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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of electronics and Information Engineers.

关键词： Demand response

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Frame Synchronization In Underwater Acoustic Communication Based on Convolutional Neural Network 11

Frame Synchronization In Underwater Acoustic Communication B...

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11th International Symposium on Telecommunication, IST 2024

作者： Khankahdani, Ali Sedaghat Jamshidi, Ali Shiraz University School of Electrical and Computer Engineering Department of Commuincations and Electronics Shiraz Iran

ISBN: (纸本)9798350356250

In this article, frame synchronization based on convolutional neural network in the underwater acoustic channel is investigated and simulated. This study considers underwater acoustic channel problems including noise, Doppler phenomenon and multipath. Network training for frame synchronization shows a reduction in frame synchronization errors. Also, the use of auto-encoder in the input of the neural network of the synchronizer and using the code obtained from the auto-encoder reduces the learnable parameters of the neural network of the synchronizer, reduces the error and increases the resistance against the Doppler phenomenon. © 2024 IEEE.

关键词： Convolutional neural networks

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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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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Martini, Simone Sönmez, Serhat Stefanovic, Margareta Rutherford, Matthew J. Valavanis, Kimon P. Ritchie School of Engineering and Computer Science University of Denver Department of Electrical and Computer Engineering United States Istanbul Medeniyet University Department of Aviation Electrical & Electronics Istanbul Turkey Ritchie School of Engineering and Computer Science University of Denver Department of Computer Science Denver United States

ISBN: (纸本)9798331513283

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. © 2025 IEEE.

关键词： Parameter estimation

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Retrofitting of Conventional Lathe Machine to computer Numerically Controlled (CNC) Machine With Bluetooth and Mobile Application Capabilities 9

Retrofitting of Conventional Lathe Machine to Computer Numer...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Sowah, Robert A. Asare-Dartey, Jacqueline D. Acakpovi, Amevi Osei, Gifty University of Ghana Department of Computer Engineering Accra Ghana Accra Technical University Department of Electrical and Electronics Engineering Accra Ghana

ISBN: (纸本)9798350385403

computer Numerically Controlled (CNC) machines play an integral and irreplaceable role in manufacturing industries. It not only realizes rapid industrial production but also saves manpower and material resources. Conventional machining tools, such as the manually operated lathe machine, are inefficient. They are quite challenging for commercial purposes due to low production capability, hence the need for CNC machines to enhance industrial automation productivity. Currently, many conventional lathe machines are used in industries in the country. The need to migrate the manual operations of these machines to full or semi-automation status is of utmost importance. It is required to convert these machines to semi-automatic lathes by retrofitting to grow the country's development in the machining industry. Converting the manual lathe into a semi-automatic lathe requires three significant modifications: electronics, mechanics, and hydraulic components. In this paper, we propose a computer numerically controlled automation system for converting the conventional lathe machine, by developing mobile and web applications to perform automation tasks. The conversion process was carried out on a mini center lathe machine at the Ghana Atomic Energy Commission at Kwabenya in Accra. Firstly, unnecessary components such as the gearbox and handwheels were removed and mounts for the various motors for the automation tasks were added to the machine as part of the mechanical system component modification. The electronic component modification involved installing stepper motors for the lathe machine's X, Y, and Z axes. The Arduino microcontroller controlled the motors via the motor drive systems. At the end of the conversion, the newly retrofitted machine could machine a designed part successfully when the machining instruction in G-code format was sent to the microcontroller. Instructions were decoded and executed successfully. © 2024 IEEE.

关键词： Lathes

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Analyzing the Effectiveness of ML and DL in Combatting Personal Condition Misinformation on Social Media

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 258-267页

作者： Babu, Jaladanki Ravindra Venkataiah, Vaadaala Narayana, Tirumalasetti Lakshmi Rao, Chegudi Ranga Raj, Ch. Deva Sairam, Rallabandi C.H. S. N. P. Anusha, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department CMR College of Engineering & Technology Telangana Hyderabad India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India Electrical and Electronics Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Mechanical Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Department R.V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Electronics and Communication Engineering Department Andhra University Andhra Pradesh Visakhapatnam India

