Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

Fuzzy Evaluation Method for Haptic Perceptual Similarity.

Tactile rendering in virtual interactive scenes plays an important role in improving the quality of user experience. The subjective rating is currently the mainstream measurement to assess haptic rendering realism, which ignores various subjective and objective uncertainties in the evaluation process and also neglects the mutual influence among tactile renderings. In this paper, we extend the existing subjective evaluation and systematically propose a fuzzy evaluation method of haptic rendering realism. Hierarchical fuzzy scoring based on confidence interval is introduced to reduce the difficulty of expressing tactile feeling with deterministic rating. After the fuzzy statistics based on the membership function, we further use close-degree and transitive closure to calculate the fuzzy equivalence matrix between different tactile renderings. Fuzzy clustering is carried out to complete the comprehensive evaluation in the case of multiple indicators. Five tactile objects are used to simulate various situations of tactile rendering. The experimental results of haptic perceptual similarity evaluation show the existence of fuzziness in the subjective evaluation and verify the feasibility of the proposed method applied to multi-indicator evaluation. We also conclude that the proposed method outperforms the existing methods in terms of time cost and labor cost.

A survey of sum-product networks structural learning.

Sum-product networks (SPNs) in deep probabilistic models have made great progress in computer vision, robotics, neuro-symbolic artificial intelligence, natural language processing, probabilistic programming languages, and other fields. Compared with probabilistic graphical models and deep probabilistic models, SPNs can balance the tractability and expressive efficiency. In addition, SPNs remain more interpretable than deep neural models. The expressiveness and complexity of SPNs depend on their own structure. Thus, how to design an effective SPN structure learning algorithm that can balance expressiveness and complexity has become a hot research topic in recent years. In this paper, we review SPN structure learning comprehensively, including the motivation of SPN structure learning, a systematic review of related theories, the proper categorization of different SPN structure learning algorithms, several evaluation approaches and some helpful online resources. Moreover, we discuss some open issues and research directions for SPN structure learning. To our knowledge, this is the first survey to focus specifically on SPN structure learning, and we hope to provide useful references for researchers in related fields.