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In the literature, we investigate the decentralized control of networked nonlinear agents with uncertainties and unknown control directions, i.e., the sign of the high-frequency-gain in agent's dynamic is unknown. A Nussbaum-type adaptive controller is designed with the aid of nonlinear damping technique for each agent such that consensus of the network can be achieved, meanwhile, the overall system is insured to be bounded. The controller for each agent is composed by a Nussbaumtype item which seeks the control direction adaptively, a distributed item to drive the system to consensus and a nonlinear damping item to handle the uncertainty in agent's dynamic. It is proved that consensus of the network can be realized via the proposed controller. Moreover, the final consensus state is indicated, which is significant for consensus of networked nonlinear systems. Simulation results are presented to verify the effectiveness of the proposed controllers.

This paper investigates the problems of absolute stability for Lur'e systems with time-varying delay and sector-bounded nonlinearity. Based on Wirtinger's inequality, a new Lyapunov functional named Wirtinger-based Lyapunov functional is constructed, in which some delay-dependent term and slack matrixes are introduced to refine the function. By using relaxed integral inequality to estimate the derivative of Lyapunov functional, less conservative stability criteria are achieved in terms of linear matrix inequalities (LMIs). The effectiveness and merits of derived conditions are verified by a numerical examples.

This paper investigates the problems of stability and dissipativity of static neural networks (NNs) with a bounded time-varying delay. A new Lyapunov-Krasovskii functional (LKF) is established, in which some delay-dependent term combined with some slack matrixes are introduced to refine the function. The proposed LKF which utilizes the information of more states and delays is crucial important to reduce the conservatism of system. In addition, by using relaxed integral inequality to estimate single integral term in the derivative of the LKF, some relaxed stability conditions are achieved. The results obtained are extended to dissipativity analysis and some relaxed sufficient conditions are derived to guarantee that considered static delayed NNs are strictly (Q,S,R.)-γ-dissipative. The effectiveness and merits of derived conditions are verified by two numerical examples.

This paper deals with the problem of delay-dependent passivity for neural networks with interval time-varying delay. The time-delay is satisfied with interval time-varying without any constraint on the rate of change. Firstly, a proper augmented Lyapunov-Krasovskii functional (LKF), which makes full use of the neuron activation function conditions, is selected. Subsequently, by fully using two new inequalities and the combining reciprocally convex approach, some delay-dependent passivity conditions are established in the form of linear matrix inequality (LMI). Finally, two numerical examples are provided to verify the superiority and effectiveness of the presented techniques.

Anti-vibration hammer is a key component in the power power line to prevent vibration of power line for a long time, resulting in power line damage, broken and other hazards. Therefore, it is important to accurately locate the position of the anti-vibration hammer in an image for the subsequent judgment, repair or replacement work. However, the traditional anti-vibration hammer positioning and identification is time-consuming, costly and inefficient. An accurate and efficient method is proposed to detect anti-vibrations in aerial images. This method use deep learning to learn insulators by the convolution neural network, and identify and locate the anti-vibrations in aerial images. The proposed algorithm is tested on lots of aerial images and experimental results show the proposed algorithm detected anti-vibration successfully and efficiently. It is promising to significantly facilitate the visual inspection and automatic identification of anti-vibration in aerial images.

Electric insulators as an indispensable device for electric power networks, maintaining its safe operation is of vital importance. Due to the large number of insulators and wide distribution, the insulator state detection based on aerial images has important practical significance. Insulator images are usually acquired by artificial or aerial collection, at a specific angle, focal length and complex background. For the labor-detection, low detection efficiency, higher detection cost and other interference, an efficient and accurate method is proposed to detect kinds of electric insulators in unmanned aerial vehicle (UAV) images. This method is based on deep learning, learning insulators characteristics through the convolution neural network in complex aerial images, and then to identify a variety of insulators. The proposed algorithm is tested on a diverse set of UAV imagery. Experimental results show that the proposed algorithm can detect electric insulators efficiently and perform better than other electric insulators detection methods. The proposed method is promising for the change detection of the electric insulators.

