Residential Demand Response Scheduling Optimization And Simulation Based On An Improved Pso Algorithm,
2021
1. School of Information Engineering, Chang'an University, Xi'an 710064, China; ;
Residential Demand Response Scheduling Optimization And Simulation Based On An Improved Pso Algorithm, Huazhen Li, Youquan Liu, Jiawei Zhu, Liao Qiang
Journal of System Simulation
Abstract: Aiming at the problems of low utilization rate of household load energy and the potential damage to the power grid caused by the lack of systematic and efficient management of household power consumption, the power consumption characteristics of controllable equipment and the energy storage characteristics of electric vehicles are modeled respectively, and the scheduling optimization objective function of household equipment under time of use price is established, and the improved particle swarm optimization algorithm is used to solve the problem. Through the example simulation, the residential power dispatching under various scenarios is analyzed. The experimental results show that the …
Self-Learning-Based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition,
2021
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;
Self-Learning-Based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition, Zhao Yu, Jifeng Guo, Yan Peng, Chengchao Bai
Journal of System Simulation
Abstract: In order to improve the ability of spacecraft formation to evade multiple interceptors, aiming at the low success rate of traditional procedural maneuver evasion, a multi-agent cooperative autonomous decision-making algorithm, which is based on deep reinforcement learning method, is proposed. Based on the actor-critic architecture, a multi-agent reinforcement learning algorithm is designed, in which a weighted linear fitting method is proposed to solve the reliability allocation problem of the self-learning system. To solve the sparse reward problem in task scenario, a sparse reward reinforcement learning method based on inverse value method is proposed. According to the task scenario, …
Hybrid System Simulation Method Based On Quantized State,
2021
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;
Hybrid System Simulation Method Based On Quantized State, Zhihua Li, Jiang De, Hanwu Shen, Zhihua Fan
Journal of System Simulation
Abstract: Hybrid system simulation and discontinuity processing have always been the difficulties of the time-discretized integration methods, while Quantized State System (QSS) is a new numerical integration method based on state variable discretization. Aiming at the hybrid systems simulation, a method of QSS+DEVS is proposed. The discrete part of hybrid system is represented as DEVS model, and the continuous part of hybrid system is discretized by QSS, which can also be represented as DEVS model. The simulation model of the whole hybrid system is obtained by coupling the two DEVS models. The accuracy, efficiency and simplicity of the QSS+DEVS method …
The Allocation Of Jamming Resources Based On Double Q-Learning Algorithm,
2021
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
The Allocation Of Jamming Resources Based On Double Q-Learning Algorithm, Xingyuan Huang, Yanyi Li
Journal of System Simulation
Abstract: In modern warfare, the multifunctional trend of radars, even multiple radars detecting targets together, enhances the anti-jamming capability of radars. However, the traditional jamming system still follows a fixed jamming strategy, and the real-time performance of decision-making facing large numbers of radars is poor. And the cognitive jamming study is urgent. The concept of reinforcement learning is explained and the difference between Q learning algorithm and double Q learning algorithm is compared. The reinforcement learning algorithm is used to establish a model based on cognitive electronic warfare to realize the allocation of radar jamming strategies. The simulation of the …
Obstacle Avoidance Path Planning Of Bridge Crane Based On Improved Rrt Algorithm,
2021
College of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024 China;
Obstacle Avoidance Path Planning Of Bridge Crane Based On Improved Rrt Algorithm, Zhimei Chen, Li Min, Xuejuan Shao, Zhicheng Zhao
Journal of System Simulation
Abstract: In view of the problem that the reasonable path can not be obtained quickly for bridge crane planning in complex environment, a rapidly exploring random tree (RRT) algorithm combined with particle swarm algorithm is proposed. According to the characteristics of the bridge crane operation, the RRT algorithm is improved. The two-way RRT algorithm is used to make the tree grow in the direction of the target according to the probability. When the path is generated, the particle swarm optimization algorithm is used to smooth the path to get a more suitable path for the operation of the bridge crane. …
Cloud Model Pid Control Of Pmsm Based On Svm Inverse System,
2021
Marine Electrical Engineering College, Dalian Maritime University, Dalian, 116026, China;
Cloud Model Pid Control Of Pmsm Based On Svm Inverse System, Li Hui, Yun Hao, Hongli Yue
Journal of System Simulation
Abstract: Aiming at the problem of multivariable, nonlinearity and strong coupling of the permanent magnet synchronous motor(PMSM), a strategy of inverse system identification which is independent of precise mathematical model and parameters based on support vector machines(SVM) is proposed. The dynamic decoupling control of PMSM is researched based on multivariable nonlinear control inverse system theory. To deal with direct inverse control open-loop system with poor robustness and inverse modeling error of SVM, a parameter self-tuning PID(Proportional Integral Differential) closed-loop controller based on cloud model rule inference is designed. The simulation results confirm that the cloud model PID control based on …
Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso,
2021
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso, Tengfei Wei, Tinglong Pan
Journal of System Simulation
