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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Huang, Wen Zhang, Dezheng Song, Zihao Wang, Haoyu Liu, Honglan Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781538662434

Short-term load forecasting (STLF) is a basic work in power network planning. The accuracy of its prediction has an important impact on the actual power generation and distribution. Power sector develop efficient and economical power generation plans also based on it. A load forecasting method is proposed in this paper, using grey relational analysis to correlate factors and fuzzy clustering to select load similar day. This preprocessing method meets the requirement of different types of electricity load forecasting. Then analytic hierarchy process (AHP) is used to model the BP neural network, particle swarm optimization support vector machine and time series intelligent algorithm, forming a combined prediction model. In experiments, the comparison between the error of the combined prediction result and the single prediction models verifies the improvement effect of the fusion model based on similar day and algorithm using analytic hierarchy process.

关键词： short-term load forecasting similar day approach intelligent algorithm analytic hierarchy process

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Designing An Optimal Control Law for Nonlinear System by Using intelligent algorithm

Designing An Optimal Control Law for Nonlinear System by Usi...

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2nd International Conference on Measurement, Information and Control (ICMIC)

作者： Rastegarpour, S. Shahmansoorian, A. Mazinan, A. H. Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran KN Toosi Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781479913909

The Purpose of this paper is based on Inverse Optimal Control method. There are two methods for designing optimal control stabilizer In order To minimize the presented cost function for any nonlinear systems, (Direct method and Inverse method). In direct method, in order to solve the optimization problem, HJB equation must be solved, in which there is no exist exact feasible mathematical techniques for solving Hamilton-Jacobi-Bellman equation, while in inverse method, by considering Control Lyapunov Function and an obtained feedback control law, a cost function will be designed so that the presented control law will be optimal for designed cost function. In this paper a new approach is presented so that there will be no need to solve the Hamilton-Jacobi-Bellman equation and the suboptimal controller will be designed without numerical method. In this approach, by using Inverse Optimal Control method and determining cost function for the system, Control Lyapunov Function and suboptimal control law are designed simultaneously in which the Control Lyapunov Function will be designed by intelligent algorithm such as PSO algorithm and GA separately. To analyze the optimal level of designed controller and Control Lyapunov Function, different performance criteria will be used. In this method any structure of Control Lyapunov Function could be considered.

关键词： Control Lyapunov Function Inverse Optimal Control intelligent algorithm Nonlinear System Level Curve

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Monitor and Analysis of Renewable Energy based on intelligent algorithm 6

Monitor and Analysis of Renewable Energy based on Intelligen...

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6th International Conference on intelligent Systems Design and Engineering Applications (ISDEA)

作者： Su, Jianhua North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781467393935

In this paper, we propose a novel method to monitor and analyze the renewable energy using intelligent algorithm. The main innovation of this paper lies in that we introduce time series algorithm to monitor and analyze of utilization of renewable energy. In particular, three types of building integrated renewable energy sources are utilized, including 1) Solar water heater, 2) Solar photovoltaic, and 3) Ground source heat pump. Next, the renewable energy monitor and analysis system is implemented using the hidden Markov model, which can effectively describe the intrinsic connection between observed data. Finally, we utilize 1) utilization efficiency, 2) solar radiation quantity and 3) COP value as the performance evaluation metric to test the performance of the proposed method. Experimental results show that the monitor and analysis results for renewable energy system by our method are very close to real values.

关键词： Renewable energy intelligent algorithm Time series algorithm Hidden Markov model

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An intelligent algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn

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Computational Economics 2024年第3期63卷 1001-1020页

作者： Rahmani, Amir Masoud Hosseini Mirmahaleh, Seyedeh Yasaman Future Technology Research Center National Yunlin University of Science and Technology Douliou 64002 Taiwan Department of Electrical Engineering Science and Technology Lille University Lille 59000 France

With the spread of COVID-19, economic damages are challenging for governments and people’s livelihood besides its dangerous and negative impact on humanity's health, which can be led to death. Various health guidelines have been proposed to tackle the virus outbreak including quarantine, restriction rules to imports, exports, migrations, and tourist arrival that were affected by economic depression. Providing an approach to predict the economic situation has a highlighted role in managing crisis when a country faces a problem such as a disease epidemic. We propose an intelligent algorithm to predict the economic situation that utilizes neural networks (NNs) to satisfy the aim. Our work estimates correlation coefficient based on the spearman method between gross domestic product rate (GDPR) and other economic statistics to find effective parameters on growing up and falling GDPR and also determined the NNs’ inputs. We study the reported economic and disease statistics in Germany, India, Australia, and Thailand countries to evaluate the algorithm’s efficiency in predicting economic situation. The experimental results demonstrate the prediction accuracy of approximately 96% and 89% for one and more months ahead, respectively. Our method can help governments to present efficient policies for preventing economic damages. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： COVID-19 Economic damage intelligent algorithm Neural network Prediction

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Computer Network Security Evaluation Based on intelligent algorithm 6

Computer Network Security Evaluation Based on Intelligent Al...

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6th International Conference on Future Generation Communication Technologies (FGCT)

作者： Sun Ming Xinxiang Vocat & Tech Coll Xinjiang Peoples R China

ISBN: (纸本)9781509067459

In our country, type and quantity of network attacks have continued to grow, China's network infrastructure and the important information system is also facing serious security challenges. A variety of network attacks and other network security incidents have become the bottleneck in the development of our national economy, and even endanger the social stability and national security of the important factors. The Computer network is an important part of modern social production and life;therefore, the security of a computer network is a problem that has been attached great importance to. Computer network security assessment is the process of identifying the behaviors that endanger the safety of computer network, and it is a relatively active defense measure. On the basis of relevant domestic and foreign theory and research, in view of the current problems, the computer network security evaluation model based on swarm intelligence algorithm was proposed in this paper. The feasibility of the algorithm was verified by simulation experiment.

