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Searchable asymmetric encryption (SAE) can also be called Public Key Encryption with Keyword Search (PEKS), which allows us to search the keyword on the data of having been encrypted. The essence of Asymmetric searchable encryption is that users exchange the data of encryption, one party sends a ciphertext with key encryption, the other party with another key receives the ciphertext. Encryption key is not the same as the decryption key, and cannot deduce another key from any one of the key, thus it greatly enhances the information protection, and can prevent leakage the user's search criteria—Search Pattern. Secure schemes of SAE are practical, sometimes, however the speed of encryption/decryption in Public-key encryption is slower than private key. In order to get higher efficiency and security in information retrieval, in this paper we introduce the concept of distributed SAE, which is useful for security and can enable search operations on encrypted data. Moreover, we give the proof of security.

In this paper, we perfect the mutual coupling of compact microstrip array antenna by designing a new defected ground structure. When the resonant frequency is 2.45GHz, array element spacing is 0.1 times of free space wavelength, we introduce new defected ground structure into antenna array. Then we use HFSS to make simulation and compare the changing of antenna's parameters before and after adding defected ground structure. The results demonstrate that the parameters representing mutual coupling in new model can reduce by 30dB, which effectively perfects the mutual coupling of compact microstrip array antenna.

As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low solution precision, lack of mining ability and easily falling into precocity. This paper proposes a new chaos electromagnetism mechanism algorithm combining chaotic mapping with limited storage Quasi-Newton Method (EM-CMLSQN). Its main idea is that it adopts limit quasi-Newton operator to replace the local optimization operator in EM algorithm for local searching in the late of algorithm. In the process of algorithm, the chaos mapping is introduced into optimization processes, and it generates new individuals to jump out of local to maintain the population diversity according to characteristics of chaos mapping random traversal. Finally, the experiments show that the new algorithm can effectively jump out of local optimal solution through comparing three continuous space test functions. The new algorithm has obvious advantages in terms of convergence speed compared to traditional EM algorithm, in addition, it is more accuracy than particle swarm optimization (PSO) algorithm. We compare the new chaos electromagnetism mechanism algorithm with ant colony optimization (ACO) algorithm, PSO algorithm, the results represent that new scheme can obtain the optimal path in the path optimization process, which shows that the new method has better applicability in the discrete domain problem.

Maneuvering target tracking is a target motion estimation problem, which can describe the irregular target maneuvering motion. It has been widely used in the field of military and civilian applications. In the maneuvering target tracking, the performance of Kalman filter(KF) and its improved algorithms depend on the accuracy of process noise statistical properties. If there exists deviation between process noise model and the actual process, it will generate the phenomenon of estimation error increasing. Unbiased finite impulse response(UFIR) filter does not need priori knowledge of noise statistical properties in the filtering process. The existing UFIR filters have the problem that generalized noise power gain(GNPG) does not change with measurement of innovation. We propose an improved UFIR filter based on measurement of innovation with ratio dynamic adaptive adjustment at adjacent time. It perfects the maneuvering detect-ability. The simulation results show that the improved UFIR filter has the best filtering effect than KF when process noise is not accurate.

In this paper, we express arc furnace electrode regulator system as a time-variant nonlinear-linear-nonlinear model. On this basis, we propose an online identification method based on nonlinear-linear-nonlinear model system. This new scheme solves the problem of model variation and prediction precision decline causing by time-varying of arc characteristic. In order to dispose the difficulty of parameters separation in the online identification process, this new method adopts the mind of update the parameters of linear parts and nonlinear parts respectively. It realizes the parameters separation of system effectively. Simulation results show that this method can track the changes of arc characteristics effectively. That it achieves the aim of real-time monitoring and controlling system parameters.

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.