bluuebunny/crossref_metadata_2025 huggingface dataset:
The Application of Improved Adaptive Kalman Filter in GPS Kinematic Positioning
Authors: Ma Xian Min , Chen Ya Ru
Published: September 2013

Model is inaccuracy sometimes when state of motion mutation, which will cause lower precision of GPS kinematic positioning. To solve this problem, an improved adaptive Kalman filter algorithm is built. In filter calculation, exponent fading factor is presented to modify prior covariance matrix, the filters stability is guaranteed and its precision is improved by adjusting the fading factor. Whats more, restriction of the experiential reserve coefficient is broken. In order to improve the kinematic performance of the Kalman filter, adaptive weighted factor and adaptive adjustment factor are introduced. The results show that, compared with adaptive Kalman filter algorithm, this algorithm has a better robustness and the kinematic performance has been improved significantly.
Parallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem
Authors: Xu Kun , Jiang Ming Yan , Yuan Dong Feng
Published: September 2013

Artificial Bee Colony Algorithm (ABCA) is a novel swarm intelligence algorithm which a colony of artificial bees cooperate in finding good solutions for numerical optimization problems and combinatorial optimization problems. Traveling Salesman Problem (TSP) is a famous combinatorial optimization problem which has been used in many fields such as network communication, transportation, manufacturing and logistics. However, it requires a considerably large amount of computational time and resources for solving TSP. To dealing with this problem, we present a Parallel Artificial Bee Colony Algorithm (PABCA) in several computers which operation system is Linux based on the Message Passing Interface (MPI). The entire artificial bee colony is divided into several subgroups by PABCA equally. Each subgroup performs an ABCA for TSP on each processor node, respectively. Each sub-colony on every processor node communicates the current best fitness function and parameters of current best fitness function according to ring topological structure during calculation process. Some well-known benchmark problems in TSP are used to evaluate the performance of ABCA and PABCA. Meanwhile, the performance of PABCA is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Experimental results show that the PABCA can obtain solutions with equal precision and reduce the time of computation obviously in comparison with serial ABCA. And PABCA have much better performance in contrast with GA and PSO.
A Novel Adaptive Match Scheme for Parallel Mesh-Decomposition in OpenFOAM
Authors: Wang Miao , Tang Yu Hua , Zhang Hong Yun
Published: September 2013

A parallel multilevel k-way partitioning algorithm is used in OpenFOAM to perform parallel mesh-decomposition. Before the parallel decomposition procedure, mesh has to be pre-decomposed by the Simple method in OpenFOAM. Match-computation for the algorithms parallel coarsening phase is based on a kind of global match scheme which introduces large amount of communication overhead. However, the domains of the mesh generated by Simple maintain good locality and continuity, which makes it unnecessary to adopt global match scheme in the parallel coarsening phase. In this paper, a novel adaptive match scheme AMS is brought forward for the parallel multilevel k-way partitioning algorithm. An adaptive critical x is calculated firstly according to the scale of the mesh and the parallel degree. For the first x stages of the coarsening phase, a local match scheme is adopted in which vertexes are only allowed to match with their adjacent unmatched vertexes with heaviest edge-weight on their local processors. For the rest stages, the traditional global match scheme is introduced and match of two vertexes on different processors is allowed. AMS can efficiently reduce the communication overhead introduced by simply adopting global match scheme. The experiment is performed on mesh of LinearPTT application on Tianhe-1A. The results show that the parallel multilevel k-way paritioning algorithm based on AMS has a better performance than that based on traditional global match scheme.
Multifocus Image Fusion Based on Multiresolution Transform and Particle Swarm Optimization
Authors: Liu Yu Shu , Jiang Ming Yan , Liao Chuan Zhu
Published: September 2013

In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a novel multifocus image fusion algorithm based on multiresolution transform and particle swarm optimization (PSO) is proposed. Firstly the source images are decomposed into lowpass subbands coefficients and highpass subbands coefficients by the nonsubsampled contourlet transform (NSCT). Then, different fusion rules are applied for low and high frequency NSCT coefficients. Finally the fused image is reconstructed by the inverse NSCT transform. The experiment results demonstrate that the proposed method is effective and can provide better performance than the method based on the wavelet transform and the nonsubsampled contourlet transform.
Wavelet-Domain High Resolution Image Reconstruction
Authors: Dong Li Wen
Published: September 2013

