To reduce the navigation graph size and redundant test paths in the generation process of Web application test cases, a Web application test case generation method based on Selenium page object design pattern and graph traversal algorithm was proposed. Firstly, by classifying the original page objects, the page object navigation graph with navigation page object class as the node and the navigation method as the migration edge was created. Secondly, with the shortest-path algorithm of graph, a Page Object Graph Algorithm (POGA) was proposed to realize the navigation graph traversal in order to generate test path set. Finally, the test paths were extracted and Faker was used to generate the simulated data, and the test cases that can be directly executed were produced. The experimental results show that, the proposed method has the reduction rate of about 89% compared with the navigation graph size generated by crawling Web applications, reduces the number of redundant and infeasible paths in comparison with the state migration method for generating Web application test cases, and further improves the reuse rate of page objects and the maintainability of test cases.

In order to solve problems in traditional gait detection algorithms, such as simplification of information, low accuracy, being easy to fall into local optimum, a gait detection algorithm for exoskeleton robot called Support Vector Machine optimized by Improved Whale Optimization Algorithm (IWOA-SVM) was proposed. The selection, crossover and mutation of Genetic Algorithm (GA) were introduced to Whale Optimization Algorithm (WOA) to optimize the penalty factor and kernel parameters of Support Vector Machine (SVM), and then classification models were established by SVM with optimized parameters, expanding the search scope and reduce the probability of falling into local optimum. Firstly, the gait data was collected by using hybrid sensing technology. With the combination of plantar pressure sensor, knee joint and hip joint angle sensors, motion data of exoskeleton robot was acquired as the input of gait detection system. Then, the gait phases were divided and tagged according to the threshold method. Finally, the plantar pressure signal was integrated with hip and knee angle signals as input, and gait detection was realized by IWOA-SVM algorithm. Through the simulation experiments of six standard test functions, the results demonstrate that Improved Whale Optimization Algorithm (IWOA) is superior to GA, Particle Swarm Optimization (PSO) algorithm and WOA in robustness, optimization accuracy and convergence speed. By analyzing the gait detection results of different wearers, the accuracy is up to 98.8%, so the feasibility and practicability of the proposed algorithm in the new generation exoskeleton robot are verified. Compared with Support Vector Machine optimized by Genetic Algorithm (GA-SVM), Support Vector Machine optimized by Particle Swarm Optimization (PSO-SVM) and Support Vector Machine optimized by Whale Optimization Algorithm (WOA-SVM), the proposed algorithm has the gait detection accuracy improved by 5.33%, 2.70% and 1.44% respectively. The experimental results show that the proposed algorithm can effectively detect the gait of exoskeleton robot and realize the precise control and stable walking of exoskeleton robot.

Aiming at the problems of mismatching and too large matching distance because of curves scaling, shifting, rotation and non-uniform sampling in the process of online signature verification, a curve segment similarity matching method was proposed. In the progress of online signature verification, two curves were partitioned into segments and matched coarsely at first. A dynamic programming algorithm based on cumulative difference matrix of windows was introduced to get the matching relationship. Then, the similarity distance for each matching pair and weighted sum of all the matching pairs were calculated, and the calculating method is to fit each curve of matching pairs, carry out the similarity transformation within a certain range, and resample the curves to get the Euclidean distance. Finally, the average of the similarity distance between test signature and all template signatures was used as the authentication distance, which was compared with the training threshold to judge the authenticity. The method was validated on the open databases SUSIG Visual and SUSIG Blind respectively with 3.56% and 2.44% Equal Error Rate (EER) when using personalized threshold, and the EER was reduced by about 14.4% on Blind data set compared with the traditional Dynamic Time Wraping (DTW) method. The experimental results show that the proposed method has certain advantages in skilled forgery signature and random forgery signature verification.

