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Abstract: A new approach of paper defect recognition is proposed. To implement this method, computer is used to acquire paper image and the subtraction algorithm is used to determine if the image contains defect at first. For the image with paper defect, wavelet packet decomposition is applied to extracting the feature vector of the defect. A BP neural network is designed for recognizing the defect type according to the feature vector. This approach provides a unified detection algorithm for different types of paper defects. The experiment data prove this method is valid and can be applied in modern paper defect inspection system. [Copyright &y& Elsevier]

Abstract: A lot of work has been done in the field of multi-valued model checking home and abroad. But some research works are not perfect. Some problems in the semantic definitions of temporal operators and corresponding model checking algorithms are found in the paper “Model Checking Using Partial Kripke Structure with 3-Valued Temporal Logic”. Some counterexamples are given to show these errors. On the basis of the discussion, some sound semantic definitions of temporal operators and correct algorithms of model checking for some temporal operators are given. [Copyright &y& Elsevier]

Abstract: Combining previous image mosaicing algorithms, this paper puts forward a kind of damaged text restore image processing algorithm. The algorithm can restore the shredder pieces tape. A series of tapes which are obtained by scanner are treated through image processing such as binary, segmentation, denoising and enhancement and so on, Then we can get the ideal original text image from some leaflet tapes image. Experimental results show that such algorithm has certain effectiveness. [Copyright &y& Elsevier]

Heat demand forecasting is in one form or another an integrated part of most optimisation solutions for district heating and cooling (DHC). Since DHC systems are demand driven, the ability to forecast this behaviour becomes an important part of most overall energy efficiency efforts. This paper presents the current status and results from extensive work in the development, implementation and operational service of online machine learning algorithms for demand forecasting. Recent results and experiences are compared to results predicted by previous work done by the authors. The prior work, based mainly on certain decision tree based regression algorithms, is expanded to include other forms of decision tree solutions as well as neural network based approaches. These algorithms are analysed both individually and combined in an ensemble solution. Furthermore, the paper also describes the practical implementation and commissioning of the system in two different operational settings where the data streams are analysed online in real-time. It is shown that the results are in line with expectations based on prior work, and mat the demand predictions have a robust behaviour within acceptable error margins. Applications of such predictions in relation to intelligent network controllers for district heating are explored and the initial results of such systems are discussed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, direct torque control (DTC) algorithms for double star permanent magnet synchronous machine alimented by two inverters is described. The double star permanent magnet synchronous machine has two sets of three-phase stator windings spatially shifted up by an angle γ=300. The double star permanent magnet synchronous is used in areas of high power industrial applications such as traction and naval propulsion. Because constitute an advantageous choice compared to classical synchronous machine, because of the relatively low torque produced. This machine is controlled by tree level direct torque with speed regulator PI, replaced by the sliding mode regulator to improve the results obtained. The implementation of the DTC multi-level applied to a double star permanent magnet synchronous machine is validated with simulated results. In this paper a method for modeling and simulation of double star permanent magnet synchronous motor drive MATLAB/Simulink. [Copyright &y& Elsevier]

Abstract: As the modern vessels have become more complex comprising an increasing number of heterogeneous interdependent subsystems. This increased complexity requires new methods for the design and operation of these marine systems. An integrated design platform for marine electric propulsion system which aims to characteristic analysis of plants and systems, DCS control algorithm trim, intelligent control strategy develop of automation and operation is presented in this paper. The design process and thoughts is described in details, and the current work is introduced. The main focus of this paper is in the development of marine integrated design platform which seeks to shift ship design to a distribution, intelligent control architecture through increased automation. [Copyright &y& Elsevier]

Abstract: The requirements of device under the multi-mission of monitoring, protecting, controlling and communicating is discussed in this paper to bring forward a design of dual CPU structure with ARM & DSP for microprocessor protection. Via the comparative analysis of several common algorithms, the algorithm of FFT, which can make the controlling of calculation accuracy easier and has a filter feature inherently, is selected to analyze the sampling data of protector and calculates relative electrical parameters. Relevant program is written for distance protection in 110kV transmissions, then debugging on software and hardware is made. The results show that the design of DSP & ARM based-microprocessor protection presented in this paper is feasible; such a device has simple and reliable circuit structure and is expandable in both software and hardware. [Copyright &y& Elsevier]