This paper is concerned with the stability problems for discrete linear systems with time-varying delays. A new summation inequality is presented in this paper, which is less conservative than other existing summation inequalities. This inequality is a discrete-time counterpart of the free-matrix-based (FMB) integral inequality. Using it to investigate the stability problem for discrete-time systems, some novel stability criteria are established. The advantage of the derived condition is illustrated by some numerical examples, while a practical example demonstrates the applicability of the method.

This paper focus on the problem of passivity of neural networks in the presence of time-varying discrete and distributed delays. By constructing a novel and suitable augment Lyapunov-Krasovskii functional(ALKF) and employing some useful inequality formula, especially generalized free-weighting-matrix(GFWM) approach, improved sufficient conditions for checking the passivity of the neural networks are established. Based on three numerical examples, the advantages of our results are illustrated through the comparison of maximal admissible delay bounds.

This paper focuses on the robust delay-dependent dissipativity problem of neural networks with a bounded time-varying delays. Firstly, a proper augmented Lyapunov-Krasovskii functional, which doesn't demand all the symmetric matrices to be positive definite, is constructed. In addition, by employing relaxed integral inequality and the combining generalized free-weighting-matrix (GFWM) approach to bound integral term in the derivative of the Lyapunov-Krasovskii functional, some less conservative sufficient conditions are deduced to ensure that considered neural networks are strictly (Q, S, R)-γ-dissipative. The advantages and effectiveness of presented techniques are demonstrated via two numerical examples.

By utilizing one coupled inductor and two auxiliary diodes, the bridgeless boost rectifier has a lower conduction loss and no diode reverse recovery problem. Detailed analysis of the novel boost rectifier is presented in this paper. The experimental results of a 300W prototype was analyzed and the excellent performance of the bridgeless boost rectifier was also shown. Dual Boost PFC brings higher EMI noise by nature. The proposed bridgeless boost rectifier can solve the problem very well. And the EMI model and analysis is validated by the Saber simulation which shows the same level with traditional Boost PFC.

In this paper, we focus on deriving a less conservative stability criterion for generalized neural networked (GNN) with interval time-varying delays. Firstly, a new augmented Lyapunov-Krasovskii functional (ALKF) with triple-integral terms is chosen to reduce the conservatism of stability criteria. In addition, by fully utilizing the integral inequality presented recently and combining the reciprocally convex approach, a improved delay-dependent stability condition is established in terms of LMI. Finally, two numerical examples are provided to verify the advantages of the presented criteria.

A contactless transformer with a shorted current sensing winding in the primary side is proposed to provide a passive current phase detection solution for a self-oscillating contactless resonant converter (SOCRC). It can detect the phase of the secondary current and feedback to the primary side rapidly and accurately regardless of the variable air gap. To guarantee the operation of the SOCRC, the influence of the coupling coefficient between the shorted current sensing winding and the secondary windings is analyzed in detail. Some experimental results on an 80W SOCRC are used to verify the analytical results of the proposed structure.

In this article, the problem of the stability for delay-dependent neural networks is concerned. A new Lyapunov-Krasovskii functional is introduced, so as to obtain some more superior delay-dependent stability criterion. Moreover, by employing the delay decomposition technique, some novel absolute stability conditions are established, which refine and improve some existing ones. Finally, the feasibility and superiority of the proposed method is demonstrated by a numerical example.

Close collaboration and desired strategy is indispensable for humanoid robots in the RoboCup soccer competition. In order to solve the problem that the convergence rate is too low in training local strategies, this paper mainly proposed a method to optimize the parameters in decision and positioning based on reinforcement learning for soccer robots. First, Markov decision process is applied to the framework for reinforcement learning. Then, we propose a relative improved method, which is known as a Sarsa Algorithm to overcome the drawback of the low convergence rate of the average reward reinforcement learning. Meanwhile, in order to deal with the large state space problems arising in the training and improve the generalization ability, this method is applied to the Keepaway local training. The training results show that, this algorithm has a faster convergent speed than other ordinary learning algorithm.