In order to improve the effectiveness of mobile cloud computing in Device-to-Device (D2D) communication network, a computation offloading scheme based on the time switch policy for energy harvesting was proposed. Firstly, the computational tasks needed to be migrated of a traffic-limited smart mobile terminal were sent to an energy-limited smart mobile terminal in the form of Radio-Frequency (RF) signals through D2D communication, and the time switch policy was used by the energy-limited smart mobile terminal for the energy harvesting of received signals. Then, the extra traffic consumption was paid by the energy-limited terminal for the relay tasks of traffic-limited terminal to the cloud server. Finally, the proposed scheme was modeled as a non-convex optimization problem for minimizing terminal energy and traffic consumption, and the optimal scheme was obtained by optimizing the time switch factor and the harvest energy allocation factor of the energy-limited terminal, and the transmission power of the traffic-limited terminal. The simulation results show that, compared with non-cooperative scheme, the proposed scheme can effectively reduce the terminal's limited resource overhead by the computation offloading through reciprocal cooperation.

Focusing on the issue that the impulse noise in Power Line Communication (PLC) system greatly affects the transmission performance, and most traditional de-noising algorithm can not effectively suppress the impulse noise, a time-frequency combination de-noising algorithm was proposed. Firstly, the impulse noise with large peak in the time domain received signal was detected and zeroed by selecting the appropriate threshold. Secondly, according to the symbols that had been decided in the frequency domain, the smaller impulse noise which had not eliminated in the time domain was reconstructed, and the accuracy of the noise reconstruction was improved by iteration. Finally, the reconstructed impulse noise was subtracted from the frequency domain received signal. Simulation experiments were conducted under the multipath channel of the power line. Compared with traditional time domain and frequency domain de-noising algorithms, the proposed algorithm could achieve the performance improvement of 2dB and 0.5dB respectively when the bit-error rate was 0.01. And as the bit-error rate decreased, the performance gap between them would be even greater. The simulation results show that the proposed time-frequency combination de-noising algorithm can improve the resistance of the PLC system to impulse noise.

Acceleration data in 3 axes during a signature process can be collected to authenticate users. Because of complex structures of Chinese signature, the process of signing in the air is hard to be forged, but it also increases differences between signatures performed by the same user which brings more difficulties in authentication. Classical verification methods applied to 2-D signature or hand gesture cannot solve this problem. In order to improve the performance of in-air Chinese signature verification, the classical Global Sequence Alignment (GSA) algorithm was improved, and the interpolation was applied to matching sequences. Different from classical GSA algorithm which uses matching score to measure similarity between sequences, two distance indexes, Euclidean distance and absolute value distance, were introduced to calculate the differences between sequences after interpolation. Experimental results show that both of the two improved GSA algorithms can improve the accuracy of authentication, the Equal Error Rate (EER) of them are decreased by 37.6% and 52.6% respectively compared with the classical method.

Considering the issue of objective weight overwhelming subjective weight when the subjective weight and objective weight is inconsistent in multi-index evaluation problem, based on G1 method and the objective weighting method, an assembled weighting model combined with G1 method improved by composite power function was proposed. Firstly, an index system was built, and the subjective ranking and subjective initial vector were determined by G1 method. Thus, each objective index vector was calculated by objective weighting method. Secondly, without changing the ranking order, the comprehensive weights integrated with both subjective and objective components were obtained by utilizing composite power function. Lastly, comprehensive evaluation was calculated by using standardized values of indices and comprehensive weights. Merchants data crawled from Dianping.com was adopted for the experiments of comprehensive evaluation. The Root-Mean-Square Error (RMSE) of the new model was 3.891, which is lower than the result of 8.818 obtained by the G1-entropy weighting and the result of 4.752 obtained by the standard deviation improved G1. Meanwhile, the coverage rate obtained by the new model was better than the two baseline models as well. On the other hand, the RMSE obtained by changing subjective ranking order is 5.430, which is higher than the result of 1.17 that obtained by changing subjective initial vector. The experimental results demonstrate that the evaluation values obtained by the new model highly match with the counterparts given by the Dianping.com, and the model can significantly weaken the effect of initial subjective values, which reflects the fundamental status of the subjective ranking.