Abstract: This paper modifies and improves the algorithm of sight distance triangle according to the traffic flow and geometric features of intersections. Using the modified crossable intervals and the traffic volume on the secondary road and the obstacles within the improved sight distance triangle as selection criteria, we put forward the control mode selection flow diagram of plane intersections in this paper, hoping to quantize the selection criteria, as well as normalize and visualize the selection process.Finally,this paper checks the accuracy and reliability of the model through the analysis of a case. [Copyright &y& Elsevier]

Abstract: This paper described the structure of a Shunt Active Power Filter system and compared various methods of active power filter harmonic detection, then the improved adaptive algorithm was used in active power filter harmonic detection; as regards the control strategy, the control method that based on optimal voltage space vector hysteresis current was presented in this paper for tracking control, And by using Matlab/Simulink simulation software, the Adaptive filter detection system and control model of Three-phase three-wire Shunt Active Power Filter were established. Simulation results showed that the selected detection method and control strategy of harmonic current played a fairly good compensation role on the harmonics which was generated by nonlinear loads and had a good dynamic compensation performance. [Copyright &y& Elsevier]

Abstract: This paper shows the determination of typical load profiles (TLPs) by using Fuzzy C-Means (FCM) clustering and Probability Neural Network (PNN) classification method. Load profiles provide useful information on electricity consumption to both consumers and suppliers. Precise knowledge on consumer''s electricity consumption becomes essential for the suppliers to design tariffs, load balancing, load planning, etc. As for consumers, such knowledge is important for them to keep track of their electricity consumption and to enable them to take part in retail market especially for those who do not have appropriate metering equipment. Hence, an effective, fast and cheap method to obtain load profile has to be developed. Objectives of this paper are to obtain groups of TLPs by using FCM clustering and to assign TLPs using PNN algorithm. One of the main issues in FCM clustering is the determination of optimal number of cluster. This paper proposes optimal number of cluster determination by using Davies-Bouldin index. PNN network is trained to directly allocate the TLPs to the representative groups of consumers. The data used in this project are obtained from Tenaga Nasional Berhad (TNB). [Copyright &y& Elsevier]

Abstract: A new design of decentralized load-frequency controller for interconnected power systems with ac-dc parallel using Particle Swarm Optimization (PSO) algorithm is proposed in this paper. A HVDC link is connected in parallel with an existing ac tie-line to stabilize the frequency oscillations of the ac system. Any optimum controller selected for load frequency control of interconnected power systems should not only stabilize the power system but also reduce the system frequency and tie line power oscillations and settling time of the output responses. In practice Load Frequency Control (LFC) systems use simple Proportional Integral (PI) or Integral (I) controller parameters are usually tuned based on classical or trial-and-error approaches, they are incapable of obtaining good dynamic performance for various load change scenarios in multi-area power system. For this reason, in this paper the PI and I control parameters are tuned based on PSO algorithm method for the LFC control in the two-area power system. A two area interconnected thermal power system is considered to demonstrate the validity of the proposed controller. The simulation results show that the proposed controller provides better dynamic responses with minimal frequency and tie-line power deviations, quick settling time and guarantees closed-loop stability margin. [Copyright &y& Elsevier]

Abstract: A large AC generator is an important component of the power system. Researches on the AC generator relaying for the next-generation ECMS and an efficient operation of protection control system are necessary. These require developments on detailed generator modeling and internal fault analysis, and prototyping for multifunctional IED device dedicated to next-generation production. In this paper, over-excitation relaying algorithm is one of the main protection elements which have an important role in DGPS(Digital Generator Protection System), was proposed. This paper first briefly introduces a principles of over-excitation and DFT-based gain compensation algorithm was adopted for the frequency measurement. Subsequently, protection scheme of over-excitation was presented. The 765[kV] modeling data of South Korea using EMTP-RV was used for simulations. The simulation result of over-excitation relaying are also provided. [Copyright &y& Elsevier]

