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Self-Adaptive Teaching-Learning-Based Optimizer with Improved Rbf and Sparse Autoencoder for High-Dimensional Problems

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SSRN 2022年

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Zhou, MengChu School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science The Lyle School of Engineering Southern Methodist University DallasTX75205 United States Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Evolutionary algorithms are commonly used to solve many complex optimization problems in different fields. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Selfadaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem. © 2022, The Authors. All rights reserved.

关键词： Radial basis function networks

An Improved Hybrid Approach for the Simultaneous Allocation of Dg Units and Tvvd Loads in Distribution Systems

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SSRN 2022年

作者： Khan, Muhammad Faisal Nadeem Ahmed, Ali Kiani, Arooj Tariq Ullah, Nasim Khan, Muhammad Adnan Mosavi, Amir Department of Electrical Engineering University of Engineering and Technology Taxila Taxila47080 Pakistan Department of Electrical Engineering Air University Islamabad Islamabad44230 Pakistan Department of Electrical Engineering College of Engineering Taif University al-Hawiyah26571 Saudi Arabia Department of Software Gachon University Seongnam Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Faculty of Civil Engineering TU-Dresden Dresden Germany

Distributed Generation (DG) studies are generally conducted considering a single type of DG units integrated with the Distribution System (DS). However, these studies may not evaluate optimum benefits offered by the DG integration. This paper presents a novel framework for individual and simultaneous allocation of different types of DG units in DS while considering varying load demand and probabilistic generation of DG units. A Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) based new hybrid method that utilizes a single controlling parameter is proposed for simultaneous allocation of DG units, aiming to minimize a multi-objective index. The strength of the proposed SSA-PSO hybrid approach is validated on seventeen benchmark functions and the performance of the novel framework for optimal allocation of DG units is validated through implementation on the IEEE 69-bus system. The results demonstrate that the proposed hybrid approach performs better as compared to other meta-heuristics techniques. Moreover, simultaneous allocation significantly reduces the multi-objective index as compared to individual DG units’ integration. © 2022, The Authors. All rights reserved.

关键词： Distributed power generation

Optimum selection of the reference signal for correlation receiver applied to marker localization 5th

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Optimum selection of the reference signal for correlation re...

5th International Conference on Advanced engineering Theory and Applications, AETA 2018

作者： Vestenický, Martin Vestenický, Peter Faculty of Electrical Engineering and Information Technology Department of Multimedia and Information-Communication Technologies University of Žilina Univerzitná 8215/1 Žilina010 26 Slovakia Faculty of Electrical Engineering Department of Control and Information Systems University of Žilina Univerzitná 8215/1 Žilina010 26 Slovakia

ISBN: (纸本)9783030149062

The localization of RFID transponders is very popular task today in industrial RFID applications. One of the special applications of RFID is the localization and identification of the underground facility networks by special RFID transponders called markers. This paper describes an optimum selection of reference signal for the correlation receiver applied to RSSI based localization of the inductive coupled RFID markers. The reference responses of the marker are generated by using the model of localization device created in Matlab – Simulink software package. The analysis takes into account the quality factor of the marker resonant circuit. The results show that the correlations coefficients between any two marker responses are not significantly dependent on the marker parameters. © Springer Nature Switzerland AG 2020.

关键词： Resonant circuits

Experimental Prototype of High-Efficiency Wind Turbine Based on Magnus Effect 27

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Experimental Prototype of High-Efficiency Wind Turbine Based...

27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Lukin, Aleksandr Demidova, Galina L. Lukichev, Dmitry V. Rassolkin, Anton Kallaste, Ants Vaimann, Toomas Belahcen, Anouar ITMO University Faculty of Control System and Robotics Saint Petersburg Russia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallin Estonia Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9781728141589

The main feature of Magnus wind turbine is the ability to produce energy at low wind speed. This benefit lets to use such types of turbines in smart homes, which are an important part of the development of distributed energy systems, growth of blockchain electricity technology and needs for duplication of the solar energy system in the private buildings. This paper presents topology of two cylinders Magnus wind turbine in order to ensure the highest values of power coefficient and the Magnus force. The optimal ratio of wind speed and cylinder rotation speed were detected to eliminate the oscillations caused by the Von Karman vortex street phenomenon. © 2020 IEEE.

关键词： Wind power

Accurate Water Quality Prediction with Attention-Based Bidirectional Lstm and Encoder-Decoder

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SSRN 2022年

作者： Bi, Jing Chen, Zexian Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate prediction of water quality indicators can effectively prevent sudden water pollution events, and control pollution diffusion. Neural networks, e.g., Long Short-Term Memory (LSTM) and encoder-decoder network, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear. Accurate prediction of water quality becomes a big challenge. This work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with bidirectional temporal attention mechanism and BiLSTM. Specifically, VBAED first adopts VMD to decompose historical data of a predicted factor, and decomposed results are used as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by a BiLSTM decoder with a bidirectional temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

Simple and Accurate Method to Evaluate Type a Standard and Expanded Uncertainties of Measurement for the Laplace Distributed Observations

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SSRN 2023年

作者： Dorozhovets, Mykhaylo Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Measuring Information Technologies Institute of Computer Technologies Automation and Metrology Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine

The article proposes simple and accurate evaluation of the standard and expanded uncertainties of the Laplace population median. The uncertainty evaluation based on the approximation of the population median distribution for n observations by the distribution of the sample median for n - 2 observations. Comparisons of the results obtained by proposed approximation with exact results shown that when 6 ≤ n ≤ 10 the differences between approximated and exact standard uncertainties are lees then 1% (0.56%), i.e. negligibly small and for a confidence level p = 0.90 and 0.95 the approximated value of expanded uncertainty differ from exact value less than 1%, and for p= 0.99 difference does not exceed ≈ 5%. When n ≥ 11 the Monte Carlo simulation results showed that the proposed method for determining both the standard and the expanded uncertainty of population median do not exceed ±1%, i.e. is very much precise. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

Improved Network Intrusion Classification with Attention-Assisted Bidirectional Lstm and Optimized Sparse Contractive Autoencoders

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SSRN 2022年

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. Currently, classification methods with autoencoders for feature learning have been proved to be suitable for the network intrusion detection. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. SABD integrates the feature extraction of stacked sparse contractive autoencoders with the classification ability of attention-based bidirectional LSTM. Specifically, stacked sparse contractive autoencoders are used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Besides, an improved optimization algorithm named genetic simulated-annealing-based particle swarm optimization is designed to optimize hyperparameters of SSCA. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Experimental results based on two different types of real-life datasets including UNSW-NB15 and KDD’99 demonstrate that the proposed SABD outperforms its state-of-the-art peers in terms of classification accuracy. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

Multi-Indicator Water Quality Prediction with Attention-Assisted Bidirectional Lstm and Encoder-Decoder

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SSRN 2022年

作者： Bi, Jing Zhang, Luyao Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for the prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called SVABEG, which combines a Savitzky-Golay (SG) filter, Variational Mode Decomposition (VMD), an Attention mechanism, BiLSTM, an ED structure, and a hybrid algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). SVABEG first adopts the SG filter and VMD to remove noise and deal with nonlinear features in the original time series, respectively. Then, SVABEG combines BiLSTM, the ED structure, and the attention mechanism to capture bi-directional long-term correlations, realize dimensionality reduction and extract key information, respectively. Furthermore, SVABEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed SVABEG outperforms current state-of-the-art algorithms in terms of prediction accuracy. © 2022, The Authors. All rights reserved.

关键词： Water quality

Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

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arXiv

arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects