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15th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Sharma, Varun Kolivand, Hoshang Asadianfam, Shiva Al-Jumeily, Dhiya Jayabalan, Manoj Liverpool John Moores University Department Computer Science Liverpool United Kingdom School of Computer Science and Maths Liverpool John Moores University Liverpool United Kingdom Qom University of Technology Faculty of Electrical and Computer Engineering Qom Iran Islamic Azad University Qom Department of Computer Engineering Branch Qom Iran

ISBN: (纸本)9798350335149

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies. © 2023 IEEE.

关键词： computer vision

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Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

Autoencoder and Teaching-learning-based Optimizer for Mobile...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Dian Zhou, Mengchu Yuan, Haitao Institute of Systems Engineering Macau University of Science and Technology Macao China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350337020

By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems;and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed Autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice. © 2023 IEEE.

关键词： autoencoder Autoencoder-embedded teaching-learning based optimization (ATLBO) High-dimensional expensive problems (HEPs) Mobile edge computing systems

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Semi-Supervised Learning Approach for Efficient Resource Allocation with network Slicing in O-RAN

arXiv

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arXiv 2024年

作者： Nouri, Salar Motalleb, Mojdeh Karbalaee Shah-Mansouri, Vahid Shariatpanahi, Seyed Pooya School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

This paper introduces an innovative approach to the resource allocation problem, aiming to coordinate multiple independent x-applications (xAPPs) for network slicing and resource allocation in the Open Radio Access network (O-RAN). Our approach maximizes the weighted throughput among user equipment (UE) and allocates physical resource blocks (PRBs). We prioritize two service types: enhanced Mobile Broadband and Ultra-Reliable Low-Latency Communication. Two xAPPs have been designed to achieve this: a power control xAPP for each UE and a PRB allocation xAPP. The method consists of a two-part training phase. The first part uses supervised learning with a Variational Autoencoder trained to regress the power transmission, UE association, and PRB allocation decisions, and the second part uses unsupervised learning with a contrastive loss approach to improve the generalization and robustness of the model. We evaluate the performance by comparing its results to those obtained from an exhaustive search and deep Q-network algorithms and reporting performance metrics for the regression task. The results demonstrate the superior efficiency of this approach in different scenarios among the service types, reaffirming its status as a more efficient and effective solution for network slicing problems compared to state-of-the-art methods. This innovative approach not only sets our research apart but also paves the way for exciting future advancements in resource allocation in O-RAN. © 2024, CC BY.

关键词： Self-supervised learning

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Koopman-Based Reinforcement Learning for LQ Control Gains Estimation of Quadrotors

Koopman-Based Reinforcement Learning for LQ Control Gains Es...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Simone Martini Serhat Sünmez Margareta Stefanovic Matthew J. Rutherford Kimon P. Valavanis Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver USA Department of Aviation Electrical & Electronics Istanbul Medeniyet University Istanbul Turkiye Department of Computer Science Ritchie School of Engineering and Computer Science University of Denver Denver USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this research, Koopman operator theory is employed to achieve faster training time and improved performance of a reinforcement learning (RL) based linear quadratic controller (LQ). The proposed methodology, called K-RLLQ, is implemented for the trajectory tracking problem of a quadrotor UAV. Using the evolution of analytically derived Koopman generalized eigenfunctions allows for the embedding of quadrotor nonlinear dynamics into a quasi-linear model. Specifically, the resulting Koopman based quadrotor dynamics has linear state matrix and state dependent control matrix. Additionally, the obtained formulation is fully actuated, hence, compared to traditional model based hierarchical control the advantages are twofold: i) the controller can be formulated using linear control strategies in Koopman formulation which will result in a nonlinear control law in the original state space; ii) the trajectory tracking task can be achieved through a single control loop. Using this formulation, an RL agent is trained to estimate the controller parameters of a linear quadratic control law. Notably, it is shown that, using a reward function and observation space based on Koopman generalized eigenfunctions over the state space, leads to a considerably faster training time and improved overall performances.

关键词： Training Trajectory tracking Dynamics Reinforcement learning Transforms Aerospace electronics Eigenvalues and eigenfunctions Mathematical models Nonlinear dynamical systems Quadrotors

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Development of Hybrid Charging Station for Electric Vehicles 1

Development of Hybrid Charging Station for Electric Vehicles

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Mosetse, Tsaone Karabo Samikannu, Ravi Yahya, Abid Kalpana, C. Ramkumar, M Siva Vidyalakshmi, R. Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Karpagam Institute of Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

Electric Vehicles (EVs) are growing more popular due to their low-carbon and ecologically ideal qualities. Many studies show the declining supply of fuels and the importance of using renewable energy systems to reduce the air pollution. The EVs capable to exceed the performance of Internal Combustion Engines (ICE). The main problem is infrastructure to facilitate EV charging. The project focuses on research, designing and implementation of the Solar Photo Voltaic (SPV) charging station for EV which will improve reliability and convenience for charging EV's. The design model won't be reliant on the grid power as the main power supply but solar energy. The design model consists of the Solar PV panel, the charge controller circuit, battery, transformer, voltage regulator, the automatic transfer changeover and the load which is connected to the automatic transfer changeover. The project report also shows the activity log of all the tasks carried out throughout the period of the design simulation and hardware development of the model. © 2023 IEEE.

关键词： Internal combustion engines

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Design and implementation of a 3D positioning and control capsule robot using magnetic waves 8

Design and implementation of a 3D positioning and control ca...