Abstract: This paper describes modeling and testing of a digital distance relay for transmission line protection using MATLAB/SIMULINK. ATP is used for detailed modeling of a power system network and fault simulation. SIMULINK is used to implement multi-resolution morphological gradient (MMG) relaying algorithm. MMG modeling is an interactive simulation environment for relaying algorithm design and evaluation. The basic principles of MMG relaying algorithm and some related algorithms such as tripping and fault location scheme are also described in this paper. Test results show that MMG relaying algorithm can detect and define fault location accurately within 1 milli-second. [Copyright &y& Elsevier]

Abstract: This paper designs and implements a intellectual emergency decision-making system for logistics emergency decision-making field combining case-based reasoning (CBR) and rule-based reasoning (RBR). It avoids the shortcomings of two reasoning methods used alone. This system designs combined mechanism of two reasoning methods. It describes formally emergency cases with frame representation methods and organizes emergency cases with stratified technology. It implements similarity algorithm and retrieval algorithm of CBR, and designs reasoning mechanism of RBR. Through the implementation of logistics emergency decision-making prototype system and empirical analysis of the case, the paper verifies the superiority of logistics emergency decision-making system combining CBR and RBR. [Copyright &y& Elsevier]

Abstract: With the development of information technology, information exchange has become the important means of modern social work and livelihood. Wired communication, as a reliable and efficient data transferring methods, plays more and more important roles. Yet few achievements on this field pay attention on the security issues. Regarding this problems, this paper presents a network accessing algorithm for the wired communication based on security state assessment. The algorithm overcomes the shortcomings of the achievements in this field and providing a safer network accessing methods for the wired communication. The case described in this paper proves that the algorithm can provide efficient network accessing service with low cost. [Copyright &y& Elsevier]

Abstract: The detection system for frame embossing surface is discussed in this paper. According to the periodic feature of frame embossing surface, one periodic embossing is inspected once. Then a fast template matching algorithm based on projection and normalized cross correlation measure is proposed in this paper. The experimental results showed that the proposed algorithm was more efficient and faster than conventional image template matching algorithms. [Copyright &y& Elsevier]

Abstract: Most of the current manual paper or electronic equipment production, but it will cause pollution of the environment .In this paper, the electronic multimedia device instructions are embedded into the processor module Flash to generate E-Manual which can be browsed in the display devices by the remote control. As Flash space provided by most of the multimedia processor module is very small, there is a high demanding for data compression. In this paper, compression problems of black and white pictures and other data embedding Flash are the main, we also do a detailed study to BI_RLE8, LZMA algorithm, and apply them to the set-top box platform that has good results. [Copyright &y& Elsevier]

Abstract: This paper mainly focused on the surface-surface intersection problem in surface border modeling. This paper introduced the intersection testing algorithm to obtain the initial intersecting lines based on the Delaunay triangulation of two surfaces. After the detection of intersecting lines’ continuity, the “weighted surface border extension algorithm” was put forward to guarantee the continuity of intersecting lines. When the discrete data is deficiency, “weighted surface border extension algorithm” is applied to guarantee the existence of intersecting lines between the surfaces which have the tendency to intersect. This paper applied “weighted surface border extension algorithm” on the result of intersecting lines to guarantee that the endpoints of intersecting lines lie on the border of both surfaces, which can be convenient to the following Constrained Delaunay Triangulation and surface cutting steps. Simulation results showed the proposed algorithm was feasible. [Copyright &y& Elsevier]