To solve the problem of local remeshing in multi-body separation numerical simulation of flight vehicle with unstructured mesh, a method for the construction of local remeshing regions was proposed based on element neighbouring indices. Firstly, the mesh quality was checked by the element radius ratio and the remeshing regions were marked. Secondly, the remeshing regions were extended by the neighboring indices of mesh elements. Finally, the around elements of the non-two manifold sides were marked for ensuring the boundary definition of remeshing regions which satisfied two sides manifold criterion. The numerical experiment of a separation simulation was conducted by the proposed method. In this simulation, the local remeshing was operated successfully for 16 times, and the average of the overall remeshing element radius ratio was improved above 0.71. The calculation results and comparative analysis of the wind tunnel experimental data shows that, the trajectory and motion of the separation were calculated accurately in the numerical experiments. It is verified that the proposed unstructured remeshing process is effective.

Concerning the limitations that the accuracy of prediction is low and the classification on box-office is not significant in application, this paper proposed a new model to predict box-revenue of movie, based on the movie market in reality. The algorithm could be summarized as follows. Firstly, the factors that affected the box and format of the output were determined. Secondly, these factors should be analyzed and quantified within [0, 1]. Then, the number of neurons was also determined, aiming to build up the architecture of the neural network according to input and output. The algorithm and procedure were improved before finishing the prediction model. Finally, the model was trained with denoised historical movie data, and the output of model was optimized to dispel the randomness so that the result could reflect box more reliably. The experimental results demonstrate that the model based on back propagation neural network algorithm performs better on prediction and classification (For the first five weeks, the average relative error is 43.2% while the average accuracy rate achieves 93.69%), so that it can provide a more comprehensive and reliable suggestion for publicity and risk assessment before the movie is on, which possesses a better application value and research prospect in the prediction field.

In order to search domain ontologies needed by users in higher precision, a heuristic ontology search mechanism was proposed on the basis of analyzing demands for ontology search and studying user search behavior, which took full advantages of the different search keywords belonging to same domain input by different users for their different domain knowledge to realize heuristic extension for users search keywords and improvement for search matching. The experimental results show that the proposed approach could help users to search and get the relevant ontologies at a higher precision and recall.

This paper mainly addressed how to identify a mass of dada in NetFlow environment. It proposed an algorithm of abnormal flow identification based on dynamic K-layer feature model. With priority strategies, index table was opened reading the abnormal behavior and matching with eigenvalues one by one. When a match was done successfully, it was marked and the type of abnormal behavior was determined. The experimental result shows that it can identify the abnormal flow quickly and efficiently. It improves the efficiency of identification, and solves network security problems and achieves design goals.

A method to locate iris was introduced,in which Integro-Differential Operators were combined with Gradient-Decomposed Hough Transform.In the Daugman algorithms,a non-scattered man-made light could arouse the bright point effect,which could be effectively reduced by the method.A new E-Bank solution was designed useing it.And the solutin can enhance the lever of security,and satisfy the customer,bank and CA.

It is very important to ascertain rationally the number and positions of split points for discretization of continuous variables. To improve the efficiency of unsupervised discretization, an entropy-based algorithm was proposed for discretization of continuous variables. It made use of the characteristics of the information content(entropy) of a continuous variable, and partitioned the continuous variable by itself for minimizing both the loss of entropy and the number of partitions, in order to find the best balance between the information loss and a low number of partitions, so then obtained an optimal discretization result. The experiments show this approach effective.

Support vector machine is a new classification algorithm based on statistical learning theory. After the discussion of the current multiclass SVMs, a novel multiclass SVM classifier based on geometric distance was proposed. The Posterior probability output of binary SVM was generalized to multiclass SVM. Without iteration computing, this method improved prediction accuracy with fast computing. The numeric experiments prove that above two methods have good generalization, which can increase prediction accuracy to unknown examples.