A Web client is constructed based on Ajax and Observer design pattern to solve the problem of poor real-time and the lower rate of code reuse for existing monitoring system. It uses the Ajax to solve the real-time graphical display problem, and combines with observer design pattern. The rate of code reuse is increased, and the coupling between modules is reduced. Demonstration system is used to validate that the proposed method is feasible.
Because of the nonlinear coupling relationship among process parameters, it is difficult for condition recognition of production process. A new criterion of kernel mapping is proposed, and the gradient optimum algorithm is used to select kernel parameter. Kernel Fisher discriminant analysis is applied to reduce dimension for realizing the multi-classification in the visual plane with layer by layer analysis method. The judgment on the current state can be realized. The Tennessee Eastman(TE) process data are used for model validation, the proposed method has more satisfactory performance on condition diagnosis, compared with kernel principal component analysis.
On the basis of studying the theory of dynamic Virtual Enterprise(VE) based on Web Service architecture, this paper proposes a new model of cooperative partner selection for dynamic VE with extension QoS constraint in the large extended, it extends the traditional QoS, monitores and processes the enterprise extension QoS in a real-time sense by using QoS monitoring system, gives partner selection process under the model, and some key issues for the implementation of partner selection are analyzed. Some evaluations of the model performance is given, experimental result shows the rationality and feasibility of the model.
A general model of wireless multiple video transmission schedule is proposed to achieve real-time monitoring of multiple scenarios. The model includes the communication setup method between the remote device and monitoring center in the beginning of transmission, the multiple video streams transmission schedule model based on the connection state, and the encrypting control method of the video stream. The model is adapted to any kind of IP network. It is applied in wireless video transmission system based on CDMA1x network, and good application effect is achieved.
Pre-FUFP algorithm updates the frequent pattern tree effectively based on the concept of pre-large items. But when there are pre-large items becoming frequent items, the algorithm need check which transactions in the original database contains the pre-large items. In this paper, an index table of pre-large items to their corresponding original transactions is proposed to find out the transactions need to be processed and fasten the process of FUFP-tree modification. The frequent patterns by using compact FP-Tree and matrix based algorithm are worked out. Experimental result shows the algorithm outperforms the pre-FUFP algorithm.
The inconsistency of interactive behavior’s sequence before and after component evolution will cause system behavior deviates from its goal. Interactive behavior between components is described based on Process Algebra(PA) and interactive behavior’s consistency is defined. The restriction which interactive behavior’s consistency will fulfill is epurated, and an approach to ensure consistency of interactive behavior is presented. An example is presented to illustrate the feasibility and validity of the approach.
The consistency verification and removal of redundancy behavior of composite component is an important issue in the field of Component Based Software Development(CBSD). The theory of interface automata is analyzed, and a more visualized presentation method of illegal state is presented by viewing composed component as a cycle process of initial state to itself. With scenario based requirement specification can always be modeled as sequence of events, digraph is used to model composed interface automata to give an algorithm of consistency verification. With the results of consistency checking, a method to remove redundancy behavior is also discussed.
Kazak as one of the minority languages and characters being universally applied or used in Xinjiang, frequency statistic of word in Kazak natural language treatment becomes the problem to be solved urgently. This paper introduces the relation of Zapf in Kazak word segmentation, which is based on frequency statistic of the word. Through the system, continuous Kazak character bunch input can be segmented, and then the cut apartment word bunch output can be gotten. The cut apartment word bunch usually is two Kazak word bunch, and dictionary can be gotten. The dictionary stores Kazak word and the frequency that the word appears in these disposal test that combines proceeding Kazak covariance of article experiment. Experimental result expresses the relation of frequency of the Kazak word, and the resulting Kazak word frequency distribution accords with power-law of Zapf.
Aiming at the incompleteness of interval-valued partial ordering relation which causes the problem of information loss in interval-valued information system which does not include decision attribute, this paper proposes a new interval-valued ordering relation based on interval-valued fuzzy number, then uses it to construct interval ordered information system, and analyzes its related monotonicity and included of upper and lower approximation. Furthermore, it presents an algorithm for attribute reduction based on indiscernibility function. Experimental result illustrates the algorithm is simple and effective.
