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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Edu, Emil-Ronald Radac, Mircea-Bogdan Department of Mechanical Engineering Timisoara Romania PUT Dept. of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9798350395914

In the present paper, the operation of high-power wind turbines of the order of mega-watts (MW), is analysed at significantly variable wind speeds over time. Experimental data from a wind turbine operating in the Dobrogea region are processed, at a higher sampling frequency of measurements. The current operating condition is investigated from an efficiency viewpoint and compared with an optimized controlled setting, in which the electric generator power is controlled dependent on the wind speed value. We find that wind speed sampling frequency is an important factor impacting control quality, while the improved control is mandatory for maximal operation efficiency. © 2024 IEEE.

关键词： Windmill

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Fully-3D-Printed Microwave Sensor for Permittivity Measurement

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IEEE Sensors Journal 2025年第12期25卷 21546-21551页

作者： Pradhan, Nrusingha Charan Koziel, Slawomir Central University of Karnataka Dept. of Electronics and Communication Engineering Karnataka India Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Poland

This paper presents the design and performance evaluation of a novel 3D-printed microwave sensor for dielectric characterization. The sensor utilizes a modified complementary split-ring resonator (CSRR) structure integrated within the ground plane. The sensor’s ability to measure both solids and liquids is demonstrated, with solid samples placed in proximity to the resonator and fluids injected into the embedded channel, thereby avoiding additional materials for fluidic channels. The sensor’s performance is evaluated based on changes in resonant frequency due to variations in dielectric constants. For solids, the resonant frequency shifts from 4.24 GHz to 2.66 GHz with increasing permittivity. For liquids, the frequency ranges from 4.24 GHz to 3.27 GHz for permittivity values between 1 and 80. An equivalent circuit model is developed to validate the sensor’s performance. The sensor is fabricated using the Fused Deposition Modeling (FDM) method with conductive layers applied via copper aerosol spray. The sensor’s versatility is demonstrated through testing with a range of solid substrates, such as Rogers 5880, FR4, Rogers 3006, and Rogers 6010, as well as four liquid samples—methanol, ethanol, propanol, and distilled water. The sensor effectively measures dielectric constants by monitoring shifts in resonant frequency, which vary with the permittivity of the tested samples. With an overall compact design of 30 mm × 40 mm × 1.6 mm, the sensor requires minimal liquid volume and achieves comparable sensitivity, with 4.05% for solids and 0.3% for liquids. The sensor’s resolution, accuracy, and low fabrication cost make it a promising tool for precise dielectric constant measurement. © 2001-2012 IEEE.

关键词： Permittivity measurement

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IoT-Cloud Integration for Enhanced Container Weight Monitoring and Alerting: A Real-Time Solution 11

IoT-Cloud Integration for Enhanced Container Weight Monitori...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Kallarackal, Stephen Philip Priya, M Krishna Menon, Sanker S Abraham, Suraj Koithot Thomas, Abraham Rajagiri School of Engineering and Technology Dept. of Applied Electronics & Instrumentation Engineering Kerala Kochi682039 India

ISBN: (纸本)9798350379136

Efficiently managing essential items in home utility containers is crucial for seamless daily operations. However, the inconvenience of not knowing when supplies are depleting can lead to unexpected shortages, causing disruptions and inconveniences. To address this, we present the "Smart Container Weight Monitoring System,"an innovative solution that leverages Internet of Things (IoT) technology to enable real-time monitoring of container weights. © 2024 IEEE.

关键词： Resource allocation

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IoT Based Utility Consumption Trend Prediction and Usage Management 5

IoT Based Utility Consumption Trend Prediction and Usage Man...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

ISBN: (纸本)9798331504403

This study presents the design, development, and evaluation of an advanced utility consumption monitoring and prediction platform aimed at facilitating efficient energy management within residential/commercial/industrial/campus settings. Using techniques such as ARIMAX modeling and Deep Q-Network (DQN) reinforcement learning, the platform offers a user-friendly interface for real-time monitoring, accurate forecasting, and intelligent usage management of electricity, water, and gas consumption. The DQN model achieved a Mean Absolute Percentage Error (MAPE) of 7.5%, significantly outperforming recent models. This demonstrates the superior accuracy and reliability of our system in trend prediction. Additionally, the platform provides enhanced energy management features, enabling users to reduce costs and improve energy efficiency. Through complex algorithms and multiple evaluation, this platform can play a significant role in promoting energy conservation and sustainability efforts in various settings. © 2024 IEEE.