The aliasing due to subsampling and the blur from the finite detector size can decrease quality of the images and make fine details and structures difficult to interpret. High resolution images can be reconstructed from several adjacent frames in a sequence by reconstruction process. This paper describes the high resolution image reconstruction method based on wavelet domain. In this approach both the image sequences and the degradation operator are presented by orthogonal wavelet with compact support. Experimental results demonstrate that the proposed method is effective to improve image details.
A New Data Classification Algorithm for Data-Intensive Computing Environments
Authors: Deng Qi Zhi , Zhang Long Bo , Qian Xin , Chen Ya Li , Wang Feng Ying
Published: September 2013

In order to solve the problem of how to improve the scalability of data processing capabilities and the data availability which encountered by data mining techniques for Data-intensive computing, a new method of tree learning is presented in this paper. By introducing the MapReduce, the tree learning method based on SPRINT can obtain a well scalability when address large datasets. Moreover, we define the process of split point as a series of distributed computations, which is implemented with the MapReduce model respectively. And a new data structure called class distribution table is introduced to assist the calculation of histogram. Experiments and results analysis shows that the algorithm has strong processing capabilities of data mining for data-intensive computing environments.
A Principal Components Analysis Self-Organizing Neural Network Model and Computational Experiment
Authors: Nong Ji Fu
Published: September 2013

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.
Formation Vector Based Shortest Path-Planning in CGF Formation
Authors: Jie Fei , Lu Zhao Han , Xie Bao Di
Published: September 2013

In order to improve the poor reality and bad flexibility of the mapping relationship which matched entities with aim locations in traditional approximation method, the formation vector shortest path-planning method was presented in this paper. By analyzing the lack of aim path-planning in the approximation method, shortest path-planning was discussed and was improved by introducing the formation vector and the idea of pheromone. Furthermore, the improved algorithm was applied in a CGF simulation system. The experimental results showed that the mapping relationship had better reality and rationality and the possibility of collision was significantly reduced than the traditional formation change process.
Convolutive Blind Source Separation Based on Wavelet De-Noising
Authors: Zhang Hong Bin , Xu Peng Fei
Published: September 2013

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.
An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network
Authors: Xin Ruo Bo , Jiang Zhi Fang , Li Ning , Hou Lu Jian
Published: September 2013

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.
A Statistical Local Binary Fitting Model for Blood Vessel Segmentation
Authors: Zhao Shi Feng , Zhou Ming Quan , Wang Kang
Published: September 2013

Network structure such as blood vessels in medical images are important features for computer-aided diagnosis and follow-up of many diseases. In this study, a new model-based segmentation method is proposed to detect blood vessels in medical images. The Local Binary Fitting (LBF) model with statistical distribution function is used for this purpose. The brain tissues and cerebral vessels in the image are modeled by Gaussian distribution and uniform distribution respectively. The region distribution combined with the LBF model is used in curve evolution. And the level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Comparisons with the LBF method show that our model can achieve better results.
Analysis and Improvement of User Authentication Framework for Cloud Computing
Authors: Chen Nan , Jiang Rui
Published: September 2013

Cloud Computing, as an emerging, virtual, large-scale distributed computing model, has gained increasing attention these years. Meanwhile it also faces many secure challenges, one of which is authentication. Amlan Jyoti Choudhury et al proposed a user authentication framework to ensure user legitimacy before entering into the cloud. However, the scheme is found to suffer from some attacks through our analysis. In this paper, we firstly analyze few attacks and the make an improvement on the user authentication framework. Our new protocol ensures that only legitimate users can access the cloud service based on smartcard. Security analysis shows our proposed scheme is secure under standard cryptographic.
A Kind of Enhanced Slotted ALOHA Algorithm for Anti-Collision in RFID System
Authors: Meng Fan Wei , Zhao Yue , Ma Cui Hong , Yang You Liang
Published: September 2013

in the RFID system, the conflict caused by the multi-label has been affecting system performance. This paper proposed a grouping dynamic framed slotted ALOHA algorithm based on the analysis of ALOHA algorithm. According to the conflict situation, to make real-time estimates the number of tags on the dynamic framed slotted ALOHA algorithm from the mathematical point of view, Dynamically change the frame length or the labels are grouped to reduce the label probability of collisions, thereby improving the efficiency of the recognition.
RFID Localization Algorithm Based on Taylor Series Expansion
Authors: Yang You Liang , Zhang Jing , Meng Fan Wei , Ma Cui Hong
Published: September 2013

