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International Conference on computer and Information technology (ICCIT)

作者： Md. Fahim-Ul-Islam Mehedi Hasan Abrar Ahsan Efaz Faijah Rashid Md. Minhaz Uddin Muhammad Iqbal Hossain Amitabha Chakrabarty Department of Computer Science and Engineering Brac University Dhaka Bangladesh Department of Electrical and Computer Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Precise power load forecasting is essential for efficient Smart Grid management, although it poses difficulties due to the nonlinear characteristics of electricity use. Although deep learning has demonstrated potential across multiple domains, its utilization in load forecasting requires a thorough assessment. This study fills the gap by doing a comprehensive evaluation of various deep learning architectures for short-term electric load forecasting, utilizing actual energy consumption data. We present TempFocusNet, a hybrid model that integrates Long Short-Term Memory (LSTM) dependency learning with the lightweight temporal attention strategy to improve prediction accuracy. Therefore, our methodology encompasses renewable energy consumption with instantaneous decision-making. Temp-FocusNet shows a 2.13% increase in R 2 , a 99.92% decrease in RMSE, a 45% improvement in MAPE, and a 91.74% decrease in MAE compared to ARIMA, the next best-performing model in terms of R 2 .

关键词： Deep learning Energy consumption Renewable energy sources Accuracy Load forecasting Predictive models Smart grids Forecasting Long short term memory Load modeling

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Super Hybrid Deep Learning Framework: Transformer-LSTM-CNN-DNN for Accurate Load Forecasting

Super Hybrid Deep Learning Framework: Transformer-LSTM-CNN-D...

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International Conference on computer and Information technology (ICCIT)

作者： Arnab Barua Shah Nawaz Haider Steve Austin Sarowar Morshed Shawon Department of Electrical and Electronic Engineering University of Science and Technology Chittagong Chattogram Bangladesh Department of Computer Science and Engineering University of Science and Technology Chittagong Chattogram Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The short-term demand forecasting of electric energy allows today’s power grid operations to run without interruptions. The improvements of Machine Learning and Deep Learning together with higher computational performance, have considerably improved the precision of the forecasts. In Bangladesh, where effective resource management can be a crucial factor, power load forecasting plays a vital role in energy security. In this research, a hybrid feature generation model using a combination of Transformer, LSTM and CNN which was then fed into a DNN model, was built for a robust predictive performance. The study was conducted on historical daily load data, spanning the period from January 2014 to April 2023, covering all eight divisions of Bangladesh, which was also subjected to a series of data cleaning, normalization and partitioning into training and testing subsets. Validation of the model was done for all the divisions, with Sylhet and Rajshahi divisions yielding the best R 2 of 0.993 and 0.989, respectively. The Transformer, LSTM and CNN layers captured both long-term dependencies and short-term variations, while the prediction accuracy of the DNN model provided a robust approach to load forecasting. This super-hybrid approach accentuates the benefits of flexible modern deep learning models to the electricity distribution system, especially for countries like Bangladesh, where efficient utilization of energy resources is crucial due to limited resource and growing demand.

关键词： Deep learning Accuracy Load forecasting Focusing Predictive models Transformers Feature extraction Data models Long short term memory Load modeling

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO EXPLICIT APPLICATIONS OF AI

INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Rathi, Vikas Vijayalakshmi, V.J. Mahesh, Bhandari Ninoria, Shalini Gupta, Ankur Kumar Premananthan, G. Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Electrical And Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India CCSIT TMU India School of Computer Science and Applications IIMT University Meerut India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore6410 India

ISBN: (纸本)9798350355338

One of the biggest developments in intelligent machines has been the development of evolving fuzzification. They are flexible system designs created using evolving methods. Fluid simulation now has excellent capabilities to use in a variety of data scientific situations as a result of this combination. The goal with this addition is to create a policy document that provides a thorough study of the evolving hazy ecosystems field of study. In order to comprehend the present setting of this subject and its importance, queries are asked and answered with some of this goal in mind. It will be made clear why evolving neural nets are significant from an explicable perspective, even before they started, how much they are utilized for, and where scholars should devote themselves in this field going forward. People must really be crucial to the developing field of data-driven schooling. © 2024 IEEE.

关键词： Adversarial machine learning

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Practical engineering Education: Use of collaborative robots in the context of Industry 5.0

Practical Engineering Education: Use of collaborative robots...

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2023 IEEE IFEES World engineering Education Forum and Global engineering Deans Council, WEEF-GEDC 2023

作者： Vogel, Christian Lindner, Fabian Kratzsch, Alexander Zittau/Görlitz University of Applied Science Faculty of Electrical Engineering and Computer Science Zittau Germany Zittau/Görlitz University of Applied Science Faculty of Business Administration and Engineering Zittau Germany Zittau/Görlitz University of Applied Science Institute of Process Technology Process Automation and Measuring Technology Zittau Germany

ISBN: (纸本)9798350316025

This scientific paper examines the use of collaborative robots in the context of Industry 5.0 and its impact on practical engineering education. The aim of this paper is to highlight the importance as well as the challenges of using robots in Industry 5.0 and to analyze how this can be integrated into engineering education. The paper discusses various aspects such as the technological advances, the need for interaction between humans and robots, and the competencies that engineers need in an increasingly digitalized working world. Furthermore, practical methods and approaches in engineering education at the Zittau/Görlitz University of Applied sciences are presented to prepare students for the requirements of Industry 5.0. Additionally, a draft of a future interdisciplinary learning factory 5.0 will be presented, in which networked technologies with the focus on robotics run through various scenarios in the context of Industry 5.0 at different locations. © 2023 IEEE.