Abstract: To improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (ACMPSO-LSTM) based on long-short memory neural network (LSTM) optimized by adaptive Cauchy mutation particle swarm optimization (ACMPSO) is proposed. For the problem of difficult selection of LSTM model parameters, ACMPSO is used to optimize model parameters, and non-linear changing inertia weights are adopted to improve the global optimization ability and convergence speed of PSO algorithm. In the optimization process, a mutation operation based on genetic algorithm is added to reduce the risk of particles falling into local optimal solutions. The simulation results show that …
Actuator Fault Status Evaluation Based On Two-Class Nmf Network,
2021
Department of Automation, North China Electric Power University, Baoding 071003, China;
Actuator Fault Status Evaluation Based On Two-Class Nmf Network, Yinsong Wang, Tianshu Sun
Journal of System Simulation
Abstract: In the feedback control loop, the adjustment ability of controller covers up the performance degradation of the actuator to some degree. A fault state evaluation algorithm based on a two-class non-negative matrix network is proposed to implement online fault state monitoring of the actuator, including fault classification and degradation assessment. The local static features of the samples are extracted, and a classifier model is established to form a network. The similarity is introduced to describe the dynamic characteristics between samples. To fulfill the actuator fault status assessment, the static distance and dynamic changes of the network output are merged …
Method Of Battlefield Frequency Allocation Based On Chaotic Perturbation Mechanism Particle Swarm Optimization Algorithm,
2021
Unit 31007 of the Chinese PLA, Beijing 100079, China;
Method Of Battlefield Frequency Allocation Based On Chaotic Perturbation Mechanism Particle Swarm Optimization Algorithm, Niu Kan, Li Bing, Fu Qiang
Journal of System Simulation
Abstract: In order to carry out the frequency allocation in the electromagnetic environment of battlefield and reduce the frequency equipment interference of various forces, a frequency allocation method based on chaotic perturbation mechanism particle swarm optimization algorithm is proposed. Which transforms battlefield frequency allocation into the optimal spectrum resource search and solution problem with constraints. The frequency allocation model with the lowest interference cost is built and the frequency allocation through the improved particle swarm optimization algorithm is carries out. The chaotic perturbation mechanism is introduced to improve the population diversity and the global optimization ability of the …
Game Analysis Of Government Procurement Contract Financing Based On Blockchain Technology,
2021
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
Game Analysis Of Government Procurement Contract Financing Based On Blockchain Technology, Haitao Huang, Qinming Liu, Chunming Ye, Chen Xiang
Journal of System Simulation
Abstract: Abstract: In view of the financing difficulties of small and medium-sized enterprise and the existing credit problems of financial supply chain, the block chain technology is applied to the financing mode of government procurement contract of the Ministry of Finance. From the supply chain business aspect, the tripartite game model of government procurement departments, small and medium-sized enterprises and banks is built, and the decision of the main body is analyzed. From the block chain technology, the evolutionary game model is built and the selection of chain node is analyzed. By MATLAB, the simulation experiments are carried out to …
Research On Integrated Optimization Approach For Car-Sharing Systems,
2021
Institute of Transportation Engineering, Hohhot 010070, China;
Research On Integrated Optimization Approach For Car-Sharing Systems, Tang Jie, Jinxin Cao
Journal of System Simulation
Abstract: Effective scheduling and routing of employees and vehicles determines the efficiency of car-sharing systems. Aiming at the scheduling of shared cars within one day, with the objective of minimizing the total system costs and personnel costs, a bi-level optimization model for multiple traveling salesman problem with time windows is established. A genetic algorithm with multi-chromosome coding and the optimized complex mutation operator are developed for the problem solution. From the comprehensive computational experiments, it can be concluded that the total numbers of vehicles and employees with the joint routing plans satisfying the order constraints can be obtained in …
Passenger Flow Sensitivity Analysis Of Evacuation Time For Standard Subway Station,
2021
1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; ;
Passenger Flow Sensitivity Analysis Of Evacuation Time For Standard Subway Station, Guoao Zhang, Ma Si, Wang Lin
Journal of System Simulation
Abstract: Underground two-level island platform stations widely existed in urban rail transit system. In order to analyze the relationship between the evacuation time and the character of passenger flow, the capacity of main evacuation facilities and the evacuation bottleneck of stations are studied, and groups of sensitivity analysis experiment are designed. The variability of the evacuation process is analyzed by comparing the output of each simulation model in a group and between groups. The simulation result shows that the station evacuation time increases within a certain limit at peak hour. And the station evacuation time is mainly affected by the …
Exploiting Block Structures Of Kkt Matrices For Efficient Solution Of Convex Optimization Problems,
2021
Michigan Technological University
Exploiting Block Structures Of Kkt Matrices For Efficient Solution Of Convex Optimization Problems, Zafar Iqbal, Saeid Nooshabadi, Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra
Michigan Tech Publications
Convex optimization solvers are widely used in the embedded systems that require sophisticated optimization algorithms including model predictive control (MPC). In this paper, we aim to reduce the online solve time of such convex optimization solvers so as to reduce the total runtime of the algorithm and make it suitable for real-time convex optimization.We exploit the property of the Karush–Kuhn–Tucker (KKT) matrix involved in the solution of the problem that only some parts of the matrix change during the solution iterations of the algorithm. Our results show that the proposed method can effectively reduce the runtime of the solvers.
