cropland are the main land use types. (3)As for the landscape pattern, the degree of dominance and evenness is rather low while that of diversity and fragmentation is rather high for the land use in Beijing suburb, which is regional differentia. (4) The reserved land resources are very limited in their development potentials, which will lead more serious contradictions between the demands and the supplies for land. So the present land used is desiderated to be developed more deeply.

Mathematical morphology is a tool for processing shapes in image processing. Adaptively finding the specific morphological filter is an important and challenging task in morphological image processing. In order to model the filter and filtering sequence for morphological operations adaptively, a novel technique based on binary particle swarm optimization (BPSO) is proposed. BPSO is a discrete PSO, where the components values of a particle position vector are either zero or one. The proposed method can be used for numerous types of applications, where the morphological processing is involved including but not limited to image segmentation, noise suppression and patterns recognition etc. The paper illustrates a fair amount of experimental results showing the promising ability of the proposed approach over previously known solutions. In particular, the proposed method is evaluated for noise suppression problem.

As a system of credit risk, it has subjective complexity equally. In previous studies of credit risk, especially from the perspective of a large system, the subjective complexity of the credit risk system is often overlooked, while the identification of the driving factors of the derivative process directly restricts the inference of credit risk. By establishing a set of indicating factors of the driving factors, this paper has done a fuzzy comprehensive analysis related to the driving factors, and tested the set of driving factors of the pre-stage credit risk. The results indicate that: the index set to the default risk has a strong subjective relationship, which means the selected driving factors are of rationality to infer the risk derivative process.

In order to analyze mechanical behavior of blood vessel robot (student’s CDIO production) in curved blood, and provide the data for outline design of robot, the flow field out side of robot is numerical simulated. The results show that the vessel shape has significant influent to flow field out side of robot, in curved blood vessel, the wall shear stress (WSS) is greater obviously than that in the straight blood vessel. The radius of blood vessel has influent to WSS, along the radius increases, the WSS on the surface between robot and the inner ring is closer to agreement that of the outer ring gradually. The WSS distribution is also influent by its position in the blood vessel.

There is a great challenge in identifying the early stages of the Alzheimer’s disease, which has become the most frequent cause of dementia in the last few years. The aim of this work is to determine which tests, from a battery of tests, are relevant and would detect faster whether the patient is having a normal aging or developing the disease. This will be made applying the method ORCLASS and the Aranaú Tool, a decision support system mainly structured on the ZAPROS method. The modeling and evaluation processes were conducted based on bibliographic sources and on the information given by a medical expert.

The choice of materials to be used on the construction of earth dams is made in an empirical way, taking into account previous projects and the experience of the involved engineers. An engineer often specifies the materials and their quantities that will be used based on previous projects, on the geotechnical information about the materials available and on common sense. In order to improve this process we propose a multicriterion model to aid in the decisions making concerning the choice of materials on the construction of earth dams with homogeneous and zoned sections. This will be made by means of the Aranaú Tool, a decision support system mainly structured on the ZAPROS method. The case study was applied to the dam Frios project (Ceará, Brazil).

This article according to the ShengLi Open-pit Coal Mine landslide and the slope project special details, used the FLAC numerical calculus analysis software to conduct the research to the landslide cataclysm mechanism, has carried on the optimized analysis to the reinforcement plan.Has obtained the pre-stressed anchor rope frame beam + high pressure splitting grouting reinforcement plan government landslide most superior processing plan through the numerical calculus, thus active control ShengLi Open-pit Coal Mine slope distortion destruction.

Radial basis functions provide powerful meshfree method for multivariate interpolation for scattered data. But both the approximation quality and stability depend on the distribution of the center set. Many methods have been constructed to select optimal center sets for radial basis function interpolation. A review of these methods is given. Four kinds of center choosing algorithms which are thinning algorithm, greedy algorithm, arclength equipartition like algorithm and k-means clustering algorithm are introduced with some algorithmic analysis.

A novel design method of all phase frequency sampling filter are proposed, and can be used to design half-band filter according to the length of filter. Because the amplitude frequency response of this kind of half-band filter is between 0 and 1, no negative, so can be spectral factorized directly to get analysis filter of all phase PR-QMF. The power complementary of the analysis filter is up to 4×10

− 12

dB due to the high spectral factorization accuracy, while the traditional method is only 4×10

− 3

dB.The ratio of signal to noise and the signal reconstruction error of all phase PR-QMF is four times higher and 10

9

lower than traditional PR-QMF respectively.

In order to solve the modeling problem of geotechnical engineering numerical simulation in complex geological condition, a new and practical modeling method (Geologic Model Transforming Method) of numerical analysis by combining geologic model and numerical model has been presented. In GMTM, there are three key techniques be used to realize the model transformation from geological model to numerical model. These techniques include calculation region cutting, surface model reconstructing and finite element auto-meshing. Calculation region cutting as the first key technique in GMTM is introduced mainly in this paper. The realizing process of the algorithm is exposited in detail. Finally, an example is given to illustrate the application of the technique. By region cutting technique, local model in 3D geological model could be extracted dynamically and the construction process could be simulated conveniently and the excavation boundary must not be defined in advance. Some useful references would be given by the proposed technique to the continuous researches on this subject.

Item-based collaborative filtering (

CF

) is one of the most popular recommendation approaches. A weakness of current item-based

CF

is all users have the same weight in computing item relationships. In order to solve the problem, we incorporate

userrank

as weight of a user based on

PageRank

into item similarities computing. In this paper, a data model for

userrank

calculation, a user ranking approach, and a

userrank

-based item-item similarities computing approach are proposed. Finally, we experimentally evaluate our approach for recommendation and compare it to traditional item-based Adjusted Cosine recommendation approach.