RFID is widely used in indoor positioning, as one of the typical system LANDMARK introduced reference label, used the nearest neighbor data correlation algorithm. This article uses the Taylor series expansion method, by the LANDMARK law get positioning results for further processing, resulting in more accurate positioning results.
The License Plate Image Processing Based on MATLAB
Authors: Li Wen Jie , Tian Ke Lun , Zhang Jie
Published: September 2013

This paper realizes the license plate image preprocessing, segmentation and positioning using MATLAB software. By image processing, it indicates the convenient, simple, and effectiveness of MATLAB recognition, achieving the desired effect.
A Cross-Entropy Method for Solving Passenger Flow Routing Problem
Authors: Jiang Yang , Zhang Xing Chen , Gan Tian Tian
Published: September 2013

In this paper, a new design optimization method-cross entropy methods for passenger flow routing in passenger hubs is employed, in order to develop rational and efficient passenger flow routing program, which will help improving the passenger flow organization. According to the description and characteristics of the problem, we transform the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and can be well applied in passenger flow routing design problem. Systematic studying the passenger terminal passenger flow routing design optimization techniques and methods and developing rational and efficient passenger flow routing program will help improving the organization of passenger flow. According to the description of the problem and the characteristics of itself, the paper transforms the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The paper also employs the cross entropy method to solve the problems. Numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and this method can be well applied in passenger flow routing design problem.
Code-Switching Speech Detection Method by Combination of Language and Acoustic Information
Authors: Zhang Hong Ji
Published: September 2013

In this paper, we propose a new speech detection method to English-Mandarin code-switching speech. Unlike previous methods, in this method we first train a support vector machine (SVM) model based on feature parameters and Gaussian Mixture Model (GMM) , then integrate the language identification (LID) information based on SVM model and acoustic information into the decoding process. Lastly, we develop a prototype system to present the method. Experiments proved that our method we can improve the accurancy of code-switching speech recognition at a certain degree compared with previous methods.
Algebraic Techniques in Linear Cryptanalysis
Authors: Wei Hong Ru , Zheng Ya Fei
Published: September 2013

Linear cryptanalysis is a statistical analysis method. Linear cryptanalysis constructs probabilistic patterns first and then distinguishes the cipher from a random permutation using lots of plaintext-ciphertext pairs. Linear cryptanalysis has a big data complexity. Algebraic attack attempts to exploit the algebraic structure of the cipher by expressing the encryption transformation as a set of polynomial equations and then attempts to solve the system to recover the encryption key. Algebraic attacks do not need too much data. This paper combines these two methods by using algebraic techniques in linear cryptanalysis, and proposes a new cryptanalysis method called Algebraic Techniques in Linear Cryptanalysis. This new method is used in the existing linear cryptanalysis of PRESENT. To recover 8-bit key information of 21-round PRESENT, the data complexity is2'and the time complexity is 2'.Compared with the result of linear cryptanalysis, the data complexity is obviously decreased.
The Software Design of Intelligent Diagnosis System for Partial Discharge Fault of GIS
Authors: Shen Jian Wei , Su Wei Min , Lv Xiao Rong , Huang Xiao Xia
Published: September 2013

The correct identification of GIS partial discharge type is essential for assessing the insulation status of GIS and developing a reasonable maintenance strategy. After the analysis of the intelligent diagnosis system design, the software architecture of host and remote computer is given by this article. Site host computer completes the data preprocessing and the remote host computer completes the function of real-time observation, data storage and inquiry, spectrum analysis and pattern recognition. The test shows that the software can complete the intelligent recognition of partial discharge.
Parallel Outlier Detection in Dial-Back Fraud Calls
Authors: Wu Bin , Liao Fan Di , Zhang Di
Published: September 2013