Abstract: Modulation transfer function (MTF) is more authoritative in evaluating the performance of optical imaging system. Therefore, measuring MTF directly from the information of remote sensing image is an important method to monitor on-orbit satellites. This paper chooses knife-method which is applicable for MTF measurement of on-orbit satellites. In the process of calculating MTF, a new-type nonlinear least squares iteration algorithm, instead of simulated annealing algorithm, is selected to approach ESF using the Fermi function. Simulation results show that the new algorithm has greater superiority in the precision and speed. Furthermore, on the basis of implementation of MTF measurement, this paper studies the influence of edge height, angle, and gray contrast on MTF, and tests the precision of the method. [Copyright &y& Elsevier]

Abstract: In this paper it proposes a meaningful digital watermarking algorithm for remote sensing image based on DCT and fractal theory. Firstly, it determines self-similar blocks for watermark embedding by using fractal technology, then scrambles the watermark with Arnold transformation and the scrambled watermark image next be scanned into onedimensional sequence, at last it applies DCT to the selected self-similar blocks and embedds the watermark into the blocks. Experiments have shown that the algorithm proposed in this paper has good invisibility, simple calculation, easy realization, and extracting watermark without the original remote sensing image. So it is robust against most geometric attacks and has some value for copyright protection of remote sensing images. [Copyright &y& Elsevier]

Abstract: This paper tries to widen the notion of adaptation to take account of bidirectional adaptations between the context and activities so as to ensure not only the activity adaptation but also the adaptation of the context of the user to the requirements of the activities. What''s more, this paper presents a bidirectional adaptations workflow management algorithm (BAWM) for the route-choice of campus navigation, which can dynamically adjust workflow execution behaviors based on current context information and the user activities. This paper also manages to show that the correctness of the BAWM algorithm by formulating it as a Petri-net model. [Copyright &y& Elsevier]

Abstract: In order to improve the application security of smart card, this paper has proposed an encryption scheme of smart card combined with AES encryption algorithm and high-speed key generation algorithm. Furthermore, combined with the characteristics of M1 card, this paper has introduced the work of the program and implemented the program. [Copyright &y& Elsevier]

Abstract: This paper summarized the defect of tree cover network and P2P resources search method, according to the characteristics of grid resources, based on A* algorithm, took search state as a node, built resources search state space. Through the implementation of the minimum cost of operation, to change the search status, to get the appropriate grid resources and to create the optimal search path being the guidance information to the next search course. In this paper a simulation experiments was carried out to compare search efficiency of several ways to search the grid resources. [Copyright &y& Elsevier]

Abstract: In data mining, outlier analysis is an important task. In recent years, many techniques have been developed for outliers mining. This paper provides an overview of the existing methods for outlier analysis. And on the base of these researches, we first propose the definition of local outlier and some formulas, and then develop a clusteringbased approach to mining local outliers. The clustering algorithm used in this paper is based on the maximal frequent itemsets in association rule mining, the properties of maximal frequent itemsets are applied to clustering. After clustering, the objects which have many common features are grouped together. By calculating the standard deviation of a cluster with respect to a certain attribute and local outlier factor of an object with respect to a certain attribute, local outliers are identified. The experimental results show that our approach is effective and appropriate to mining local outliers. [Copyright &y& Elsevier]

Abstract: In this paper, by studying the modified Ishikawa''s iteration method, we establish a weak convergence theorem for the asymptotically strict pseudo-contraction in Hilbert spaces. We also modify this iteration method by applying projections onto suitably constructed closed convex sets to get an algorithm which can generate a strongly convergent sequence. The results presented in this paper improve and extend the previous known results. [Copyright &y& Elsevier]

Abstract: Fault classification and location is a major area of investigation among power system problems and intelligent system application. A novel hybrid fault classification and location algorithm based on S-transform and artificial neural network for UHVDC line is proposed in this paper. The BP artificial neural networks are designed to distinguish the non-fault transient, classify and locate faults with transient energy which are extracted by S–transform from DC voltage signals. The effect of the high ground fault resistance, transients caused by lightning strokes and different fault location are considered in this paper. [Copyright &y& Elsevier]

