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IEEE Instrumentation and Measurement technology Conference

作者： Herti Miawarni Tri Arief Sardjono Eko Setijadi Wijayanti Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Biomedical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Architecture Faculty of Engineering Universitas Diponegoro Semarang Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

Recently, many countries have been dealing with an increasing amount of elderly who are living alone. Unfortunately, fall incidents in the elderly tend to happen, and without proper dependable care would lead to fatal injuries. The elderly must be closely monitored under constant observation. Accidental falls are a leading cause of injury, mortality, and mobility limitations in the elderly. The expenses of national healthcare systems are also significantly impacted by accidental falls. These findings highlight the importance of investing heavily in the study and advancement of fall detection and intervention technologies. It is essential to recognize them early and provide fast assistance by developing technologies that can simultaneously improve the standard and the safety of the elderly's living environment. In this paper, we present a computer vision algorithm for falling incident detection using a low-cost camera and deep learning model. Ultimately, this algorithm can be used for the cases of elderly, in which falling risk is high and fatal. Furthermore, one self-developed dataset was used to validate the suggested approach experimentally. We used an OpenPose-based feature using a low-cost camera input and then classified each event or activity as either fall or non-fall. Classification tasks are carried out by Long Short-Term Memory (LSTM) with multiple validation metrics. The LSTM in this work is also optimized using Bayesian method. Finally, our approach to recognize falls has accuracy of 99.5%, which is distinct from other past works.

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Broadband Corrugated Modified Vivaldi Antenna for Microwave based Imaging Applications 24

Broadband Corrugated Modified Vivaldi Antenna for Microwave ...

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24th International Conference on computer and Information technology, ICCIT 2021

作者： Talukder, Md Siam Samsuzzaman, Md Paul, Liton Chandra Masud, Md Abdul Azim, Rezaul Moniruzzaman, Md Patuakhali Science and Technology University Faculty of Computer Science and Engineering Patuakhali Bangladesh Pabna University of Science and Technology Dept. of ElectricalElectronic and Communication Engineering Pabna Bangladesh University of Chittagong Dept. of Physics Chittagong Bangladesh Universiti Kebangsaan Malaysia Dept. of ElectricalElectronic and Systems Engineering Bangi Malaysia

ISBN: (纸本)9781665494359

This paper explored a Broadband Corrugated Modified Vivaldi Antenna for head imaging applications using microwaves. The antenna is made up of a tapered feeding circular slotted patch and a rectangular modified wing shape ground with an electrical dimension of $0.49\lambda\times 0.41\lambda\times 0.009\lambda$ at lower frequency of 1.67 GHz. Front and back radiator are placed on a substrate of FR-4 material having a permittivity of 4.3 and a thickness of 1.5 mm, which are less expensive and more widely accessible. The designed antenna works between 1.67 and 6.37 GHz, allowing the imaging system to function over a large fractional bandwidth of about 116.92 %. With an appropriate impedance matching and a directionally stable radiation characteristics, the antenna has a decent gain of 9 dBi. A 50$\Omega$ microstrip line feeds the antenna. The simulation tools CST was used to evolve and optimize the design. © 2021 IEEE.

关键词： Microstrip antennas

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Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

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IFAC-PapersOnLine 2022年第37期55卷 235-240页

作者： Jilles van Hulst Maurice Poot Dragan Kostić Kai Wa Yan Jim Portegies Tom Oomen Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology The Netherlands Center of Competency ASM Pacific Technology The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

关键词： Nonlinear system identification Identification for control Iterative learning control Data-based control Motion Control Applications in semiconductor manufacturing

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Recurrent Graph Neural Network-Based Identification of Replay Attacks in Power Networks

Recurrent Graph Neural Network-Based Identification of Repla...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Faaiz Joad Rachad Atat Hayat Mbayed Abdulrahman Takiddin Dept. of Electrical & Computer Engineering Florida State University Tallahassee FL USA Computer Science Department Lebanese American University Beirut Lebanon Center for Advanced Power Systems Florida State University Tallahassee FL USA

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

As cyber-physical systems, power networks exchange data to ensure smooth operation. Through communication networks, the exchanged data becomes vulnerable to cyberattacks where malicious entities establish malevolent connections to falsify the data, which exploit the data integrity, leading to network instability. Machine learning-based solutions have been proposed to identify such malevolent actions, including false data injection attacks. This paper aims to (a) enhance the attack detection performance and (b) quantify the performance of non-recurrent and recurrent machine learning-based attack detection systems against multiple replay attacks that involve falsifying data via presenting normal operation patterns from previous timestamps. It turns out that capturing temporal aspects from the data using recurrent models outperforms non-recurrent models by 5-32% in accuracy (ACC). Specifically, we propose a recurrent graph neural network (RGNN) model that captures the temporal and spatial aspects of the network data. The RGNN model outperforms non-recurrent models by 14 – 32% and other recurrent models by 6 – 13% in ACC. The experiments are conducted on IEEE 14, 39, and 118-bus power networks where the model performance is enhanced (by up to 8% in ACC) in larger systems due to capturing more spatial and temporal features.

