Vol 50, No 5 (2016)

Chassis serves as a backbone by supporting the body and diverse parts of the automobile. It ought to be sufficiently rigid to endure the shock, twist, vibration and extra stresses. Then, a vital consideration in chassis design is the strength (Equivalent Stress) for sufficient bending stiffness (Deflection). The primary goal of the research is to build up an Artificial Neural Network (ANN) model for identical stress prediction. Two side members joined to a series of cross members to make the chassis frame. The number of cross members and their locations, cross-section and the sizes of the side and the cross members turn into the design variables. The chassis frame model is created in Creo 3.0 and dissected using Ansys. Since, the number of parameters and levels are more, so the probable models are too much. By changing the Parameters, using the orthogonal array the weight of the sidebar is decreased. Then, FEA is performed on those models. ANN model is prepared by using the results of FEA. For training the ANN model, the standard back-propagation algorithm is observed to be the best. A multi-layer perception network is used for non-linear mapping between the input and the output parameters. FEA-ANN hybrid model can save material used, production cost and time.

Face recognition, one of the biological recognitions, has received extensive concern due to its secrecy and friendly cooperation. Gabor wavelet is an important tool in face feature description. In order to reduce the loss of useful information during down sampling, this work puts forward a Gabor feature representation method based on block statistics, which enhances the efficiency of Gabor feature representation. This study was designed to explore face recognition algorithms on the basis of highly recognizable and real-time collaborative representation. Experimental results indicated that, the face recognition based on block Gabor feature collaborative representation not only guaranteed the calculation speed, but also took full advantage of the robustness of Gabor feature. Besides, the block Gabor feature containing more details further improved the recognition rate.

This paper presents a CDM-backstepping strategy for motion control of Electrically-driven manipulator under the conditions of uncertainty and the action of external disturbance, while incorporating a nonlinear observer. Based on this model, a systematic analysis and design algorithm is developed to deal with stabilization and trajectory tracking of elbow robot, one feature of this work is employing the backstepping observer to achieve the exponential stability with position and velocity estimations. The results of computer simulations demonstrate that accurate and robust motion control can be achieved by using the proposed approach.

Vulnerabilities such as design flaws, malicious codes and covert channels residing in hardware design are known to expose hard-to-detect security holes. However, security hole detection methods based on functional testing and verification cannot guarantee test coverage or identify malicious code triggered under specific conditions and hardware-specific covert channels. As a complement approach to cipher algorithms and access control, information flow analysis techniques have been proved to be effective in detecting security vulnerabilities and preventing attacks through side channels. Recently, gate level information flow tracking (GLIFT) has been proposed to enforce bittight information flow security from the level of Boolean gates, which allows detection of hardware-specific security vulnerabilities. However, the inherent high complexity of GLIFT logic causes significant overheads in verification time for static analysis or area and performance for physical implementation, especially under multilevel security lattices. This paper proposes to reduce the complexity of GLIFT logic through state encoding and logic optimization techniques. Experimental results show that our methods can reduce the complexity of GLIFT logic significantly, which will allow the application of GLIFT for proving multilevel information flow security.

Geiger iterative algorithm is very strict to the initial value. If the initial value is not selected suitably, it is difficult to enter the convergence range, thus increasing the number of iterations. The acoustic emission source location based on phase difference time delay estimation method reduces the error of acoustic emission location, but it has some shortcomings in accuracy. Based on the above problems, this paper presents a new algorithm for Geiger optimization based on source localization. Firstly, the initial value of Geiger is obtained by using the phase difference method. Then, the optimal solution is obtained by the iterative solution of Geiger algorithm and the least square method. The simulation results show that this method can effectively solve the problem of selecting the initial value of Geiger, so that it can quickly enter the convergence range, improving the convergence speed and positioning accuracy, comparing the positioning results of the United States PCI-2 type acoustic emission instrument, the average error reduced by about 5 mm.