关键词： intelligent algorithm Computer network Network security evaluation

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An intelligent algorithm for Mapping of Applications on Parallel Reconfigurable Systems 6

An Intelligent Algorithm for Mapping of Applications on Para...

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6th International Conference on Signal Processing and intelligent Systems (ICSPIS)

作者： Rad, Masoud Yazdani Shahbandegan, Shakiba Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ISBN: (纸本)9781728186290

An increasing demand for computational power in daily used applications has led to implementation of different computing platforms. Among them, reconfigurable platforms which are based on FPGAs, have been widely used in recent years. While they are flexible, the communication cost between different computational tasks implemented in different locations of FPGA is one of the main challenges when it comes to improving the performance. In this paper, mapping of application tasks into processing platforms is studied. We presented a new method based on neural networks. Firstly, using Node2vec embedding algorithm, dimensions reduction, and rotation and scale, an initial mapping of task interaction graph nodes into processing structure FPGAs was obtained. Secondly, the dilation and maximum capacity utilization optimization were done using the stochastic gradient descent (SGD) method and loss functions. After performing experiments and comparing the proposed method with a similar method, results showed that the proposed method was superior in performance in terms of dilation (11.28%) and showed a poorer performance in terms of maximum capacity utilization (4.30%).

关键词： intelligent algorithm reconfigurable computing task interaction graph stochastic gradient descent mapping algorithm

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Capacitor Monitoring for Modular Multilevel Converters Based on intelligent algorithm 4

Capacitor Monitoring for Modular Multilevel Converters Based...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ding, Hao Wang, Qingsong Deng, Fujin Cheng, Ming Buja, Giuseppe Southeast Univ Sch Software Engn Suzhou Peoples R China Southeast Univ Sch Elect Engn Nanjing Peoples R China Jiangsu Key Lab Smart Grid Technol & Equipment Nanjing Peoples R China Univ Padua Dept Ind Engn Padua Italy

ISBN: (纸本)9781665479141

As a kind of modularized design of high power converter, modular multilevel converter is widely used in various high-voltage and high-power applications, because of its good topology structure and expansibility, which is realized by sub-modules. As a result, a large number of sub-modules are integrated in the converter, within which the capacitors greatly affect the reliability of the converter. Thus, it is necessary to monitor the health of the capacitors in the sub-modules. In this paper, an intelligent algorithm-based method is proposed to monitor the sub-module capacitance in modular multilevel converter, by selecting bridge arm currents, dc voltage and sub-module switch signal integration as the characteristics of capacitor degradation. Neural network algorithm is selected as an example to monitor the sub-module capacitor value. Simulation results show the effectiveness and accuracy of the proposed method.

关键词： capacitance monitoring intelligent algorithm modular multilevel converter neural network

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Application of intelligent algorithm to Solve the Fractional Heat Conduction Inverse Problem 21st

Application of Intelligent Algorithm to Solve the Fractional...

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21st International Conference on Information and Software Technologies (ICIST)

作者： Brociek, Rafal Slota, Damian Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9783319247700;9783319247694

This paper describes an application of an intelligent algorithm to reconstruct the boundary condition of second kind in the heat conduction equation of fractional order. For this purpose, a functional defining error of approximate solution was minimized. To minimize this functional Ant Colony Optimization (ACO) algorithm was used. Calculations has been performed in parallel way (multi-threaded), so the computation time is significantly shortened. The paper presents examples to illustrate the accuracy and stability of the presented algorithm.

关键词： intelligent algorithm Inverse problem Identification Time fractional heat conduction equation

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Implementation of intelligent algorithm in Mathematical Modeling Teaching 1

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2nd EAI International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Li, Zhen Jiangxi Technical College of Manufacturing Nanchang Jiangxi Nanchang330095 China

ISBN: (数字)9783031239441

ISBN: (纸本)9783031239434

Mathematical modeling is to refine the actual problems in the real world, abstract them into a mathematical model, find the solution of the model, verify the rationality of the model, and use the solution provided by the mathematical model to explain the real problems. This paper first introduces the common intelligent algorithms and expounds the classification of solving categories, and then explores the implementation of intelligent algorithms in mathematical modeling teaching from four aspects: the existing situation, the proposal of mathematical modeling teaching mode, the implementation methods and implementation results of intelligent algorithms in mathematical modeling teaching. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： intelligent algorithm Mathematical modeling teaching Modeling Competition

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Research on intelligent algorithm Evaluation Technology of AI Software 3

Research on Intelligent Algorithm Evaluation Technology of A...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Yaming Shen, Xiaomei Ding, Jianyang Wu, Lijin Tang, Longli China Institute of Marine Technology & Economy Beijing China

ISBN: (纸本)9798350380989

AI software is regarded as the key to the formation of new domain and new quality combat capabilities in the future. In response to the characteristics of randomness, autonomy, and learning of AI software, traditional testing methods lack comprehensive coverage of non-functional requirements, test data is difficult to obtain and annotate, and evaluation indicators are not comprehensive. This paper focuses on the research of intelligent algorithm evaluation technology, proposing a multi-perspective and multi-attribute evaluation method for intelligent algorithms from four aspects: accuracy evaluation, efficiency evaluation, data robustness evaluation and algorithm coverage evaluation. This can effectively support future AI software evaluation work and improve the AI software testing technology system. © 2024 IEEE.

关键词： algorithm accuracy algorithm coverage algorithm efficiency algorithm robustness intelligent algorithm

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