关键词： Mechatronics Accuracy Data integrity Computational modeling Cyber-physical systems Data models Graph neural networks Communication networks computer crime

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Electromechanical behavior of red blood cells in insulator-based dielectrophoretic device

Electromechanical behavior of red blood cells in insulator-b...

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International electrical engineering Congress (iEECON)

作者： Kitipob Vijitnukoonpradit Nitipong Panklang Mati Horprathum Boonchai Techaumnat Dept. of Electrical Engineering Chulalongkorn University Bangkok Thailand Dept. of Electrical Engineering Rajamangala University of Technology Thanyaburi Pathumthani Thailand Spectroscopic and Sensing Devices Research Group National Electronic and Computer Technology Center (NECTEC) National Science and Technology Development Agency Pathumthani Thailand

We investigated the dielectrophoresis (DEP) of red blood cells by observing the critical frequencies that cause the cells to change between positive DEP to negative DEP. We fabricated a microfluidic device composed of a microchannel and electrodes. Red blood cells suspended in a conductive buffer were fed into the device and the DEP experiment was performed to determine the critical frequency of the red blood cells. Subsequently, the experimental critical frequencies were used to calculate electrical parameters, cytoplasmic conductivity and membrane capacitance, of the cells.

关键词： Performance evaluation Electrodes electrical engineering Red blood cells Cells (biology) Conductivity Capacitance

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Design and Control of a Two Switch Forward Converter with Supertwisting Sliding Mode

Design and Control of a Two Switch Forward Converter with Su...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Korhan Kayisli Ruhi Zafer Caglayan Resul Coteli Mariacristina Roscia Abdelkader Harrouz Abdelhakim Belkaid Electrical-Electronic Engineering Engineering Faculty Gazi University Ankara Turkiye Electrical- Electronic Engineering Engineering Faculty Computer Engineering Engineering Faculty Gazi University TED University Ankara Türkiye Dept. of Energy Systems Eng. Technology Faculty Firat University Elazig Turkiye University of Bergamo Bergamo Italy Dept. of Hydrocarbon and Renewable energy LDDI laboratory Ahmed Draia University Adrar Algeria Dept. of Electrical Engineering University of Bejaia Bejaia Algeria

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper presents the design and control of a two-switch forward converter utilizing a supertwisting sliding mode control (STSMC) strategy. The two-switch forward converter, known for its efficiency in medium-power applications, is equipped with a robust control method to ensure improved stability and dynamic performance under varying operating conditions. The supertwisting sliding mode approach is chosen for its ability to mitigate chattering effects, a common drawback in traditional sliding mode control, while ensuring precise tracking and disturbance rejection. Through comprehensive simulation, the proposed system demonstrates enhanced performance in terms of voltage regulation and improved transient response. The results indicate that the integration of STSMC significantly improves the converter's efficiency and robustness, making it suitable for various power conversion applications.

关键词： Robust control Transient response Renewable energy sources Simulation Switches Robustness Stability analysis Power conversion Voltage control Sliding mode control

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Optimizing Mobility IoT Device Networks with Dynamic RIS in mMIMO-Cooperative NOMA 6G systems

Optimizing Mobility IoT Device Networks with Dynamic RIS in ...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Khalil Hassan Mohamed Hassan Khalid Hamid Elmuntaser Hassan Rania A. Mokhtar Mamoon M. Saeed Dept. of Comm. Systems Uni. of Science & Technology Omdurman Sudan Dept. of Electrical Eng Omdurman Islamic Uni. Omdurman Sudan Dept. of Computer Science Al-Neelain University Khartoum Sudan Department of Computer Engineering Taif University Taif KSA Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Advanced technologies like reconfigurable intelligent surfaces (RISs), massive multiple-input multiple-output (mMIMO), and non-orthogonal multiple access (NOMA) are crucial for the upcoming 6G communications landscape, particularly for IoT applications. This study uniquely explores the effects of RIS dynamics on the performance of mMIMO downlink (DL) cooperative NOMA systems in dense IoT environments. It introduces a novel methodology for modeling dynamic traffic distribution and integrating RIS, focusing on key performance metrics such as network capacity, spectral efficiency (SE), latency, and energy efficiency (EE). Various scenarios are evaluated, considering different distributions of IoT devices and signal-to-noise ratios (SNRs). Using simulation software, the research analyzes performance under unstable channel conditions with varying device distances, speeds, and power locations, employing 256-QAM modulation and successive interference cancellation (SIC). The results demonstrate that dynamic RIS significantly enhances capacity, spectrum efficiency, latency, and EE, paving the way for optimized IoT network architecture in future wireless systems.

