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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Benaissa, Mohamed Chaabane, Abdelhalim Aissaoui, Djelloul Attia, Hussein Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Science and Technology Djelfa17000 Algeria Université 8 Mai 1945 Guelma Laboratoire des Télécommunications-LT BP 401 Guelma24000 Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350309249

This study presents an innovative ultra-miniaturized antenna system designed for 2.45 GHz ISM band operation, tailored to the distinct requirements of biomedical implants. The proposed antenna boasts an exceptional level of compactness, with a mere 3.3 mm3 in volume and physical dimensions of 3 mm × 3 mm × 0.37 mm. In a three-layer body model incorporating skin, fat, and muscle, the antenna system achieves an impressive peak gain of -25.4 dB at 2.45 GHz while maintaining a remarkable impedance bandwidth of -10 dB, spanning 33.8%. This accomplishment is particularly noteworthy, given the antenna's compact design. Additionally, it exhibits outstanding omnidirectional radiation patterns. Furthermore, an evaluation and analysis of the antenna's specific absorption rate (SAR) are conducted. These results emphasize the practicality and potential applications of this antenna system within biomedical devices. © 2024 IEEE.

关键词： Microwave antennas

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Motion Detection Method of Electrostatic MEMS Resonators Operating in Aqueous Media

Motion Detection Method of Electrostatic MEMS Resonators Ope...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Mohamed Hemid Basil Alattar Mehdi Ghommem Alaaeldin Ahmed Eihab Abdel-Rahman Rana Sabouni Mechatronics Graduate Program American University of Sharjah Sharjah UAE Mechanical Engineering Department American University of Sharjah Sharjah UAE Systems Design Engineering University of Waterloo Ontario Canada Chemical Engineering Department American University of Sharjah Sharjah UAE

This paper introduces an experimental method to detect the motion of an electrostatic Micro-Electro-Mechanical systems (MEMS) resonator in aqueous media. The resonator comprises a micro cantilever beam subject to electrostatic actuation through a side electrode. A Finite Element Method (FEM) model of the resonator is developed to determine the in-plane mode shapes and their natural frequencies in order to facilitate the experimental study. The motion of the resonator lead to variations in its capacitance and induce a current. The developed experiments demonstrate that motion-induced current can be measured and analyzed to detect the motion of the resonator's higher-order modes and can be used in chemical sensing.

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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards

Safe CoR: A Dual-Expert Approach to Integrating Imitation Le...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Hyeokjin Kwon Gunmin Lee Junseo Lee Songhwai Oh Interdisciplinary Program in Artificial Intelligence and ASRI Seoul National University Seoul Korea Department of Electrical and Computer Engineering and ASRI Seoul National University Seoul Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as complex driving situations. To overcome these challenges, we present the safe constraint reward (Safe CoR) framework, a novel method that utilizes two types of expert demonstrations—reward expert demonstrations focusing on performance optimization and safe expert demonstrations prioritizing safety. By exploiting a constraint reward (CoR), our framework guides the agent to balance performance goals of reward sum with safety constraints. We test the proposed framework in diverse environments, including the safety gym, metadrive, and the real-world Jackal platform. Our proposed framework improves algorithm performance by 39% and reduces constraint violations by 88% on the real-world Jackal platform, highlighting its effectiveness. Through this innovative approach, we expect significant advancements in real-world performance, leading to transformative effects in the realm of safe and reliable autonomous agents.

关键词： Navigation Imitation learning Focusing Reinforcement learning Autonomous agents Safety Reliability Optimization Intelligent robots Faces

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Millennials' social media-driven online entrepreneurship: Assessing their aspirations for multi-level marketing jobs 24

Millennials' social media-driven online entrepreneurship: As...

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Proceedings of the 10th International Conference on Industrial and Business engineering

作者： Ahmad Sattam Alhawamdeh Ierenne Novendah Yogi Tri Prasetyo Reny Nadlifatin Maela Madel L. Cahigas Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan China International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan China Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia School of Industrial Engineering and Engineering Management Mapua University Manila Philippines

ISBN: (纸本)9798400710742

Abstract: With the development of Internet technology and the popularization of mobile devices and so on, e-commerce has entered a period of high-speed development, consumers are faced with a wealth of channel choices, and the competition for traffic in different channels is becoming more and more intense, which poses a great challenge to enterprises. From the perspective of PPM theoretical model to study the consumers' migration intention of different online channels, using SPSS software and Amos to construct structural equations for the influencing factors of consumers' migration intention of different online channels, the results show that the push force and pull force positively affect the consumers' migration intention, and the push force has the greatest influence on the consumers' migration intention. It provides a basis for the live e-commerce platform to propose measures to enhance user channel stickiness.

关键词： Consumers

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Per- and polyfluoroalkyl substances behavior: Insights from autothermal thermophilic aerobic digestion - Storage nitrification-denitrification reactors

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Chemosphere 2024年 365卷 143357页

作者： Alukkal, Caroline Rose Lee, Linda S. Staton, Kevin Interdisciplinary Ecological Sciences & Engineering Purdue University West LafayetteIN United States Department of Environmental & Ecological Engineering Purdue University West LafayetteIN United States Department of Agronomy Purdue University West LafayetteIN United States Thermal Process Systems Crown PointIN United States

