Xiaodong Liu

In order to improve the practicability of stochastic resonance (SR) theory in the field of underwater target weak signal detection, taking the parameters of stochastic resonance system as the research object, a signal detection method based on multi feature fusion measure based on particle swarm optimization algorithm is proposed. In this method, the multi-feature fusion measure is taken as the fitness function, and the stochastic resonance problem is transformed into a multi parameter parallel optimization problem. To prove the effectiveness of the above method, we did a set of computer simulation experiments. This method can realize the signal detection of underwater weak and small targets in long-distance low SNR marine environment without relying on a priori information, further improve the signal detection probability, and improve the effectiveness and reliability of underwater weak and small target detection.

As the application of "China Intelligent Manufacturing" in the field of production becomes more and more extensive, it becomes crucial to ensure the security of information interaction between production equipment. China's domestic encryption algorithm - SM4 block encryption algorithm came into being. The algorithm consists of a key expansion function and an encryption and decryption function. The key expansion function collects 128-bit keys at a time, and generates thirty-two 32-bit encryption keys after XOR operation, nonlinear permutation operation (S-box) and linear shift operation.The XOR operation parameter CK of the original key expansion function is obtained by the modulo method, which is similar to the system parameter FK and is a fixed value. In order to reduce the risk of deciphering caused by fixed parameters, the lightweight two-dimensional chaos algorithm Hemon is introduced for optimization design. The system parameters used in the SM4 algorithm are randomly generated with the change of the key, which improves the security of the algorithm. And use the hardware encryption platform ZYNQ-7000 series model xc7z100ffv900-1 FPGA side design and implementation, relying on the hard to decipher of hardware encryption to complete the optimization of SM4 algorithm key exchange.

This paper develops a generalized Copula-polynomial chaos expansion (PCE) framework for power system probabilistic power flow that can handle both linear and nonlinear correlations of uncertain power injections, such as wind and PVs. A data-driven Copula statistical model is used to capture the correlations of uncertain power injections. This allows us to resort to the Rosenblatt transformation to transform correlated variables into independent ones while preserving the dependence structure. This paves the way of leveraging the PCE for surrogate modeling and uncertainty quantification of power flow results, i.e., achieving the probabilistic distributions of power flows. Simulations carried out on the IEEE 57-bus system show that the proposed framework can get much more accurate results than other alternatives with different linear and nonlinear power injection correlations.

This paper presents a methodology for empirically identifying the key player, whose removal from the network leads to the optimal change in aggregate activity level in equilibrium (Ballester et al., 2006), allowing the network links to rewire after the removal of the key player. First, we propose an IV-based estimation strategy for the social-interaction effect, which is needed to determine the equilibrium activity level of a network, taking into account the potential network endogeneity. Next, to simulate the network evolution process after the removal of the key player, we adopt the general network formation model in Mele (2017) and extend it to incorporate the unobserved individual heterogeneity in link formation decisions. We illustrate the methodology by providing the key player rankings in juvenile delinquency using unique information on friendship networks among U.S. teenagers. We find that the key player is not necessarily the most active delinquent or the delinquent who is the highest in standard (not microfounded) centrality measures. We also find that, compared to a policy that removes the most active delinquent from the network, a key-player-targeted policy leads to a much higher delinquency reduction.

This paper introduces a discrete-choice simultaneous-equation social interaction model. We provide a microfoundation for the econometric model by considering an incomplete information game where individuals interact in multiple activities through a network. We characterize the sufficient condition for the existence of a unique BNE of the game. We discuss the identification of the econometric model and propose a two-stage estimation procedure, where the reduced form parameters are estimated by the NPL algorithm in the first stage and the structural parameters are recovered from the estimated reduced form parameters by the AGLS estimator in the second stage. Monte Carlo experiments show that the proposed estimation procedure performs well in finite samples and remains computationally feasible when networks are large. We also provide an empirical example to illustrate the empirical relevance of the proposed model.

Summary: This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the empirical salience of this model. Using the Add Health data, we find that a student's academic performance is not only affected by academic performance of his peers but also affected by screen‐related activities of his peers.