Abstract: Underground Magnetic CT technique is a newly geophysical prospecting method widely used in detecting inner structure of geologic bodies. At present two most popular image reconstructing algorithms in computerized tomography are algebra reconstruction technology (ART) and simultaneous iterative reconstruction techniques (SIRT). Both of the two algorithms have their own drawbacks. In this paper we studied the theory of ART and SIRT technique, analyzed the causes of drawbacks of the two algorithms and then we improved SIRT algorithm by selecting the data processed by ART as initial value of iteration in SIRT. The result of the forward simulation showed that the improved method overcomes the instability of SIRT and enhanced accuracy of SIRT. At the end of this paper, we applied the improved SIRT method to reconstruct image in an electromagnetic prospecting. [Copyright &y& Elsevier]

Abstract: In order to make use of a great many blurred images captured by Hadamard Transform Spectral Imager(HTSI) in projects, an ameliorated algorithm of Spectral Recovery of Hadamard Transform(HT) has been proposed in this paper. The ameliorated algorithm has been deduced by bring the average of multiple images into the Adverse Hadamard Transform. The impact of this new method on Gaussian noise in images has been analyzed. The contribution of improving the quality of the false color image and added image has been proved and demonstrated in this paper. [Copyright &y& Elsevier]

Abstract: Currently, the cloud computing is welcome in scholars and enterprise, because of its good features such as low investment, easy maintenance, Flexibility and fast deployment, reliable service. However, when the cloud computing is implemented, the security and credibility is one of the biggest obstacles. In cloud computing, users directly use and operate software of cloud service providers, operating system, even programming environment and network infrastructure, so the impact and breakage users caused for cloud software and hardware resources more serious than the current Internet users to share resources. So whether the behavior and behavior subject is credible or not, how to authenticate and control user behavior, these are important parts of the cloud computing study. This paper presents the concept of user behavior authentication, discusses how to authenticate and control user behavior in the cloud computing environment according to the user''s behavior, include establishment of behavior authentication set, mechanisms of behavior authentication and control, corresponding mode of Stochastic Petri Nets, False Negative rates and algorithm performance etc. Paper lays the theoretical and practical foundation for the real application of cloud computing. [Copyright &y& Elsevier]

Abstract: Fine scalable video coding is a common method for video quality scalable of H264 and MPEG-4, but it has not been used on AVS as yet. This paper presents a new algorithm of fine granularity scalability based on AVS. The DCT residue coefficients of the FGS enhancement layer proposed in this paper are firstly scanned by zigzag and then scaled with the energy distribution characteristic of frequency domain. Then, the scalable coefficients are coded by bit-plane coding and Exp-Golomb coding. Finally, we simulate the algorithm on the software codec model version rm52k of AVS-P2. The experimental results show that the ameliorated scalable coding method can improve the coding efficiency obviously compared with the original FGS in terms of average PSNR. [Copyright &y& Elsevier]

Abstract: The partitional clustering algorithms are used more widely in text clustering. However, the traditional algorithms based on partition treat all the attributes equally in clustering process. They all suppose that the importance of each attribute is equal. These algorithms will have a lower accuracy. In order to handle this problem, this paper provides a new clustering algorithm-attribute weighted fuzzy c-means algorithm. During the iteration of this algorithm, it can find the important attributes. Moreover, this algorithm also can find the cluster structure hiding by the unimportant attributes. The simulation of this algorithm on test documents can prove the algorithm provided by this paper can gain a good computation speed, found the latent structure and can remark the different importance of each attribute. [Copyright &y& Elsevier]

Abstract: Aerial imagery sensors can provide sufficient resolution to sense vehicle locations and movements across broader spatial and temporal scales. In this paper, an approach for collecting and analyzing aerial imagery is given. This paper presents a method to detect vehicle from a moving camera. The detection component involves a cascade of modules. First, road is detected based on edge detection and blob analysis, then our proposed motion detection (simplified Lucas-Kanade algorithm) is used to obtain motion vectors from two consecutive input frames. Third, frame differencing method based on obtained motion vectors is applied to generate differenced image. At last, moving vehicles are detected based on blob analysis. [Copyright &y& Elsevier]