The widespread broadcast of fake health news offers a huge challenge that puts the health and well-being of the general population in peril. This is especially true in a time when there is an excess of information available. In order to develop automated systems that are capable of spotting fraudulent health-related information, researchers have applied artificial intelligence methodologies such as machine learning (ML) and deep learning (DL). These approaches have been utilized in order to construct these systems. In order to address the problem that is now being faced, this is being done. The objective of this research is to give an analysis of the method, performance measurements, and problems that are linked with machine learning (ML) and deep learning (DL) systems for the purpose of recognizing fake health news. This analysis is presented in the course of this research. We study the many ways that are applied in the process of feature engineering, that of model architectural design, and that of evaluation metrics throughout the machine learning (ML) and deep learning (DL) paradigms. These approaches are utilized in the process of feature engineering. Additionally, we evaluate the significance of our findings for the development of the efficiency of systems that are meant to identify false health news and offer prospective areas for additional research in this vitally important sector. Lastly, we conclude that our findings have the potential to improve the effectiveness of these systems. © (2025), (International Association of Engineers). All rights reserved.

关键词： Adversarial machine learning

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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Development of an Intrusion Detection Model using Long Short Term Memory Algorithm 5

Development of an Intrusion Detection Model using Long Short...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Adetunji, Olusogo Julius Tolulope Ibitoye, Oladapo Bells University of Technology Department of Computer Engineering Ota Nigeria Afe Babalola University Department of Electrical Electronics and Computer Engineering Ado Ekiti Nigeria

ISBN: (纸本)9798331542559

Intrusion detection model is a cyber-security technology that is made to keep an eye on and examine network or system activity for indications of unauthorized or hostile activity. It functions by looking through system logs or network traffic and comparing the data with predefined signatures or patterns of activity that point to possible security risks. This study is aimed at developing deep learning model specifically tailored for Intrusion Detection Systems (IDS) by exploring parameter tuned LSTM (Long-short-Term Memory) network that analyzes datasets by carrying out sequential learning and help to improve traditional IDS to identify threats more accurately and classify intrusions into DoS (Denial of Service), Probe, R2L (Remote to Local), U2R (User to Root), focusing on minimizing false positives and false negatives through continuous model refinement;implement mechanisms for continuous learning to stay ahead of emerging threats using appropriate metrics to access the performance of enhanced IDS. The implementation was carried out on Google Colab platform with 8GB RAM hard ware using python programming language. The results of training and validation accuracy and loss respectively of 10 epochs are recorded. Optimal training and validation accuracy are obtained at 9th and 10th with 99.45% and 99.65% respectively. The results also showed, training and validation loss of 0.0730 and 0.020 respectively. At these optimal validated results, corresponding confusion matrix are obtained and sorted and, accuracy, specificity, f1 score, precision and recall of 99.65%, 99.35%, 99.59%, 99.29% and 99.9% respectively are obtained and compared with other existing models. The initiative of this work aimed at reducing false positives and false negatives, give security analysts more dependable alerts, and make resource allocation more effective. © 2024 IEEE.

关键词： Network intrusion

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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2025 IEEE International Students' Conference on electrical, electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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AI based Compact Controller for Wind farms Economical Operation, Control and Monitoring 2

AI based Compact Controller for Wind farms Economical Operat...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sasikala, C. Palpandian, P. Muthuram, G. Kumar, S. Siva Karthik, S. Sam Raj, C. Mohan Arjun College of Technology Department of Electronics and Communication Engineering Coimbatore642 120 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 032 India Hindusthan College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641 105 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 105 India Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 202 India

ISBN: (纸本)9798350368413

In India, 42.6 GW power is generated by wind turbine generators for supplying electricity to the load. Permanent Magnet DC generator, Synchronous generator and Induction generator are the most commonly used generators in the wind mill. Though the generators are designed for producing the power with maximum efficiency, monitoring, operation and control of wind mill plays an important role. The instantaneous faults like open circuit fault and least resistance fault can be easily detected and removed by the protection scheme. But the inter turn fault, core saturation condition, loss of dielectric due to ageing of machines cannot be detected until the fault occurs. The economic operation of the wind mill is based on the fault detection and prediction, uninterrupted operation of the wind mill, monitoring of parameters from the remote location. The AI based compact controller R04CPU with remote I/O module is used to monitor, control the windmill and ensures the economic operation/fault detection. © 2024 IEEE.

关键词： Windmill

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