HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Engineering with Computers - The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of...     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.

     

基于USB总线的新型多路自整角信号发生器设计

目前,常用的自整角信号发生器大都采用像AD2S100这样的专用器件进行信号转换;这类器件往往都是国外制造,价格不菲,然而信号转换精度却只有0.2°～0.5°,对于某些高精度应用难以满足要求;为此,以C8051F340单片机为核心设计了一种基于USB总线的多路自整角信号发生器;设计摒弃了对专用芯片的需求,采用软件模拟方式进行信号解算和误差补偿,大大降低生产成本的同时,使信号全范围误差小于0.2°。经应用证明该信号发生器工作稳定、抗干扰强,有很高的实用价值。     

基于TMS320C32的CAN总线通信系统设计

为实现空间相机控制器与星务计算机之间的可靠数据通讯,设计了基于TMS320C32的CAN总线通信系统;针对TMS320C32和SJA1000的工作时序特点,利用可编程逻辑器件FPGA实现了接口逻辑转换,编写激励文件利用Modelsim软件进行了功能仿真,给出了CAN总线通信系统的软件设计,包括SJA1000的初始化、数据的发送接收及位定时参数的设定;最后通过周立功公司的ZLGCANTest软件对设计的CAN总线通信系统进行了测试;实验结果表明,系统的通信速率达到307.2kbps,通讯无误,满足相机控制器可靠性的要求。     

基于北斗卫星的森林火灾监控终端设计

针对传统森林火灾监控方式存在的受地形限制,导致火灾发现不及时和定位难的缺陷,设计了基于北斗卫星的森林火灾监控系统,实现了火灾智能识别和预警,并且可以对火源、火场进行快速定位和信息通报;终端包括烟采集处理模块、图像采集模块、DSP处理模块、ARM主控模块和北斗卫星通信模块五部分,从烟和火焰两个方面对森林实时进行监控,分别采用离子烟雾传感器和DSP火焰识别混合算法,提取火焰图像差值和火焰图像边界参数的方法,提高了识别的准确率;试验证明,系统具有结构简单、使用方便和识别智能化的特点。     

实现多无线接入点网络连接模型的研究

本文提出一种负载均衡模型,此模型不仅考虑AP的信号强度,而且要考虑现有AP已接入无线工作站的数量还有连接质量因素。除此之外,还讨论了实现此模型的无线协议的扩充。     

Robust ℋ︁∞ PID control for multivariable networked control systems with disturbance/noise attenuation

In this paper, we study the design problem of PID controllers for networked control systems (NCSs) with polyhedral uncertainties. The load disturbance and measurement noise are both taken into account in the modeling to better reflect the practical scenario. By using a novel technique, the design problem of PID controllers is converted into a design problem of output feedback controllers. Our goal of this paper is two‐fold: (1) To design the robust PID tracking controllers for practical models; (2) To develop the robust ??_∞ PID control such that load and reference disturbances can be attenuated with a prescribed level. Sufficient conditions are derived by employing advanced techniques for achieving delay dependence. The proposed controller can be readily designed based on iterative suboptimal algorithms. Finally, four examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.