关键词： 6G mobile communication Wireless communication NOMA Interference cancellation System performance Reconfigurable intelligent surfaces Network architecture Dynamic scheduling Internet of Things Resource management

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Progressive Tokenization with Spectrogram Transformer for Enhanced Speaker Recognition and Noise Adaptation

SSRN

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SSRN 2024年

作者： Hamsa, Shibani Iraqi, Youssef Shahin, Ismail Damiani, Ernesto Werghi, Naoufel Center for Cyber-Physical Systems Dept. of ECE Khalifa University Abu Dhabi United Arab Emirates School of Computer Science Mohammed VI Polytechnic University Morocco Dept. of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

In the realm of audio signal processing using Artificial Intelligence (AI), our research addresses two critical challenges. Firstly, we confront the limitations posed by current spectrogram transformer topologies, particularly in handling emotional expressions and background noise during authentic discussions. We identify discrepancies between features extracted by the popular Audio Spectrogram Transformer (AST) and conventional Convolution Neural Network (CNN)-based spectrogram architectures, underscoring the shortcomings of spectrogram Transformers in capturing local features. To remedy this, we introduce progressive tokenization, facilitating the aggregation of neighboring tokens to effectively model local structural nuances. Secondly, we address the issue of speech comprehension in the presence of various concurrent sources, such as reverberation or crowd noise, without requiring additional denoising preprocessing. Traditional techniques reliant on learned source separation or denoising can introduce complexity and risk losing essential features. Our proposed solution, the Progresssive Token (P-Token) Spectrogram Transformer, adept.y handles these challenges with an end-to-end design, obviating the need for cumbersome preprocessing. This approach streamlines speech enhancement and speaker recognition tasks, demonstrating superior performance in demanding scenarios. Through methodical experimentation and rigorous evaluation, we substantiate the efficacy of the P-Token Transformer in speaker recognition within challenging acoustic environments. This transformative approach not only enhances accuracy but also offers practicality and resource efficiency, poised to redefine speaker recognition in real-world applications. © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

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Diagnosis of Epileptic Seizures in TLE Patients by Improved Cepstrum Analysis

Diagnosis of Epileptic Seizures in TLE Patients by Improved ...

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electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Bahar Tajadini Hani Attar Saeid Reza Seyd Nejad Soheila Rezakhani Reza Boostani CSE & IT Dept. CSE&IT Dept. Electrical and Computer Engineering Faculty Shiraz University Shiraz Iran Faculty of Engineering Zarqa University Zarqa Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia Electrical Engineering Dept. Electrical Engineering Dept. Shahid Bahonar University Kerman Iran Neurology Dept. Neurology Dept. Neuroscience Research Center School of Medicine Medical Sciences University Kerman Iran CSE & IT Dept. CSE&IT Dept. Electrical and Computer Engineering Faculty School of Engineering Shiraz University Shiraz Iran

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Epilepsy is a common chronic brain disease characterized by recurrent seizures. The most effective way to detection the arrival of an epileptic seizure is EEG analysis. Various methods have been proposed in the detection of epilepsy. The article proposes the combination of the AutoRegressive and Cepstrum methods, taking into account the periodic characteristics of the amplitude and frequency of epileptic seizures compared to seizure-free periods in brain signals. Once the signal is received and pre-processed, the AR model of the signal is calculated and the Cepstrum is obtained on the modeled signal, then the Cepstrum coefficients are averaged within the given interval, and a detection of epileptic seizure is made by thresholding the averaged coefficients. TLE patients were selected in this study due to their higher prevalence compared to other types of epilepsy. The proposed detection algorithm was examined on 11 EEG patients with temporal lobe epilepsy. The results indicate that the proposed method has an accuracy of 98.3% and a false positive rate of 0.5%, which demonstrates its superiority over existing methods, especially considering the presence of various artifacts in the available data. Moreover, owing to its low computational complexity, the proposed method can be integrated into real-time implant detection systems.

关键词： Temporal lobe Accuracy Sensitivity Epilepsy Cepstrum Training data Surgery

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A Decision-Dependent Chance-Constrained Planning Model for Distribution Networks Under Extreme Weather Events

A Decision-Dependent Chance-Constrained Planning Model for D...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Zhou, Anping Sun, Hongbo Kitamura, Shoichi Nikovski, Daniel Dept. of Electrical and Computer Engineering Southern Methodist University DallasTX75205 United States Data Analytics Group Mitsubishi Electric Research Laboratories CambridgeMA02139 United States Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki661-8661 Japan

ISBN: (纸本)9798350396782

Extreme weather events have posed tremendous challenges to the operation of distribution networks. In this paper, we propose a decision-dependent chance-constrained model for the optimal planning of diesel generators, renewable distributed generations (RDGs), energy storage systems, and switches under contingency. A promising moment-based ambiguity set that incorporates the information of decision variables is employed to depict the uncertainty arising from RDGs. By leveraging effective approximation methods such as the Bonferroni approximation method to handle the considered joint chance constraints, the proposed model is transformed into a tractable mixed-integer second-order conic programming problem, which means it can easily be implemented. Numerical experiments are put forward on the IEEE 33-bus test system to validate the effectiveness of the developed approach. © 2023 IEEE.

关键词： Chance constraint contingency distribution network distributionally robust optimization renewable energy

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