The high performance of Solid State Disk(SSD) has failed to be improved in the existing database systems. Aiming at this problem, a new connection method is proposed in this paper. The algorithm is optimized Flash database connection on the column files. Using the small size of column storage and the advantages of SSD high-speed random read, the algorithm overcomes the mismatch with the flash memory. Experimental results show that compared with the original algorithm, the query time of the new algorithm is less under the conditions of different query rate and different memory.
In order to solve the problem of network congestion, this paper proposes a network congestion control algorithm based on Tabu Search (TS) genetic optimization named TSGA, which combines TS and Genetic Algorithm(GA). An optimization mathematical model on multiple constrained QoS routing with the objectives of network resource consumption minimization and load distribution balance is presented. Simulation experimental results prove that the algorithm can realize network congestion control, and improve network performance effectively.
This paper proposes a Wireless Sensor Network(WSN) node positioning algorithm based on studying Received Signal Strength Indicator(RSSI) and Support Vector Classification(SVC). It considers node RSSI value as a multi-classification problem of characteristic quantity, converts RSSI into node position directly by SVC which has good generalization ability to realize symbolic positioning and physical positioning, and achieves a higher positioning accuracy. Experimental results show that the symbolic positioning effect of the algorithm is good, when the anchor node density is 20%, 98.19% of the nodes can get correct position.
Previous dynamic cluster configuration methods are based on the specific physical experimental models without the description of mathematical models. Aiming at the problem, this paper proposes a prediction-based dynamic clusters configuration strategy, which uses least mean square to predict the situation of service requests in the future time according to the network historical information of service requests. On the basis of the load requests and the clusters processing power, it decides the servers’ scale and dynamically adjusts the opening and shutdown of the computers in the server cluster. Experimental result verifies the feasibility and superiority of the schedule strategy.
The controller design for networked controller with bandwidth constraints is studied in this paper. The scheduling strategy based on stochastic communication logic is presented using Poisson process with time-dependent intensity. Then the limit updates are obtained. According to its Markov jump linear characteristic, the controller is co-designed based on update time. Simulation results show that the times of state update are reduced, the influence of bandwidth on control performance is decreased, and the dynamic performance of the system is improved by means of introducing stochastic communication logic.
Network coding is inherent vulnerable to the data pollution attacks. To address this problem, it discusses two random linear network coding pollution data detection schemes, one is based on homomorphic hash function which deduces the general formation and proves its correctness. The other is linear space signature. It comparatively analyzes their computational cost and payload efficiency under different data block size conditions, and proposes a new combinatory detection scheme for this problem.
Aiming at the coding characteristic of GIF image, this paper presents an image selection encryption algorithm under degradation and confidentiality modes. Intra-set shuffle index method is used to control image degradation visibility for degradation model. It encrypts only fractal of the image, the whole image is protected for confidentiality model. Analysis results show that this algorithm can meet the requirements of security, format-compliance, and it also can constant compression-ratio.
According to the changes of the historgrams of the first digital distribution and the individual DCT coefficients caused by the steganography algorithm, such as Outguess, F5, Steghide, this paper proposes a new detection algorithm for the JPEG Image. It extracts 144 features by using the first digital distribution and the histograms of individual DCT coefficients, and uses Fisher classifier for recognition. Experimental results show that the algorithm has high detection rate and chronic adaptability.
In Ad hoc networks, group key management is one of the most important secure problems. This paper proposes a hierarchical group key management scheme adapted to large groups according to network characteristics. The scheme puts STR hierarchical tree structure into distributed subgroup management model effectively, and combines layer-based key management with cycle-based key management, in order to reduce the communication and computation cost greatly. Experimental result shows that this scheme is of good attribute in expansibility and lange Ad hoc networks.
This paper analyzes a certificateless verifiably encrypted signature scheme which is proposed by Zhou Min et al and points out that their scheme can’t resist the public key replacement attack, it does not satisfy two basic security properties of verifiably encrypted signature: unforgeab- ility and extractability. Moreover, their scheme has key escrow issue, so it has not the advantages of certificateless public key cryptosystem.
