Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

基于区块链的物联网信息安全技术研究

物联网的发展在这几年获得的成果非常明显,在实现智能化管理与操作的同时,存在的隐私安全以及漏洞问题也暴露出来。物联网规模非常庞大,并且还具有分散的拓扑结构。传统的安全与隐私保护方式具有局限性,对于现阶段的物联网操作系统并没有太大的作用。因此,为了保障物联网信息的安全性,弥补传统防护技术上的缺陷,物联网信息防护技术的研究迫在眉睫。     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Computational Economics - The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory...     

Design and analysis of turnover platform for welding robot北大核心CSCD

A turnover platform for welding robot was designed for the application of welding robot with lower accuracy requirement, which was of low cost and higher position accuracy. In this turnover platform, the pneumatic motor was used as the power output, and the indexing mechanism with high accuracy was the transmission system with high transmitting ratio based on worm and wheel. The position information was acquired by using the photoelectric encoder, and the turnover motion with high accuracy was realized through the closed-loop controller. Simulation results showed that the maximum speed of the welding platform approached 14 r/min, and the platform could meet the requirements of most welding products. Such a turnover platform can offer the application program of the welding robot with low cost for the middle and low level products, and reduce the cost of welding robot and improve welding productivity.     

设计的进化与价值

<正>一、前言设计是人们对于装备制造、工程建设、经营管理和服务创新的创意设想、计划和计算,是将信息、知识和技术转化、集成为整体解决方案并实现价值的发明创造,是一切有目标创新实践的先导、准备与关键环节。设计决定着创新创造的方向、目标、路径和价值。设计创新引领推动人类社会的文明和进步,设计的知识技术、价值理念、人才团队、方式环境也随着人类文明进化而发展。从旧石器时代起,我们的祖先已利用天然材料,朦胧地构想制作     

La<Subscript>1−x</Subscript>Ag<Subscript>x</Subscript>FeO<Subscript>3</Subscript>/halloysites nanocomposite with enhanced visible light photocatalytic performance

La_1?xAg_xFeO₃/halloysites nanotubes (HNTs) nanocomposite was synthesized by sol–gel method. It was characterized by X-ray diffraction, transmission electron microscope, Fourier transform infrared spectroscopy and UV–visible diffused reflectance spectroscopy measurements. The photo-activity of the La_1?xAg_xFeO₃/HNTs nanocomposite was evaluated via degradation of methylene blue (MB) under visible-light irradiation. The results showed that the HNTs with unique pore structure favored the adsorption of organic molecules. Adequate Ag⁺ doping improved the absorption ability for visible light. The La_0.95Ag_0.05FeO₃/HNTs demonstrated the best photocatalytic performance, which achieved as high as 99 % for MB degradation exposed 2 h irradiation. However,further increasing of Ag⁺ doping gradually reduced the photocatalytic activity. The nanocomposite catalyst showed outstanding recyclability after eight cycles which still remained up to 90 %.     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.