With the intensified competition among telecommunications industry, we focused much on the quality of service. Illegal activities, especially dial-back fraud calls, may cause annoyance and inconvenience which will reduce user experience. The detection of dial-back fraud calls is an urgent issue that needs to be addressed. The rapid development of information technology which gives rise to the accumulated huge data will pose a greater challenge. However, traditional detecting methods to identify illegal activities cannot get acceptable accuracy. On the other hand, those methods become very inefficient or even unavailable when processing massive data. In this paper, we introduce a distributed outlier detection approach to locate illegal acts of the illegal users who have the characteristics as outliers. For a higher hit rate, we combine outlier detection with cluster coefficient. Besides, the method exploits parallel computation based on MapReduce in order to obtain vast time savings and improve the processing capability of the algorithm on large data. Extensive experimental results demonstrate the efficiently performances of proposed algorithm according to the evaluation criterions of speedup and scale up.
Multi-Objective Optimization of Quality in Laser Cutting Based on Response Surface Model
Authors: Hao Hui Juan , Wang Mao Li , Hao Feng Qi
Published: September 2013

Prediction and optimization of quality characteristics is an important means to improve the quality of laser cutting. Kerf width (KW) and material removal rate (MRR) are selected as the quality characteristics in this paper. The fitting response surface models (RSM) of KW and MRR are considered as the optimization objective function in pulsed Nd: YAG laser cutting of alloy steel for multi-objective optimization. An improved Pareto genetic algorithm is used in the optimization, and the significant factors have been found. The predicted results are basically consistent with the experimental. Therefore, the method used in this paper can be used for optimization of KW and MRR in pulse Nd: YAG laser cutting. The study can provide theoretical basis for the prediction and optimization of quality in laser cutting.
A Blind Watermarking Algorithm Research Based on Fast DCT
Authors: Lai Si Yu , Wang Juan
Published: September 2013

This article exploits a fast algorithm to watermark implant and extract, which first transform N×N(N=2n) two dimension DCT into one dimension DCT with N numbers of points. And then the adopted algorithm implants watermark using matrix decomposition and masterly adjusting interrelation of DCT coefficients, thus to make the visual distortion to be minima after a watermark being implanted in the image, in addition, this method is a blind approach. The results indicate that the algorithm enjoy high image reducing property and robustness to several common attacks when ensuring invisibility of watermark. Finally, it is also superior to R-C, index-mapping like matrix cut algorithms and can also improve calculating speed, which make it possible to apply for image protection.
An Energy-Efficient Routing Algorithm for Disruption Tolerant Networks
Authors: Yao Yu Kun , Zheng Wen Xi , Ren Zhi
Published: September 2013

In Disruption Tolerant Networks (DTN), information is exchanged between mobile nodes with the same transmit power. It is an energy-consuming process and highlights the need for energy-efficient power control mechanisms. If the transmit power of nodes is too low, they might miss many of the opportunities of transmission. On the other hand, high transmit power is energy inefficient. In this paper, we propose ERBC (Energy-efficient Routing algorithm Based on Cross-layer design), an energy-efficient routing algorithm based on cross-layer design. Instead of using fixed transmit power, ERBC dynamically chooses the transmit power using both the cross-lay design and the RSSI technology. Meanwhile, ERBC broadcast data packets partially in order to enhance the delivery ratio. Through simulation, we demonstrate that ERBC can reduce 37% for per-bit energy consumption than that in basic epidemic routing protocol.
Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network
Authors: Wu Ze Yue , Chen Yue Hui
Published: September 2013

Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible and effective.
Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree
Authors: Chen Yi Ming , Chen Yue Hui
Published: September 2013

In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.
Study and Simulation of the Model of Stability Measurement for Integrated Semiconductor Equipment
Authors: Xue Fang , Wang Hai Fei
Published: September 2013

Performance analysis and capacity prediction of integrated semiconductor equipment is a very difficult task, it is a effective way to solve this problem by setting up a model of equipment performance measurement. This paper details the composition and related operation principle of parallel integrated semiconductor equipment. This paper also deriving a set of stability measurement model, we call it Throughput Model, for integrated semiconductor equipment according to its operation principle, which reflect the relationship between stability and output error. The model can effectively calculate the stability of equipment and help system design.
Defect Detection in Patterned Fabrics Using Wavelet Filter
Authors: Ying Yu Long , Chen Dan Er
Published: September 2013