In the proxy signature schemes from pairings, the pairing operations are the most time-consuming. In order to reduce times of the pairing operations, this paper proposes a new proxy signature from bilinear pairings. The security of the scheme depends on the hardness assumption of discrete logarithm problem and the computation Diffie-Hellman problem. Its security is analyzed and the result shows that this scheme is secure and effective, it can effectively resist the strong forgery attacks and satisfy the security properties of strong proxy signature. Compared with other schemes, the new scheme has better computational efficiency.
A trust model based on social rules and the topology characteristic of unstructured P2P network is proposed in this paper. A distributed storage mechanism of evaluation information is designed. The calculating, updating and searching methods of reputation information are given. The computation complexity and communication cost of this algorithm are low. Experimental results show that the proposed trust mechanism can resist the attack of malicious peers effectively.
A partition reconstruction method for disparity image based on homogeneous RBF network is presented. Disparity image is divided into some regions by reconcilable threshold and edge detection. Reconstruction based on RBF neural network is carried out in every region. All regions are combined and reconstruction result comes into being. When reconstruction based on RBF is executed in different region, data which have different resolving power are used in training according to structure of different region. Experimental results show that good reconstruction can be attained by the using method proposed.
To efficiently resolve action classification problem, a classification algorithm based on Action Energy Image(AEI) is proposed. The high dimensional feature space is reduced to lower dimensional space with (2D)2PCA. The nearest-neighbor classifier is adopted to distinguish different actions. It need not extract the period of the video, which is indispensable in some other methods. Experimental results show that the algorithm achieves higher classification accuracy with less running time and less memory space.
This paper proposes an advanced co-evolutionary model fitting algorithm. It optimizes the process in the course of solving the symbolic regression, especially to the shortcomings of traditional Genetic Algorithm(GA). It abstracts the modeling and optimization into a variety of inter-group co-evolution, associating these populations through exchange of fitness value, while extending the intelligent algorithm both in spatial and temporal scope when optimizing the parameters modeling. For the various groups with different gene expression, they have their nature self-contained in solving certain problems. It is more conducive to take advantages of the intelligent algorithms(GA, Genetic Programming(GP)). Compared with the traditional algorithm, the co-evolutionary model fitting algorithm shows a significant improvement in stability and convergence rate.
For the dynamic environment problem, this paper presents a self-learning function of the Symmetry Particle Swarm Optimization(SymPSO). The algorithm proposes to detect changes of the environment by using a static virtual particle swarm, and based on the thought of symmetric particles, without increasing the computational complexity, generates multiple symmetric virtual population. It can significantly expand the ability of population. To ensure the algorithm to escape from local optimum as quickly as possible, this paper proposes wide-area learning strategies to enhance self-learning ability of particles. Simulation comparative tests based on DF1 environment show that SymPSO algorithm can track the optimal value changes and escape from local optimum quickly, indicating the effectiveness of the algorithm.
In order to give more help to athletes’ exercise, a human motion simulation system based on binocular stereo vision is established. Using a mark-less motion capture algorithm, the pixel coordinates of human skeletons in the image are captured. According to binocular stereo vision theory, the world coordinates of human skeletons are computed and the 3-D human model is constructed. The human model is optimized by computing the energy function. The human motion is simulated and the parameters of human motion are figured out. Experimental results demonstrate that the simulation system can do well in motion capture and simulation of human motion.
For problem of imbalanced data learning, a gradually learning classification algorithm is proposed. This classification algorithm gradually adds the synthetic minority class examples according to attribute value-range distribution, and removes timely the synthetic examples which the stage classifier misclassifies. As the data achieves the desired degree of balance, the method uses raw data and synthetic data training learning algorithm, and gets the final classifier. Experimental results show that the gradually learning algorithm is better than C4.5, and better than SMOTEBoost and DataBoost-IM on most data sets.
It is an important task for knowledge-based systems to select and evaluate the attributes as well as a critical factor affecting systems’ performance. Using the genetic operator of the searching approach and correlation analysis, which characterizes Genetic Algorithm(GA), as the evaluation mechanism, this paper presents a new method to select the optimal subset of attributes for a given case library. Experimental results show that the proposed method can identify the most related subset to classify and predict, while reducing the representation space of the attributes whereas hardly decreasing the classification precision.