Abstract: The algorithm based on context similarity has the drawback that it will cost more time if not all the attributes are detected, this paper views the context attributes as attribute stream and the BOAS (Bayesian on the Attributes Stream) algorithm based on context knowledge is proposed in this paper. BOAS algorithm firstly calculates the undetected attribute of terrorism group identifier, and then uses conditional probability and Bayesian theorem to predict the future terrorist behavior. In this way, it avoids handling the undetected attributes in the original algorithm, but also avoids computing the context similarity based on distance function repeatedly. Thus, it improves the performance efficiency. The experiment result shows that BOAS algorithm proposed in this paper reduces computation time while it assures the accuracy of the prediction. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on Gentle Adaboost algorithm detects the ear part under complex background using two steps: off-line cascaded classifier training and on-line ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature exaction and orientation selection, Gabor-based ear is presented in this paper. Kernel Fisher Discriminant Analysis is applied for dimension reduction of the high-dimensional Gabor features. Then distance based classifier is applied for ear recognition. The experimental results on USTB ear database show effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Ordinal regression shares properties of both classification and regression, since the goal function maps the instance space into the integer set. In this paper, we studied generalization properties of ordinal regression algorithms, and we focused on the performance of goal function effecting on individual examples. The strong (weak) loss stabilities and strong (weak) score stabilities for standard regression algorithms we defined only consider changing one element of training set. With the definitions of stabilities for ordinal regression algorithms and the clipped loss function, four kinds of generalization bounds are given in this paper. [Copyright &y& Elsevier]

Abstract: Online reputation evaluation has been implemented in many practices in different ways. Based on the evaluation of several reputation mechanisms, this paper proposes a reputation evaluation for scoring and monitoring the seller''s behavior. Second-order atomic patterns are defined, based on which we propose an algorithm named Partially- Ordered Pattern Scoring (POPS). A solution for reputation monitoring is discussed. This mechanism helps to prevent several cheating strategies. At the end of the paper, comparative experiments on accuracy with Double Exponential Smoothing and on stability with different lengths of cases have shown its advantages. [Copyright &y& Elsevier]

Abstract: This paper gives a novel temporal Tomlinson-Harashima Precoding (THP) and spatial detection joint processing algorithm for Multiple-Input Multiple-Output (MIMO) frequency selective fading channels. Generally spatial and temporal signals are processed at transmitter side, or at receiver side, both are very complex indeed. In this paper, we propose a novel temporal THP (T-THP) and spatial decision feedback equalization (S-DFE) detection joint processing technique, at transmitter, T-THP to each sub-channel of MIMO channel model is performed, through this step, the channel model is changed into flat fading MIMO channel; Then at receiver side, adopt MIMO detection to cancel interference from different antenna or multi-user, and at last, the estimated signal will be got. Using this solution, we can change the space and time 2-dimension''s operation into two 1-dimension''s operation separately, and in each space, the algorithm is relative simple. From algorithm analysis and performance comparison, we can see the validation and advantage of this method suggested in this paper [Copyright &y& Elsevier]

Abstract: String match algorithm is widely used in the area of data mining. In this paper, we present an approach for elevating the performance of this algorithm via GPU (Graphic Processing Unit). With the rapid development of Graphics Processing Unit to many-core multiprocessors, it shows great potential in many applications and high performance computing. Especially, the heterogeneous architecture CPU+GPU shows enthusiastic capacity to accelerate parallel applications. Till now, some research has been done for parallel implementation of string match algorithm on GPU. But there are some constraints for acceleration of this algorithm: firstly, if-branch exists in the algorithm which can cripple the performance of the parallel implementation; secondly, improper memory access may bring heavy latency generated by bank-conflict. This paper presents the optimization of global memory access, shared memory access and the elimination of if-branch for elevating the performance of string match algorithm BMH. Meanwhile, we explore the interesting characteristics of the parallel BMH string match algorithm implemented on GPU using CUDA. We found that the performance elevation is affected by the characteristic of pattern, which also proves the potential of GPU. [Copyright &y& Elsevier]