This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal filter design method for solving this problem. Wavelet is used to extract the texture features of textile fabrics. In view of this optimal filter, a news semi-supervised segmentation algorithm is then proposed. The performance of the scheme is evaluated by using a variety of homogeneous textile images with different types of common defects. The test results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed scheme.
Blue Monitor: A PEEM-Based Online Battery Monitoring and Maintenance Platform
Authors: Ren Chao , Chang Kai Ge , Li Cai Hong , Bao Jing
Published: September 2013

with rapid development of the mobile telecommunications industry, numerous Power Equipment and Environment Monitoring System (PEEM) related products have emerged in recent years. These products provide convenient remote maintenance. However, most of the existing systems remain in the phase of collecting data, which is not very useful for further analysis to improve maintenance operations. In this paper, we introduce a PEEM-based online battery monitoring and maintenance platform called Blue Monitor system. By maintaining communication power supply system in the mobile base station, Blue Monitor can reduce fossil-fuel power generator usages, decrease operational difficulty, enable unattended monitoring service and extend battery life. It will significantly boost energy savings and financial benefits.
A Method for Extracting Signals with Specific Normalized Kurtosis Range
Authors: Zhao Yong Jian , Qu Mei Xia , Jiang Hai Ning
Published: September 2013

The famous FastICA algorithm has been widely used for blind signal separation. For every process, it only converges to an original source which has the maximum negentropy of the underlying signals. To ensure the first output is the desired signal, we incorporate a priori knowledge as a constraint into the FastICA algorithm to construct a robust blind source extraction algorithm. One can extract the desired signal if its normalized kurtosis is known to lie in a specific range, whereas other unwanted signals do not belong to this range. Experimental results on biomedical signals illustrate the validity and reliability of the proposed method.
A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph
Authors: Chen Dan Er , Ying Yu Long
Published: September 2013

With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.
Global Robust Exponential Stability of Interval Cohen-Grossberg Neural Network with Time Varying Delays
Authors: Zhang Rui , Liu Jian , Li Meng Xin , Liu Mei Ju
Published: September 2013

In this paper, the global robust exponential stability is discussed for interval Cohen-Grossgerg neural network with time varying delays. On the basis of the linear matrix inequalities (LMIs) technique, and Lyapunov functional method combined with the Bellman inequality and Jensen inequality technique, we have obtained the main condition to ensure the global robust exponential stability of the equilibrium point for this system. The obtained stability criterion is dependent on the upper bound of time varying delays. The proposed result is less restrictive, and suitable of the cases of slow or fast time varying delays, easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained result, and the simulation example is used to demonstrate the effectiveness of our result.
An Overview of Collaborative Filtering Recommendation Algorithm
Authors: Sun Ping , Li Zheng Yu , Han Zi Yang , Wang Feng Ying
Published: September 2013

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.
Boundary Segmentation Based on Improved GVF Snake Model
Authors: Huang Wen Bo , Yan Yang , Wang Yun Ji
Published: September 2013

The gradient vector flow (GVF) Snake models are active contour models: they lock onto nearby edges, localizing them accurately. GVF refers to the definition of a bidirectional external force that can capture the object boundaries from either sides and can deal with concave regions. However, we find that there is a main disadvantage of GVF Snakes---- there are some critical points in the model, the critical points must be included in the initial contour or must not be contained internal, and otherwise, it could not converge to the correct boundary. To solve this problem, we propose an improved GVF Snake model: we construct a new driving force to improve the initialization of contour. The experimental result shows that the new model we proposed not only avoid the critical points of the initial contour, but also converge to the concave boundary better.
Analysis Based on of Android Malicious Code Intrusion Detection
Authors: Liu Lei , Li Dai Ping
Published: September 2013

Due to the android platform is open source, more and more developers and manufacturers to use it. At the same time its security attracts more and more challenges, it is vulnerable to mobile phone virus attacks. In order to solve these problems, we should detect the files in the phone. Because the Boyer-Moore algorithms efficiency is higher than others, so we use it in android application intrusion detection. First we should set up a virus signature library, and store these signatures in the SQLite Database. Then scanning system of documents carries on the analysis and extraction corresponding feature codes, and use the BM algorithm to match them with virus signature, and we can find the viruses. What is new and original in this paper is that the efficiency of intrusion detection is higher.
Study on Jamming to Sidelobe Cancellation of Phased-Array Radar
Authors: Hao Yan Zhong , Jiao Jing Shan , Zhang Fu Chun , Hui Wang , Wang Shu Qiang
Published: September 2013

Antenna sidelobe cancellation is effective method to repress radar sidelobe jamming. This paper analysed the function and characteristics completely based on the principle of sidelobe cancellation, and look for jamming method more effective according to the weakness and technique crux. The results indicate that the adaptive side lobe cancellation technique can effectively antagonize the active jamming.
Research on the Algorithm of Topology Discovery Based on Switch MAC's Self-Learning
Authors: Huang Rui , Wang Jun Feng
Published: September 2013

