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Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

computers, Materials & Continua 2023年第6期75卷 6375-6393页

作者： Neelam Mughees Mujtaba Hussain Jaffery Abdullah Mughees Anam Mughees Krzysztof Ejsmont Department of Electrical and Computer Engineering COMSATS University IslamabadLahore Campus45550Pakistan School of Engineering and Technology National Textile UniversityFaisalabad37610Pakistan Department of Electrical Engineering Information Technology UniversityLahore54000Pakistan Department of Electrical Engineering GC UniversityFaisalabad38000Pakistan Faculty of Mechanical and Industrial Engineering Warsaw University of Technology02-524WarsawPoland

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by ***,they are exceedingly unpredictable since they rely highly on weather and atmospheric *** microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of *** this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in *** create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the *** of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization ***,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE *** these models were also optimized and modeled in *** results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

关键词： Deep stacked autoencoder sequence to sequence autoencoder bidirectional long short-term memory network wind speed forecasting solar irradiation forecasting

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs

The Lynchpin of In-Memory Computing: A Benchmarking Framewor...

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2024 International Conference on Neuromorphic systems, ICONS 2024

作者： Rahman Chowdhury, Md Tawsif Nguyen Vo, Huynh Quang Ramanan, Paritosh Yildirim, Murat Tutuncuoglu, Gozde Wayne State University Electrical and Computer Engineering Detroit United States Oklahoma State University Industrial Engineering and Management Stillwater United States Wayne State University Industrial and Systems Engineering Detroit United States

ISBN: (纸本)9798350368659

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics. © 2024 IEEE.

关键词： RRAM

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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Multi-Site Aggregate Production Planning Using Particle Swarm Optimization

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Journal of engineering, Project, and Production Management 2022年第1期12卷 62-69页

作者： Wibawa, Aji Prasetya Mahmudy, Wayan F. Rizki, Agung Mustika Yuliastuti, Gusti Eka Tama, Ishardita Pambudi Electrical Engineering Department Universitas Negeri Malang Malang Indonesia Computer Science Faculty of Computer Science Brawijaya University Malang Indonesia Industrial Engineering Faculty of Engineering Brawijaya University Malang Indonesia

Aggregate planning is a crucial stage in the production process because it supports other processes. Careless production planning may cause production costs to spike sharply that hurts the company financially. This study explores the novel usage of particle swarm optimization (PSO) to discover a set of solutions among the objective of a multioptimization problem in aggregate production planning. The study uses a small home textile industry with complex production processes of school uniforms as a case study. The results show that the production cost difference between actual data and the proposed method is IDR330,670,000. Thus, PSO can solve the multi-site aggregate planning by reducing the company production cost. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Aggregates

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Wheat Field Fire Smoke Detection from UAV Images using CNN-CBAM 2

Wheat Field Fire Smoke Detection from UAV Images using CNN-C...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Rochester Department of Industrial and Systems Engineering MI United States Oakland University Rochester Department of Electrical and Computer Engineering MI United States

ISBN: (纸本)9798331529529

Wheat field fires pose a significant threat to wildlife habitats, agricultural resources, and especially farmer incomes. The frequency of these fires has increased noticeably in recent years, likely due to human activity in agricultural areas and the exacerbating effects of climate change. Rapid action is crucial once smoke is detected, as it marks the first step in early fire detection. To address this challenge, we propose an enhanced CNN model specifically designed to identify smoke in wheat field fires. As part of this approach, a dataset of 3900 UAV images was compiled to depict smoke from wheat field fires. The proposed work also integrates the CBAM into the CNN to improve the model's ability to extract features for smoke patch characteristics of wheat field fires. Furthermore, the CNN architecture includes decoupled heads to extract relevant data from various sources. Testing on our wheat field fire smoke dataset has demonstrated the effectiveness of this proposed method, achieving an accuracy of up to 0.91 and outperforming the previous model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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ASSESSMENT OF THE SIGNIFICANCE OF FEATURES FOR THE IDENTIFICATION OF DOMESTIC APPLIANCES 27

ASSESSMENT OF THE SIGNIFICANCE OF FEATURES FOR THE IDENTIFIC...

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27th International Conference on Electricity Distribution, CIRED 2023

作者： Ma, Liya Schmidt, Maximilian Schegner, Peter Technische Universitaet Dresden Institute of Electrical Power Systems and High Voltage engineering Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9781839539596

The load identification technologies in the domestic field, also called Non-Intrusive Load Monitoring (NILM), aim to break down the total electricity consumption at household level to the appliance level. This detailed information helps to identify the behaviour of users, deduce energy saving concepts, or provide live assistance for the residents. The NILM approaches are carried out with appliance features, such as the absorbed power, the voltage and current profile. They are defined as appliance-specific characteristics or features, which supposed to make the appliance clearly distinguishable from others. However, most of the publications focus on the identification algorithms. There is barely a contribution to analyse the significance of features. Hereby, the significance means, how consistent and reliable a feature differs from one appliance to another. This paper works on this question. Based on analyses of publications and own measurements, this paper gives an overview of the significance of representative features. Furthermore, suitable features for one-second data are selected and NILM is carried out. © The Institution of engineering and Technology 2023.

关键词： Domestic appliances

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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A Reinforcement Learning Model for industrial Filling Process Control 30

A Reinforcement Learning Model for Industrial Filling Proces...

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30th IEEE International Conference on Electronics, Circuits and systems, ICECS 2023

作者： Emeksiz, Omer Sabri Agcabay, Mert Eren Masazade, Engin Selim, Suat Boysan, Taner Electrical and Electronics Engineering Faculty of Engineering Marmara University Istanbul Turkey BAYKON Industrial Weighing Systems Istanbul Turkey

ISBN: (纸本)9798350326499

In this paper, we propose an industrial filling process based on reinforcement learning. Despite the desired control can be achieved by using a simple PID controller, in many practical cases action space can be discrete as coarse and fine feed, there can be some residual material which may lead to overflow after the predefined setpoint is reached, or type and temperature of the material may affect the flow of the filling process. For such instances where the PID controller may struggle, in this work, we define a data-driven filling process based on reinforcement learning. We record different filling processes as episodes, define state-action pairs, and rewards to minimize the total filling duration, and set penalty functions for overflow and underflow situations. We use Monte Carlo method to find the best action in each state. The performance of the algorithm is illustrated with numerical examples. © 2023 IEEE.

关键词： Monte Carlo methods

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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