Velocity control of longitudinal vibration ultrasonic motor using improved Elman neural network trained by CQPSO with Lévy flights

Longitudinally vibration ultrasonic motor (LV-USM), a canonical nonlinear system, utilizes the inverse piezoelectric effect of piezoelectric ceramic to generate the mechanical vibration within the scope of ultrasonic frequency. However, it is very difficult to establish a strict and accurate mathematical model. Hence seeking a dynamic identifier and controller for LV-USM avoiding the accurate mathematical model becomes a feasible approach. In this paper, a novel learning algorithm for dynamic recurrent Elman neural networks is present based on a particle swarm optimization (PSO) to identify and control an LV-USM. To overcome the PSO’s global search ability, Lévy flights, a kind of random walks, are imported to improve the ability of exploration rather than Brownian motion or Gauss disturbance based on Cooperative Quantum-behaved PSO (CQPSO). Thereafter, a controller is designed to perform speed control for LV-USM along with the nonlinear identification also using this kind of neural network. By discrete Lyapunov stability approach, the controller is proven to be stable theoretically and the latter trial shows its robustness of anti-noise performance. In the experiments, the numerical results illustrate that the designed identifier and controller can achieve both higher convergence precision and speed, relative to current state-of-the-art other methods. Moreover, this controller shows lower control error than other approaches while the displacement of the rotor disc in LV-USM appears more smooth and uniform

The Service Quality Evaluation of Mobile Communication from Quality Improvement Perspective ----a case study on China telecom in Wuchang District Wuhan City

Based on SERVAUAL model, this paper brings in the entropy method to rank quality improvement (QI) priority for service attributes, and a service quality evaluation(SQE) model integrating competitive analyses has been structured to evaluate the mobile communication service quality (SQ) for Wuhan Branch of China Telecom(WBCT). The research shows that the QI priority of 22 service attributes has changed as adopts entropy method comparing with gap-based SERVQUAL. The service attributes that finally should be improved have changed from Q20(Various business charges reasonable) and Q22(Record customer complaints and improve) to Q21(provide customers all kinds of value-added services) and Q11(Staff serves with high efficiency)

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.Comment: KDD 201

US-China trade war and China’s stock market: an event-driven analysis

The US-China trade war, initiated in March 2018, substantially transformed the trading partnership between the two largest economic powers. It directly influenced the profitability of domestic enterprises related to the export chain and harmed the domestic economy in China and its stock market. This study empirically examines the effects of the trade war on China’s stock market based on chronological events and tests whether it is the contagion effect or the present value effect. The empirical study supports the contagion effect because the impact of the US-China trade war differed significantly in different sectors only when the US announced its imposition of more tariffs on US$50 billion worth of Chinese products. However, there is no apparent difference between the industries for other events, nor is there any significant difference between the industries in terms of longterm impact