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From the view of information flow, a super-network equilibrium optimization model is proposed to compute the solution of the operation architecture which is made up of a perceptive level, a command level and a firepower level. Firstly, the optimized conditions of the perceptive level, command level and firepower level are analyzed respectively based on the demand of information relation, and then the information supply-and-demand equilibrium model of the operation architecture super-network is established. Secondly, a variational inequality transformation (VIT) model for equilibrium optimization of the operation architecture is given. Thirdly, the contraction projection algorithm for solving the operation architecture super-network equilibrium optimization model with fuzzy demands is designed. Finally, numerical examples are given to prove the validity and rationality of the proposed method, and the influence of fuzzy demands on the super-network equilibrium solution of operation architecture is discussed.

Bionic electronic skin can accurately sense and locate surface pressure, which is widely demanded in many fields. Traditional electronic skin design usually relies on grid-architecture sensor arrays, requiring complex grid and interconnection arrangements as well as high cost. Grid-less planar sensors can solve the problem by using electrodes only at the edges, but they usually require the use of mapping software such as electrical impedance tomography to achieve high precision. In this work, a design method of high-precision grid-less planar pressure sensors based on the back-propagation (BP) neural network is proposed. The measurement precision of this method is demonstrated to be over two orders of magnitude higher than that of a grid-structure sensor array with the same electrode distribution density. Moreover, this method can be used for irregularly-shaped and non-uniform sensors, which further reduces the manufacturing difficulty and increases the application flexibility.

In order to modify the local luminance heterogeneity of target object image, restrain the phenomenon of overexposure and preserve enough original image information, we analyzed the adaptability of the adjustable projection which is applied to exposure compensation. In this paper, a novel iterative method by using the improved algorithm for absolute center moment (IACM) is proposed. A projector-camera system with the iterative algorithm is built to adjust the adaptive projection according to the inversion theory, the differences of depth information and the illumination intensity. Among them, the role of depth information is correspond to different light intensity at different heights. Through the experiment, the result shows that the camera could achieve uniform luminance and the index convergence is evaluated by IACM. This system can output the image with better definition based on iterative stop criterion, thereby the number of iterations is limited and objectively countable. This technique is suitable for industrial measurement, defect detection and quality control in real-time measurement.

In recent years, with the improvement of the people’s living conditions and the arousal of consumers’ consciousness, the problem of Agricultural Product Quality Safety becomes a hot topic that more and more people pay attention to. This paper presents a safety risk assessment model for agricultural product quality and safety. The effectiveness and feasibility of applying neural network models to Risk Assessment are analyzed. Aiming at the problems of the disappearance of gradients in traditional recurrent neural networks when stretching the length of the network, an improved recurrent neural network algorithm is proposed. It can predict the risks better, and finally, achieve the purpose of providing a solid foundation for improving the agricultural product quality and risk early warning system in the future, thereby effectively reducing food safety risks.

Agricultural news usually describes objective facts, and its subjectivity is weak. Conventional sentiment analysis methods which can get high accuracy by definite sentiment trend are difficult to apply to the agricultural news filed. We propose a simple yet effective framework that allows us to accurately analyze the sentiment of Chinese Agricultural News. The basic idea is to create a dictionary of a positive and negative sentiment of agricultural news as prior knowledge and construct a sentiment classification model with a joint emotion-subject model (JST) and the dictionary. The practical merits of our approach are demonstrated in real-world datasets.

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems. In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection (DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS (MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.