The switch is applied into topology discovery areas has become a hot research topic in network management technology. Through summarizing many defects of the SNMP (simple network) for the link layer discovery protocol, this paper presents a new topology discovery algorithm based on self-learning switch MAC. Research on the algorithm is about assumptions, definitions, principles and process. Through compare advantages and disadvantages in SNMP and MACs self-learning, the theoretical support will be provided for other scholars in the field of network management.
Optional Feature Vector Generation for Linear Value Function Approximation with Binary Features
Authors: Ren Bo Yan , Qin Zheng , Zhao Feng Fei
Published: September 2013

Linear value function approximation with binary features is important in the research of Reinforcement Learning (RL). When updating the value function, it is necessary to generate a feature vector which contains the features that should be updated. In high dimensional domains, the generation process will take lot more time, which reduces the performance of algorithm a lot. Hence, this paper introduces Optional Feature Vector Generation (OFVG) algorithm as an improved method to generate feature vectors that can be combined with any online, value-based RL method that uses and expands binary features. This paper shows empirically that OFVG performs well in high dimensional domains.
DOA Estimation for Antenna Array with Partially-Well Sensors via Low-Rank Matrix Completion
Authors: Yu Hua Xing , Zhang Xiao Fei , Li Jian Feng , Ben De
Published: September 2013

In this paper, we address the angle estimation problem in linear array with some ill sensors (partially-well sensors), which only work well randomly. The output of the array will miss some values, and this can be regarded as a low-rank matrix completion problem due to the property that the number of sources is smaller than the number of the total sensors. The output of the array, which is corrupted by the missing values and the noise, can be complete via the Optspace method, and then the angles can be estimated according to the complete output. The proposed algorithm works well for the array with some ill sensors; moreover, it is suitable for non-uniform linear array. Simulation results illustrate performance of the algorithm.
Comparison and Improvement of Intelligent Optimization Algorithms
Authors: Wang Meng Lan
Published: September 2013

This paper describes the basic ideas of several commonly used intelligent optimization algorithms, summarizes their essential features, classifies them, and points out the improvement directions.
Smooth Edge Feature Lines Extraction from Point Clouds of Eroded Fractured Fragments
Authors: Lin Jian Bin , Zhou Ming Quan , Wu Zhong Ke
Published: September 2013

This paper presents a novel method to extract edge lines from point clouds of these eroded, rough fractured fragments. Firstly, a principal component analysis based method is used to extract feature points, followed by clustering of these feature points. Secondly, a local feature lines fragment is constructed for each cluster and afterwards a smooth and noise pruning process for each local feature lines fragment. Thirdly, these separated local feature lines fragments are connected and bridged in order to eliminate the gaps caused by the eroded regions and construct completed global feature lines. Fourthly, a noise pruning process is performed. The output of this method is completed, smoothed edge feature lines. We illustrate the performance of our method on a number of real-world examples.
A Root-MUSIC Algorithm for Angle Estimation in Bistatic MIMO Radar
Authors: Liu Jia Wei , Cao Ren Zheng , Zhang Xiao Fei
Published: September 2013

This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation using the root multiple signal classification (MUSIC) algorithm in a bistatic multiple input and multiple output (MIMO) radar. The proposed algorithm gets the estimation of DOA and DOD via computing the roots of polynomials and it avoids the spectral peak searching in the conventional MUSIC algorithm. Thus the Root-MUSIC algorithm has much lower computational load. Simulation results illustrate our proposed algorithm has better angle estimation performance than the conventional algorithms.
SAR Image Recognition Combined Bidirectional 2DPCA with PCA
Authors: Wang De Gong , Yang Zuo Long , Chang Shuo
Published: September 2013

The method of Principal Component Analysis (PCA) needs to convert image matrix to high-dimensional column vector used in feature extraction. The 2-dimensional PCA (2DPCA) offsets disadvantages of PCA. However, 2DPCA compresses image along the rows or columns only, the number of features is still large. In order to solve the above problems, bidirectional 2DPCA was used to compress image matrix along row and column meanwhile, then use PCA reduce the number of computations and feature dimensions. Three kinds of ground static military targets images acquired by SAR were used as the experimental data. The experimental result shows that, the method of SAR image recognition presented by this paper reduced the dimensions of feature matrix and raised the recognition rate.
SAR Images Recognition Combined Bidirectional 2DPCA with KPCA
Authors: Wang De Gong , Li Yong , Jin Fu Lu
Published: September 2013