关键词： AutoRegressive Integrated Moving Average with eXogenous inputs (ARIMAX) Deep Q-Network (DQN) energy management monitoring prediction Utility consumption

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Dependable Wireless Powering for the Wireless Sensor Station by Using Combination of Solar Cell and Rectenna

Dependable Wireless Powering for the Wireless Sensor Station...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Yoshida, Satoshi Nishikawa, Kenjiro Ryukoku Univ. Dept. of Electronics and Informatics Shiga Japan Kagoshima Univ. Dept. of Electrical and Electronics Engineering Japan

ISBN: (纸本)9781665442282

This paper proposes an experimental evaluation result of the combination of solar cell and rectenna for wireless sensor network to realize the dependable wireless powering. 100 W class microwave power transmission (MPT) and 3000 Ix illuminance photovoltaic power generation are combined to the system. The measurement results of the output DC power were 325 mW and 160 mW for the configuration of combination of MPT and 3000 Ix photovoltaic power generation and the configuration of MPT only, respectively. The combination of the MPT and photovoltaic power generation is effective to get large DC power. © 2023 IEEE.

关键词： Solar energy

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Effect of the Shielding for Opened Windows of the Reusable Vehicle Test for 5.8-GHz-Band Microwave Power Transmission 35

Effect of the Shielding for Opened Windows of the Reusable V...

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35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023

作者： Yoshida, Satoshi Nishikawa, Kenjiro Ryukoku Univ. Dept. of Electronics and Informatics Shiga Japan Kagoshima Univ. Dept. of Electrical and Electronics Engineering Japan

ISBN: (纸本)9789463968096

This paper summarized the effect of the shielding for opened windows of the Reusable Vehicle Test (RVT) for 5.8-GHz-band microwave power transmission (MPT). The MPT system was equipped to power the 2.4-GHz-band ZigBee wireless data communication system. Experimental evaluations were performed using the RVT. The measurement result showed that output DC power from the rectifier increased under the condition of closing all windows while no affect for data communication was observed. It is concluded that the shielding window is effective to get large DC power. © 2023 International Union of Radio Science.

关键词： Plasma jets

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Advances and Adaptation in Artificial General Intelligence Based on Novel Q∗ Framework 27

Advances and Adaptation in Artificial General Intelligence B...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upreti, Meenakshi Bhatnagar, Abhinav Birla Institute of Applied Sciences Dept. of Master of Computer Applications Bhimtal India Birla Institute of Applied Sciences Dept. of Electronics and Communication Engineering Bhimtal India

ISBN: (纸本)9798350392319

A priority in the researches on artificial intelligence is the creation of Artificial General Intelligence. Even though the regular artificial intelligence was making some progress in certain areas, there were challenges and untried domains. This study introduces a novel Q∗ framework which is intended for improving AGI. Understanding, obtaining and using machine-generated knowledge across multiple domains are some of the aspects of AGI. It integrates concepts form cognitive psychology and reinforcement learning to produces a cohesive approach that includes learning as well as introspection and flexibility. By leveraging the advantages of Q-learning and refining existing methods, achieving AGI through Q∗ is feasible. Q* prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly. This makes it possible to be adaptable and learn well in unfamiliar settings. The paper gives a detailed explanation of the theoretical underpinnings and possible applications of AGI, emphasizing Q∗′ 's commitmentto advancing its pursuit. We discuss the conceptual framework proposed by Q∗ and speculate on its implications for future AI research. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Algorithms for Predicting Chronic Diseases 3

Machine Learning Algorithms for Predicting Chronic Diseases

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3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Bulus, Furkan Saoud, Bilal Shayea, Ibraheem Syzdykova, Zuleikha Faculty of Electrical and Electronics Engineering Dept. Electronics & Communications Engineering Istanbul34469 Turkey University of Bouira Faculty of Sciences and Applied Sciences Lisea Laboratory Dept. Electrical Engineering Bouira10000 Algeria Astana It University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350384598

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Efficient Matrix Multiplication Processing Elements in Systolic Arrays using Shift and Bit Dependent Conditional Add Approach 3

Design of Efficient Matrix Multiplication Processing Element...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Tenneti, Srimanth Indrakanti, Raghu Shankarpally, Swetha University of Cincinnati Dept. of Engineering and Applied Science OH United States Anurag University Dept. of Electronics and Communication Engineering Hyderabad India CVR College of Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9798350372120

Real-time applications like audio signal processing and wireless communication highly demand the need for powerful and efficient multiplier and accumulator units (MACs) for achieving high performance. The overall system performance is closely correlated with the power, performance, and area (PPA) of the MAC unit. This paper proposes a novel modified Shift and Add (mSAC) architecture to perform the MAC operations. The mSAC cells serve as the processing elements (PE's) of a systolic array. The proposed architecture's credibility is verified with two design examples based on the simulations performed on Cadence Xcelium simulator and the hardware implementation has been done on the Cadence Genus Synthesis solution at a clock frequency of 2 GHz. The proposed hardware model achieves a lower power and area profile compared to state-of-the-art architectures. © 2024 IEEE.

关键词： Systolic arrays

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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