Abstract: With the rapid development of the Intemet, information overload and isotropic becomes worse and worse. Personalized services system''s birth partly resolved this problem. The traditional recommendation methods, such as content based recommendation and collaborative filtering, do help a lot. However, to some extent, it couldn’t authentically understand the preference of users. Considered the limitations of the traditional methods, tag recommedation spring up. This paper makes expanded research based on tag reccommendation. With semantic matching, a certain tag turns to a similar tag set. Acording to co-occurence of tags, a tag is expended to a ralated tag set. Integrate these tag set to generate recommendation list is better than single tag recomendation by experimental observation. Besides, this paper also adopt time degradatioin aglorithm which improved the recommender''s accuracy and efficiency. [Copyright &y& Elsevier]

Abstract: Semi-supervised cluster ensemble combines semi- supervised learning and cluster ensemble to increase the precision of clustering. And it conquers the disadvantages of the selectivity, bias, low precision and bad generalization. And conquers that unsupervised learning can’t take advantages of known information of datasets. As a result the precision, robustness and stability of cluster ensemble are increasing. Now semisupervised cluster ensemble is researched at the very outset. The paper''s main goal is to construct an algorithm of semi-supervised cluster ensemble. In this paper it includes as follows. First the advantages of semisupervised cluster ensemble are stated; Second the algorithm of semi-supervised cluster ensemble is established; Third the experiments of semi- supervised cluster ensemble, such as the results,the datasets, the types and the steps of experiments, are studied. [Copyright &y& Elsevier]

Abstract: HTTP tunnel Trojans encapsulate all outgoing traffic into semantically valid HTTP requests that are then sent to the attacker. This paper presents a new method to detect the HTTP tunneling Trojans by using network behavior characteristics. In this paper, the communication processes of tunnel Trojan were divided into two stages. The difference between normal HTTP session and Trojan session with HTTP tunneling were depicted by eight statistics attributes, and statistical analysis and C4.5 decision tree classification algorithm were introduced to classify the two sessions. The experimental results showed that our method could efficiently detect most of the known HTTP Tunnel Trojans and could find some unknown HTTP Tunnel Trojans. [Copyright &y& Elsevier]

Abstract: In this paper the operation principle of magnetic suspending switched reluctance motor (MSRM) is introduced. The math model of suspending force is given. The control scheme based on BP neural network combined with PID was presented for serious non-linearization of suspending switched reluctance motor radial force. The control algorithm is detailed. Simulations are carried out under the non-load and the load. The results both show that the rotor displacement can quickly follow the given displacement, which verifies the proposed method. The paper provides the basis for further experiment research. [Copyright &y& Elsevier]

Abstract: Aiming at network teaching of high school, personalized learning system model is proposed in this paper. The model includes data source preprocessing, students feature modeling and data mining platform. According to major feature, gender feature, personality feature and cognitive feature, this paper proposes a student feature analysis model under network. Based on the model and applying the ID3 classification algorithm, we can make a decision to forecast whether students have the ability to pass test. Besides, it also provides the decision-making supporting for individualized teaching. [Copyright &y& Elsevier]

Abstract: Static separation of duty (SSOD) is an important approach to enforce separation of duty (SOD) which is widely considered to be a fundamental principle in computer security. SSOD requires that the privileges are separately assigned to different users to complete a sensitive task. On the other hand, deduction relation which exists between privileges tend to undermine the isolated state of privileges. Therefore, deduction relations between privileges may be inconsistent with SSOD policy. However, few researches on this problem are found at present. In this paper, based on the privilege assignment state in RBAC, the definition of compatibility between SSOD policy and privilege deduction relations is given. Furthermore, through a novel concept of minimum deduction cover of a privilege set, the compatibility decision theorem is presented, and an efficient compatibility decision algorithm is designed accordingly. The application example shows that the work of this paper is useful to eliminate the conflicts between SSOD policy and the implicit authorization caused by privilege deduction. [Copyright &y& Elsevier]