Per- and polyfluoroalkyl substances (PFAS) have emerged as significant environmental contaminants due to their persistence, bioaccumulative properties, and potential adverse impacts on health and ecosystems. Water Resource Recovery Facilities (WRRFs) play a crucial role in the management of PFAS, given their widespread presence in consumer products and subsequent reintroduction into the environment. This study investigated the dynamics of PFAS within the solids stream treatment processing that utilized autothermal thermophilic aerobic digestion (ATAD) followed by a storage nitrification-denitrification reactor (SNDR). PFAS analysis included 60 PFAS analyzed via liquid chromatography-triple quadrupole time-of-flight mass spectrometry of pre-ATAD, post-ATAD, and post-SNDR samples. Complexities such as volatile solids loss during the treatment processes were considered in assessing the effect of ATAD and SNDR on PFAS concentrations. Significant changes were observed in the relative contributions of various PFAS classes throughout the treatment processes due to biotransformation;similar changes were reflected in both 2019 and 2021. The relative contribution of perfluoroalkyl alkyl acids (PFAAs) increased while phosphorus-containing PFAS (e.g., di-substituted polyfluoroalkyl phosphate esters) and fluorotelomer carboxylic acids decreased. Shorter-chain PFAAs were enriched during ATAD, whereas most PFAS increased during SNDR except diPAPs and FTCAs, reflecting treatment conditions' impact. Overall, minor decreases in total PFAS concentrations during ATAD as well as SNDR were observed and hypothesized to be due to enhanced biotransformation to ultra-short PFAS that were not quantified. Even with up to 60 PFAS quantified in the samples, PFAS accounted for © 2024

关键词： Nitrification

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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Risk Management Practices using the House of Risk for Safety, Health, etc.(HORshe) Method: A Case Study on Wastewater Treatment in the Pharmaceutical Industry

Risk Management Practices using the House of Risk for Safety...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Aswin Pasaribu Adithya Sudiarno Interdisciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Industrial and Systems Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

The pharmaceutical industry in Indonesia is expanding rapidly despite environmental challenges. PT. XYZ, a major player, faces significant difficulties in managing liquid waste. The current Liquid Waste Disposal Permit (IPLC) is deficient, inadequately addressing domestic wastewater quality standards and broader environmental sustainability measures. A comprehensive legislative framework covering all technical and environmental aspects, including household wastewater, is necessary. This study introduces the House of Risk for Safety, Health, etc. (HORshe) approach, an advanced model integrating methodologies from HOR (House of Risk), HoQ (House of Quality), FMEA (Failure Mode and Effect Analysis), and HIRA (Hazard Identification and Risk Analysis). HORshe expands risk management to include safety, health, and environmental considerations, emphasizing preventive measures to mitigate risk agents. The research utilizes the HORshe model to identify effective wastewater control strategies through qualitative interviews and Focus Group Discussions (FGDs). The study's significance lies in its application to pharmaceutical industrial waste management, addressing critical environmental risks and consumer safety concerns. The objective is to refine risk analysis across multiple sectors using the HORshe model and contribute to future research. The results suggest 11 preventive measures for 6 key risk factors out of a total of 12, with ultrafiltration identified as the most crucial preventive strategy, while reverse-osmosis control systems are deemed less significant. This highlights the research's impact on sustainable waste disposal practices in the pharmaceutical industry and other domains, providing a foundation for future improvements in environmental risk management.

关键词： Costs Liquids Green products Control systems Risk management Wastewater Waste disposal Interviews Standards Pharmaceuticals

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Modeling and Assessing the Performance of a Low-Concentration Photovoltaic System

Modeling and Assessing the Performance of a Low-Concentratio...

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Sustainable Power and Energy Conference (iSPEC)

作者： Mohammad Azad Alam Faisal Masood Kashif Irshad Mohammed Zayed Javed Akhter Shafiqur Rehman Interdisciplinary Research Centre for Sustainable Energy Systems (IRC-SES) Research Institute King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Department of Electrical Engineering University of Engineering and Technology Taxila Rawalpindi Pakistan Mechanical Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Mechanical Engineering Department University of Engineering and Technology Taxila Pakistan

ISBN: (数字)9798350395075

ISBN: (纸本)9798350395082

One of the most promising renewable energy sources that could help solve a lot of the problems the current world faces is solar energy. Low-concentration photovoltaic (LCPV) systems offer significant advantages, including reduced unit costs and improved efficiency in solar power generation compared to non-concentrated photovoltaic modules. This study introduces a performance prediction model for an LCPV system that incorporates a crystalline silicon solar module positioned at the outlet aperture of a compound parabolic concentrator (CPC). Designed for a concentration level of 2.5x, the CPC serves as the concentrating element. The proposed LCPV system is modeled and simulated using MATLAB to evaluate variations in output power, short-circuit current, and open-circuit voltage under changing solar irradiance and temperature conditions. The current-voltage and power-voltage characteristics curves have been drawn for different incident solar irradiances and temperatures. The extreme power point has been identified, and the corresponding values of voltage and current have been recorded. It has been found that the open-circuit voltage rises logarithmically with increasing light intensity, whereas the short-circuit current grows linearly with sunlight concentration. It is further examined that lower module temperature resulted in higher maximum power and larger open-circuit voltage, which has a profound effect on system performance. Higher irradiance levels resulted in larger currents while voltage remained relatively constant. The developed model can be adequately used for electrical characterization and performance estimation of LCPV systems under diverse operating conditions.

关键词： Solar irradiance Temperature Short-circuit currents Voltage Predictive models Mathematical models Silicon Solar panels Compounds MATLAB

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Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

Random suppression of ECG artifacts using a NLSRLMMN-based c...

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Asilomar Conference on Signals, systems & Computers

作者： Mohammed Mujahid Ulla Faiz Azzedine Zerguine Izzet Kale Department of Electrical and Computer Engineering Presidency University Bengaluru Karnataka India Electrical Engineering Department Interdisciplinary Research Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Department of Computer Science and Engineering Applied DSP and VLSI Research Group (ADVRG) University of Westminster London United Kingdom

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages.

关键词： Computers Adaptation models Adaptive systems Mean square error methods Interference Electrocardiography Muscles Noise cancellation Signal to noise ratio Motion artifacts

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