Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

On voltage collapse and apparent-power losses

The paper proposes a new concept for assessing vulnerably of a power system in terms of voltage stability. The concept is based on the apparent-power losses and uses only local data, i.e. voltage and current phasors at every line end in the system. In the paper, we prove that, at the voltage-collapse point, the entire increase in loading of the most critical line is due to increased transmission losses, which also coincides with the singularity of the Jacobian matrix. It is shown that this information can be used as an indication of a voltage-instability proximity. The proposed local method has several advantages over the existing static and dynamic methods, which is also shown in the paper.     

Thermoelastic evaluation of composite laminates using digital imaging processing

The study of the variation of the curvature of non-symmetric composite laminates with temperature provides a measure of the magnitude of the thermal stresses and the mechanical behavior of the material with temperature. In the present work, an experimental method based on the pioneer use of a digital camera and image processing method is proposed to perform such measurements. Taking advantage of the method and by comparing experimental and CLT, extended-CLT and FEM simulation results, an evaluation of the residual stresses is described and an approach providing more precise determination of the residual thermal stresses in non-symmetric laminates is proposed.     

On-demand multicast routing protocol with efficient route discovery

In this paper, we introduce an efficient route discovery mechanism to enhance the performance and multicast efficiency of On-Demand Multicast Routing Protocol (ODMRP). Our framework, called limited flooding ODMRP, improves multicasting mechanism by efficiently managing flooding mechanism based on delay characteristics of the contributing nodes. In our model, only the nodes that satisfy the delay requirements can flood the Join-Query messages. We model the contributing nodes as M/M/1 queuing systems. Our framework considers the significant parameters in delay analysis, including random packet arrival, service process, and random channel access in the relying nodes, and exhibits its best performance results under high traffic load. Simulation results reveal that limited flooding ODMRP drastically reduces the packet overhead under various simulation scenarios as compared to original ODMRP.     

基于抽象原则和模型检测的网络协议安全分析

提出了一种可以缓解状态空间爆炸的抽象原则,对模型设计过程中的辅助变量、报文字段、自动状态机数量进行科学约简,在尽量不影响验证结果准确度的前提下,降低了模型的复杂度.在此基础上,提出了一种半自动化建模框架,只需用户提供少量必须的输入,不需要学习语法,就可以自动生成具有统一规范的模型,方便研究人员查阅和使用.实验结果表明,采用所提的抽象原则和半自动化建模框架创建的模型,可以验证网络协议的相关属性.     

The response of plasma parameters and energy transport in the plasma sheet to interplanetary magnetic field Bz

Science China Technological Sciences - We use 9 years data of Cluster to study the dependencies of plasma parameters and energy transport in the plasma sheet on the lasting time of...     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.     

交通监控场景中的车辆检测与跟踪算法研究

考虑多目标跟踪过程中存在的实时性和身份跳变问题,提出一种基于检测的多车辆跟踪算法。首先利用Mobilenetv2替换YOLOv3检测算法的主干网络,构建目标检测模块YOLOv3-Mobilenetv2,减少检测算法模型参数,提高检测模块的运行速度;在Mobilenetv2中引入Bottom-up连接,增强多尺度特征图间的信息融合;然后构建基于LSTM的运动模型,解决卡尔曼滤波在非线性系统中产生的预测误差,基于Deepsort跟踪算法,引入LSTM运动模型,形成L-Deepsort跟踪算法;改进L-Deepsort跟踪算法外观匹配策略,提升目标间的关联性;最后融合轻量级目标检测算法YOLOv3-Mobilenetv2与多目标跟踪算法L-Deepsort,形成MYL-Deepsort多车辆跟踪算法,实现多车辆的实时准确跟踪。实验结果表明,该方法在跟踪性能提升的情况下,速度较YOLOv3-Deepsort提高21 frame/s,在TX2平台达到13 frame/s。