In order to solve the difficulties of online measurement in the marine protease fermentation process of crucial biological variables (such as biomass concentration, substrate concentration and enzyme activity, etc), a soft sensing method based on KPCA-RBF neural network is proposed by combining the kernel principal component analysis (KPCA ) with the radial basis function (RBF) neural network. Establishing the soft sensing model of KPCA-RBF neural network, KPCA is applied to compress data, and choose the nonlinear component as the input of RBF neural network and biomass concentration, substrate concentration, relative enzyme activity as the output. Simulation results indicate that this model has a higher accuracy, better tracking performance when compared with RBF and PCA-RBF neural network model. Therefore, the proposed method can satisfy the requirements of on-line measurement of biological parameters and is proved to be an efficient modeling method.

This paper studies the problems for the stability analysis and controller design of wireless sensor networks (WSNs) measurement and control systems. In order to ensure the stability of the systems, we construct proper Lyapunov-Krasovskii functional and employ integral inequalities to analyze the WSNs measurement and control systems based on Lyapunov theorem. At the same time, in combination with the actual conditions, it makes a concrete analysis of the WSNs measurement and control system in greenhouse with temperature and humidity parameters as the objectives of research. A numerical example demonstrate that the criterion obtained in this paper can make the WSNs measurement and control systems steady, which is better than some published results.

The problem for the stability of train network control systems on electric multiple unit (EMU) with network-induced delay and packet dropout has been investigated.An equivalent continuous-time model is proposed to account for the train network control systems .Based on the obtained train network control systems model,employing Lyapunov theory together with some novel analysis techniques,less conservative results are derived.The effectiveness of the proposed method is illustrated by an numerical example.

This paper is focused on the absolute stability of Lur'e systems with interval time-varying delay. By constructing a complete delay-decomposing Lyapunov-Krasovskii functional and considering the relationship between the time-varying delay and time-varying interval, less conservative delay-range-dependent stability criteria in terms of linear matrix inequalities (LMIs) are obtained, which have wider application than the existing ones because the low bound and upper bound of time-varying delay can be freely decomposed at one's discretion. A numerical example is given to show the effectiveness of the proposed method.

This paper is based on the principles of chromatography which was raised from chemistry field. We summarize and classify the traditional economic principles and operational applications. And we present the feasibility of chromatography in the classification of economic applications. We propose the feasibility of application of correlating principle analysis in the economic analysis. We also point out the difficulties of application classification system of economic phenomena.

MicroRNAs constitute a growing class of non-coding RNAs that are thought to regulate gene expression via translational repression in animal. Single nucleotide polymorphisms (SNPs) in microRNA may modify various biological processes by influencing the processing and/or target selection of microRNAs. Therefore, genetic background differences in distribution of micoRNA gene polymorphisms may be an important factor in determining phenotypic variation. In the present study, we investigated a T to C polymorphism in the seed region of mature mir-1644 sequence with forced PCR-RFLP using HpaII nuclease in six chicken populations: 102 individuals from Jiningbairi, Langya, Siyuwugu, Wenshangluhua, Beijingyou and Leghorn Chicken breeds. The results showed that genotype frequencies of the mir-1644 T>C did not differ significantly from those expected under the Hardy-Weinberg equilibrium, and Wenshangluhua chicken have significant differences in the frequency and other chicken breeds. Bioinformatics analyses indicated that mir-1644 T>C polymorphism may alter target selection and secondary structure.Thus exert profound biological effects in the formation of some special phenotype of Wenshangluhua chicken‥

According to network security problems, a new more initiative distributed network of safety warning model P2DWR2 is developed in this paper, based on the available P2DR model. The key technologies of P2DWR2 model, i.e. deployment of security policy, cooperation of security components etc., were discussed. The new model compensates the insufficiencies of P2DR model, such as low automatic level and requirement of human involvement to complete the security incident response deficiencies. P2DWR2 model provides the policy uniform defines, automatic distribution, self-adaptive management functions and so on. And it makes the core effect of security policy realized.