Abstract: The problem of test point selection is important in the area of fault diagnosis and circuit testing. In this paper, a method based on immune clonal selection algorithm (ICSA) and integer-coded fault dictionary is proposed for selecting the optimum test points of analog circuits. The problem of test points selection is transformed into the selection of dictionary columns to isolate the rows by using integer-coded fault dictionary. Then, the ICSA-based method was used to find the optimum test points. In the end of this paper, the presented approach is illustrated by a practical example of analog circuit and statistical experiments. The experimental results indicate that the proposed method is easier to find the global optimum test points with acceptable time consumption. Compared with GA-based method, this method has fewer parameters to set. [Copyright &y& Elsevier]

Abstract: According to De Casteljau‘s Bézier curves and surfaces discrete generation algorithm, in this thesis built up a new model and introduced a fast discrete generation algorithm based on the model. This algorithm provides the Casteljau discrete generation process better flexibility and detailed controllability, and this algorithm also introduced the thought of adaptive forward differencing algorithm. First in this paper built up a similar binary tree structure induced from the Casteljau‘s discrete generation algorithm. In the model, each group of curve control points is formed into a node in the similar binary tree structure. Based on this model, a relative algorithm is described in the paper, which can calculate each node in the model directly. This feature makes it easy to generate control points that meet the requirement of fitting degree, and since it reduces the generation of intermediate control points, this algorithm greatly reduces the overheads and computing time and makes a fast calculation when displaying curves or surfaces. [Copyright &y& Elsevier]

Abstract: With unceasing appearance of each kind of new network applications and the new wooden horse virus, the massive P2P video frequency transmission services and immediate communication services, the traditional network management technology and method can’t satisfy the need of network development and change. It is urgent needing the effective network behavior sensation technology to analyze and forecast the development and change of network applications, and then provide the important reference. This paper combines machine learning method based on artificial intelligence and network survey and network management technology. It uses the method of BP artificial neural networks (BP ANN) to recognize each kind of flow in network, to sense each kind of behavior in network, then carries on scientific and effective management and tendency forecast to the network behavior. The corresponding improved model and algorithm has been given in the paper. Finally, the validity of the model and the method has been confirmed through experiments. [Copyright &y& Elsevier]

Abstract: Distributed Generation (DG), and especially microturbine generation (MTG) system, plays a significant role in Smart Grids. One of the most important variables of a MTG system is the governer speed. This paper defines a problem on transient speed response of MTs for a step load. The aim is to optimize the speed governer parameters in order to minimize the transient response of MTG. Differential Evolutionary Algorithm (DEA) is employed to solve the problem. The stability issue of the MTG system is also discussed in the paper. The simulation results show the effectiveness of a fine-tuned speed governer by using the proposed method. [Copyright &y& Elsevier]

Abstract: Distributed generation (DG), and especially microturbine generation (MTG) system, plays a significant role in Smart Grids. One of the most important variables of a MTG system is the fuel consumption. This paper defines a problem on fuel consumption of MTs for a sample load change that can be used for a 24-hour load change pattern. The aim is to optimize the speed governer parameters in order to minimize the fuel consumption of MTG. Differential Evolutionary Algorithm (DEA) is employed to solve the problem. The stability issue of the MTG system is also discussed in the paper. The simulation results show the effectiveness of a fine-tuned speed governer by using the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents a fast optimal generator shedding algorithm which is on the basis of predictor-corrector method and time domain simulation for transient stability. It includes two parts that are the fast time domain simulation and optimal generator shedding. On the basis of the generator''s classical model and load''s constant impedance model, this paper use fourth-order Hamming predictor-correction formula to compute differential equations of the system with large step length, in order to improve computing speed. The second part shows how to calculate the sensitivity of transient stability constraint function to control variables, by the application of optimal control theory. Combined with the discrete characteristics of generator shedding, “Micro-step discrete method” is proposed, which is fast, accurate and so on. The advantages of this method are verified by analysis results of New England 39-bus test system. [Copyright &y& Elsevier]