Applied Mechanics and Materials Vols. 347-350

Abstract: Equipment maintenance training is one of the most important tasks in peacetime and wartime equipment support. Traditional maintenance training is mainly carried out on real equipment. It is subject to many limited conditions. Virtual maintenance training can be an effective solution to the shortcomings of the actual equipment training, but it also needs to be improved in the aspect of offering intuitive image operation interface for trainees. Maintenance simulation training opens up new ideas for equipment maintenance training. The article studies the advantages and disadvantages of the three maintenance training methods. Specific application of simulation maintenance training in radio maintenance training is given. The article has a certain reference value for maintenance training.

Abstract: By investigating insufficiency of typical artificial intelligence algorithms aiming at the high rate of False-Positives and False-Negatives in the Intrusion Detection Systems (IDS), this paper presents an approach that Support Vector Machine (SVM) is embedded in Network Intrusion Detection System (NIDS). At the same time, by using online data and K-fold cross-validation method, this paper proposes a method to optimize the attributes and model of SVM respectively. Experimental results show that by using this method as the detection core of the intrusion detection system, the rate of False-Negatives in IDS can be reduced significantly.

Abstract: The decoding parallelism of quasi-cyclic low density parity check (QC-LDPC) codes, an important realization factor of LDPC codes, is limited by the block size. Joint row-column (JRC) decoding algorithm, an efficient decoding technique having low computation cost and small iteration number, also suffers from the decoding parallelism limitation in QC-LDPC application. In this paper, a novel LDPC-code construction method is presented and validated. This method constructs the row sets to eliminate the data access conflicts, and it clearly enhances the decoding parallelism while avoids any degradation of bit error rate (BER) performance.

Abstract: With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPUs, and calculated the overall speedup. Our results show that a speedup of 36.1 times using 1 GPU and more than 120 times for 4 GPUs on 1920x1080 sequences. Further, our parallel algorithm demonstrated the potential of nearly linear speedup according to the number of GPUs in the system.

Abstract: At present, Cross Site Scripting (XSS) vulnerability exists in most web sites. The main reason is the lack of effective validation and filtering mechanisms for user input data from web request. This paper explores vulnerability detection method which based on taint dependence analysis and implements a prototype system for Java Web program. We treat all user input as tainted data, and track the flow of Web applications, then we judge whether it will trigger an attack or not. The taint dependent analysis algorithm mentioned in this paper is used to construct the taint dependency graph. Next the value representation method of the string tainted object based on finite state automata is discussed. Finally, we propose the vulnerability detection method for the program. The experiment result shows that the prototype system can detect reflection cross-site scripting vulnerability well in those programs which dont have effective treatment for the user input data.

Abstract: Speedy IP Traceback method (SIPT) is deficient in monitoring and position attack sources, this paper puts forward an improvement scheme based on SIPT, it only marks two-three times for the packet on the key locations, and the propose also overload rarely used fields on IP packet header to store the checkpoint information, which can traceback the attack source use these marking information, which would help to attack path-reconstruction with a low false-positive rate as a guid.

Abstract: this article presents an improved classifier vehicle identification algorithm to improve the efficiency of the existing vehicle recognition algorithm. First, using edge orientation histograms to extract image characteristics, then, Error correction coding is applied to the classification of classifier, the multi-class classification problems turned into multiple binary classification problems. A large number of experimental analysis shows that the improved vehicle identification algorithm has good recognition performance and robustness. Therefore, the algorithm which this article used has high theoretical and practical value.

Abstract: This paper presents a novel Dempster-Shafer evidence construction approach for aircraft aim recognition. The prior-probability of the properties of aircraft was used for establishing a probabilistic argumentation system. Dempster-Shafer evidence was constructed by assumption-based reasoning. Therefore, additional information could be provided to the classification of the data fusion system. Experiments on artificial and real data demonstrated that the proposed method could improve the classification results.

Abstract: In order to enhance the extensibility of current attack feature extracted and detection means for App-DDoS(Application Layer Distributed Denial of Service, App-DDoS) attacks, a novel feature extracted method based on marking access and a new detection algorithm named d-SVDD are proposed. After expressing kinds of App-DDoS attacks as characteristic vectors by access marked strategy and feature extracted strategy, d-SVDD algorithm is used for secondary classification and detection of pre-set area around decision boundary based on SVDD. It is proved by experiments that the proposed feature extracted and detection means can realize effective detection for kinds of App-DDoS attacks, both have satisfying time, space and extensibility performance.

Abstract: Material supply isthe basic guarantee for the large ship sailing to ocean. In order to improve the efficiency of the supply of the large ship, the support of materials handling system is needed. According to the typical material handling system of the large ship, with the material handlings time shortest as the decision goal and the handling sequence as the decision variable, the analysis and construction of the model for optimizing the systems operation is given in this paper. The improved genetic algorithm is put forward. The specific model is also given and its proved that the analysis and modeling method in this paper can effectively improve the efficiency of the material handling of the large ships.