The bidirectional 2DPCA (two-dimensional principal component analysis) method for SAR images recognition, can compress the columns and rows of images matrix and reduce the number of feature dimensions. However, it fails to use high order statistics information of image data, neglects the nonlinearity correlation between pixels. Therefore, this paper presents the method combined bidirectional 2DPCA with KPCA (Kernel Principal Component Analysis). This method not only compresses the dimensions of images data, but develops the superiority of KPCA in describing correlation between many pixels. Experimental results show that: this method can decrease calculated amount and raise recognition rate of SAR target effectively.
Extraction and Classifier Design for Image Recognition of Insect Pests on Field Crops
Authors: Zhang Hong Tao , Hu Yu Xia , Zhang Heng Yuan
Published: September 2013

Feature extraction and the classifier design were the crucial parts for image recognition of insect pests on agriculture field crops. The hardware of the detection device for insect pests included the trapping, stunning and buffering unit, the even illumination unit, the scattering and transporting unit, and the image vision unit. The seven morphological features from binary images of the insect pests were extracted and normalized, such as area, perimeter, and complexity. The standard vector model library and the membership functions were established based on the feature mean and the feature standard deviation of the nine species of pests. The fuzzy decisions were analyzed based on the fuzzy principle of the minimum and maximum membership degree. The results showed that the fuzzy classifier could identify the nine species of pests that harmed seriously, and the recognition accuracy was over 86%.
Research on a Kind of Algorithm Cell Search in TD-LTE System
Authors: Chen Fa Tang , Xu Chi Yun
Published: September 2013

According to the theoretical analysis of Zadoff-Chu sequence and theoretical derivation.A kind of low complexity implementation method of algorithm in cell search in TD-LTE system is proposed in this paper. Both the theory analysis and simulation results show that the algorithm proposed can reduce computational complexity and this algorithm is feasible and efficient, which gains good performance of cell search in TD-LTE system.
Fast Image Positioning Based on Mark
Authors: Zhang Wei Guo , Zhang Chuan You
Published: September 2013

MARK tag is a common method in industrial image location. In allusion to the disadvantages of conventional MARK located, such as large numbers of templates training set,no scale and rotation ,harsh conditions and slow processing speed ,a improved matching strategy based on NCC is proposed in this paper. The method search and correct the sole MARK in the template, extract 25 grids normalized feature sequence. Then matching with all contour feature sequences extracted in the target image and finally locate the target MARK. The experiment results show that , the method has much robustness to scale and rotation and can meet the real-time processing requirements
Microscopic Image Segmentation of Chinese Herbal Medicine Based on Region Growing Algorithm
Authors: Liu Qing , Zhang Li Jun , Liu Xi Ping
Published: September 2013

In order to effectively separate the target region of the microscopic image of Chinese Herbal Medicine (CHM), and lay the foundation for the subsequent image recognition processing, a microscopic image segmentation method of CHM by using region growing (RG) algorithm is put forward based on the characteristics of the plant microscopic images. Firstly, the CHM microscopic images with different cell structure are regarded as a multi-dimensional matrix to process and established seed label matrix. Secondly, in a given region threshold conditions, the different seed growth points are selected to segmented the different images. Finally, given a fixed growth points, the microscopic images are processed by choosing a different threshold. The experimental results show that CHM image segmentation threshold and seed selection decide the image target extraction. For different CHM images, according to a certain method, the better image segmentation results can be achieved in the case to obtain a suitable threshold value using image information and the seed point adjustment.
The Grid-Connected Inverter of Simulation on Direct-Drive Wind Power System Based on MATLAB
Authors: Wang Xiao Ming , Liu Wan Ci , Xiao Wen Juan
Published: September 2013

Based on working Principle of the direct-drive wind power systems grid-connected inverter was research, the mathematical model for inverter in Three-phase rotating coordinate was established and analyzed. A double-loop control strategy that based on feed forward decoupling were introduced, Meanwhile, combining the implementation effective control of the AC-side output current by the Space Vector Pulse Width Modulation method (SVPWM), it could generate sine wave current that had the same frequency and the same phase with network voltage. To a great degree, reduced harmonic pollution in power system, moreover, improved the Grid-connected wind power systems efficiency and reliability.
An Electrical Design for Crude Oil Dilution
Authors: Guan Da Lu , Yang Fang
Published: September 2013

