Generally, there are two objectives in the optimization of the measurement noise covariance matrix R of Kalman filter. However, most of the traditional optimization methods of Kalman filter only focus on one objective. In this paper, we proposed a new method to optimize the parameter R based on Multi-Objective Memetic Algorithm (MOMA). Compared with traditional methods, it can optimize multiple objectives simultaneously. In this method, the decision vector is the diagonal elements of matrix R, the first objective function f1 is the mean of the residual vectors, and the second objective function f2 is the degree of mismatching between the actual value of the residual covariance with its theoretical value. In the MOMA, the global search based on NSGA-II is utilized to minimize the two objective functions, and the local search based on Simulated Annealing (SA) is just used to minimize the f1. The experimental results demonstrate that the Kalman filter optimized by MOMA, namely MOMA-Kalman, can get much smaller filtering error than regular Kalman filter and other adaptive filter algorithms, such as SageHusa-Kalman and Fuzzy-Kalman.
In this paper we describe an algorithm which is based on Differential Evolution Algorithm using self-adaption and opposition-Based Mechanisms (SAODE). The mutation control parameterfiis self-adapted according to the deviation of search parameters in each generation. Opposition-based optimization is included in the initialization, and in the evolutionary process itself. In order to demonstrate the behavior of our algorithm we applied it in 4 benchmark functions with a combination of self-adaptive and opposition-based optimization. According to the obtained results, SAODE surpasses DE on 3 functions among 4 test functions about the convergence speeds.
The article proposed one-pass authenticated key establishment protocol from optimal eta pairings in random oracle for Wireless Sensor Networks. Security of the protocol relies on Computational Diffie-Hellman Problem on Optimal Eta Pairings. In one-pass key establishment protocol, the initiator computes a session key and a re1ated message. The key token is to be sent to the intended receiver using receiver's public key and sender secret key. From the received key token the receiver compute the session key, which is the same as the one computed by the sender,using sender public key and receiver's secret key. Because of low communication overhead, the scheme is better suited for Wireless Sensor Networks (WSNs) than the traditional key establishment protocol to establish the session key between two adjacent nodes.
Thorax injuries are common in vehicular accidents, second only to head injuries. Unbelted drivers of vehicles are more likely to suffer thorax injuries from steering wheel contact in frontal impacts. The objective of this study is to investigate the effects the steering wheel tilt angle (0, 20, 40, and 60) impact to the thorax of human body model with respect to thorax deflection and steering wheel rim contact interaction. To understanding of the human thorax sensitivity to steering wheel tilt angle on the force and deflection response using finite element simulations. It was found that the thorax response is sensitive to changes in steering wheel tilt angle. The contact force, Sternal displacement were the key parameters to be observed and compared. The results show that the contact force increased when the steering wheel tilt angle was bigger, the response was quicker. Low steering wheel tilt resulted in greater deformation. The greater the contact force, the deformation of the sternum but reduced when thorax impact the steering wheel, According to ECE R12 steering wheel regulation ,use force regulations to assessment the injury of the thorax is not accurate enough when human thorax impact the steering wheel.
Differential Evolution (DE) algorithm is a heuristic random search algorithm based on the group difference, and it is also an optimization algorithm which processes random search in the continuous space with actual number vector coding. In uranium ore classification, its impossible to know in advance the number of best features to select. This paper puts forward a new algorithm named Variable-number Differential Evolution (VDE) algorithm, and applies it to selecting the best features of hyperspectral data. Then applies k-nearest neighbor, decision tree, naïve bayes, naïve bayes tree and support vector machine algorithm on the newly obtained data set only containing the selected features, records average accuracy by 10-fold cross-validation. The results show that new algorithm can improve the accuracy of classification compared with genetic algorithm (GA) and original DE algorithm.
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