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8th International Conference on Control, Instrumentation and Automation, ICCIA 2022

作者： Mahmoodzadeh, Hamidreza Majd, Vahid Johari Ehsani-Seresht, Abbas Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran Tarbiat Modares University School of Electrical and Computer Engineering Tehran Iran Hakim Sabzevari University Department of Mechanical Engineering Sabzevar Iran

ISBN: (纸本)9781665495691

Capsule endoscopy robots have been used to detect digestive diseases in recent years. This paper explores the design and implementation of capsule robot localization and control systems using magnetic waves. The built system includes five external electromagnetic coils, and a permanent magnet added to the inactive capsule robot, which is available on the market. The instantaneous position of the robot is calculated through image processing. Moreover, The speed of the robot and the coil's current is measured using the rate of the position and the current sensor. A sliding mode controller is designed for 3D motion of the robot in the digestive system test environment in the lab. Our results show the suitable performance of the robot in tracking the desired position. © 2022 IEEE.

关键词： Endoscopy

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Moving Average-Based Performance Enhancement of Sample Convolution and Interactive Learning for Short-Term Load Forecasting

Moving Average-Based Performance Enhancement of Sample Convo...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Yin, Du Miao, Lingfeng Li, Guanzhi Zhan, Choujun Sun, Lulu School of Computer Science South China Normal University Guangzhou China School of Electrical and Computer Engineering Nanfang College Guangzhou China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Efficient and accurate short-term load forecasting (STLF) is significance in modern electricity markets. However, accurate short-term load forecasting is challenging due to the non-stationary power load patterns. In this work, we propose a short-term load forecasting framework based on maximal information coefficient (MIC), moving average filter (MAF) and sample convolution and interactive learning (SCINet). Firstly, MIC is used for feature selection. Secondly, the filtered input features are decomposed using MAF individually. Finally, the data are used in an advanced SCINet for short-term load forecasting. The performance of the proposed method is evaluated using datasets from two different regions of the US electricity market. In addition, we compare the prediction results with support vector regression machines (SVR), long short-term memory networks (LSTM), temporal convolutional networks (TCN), light gradient boosting machine (LightGBM), artificial neural network (ANN), random forest (RF), and sample convolution and interaction networks (SCINet). The proposed model achieves accurate prediction results among all the machine learning models used in this paper. © 2022 IEEE.

关键词： Convolution

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Solving constrained optimization problems via the variational quantum eigensolver with constraints

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Physical Review A 2024年第2期110卷 022430页

作者： Thinh Viet Le Vassilis Kekatos Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette 47906 IN United States

Variational quantum approaches have shown great promise in finding near-optimal solutions to computationally challenging tasks, including solving optimization problems. Nonetheless, optimization problems with constraints may not have been handled in a disciplined fashion thus far. To address this gap, this work proposes a hybrid quantum-classical algorithmic paradigm termed the variational quantum eigensolver with constraints (VQEC) that extends the celebrated VQE to handle optimization with constraints. As with the standard VQE, the vector of optimization variables is captured by the state of a variational quantum circuit (VQC). To deal with constraints, VQEC optimizes a Lagrangian function classically over both the VQC parameters as well as the dual variables associated with constraints. To comply with the quantum setup, variables are updated via a perturbed primal-dual method leveraging the parameter shift rule. Among a wide gamut of potential applications, we showcase how VQEC can approximately solve quadratically constrained binary optimization problems, find stochastic binary policies satisfying quadratic constraints on the average and in probability, and solve large-scale linear programs over the probability simplex. Under an assumption on the error for the VQC to approximate an arbitrary probability mass function, we provide bounds on the optimality gap attained by a VQC. Numerical tests on a quantum simulator investigate the effect of various parameters and corroborate that VQEC can generate high-quality solutions.

关键词： Optimization problems Quantum algorithms & computation

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Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

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Digital Communications and networks 2022年第3期8卷 289-302页

作者： Zihan Chen Guang Cheng Ziheng Xu Shuyi Guo Yuyang Zhou Yuyu Zhao School of Cyber Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Ministry of EducationNanjing211189China Purple Mountain Laboratories Nanjing211111China

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security ***,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a ***,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version *** this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 *** experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and *** to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

关键词： Encrypted internet traffic Encrypted traffic service classification Multi PDU length sequence Length sensitive composite deep learning TLS-1.3

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Identifying Motor Tasks From EEG Signals of Planning and Execution Phases by CNNs in a Motor Learning Procedure

Identifying Motor Tasks From EEG Signals of Planning and Exe...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Ali Darzi Masouleh Fariba Bahrami Ahmad Kalhor School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

In this research we analyzed EEG signals to classify motor planning and execution phases during a set of motor learning task. We began by preprocessing the EEG data and then displaying topographical plots (topoplots) of different frequency bands of EEG data to classify the phases. Afterward, we tested convolutional neural network (CNN) models, ResNet18 and EfficientNet to further enhance the processing and classification of the EEG signals. The CNN models were examined based on performance measures like accuracy, precision, recall, and F1-score. Primary findings suggested that ResNet-18 and EfficientNet models successfully distinguished the motor planning and execution phases, particularly EfficientNet presented better results. This study highlights the utilization of deep learning models as a potential way to classify EEG signals. This will help us to understand motor learning in humans better. Ultimately, after classifying the phases, we proceeded to classifing different drawn patterns and identify them using these two models. For classifying the phases using ResNet18 and EfficientNet networks, we managed to achieve accuracies of $86.3 \%$ and $89.2 \%$, respectively. Additionally, for pattern classification, accuracies of $\mathbf{8 7. 9 \%}$ for ResNet18 and $\mathbf{9 6. 9 \%}$ for EfficientNet were obtained.

关键词： Deep learning Accuracy Pattern classification Motors Brain modeling Electroencephalography Planning Convolutional neural networks Biomedical engineering

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