The paper introduces an electrical design to dilute heavy crude oil. The design is based on the feedback principle and includes detection part, control part and actuator part. The practice proves the design is reliable and accurate to realize the proportion between the crude oil flow and dilution liquid flow.
Display System of OLED Based on FPGA
Authors: Zhan Zhao
Published: September 2013

Considering narrow temperature and viewpoint of LCD for most portable display system,OLED is used as display device.Focusing on the problem of low contrast of generic driver method for portable display system.
The Simulation Research of Self-Excited Startup for Wind Generator Based on RTDS
Authors: Hu Yu Lan , An Ran Ran , Mei Jing Cheng , Qian Luo Jiang , Yan Jun
Published: September 2013

Concerned that wind generators start up and establish the stable terminal voltage of the stator which is connected to the grid, this paper presents that wind generator can be operated as the independent generator at a certain voltage and frequency by means of capacitive excitation. The paper also deduces the calculation method for the proper capacitance value of self-exciting startup of wind generator. The paper builds a simulation model based on real time digital simulator (RTDS) and takes a case for simulating self-excited startup of wind generator. Simulation results demonstrate that the wind generator with self-excitation can successfully start up and be connected to the grid, it make a good agreement of actual situation. Furthermore, the results of RTDS simulation show that only proper capacitors value from optimized calculation can lead to a normal terminal voltage characteristic. The validity of proposal methods and designed simulation model is verified by experimental and computed results.
Compensation Effect and Simulation of Series-Parallel Hybrid Dynamic Voltage Restorer
Authors: Shi Yi , Wang Xiao Lan
Published: September 2013

Aiming to the requirements for stable voltage of system. The topology of series-parallel dynamic voltage restorer (DVR) used in the system is proposed in this paper. The simulation results under multiple of voltage fault show that the DVR can provide voltage support continuously, and improve the reliability of bus voltage in system effectively.
The Main Circuit Studies of Current Mode APF Based on SPWM Technology
Authors: Zhou Hong Yu
Published: September 2013

It is become a relative mature technique now that Active Power Filter can do harmonic suppression and reactive compensation to alternating current net of nonlinear load. This paper contrasts passive and active filter technology in harmonic suppression, and introduces the classification of passive filter as well as working principle. This paper also discusses current-source APF consisting of multi-modular and phase shifted SPWM converters and cascade multilevel converters applied in current-source APF which can carry out high switch frequency effect under the lower switch frequencies. Finally, this paper gives a comprehensive discussion on APF technique and prospects its development in China.
Nonlinear Machine Parameter Model of a High Performance Drive System
Authors: Wu Mao Lin
Published: September 2013

For sake of researching PMSM nonlinear parameter of a drive system, a load test is designed to get input-output property under the condition of different load. The main parameters of machine are estimated by applying predicting-error algorithm, then the data sets are used to train four neural network parameter models. To validate the model simulation method is used to study the speed and torque response. The results show that the way can optimize parameters of machine effectively and can be utilized for on-line parameter identification and electric drives.
Calculation on Energy Saving Potential of a New Dry-Type Air-Conditioning System
Authors: Hu Xiao Wei , Zhang Yu Feng , Wei Li Li , Wu Xin Hui
Published: September 2013

Temperature and humidity independent control can be made by a new dry-type air-conditioning system, the combination of silica gel rotor and heat pump. Heat pump cycle can simultaneously cool the process air and heat the regeneration air, so it can cancel each other out in the cold and heat energy consumption within the system. This novel form of system not only solves the regeneration energy issues fundamentally but also avoids energy waste by reheating in conventional cooling dehumidification air-conditioning system. Based on the establishment of the component model, MATLAB program has been compiled to simulate the combined system running at nominal operating conditions and calculate the performance parameters and energy efficiency. Comparison of the energy efficiency was made of this dry-type air-conditioning system with ones of other regeneration desiccant wheel air conditioning system and conventional cooling dehumidification air-conditioning system, the results of which show that the new dry-type system provided regeneration energy, overcomes the restriction from climate, region and using conditions of other regeneration. Moreover, compared with conventional re-heat air-conditioning system, its energy saving is up to 35.1% and the energy efficiency is increased by 38.1%.