关键词： engineering education

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FairXbar: Improving the Fairness of Deep Neural Networks with Non-Ideal in-Memory Computing Hardware

FairXbar: Improving the Fairness of Deep Neural Networks wit...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Mugdho, Sohan Salahuddin Guo, Yuanbo Rogers, Ethan G. Zhao, Weiwei Shi, Yiyu Wang, Cheng Department of Electrical and Computer Engineering Iowa State University of Science and Technology Ames IA 50010 Computer Science and Engineering University of Notre Dame Notre Dame IN 46556

ISBN: (纸本)9783982674100

While artificial intelligence (AI) based on deep neural networks (DNN) has achieved near-human performance in various cognitive tasks, such data-driven models are known to exhibit implicit bias against specific subgroups, leading to fairness issues. Most existing methods for improving model fairness only consider software-based optimizations, while the impact of hardware is largely unexplored. In this work, we investigate the impact of underlying hardware technology on AI fairness as we deploy DNN-based medical diagnosis algorithms onto in-memory computing hardware accelerators. Based on our newly developed framework that characterizes the importance of DNN weight parameters to fairness, we demonstrate that device variability-induced non-idealities such as stuck-at faults and noises due to variation can be exploited to deliver improved fairness (up to 32% improvement) with significantly reduced trade-off (less than 1% loss) of the overall accuracy. We additionally develop a hardware non-idealities-aware training methodology that further mitigates the bias between unprivileged and privileged demographic groups in our experiments on skin lesion diagnosis datasets. Our work suggests exciting opportunities for leveraging the hardware attributes in a cross-layer co-design to enable equitable and fair AI. © 2025 EDAA.

关键词： Deep neural networks

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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Speech Watermarking for Tampering Detection Using Singular Spectrum Analysis with a Psychoacoustic Model 26

Speech Watermarking for Tampering Detection Using Singular S...

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26th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2023

作者： Khanti, Phondanai Sathirasattayanon, Pannathorn Kaewcharuay, Patthranit Termkoh, Nanthayod Phaisangittisagul, Ekachai Galajit, Kasorn Karnjana, Jessada National Science and Technology Development Agency National Electronics and Computer Technology Pathum Thani Thailand Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Kasetsart University Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350344028

Unauthorized modification of speech signals can lead to misinformation, invade privacy, and reduce the reliability of individuals and agencies. This paper proposes a speech tampering detection scheme that employs semi-fragile watermarking based on singular spectrum analysis (SSA) enhanced by a psychoacoustic model. The SSA technique is used to analyze speech signals and extract the singular spectra. The watermark is embedded into speech signals by modifying some parts of the singular spectra, suggested by psychoacoustic model, to maximize transparency. After embedding the watermark into the host signal, it is possible to verify if someone has tampered with the signal. To determine whether a watermarked signal has been modified, the extracted watermark from the watermarked signal is compared to the original watermark. The evaluation results demonstrate that the proposed scheme is sensitive to malicious attacks, effectively detecting any deliberate attempts to modify the speech signal. However, it remains resilient to unintentional alterations, such as those caused by transmission errors or signal processing operations. Additionally, the evaluation results highlight the effectiveness of the proposed scheme in preserving the quality of the watermarked speech signal. The embedded watermark introduces minimal distortion, ensuring that the overall quality of the speech remains intact. Compared to a conventional SSA-based method, the proposed approach yields a notable improvement in sound quality by 7.69% and 6.91% for the objective difference grade and signal-to-distortion ratio, respectively, and the log-spectral distance is reduced by 52.17%. © 2023 IEEE.

关键词： Speech communication

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A Discontinuous Galerkin Time-Domain Scheme to Simulate Semiconductor Lasers

A Discontinuous Galerkin Time-Domain Scheme to Simulate Semi...

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European Conference on Antennas and Propagation, EuCAP

作者： Ming Dong Liang Chen Ran Zhao Hakan Bagci Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

A computational framework, which relies on the discontinuous Galerkin time-domain scheme, is proposed to simulate transient lasing generated by interactions of light with an active medium. The proposed scheme solves a coupled system of the Maxwell and the rate equations. The active medium inside the laser region is described quantum-mechanically by the rate equations to account for the atomic transitions of a multi-level system, while electromagnetic field interactions are described classically by the Maxwell equations. Numerical examples are provided to demonstrate the accuracy and the applicability of the proposed framework.

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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