Leveraging Machine Learning Techniques Towards Intelligent Networking Automation,
2021
The University of Western Ontario
Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez
Electronic Thesis and Dissertation Repository
In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …
Client Access Feature Engineering For The Homeless Community Of The City Of Portland,
2021
University of Texas at Austin
Client Access Feature Engineering For The Homeless Community Of The City Of Portland, Oswaldo Ceballos Jr
altREU Projects
Given the severity of homeless in many cities across the country, the project at hand attempts to assist a service provider organization called Central City Concern (CCC) with their mission of providing services to the community of Portland. These services include housing, recovery, health care, and jobs. With many different types of services available through the works of CCC, there exists an abundance of information and data pertaining to the individuals that interact with the CCC service system. The goal of this project is to perform an exploratory analysis and feature engineer the existing datasets CCC has collected over the …
Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges,
2021
CUNY New York City College of Technology
Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva
Publications and Research
This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.
Learning To Interpret Fluid Type Phenomena Via Images,
2021
Louisiana State University and Agricultural and Mechanical College
Learning To Interpret Fluid Type Phenomena Via Images, Simron Thapa
LSU Doctoral Dissertations
Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the …
Panoramic Learning With A Standardized Machine Learning Formalism,
2021
UC San Diego
Panoramic Learning With A Standardized Machine Learning Formalism, Zhiting Hu, Eric P. Xing
Machine Learning Faculty Publications
Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences. In handling a wide variety of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly-focused methods also make standardized, composable, and reusable development of learning solutions difficult, and make it costly if possible to build AI agents that panoramically learn from all types of experiences. This paper presents a standardized ML …
Forensicast: A Non-Intrusive Approach & Tool For Logical Forensic Acquisition & Analysis Of The Google Chromecast Tv,
2021
University of New Haven
Forensicast: A Non-Intrusive Approach & Tool For Logical Forensic Acquisition & Analysis Of The Google Chromecast Tv, Alex Sitterer, Nicholas Dubois, Ibrahim Baggili
Electrical & Computer Engineering and Computer Science Faculty Publications
The era of traditional cable Television (TV) is swiftly coming to an end. People today subscribe to a multitude of streaming services. Smart TVs have enabled a new generation of entertainment, not only limited to constant on-demand streaming as they now offer other features such as web browsing, communication, gaming etc. These functions have recently been embedded into a small IoT device that can connect to any TV with High Definition Multimedia Interface (HDMI) input known as Google Chromecast TV. Its wide adoption makes it a treasure trove for potential digital evidence. Our work is the primary source on forensically …
Forensic Artifact Finder (Forensicaf): An Approach & Tool For Leveraging Crowd-Sourced Curated Forensic Artifacts,
2021
University of New Haven
Forensic Artifact Finder (Forensicaf): An Approach & Tool For Leveraging Crowd-Sourced Curated Forensic Artifacts, Tyler Balon, Krikor Herlopian, Ibrahim Baggili, Cinthya Grajeda-Mendez
Electrical & Computer Engineering and Computer Science Faculty Publications
Current methods for artifact analysis and understanding depend on investigator expertise. Experienced and technically savvy examiners spend a lot of time reverse engineering applications while attempting to find crumbs they leave behind on systems. This takes away valuable time from the investigative process, and slows down forensic examination. Furthermore, when specific artifact knowledge is gained, it stays within the respective forensic units. To combat these challenges, we present ForensicAF, an approach for leveraging curated, crowd-sourced artifacts from the Artifact Genome Project (AGP). The approach has the overarching goal of uncovering forensically relevant artifacts from storage media. We explain our approach …
