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SSRN 2025年

作者： Mian Qaisar, Saeed Say, Dalila Krichen, Moez Zidi, Salah Hatem Bettaher Laboratory IResCoMath University of Gabes Gabes6029 Tunisia Electrical and Computer Engineering Department Effat University Jeddah21478 Saudi Arabia Faculty of Computer Science and Information Technology Al-Baha University ReDCAD Laboratory National School of Engineers of Sfax University of Sfax Al-Baha65528 Saudi Arabia

Non-Destructive Testing (NDT) is an appealing technique for confirming the welding quality and ensuring structural integrity without causing damage. It assists in regulatory compliance, risk mitigation, and process optimization for improved industrial reliability. This study reviews potential NDT approaches. Through a rigorous analysis of existing surveys, we aim to decipher the current landscape and highlight the significant advancements in the field. Because of the potential of artificial intelligence (AI) assistive X-ray imaging-based NDT, we particularly examine the integration of AI algorithms and X-ray imaging in the NDT of welds. This convergence represents a paradigm shift, redefining traditional methods and ushering in a new era of precision, automation, and efficiency. As we navigate through potential X-ray imaging datasets, delve into crucial image processing techniques, examine feature extraction methods, and explore AI algorithms, our survey reveals the intricate interplay of technologies that drive automated weld defect categorization. The review broadens its focus by including various practical applications, highlighting the adaptable utility of AI-assistive X-ray imaging for weld defect detection in potential industrial applications. Moreover, the promising opportunities and nuanced challenges associated with integrating X-ray imaging and AI in weld integrity assessment are highlighted. It provides a comprehensive perspective on this rapidly evolving field. © 2025, The Authors. All rights reserved.

关键词： Fracture mechanics

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Optimal scheduling of ESS in distribution system with multiple DG 13

Optimal scheduling of ESS in distribution system with multip...

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13th International Conference on electrical and Electronics engineering, ELECO 2021

作者： Tunçel, Suleyman Seker, Ayse Aybike Oskouei, Morteza Zare Gozel, Tuba Mohammadi-Ivatloo, Behnam Abapour, Mehdi Gebze Technical University Department of Electronics Engineering Kocaeli41400 Turkey Department of Electrical and Electronics Engineering Iskenderun Technical University Ratay 31200 Turkey Smart Energy Systems Laboratory University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9786050114379

Increasing tendency to highly penetrate distribution networks with renewables due to environmental concerns, has lead distribution network operators to establish energy storage systems (ESS) for obtaining required flexibility. In this paper, the role of a widely used ESS, namely, compressed-air energy storage (CAES) is evaluated considering hosting distributed generation capacity of network. A deterministic scheduling strategy is proposed to minimize cost of operation and maintenance of network in presence of PV and wind generation as renewable sources. By utilization of CAES, renewable energy curtailment has been reduced while total electricity cost injected from the grid is minimized. Optimal charge and discharge scheduling of CAES has allowed excess renewable energy to be stored and then injected to the network on peak hours in order to reduce cost by decreasing power grid dependency on peak hours. © 2021 Chamber of Turkish electrical Engineers.

关键词： Compressed air

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Information-Bearing RIS Enabled NOMA System

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Shuai Sun, Zeyang Xu, Sai Xie, Dan Li, Cheng Harbin Institute of Technology School of Electronics and Information Engineering Heilongjiang Harbin150001 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Memorial University Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sA1B 3X5 Canada

In this correspondence, we investigate an information-bearing reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) system. In this system, the RIS is segmented into multiple sub-RIS units, each serving as a passive transmitter to deliver information to users via extended backscatter modulation under the NOMA scheme. We aim to maximize the system's weighted sum rate (WSR) by jointly optimizing the active beamformer at the power beacon (PB) and the RIS coefficient vectors, while adhering to constraints related to PB transmission power, RIS coefficients, successive interference cancellation (SIC) decoding rate, and rate fairness. Extensive simulation results verify the feasibility and superiority of the proposed scheme. © 2025 IEEE.

关键词： Beamforming

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Distantly Supervised Relation Extraction Based on Multi-head Self-attention and Gate Mechanism 14th

Distantly Supervised Relation Extraction Based on Multi-head...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Xu, Fengzhu Li, Meijing Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh700000 Viet Nam Biomedical Engineering Institute Chiang Mai University Chiang Mai50200 Thailand Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811684296

In the field of distant supervision relation extraction, PCNN (piecewise convolution neural network) is normally involved to trap local features of sentences, and has achieved good results. However, the existing PCNN-based methods are unable to capture the features of long-distance interdependence in sentences and cannot distinguish the influence of the three segments of PCNN on relation classification, which probably results in missing some vital information. To address the above two issues, we come up with a new model with multi-head self-attention and gate mechanism for distant supervised relation extraction. Firstly, to capture the features of long-distance interdependence in sentences, we employ an internal multi-head self-attention in PCNN model, which can grab information in different representation subspaces. Secondly, to distinguish the influence of the three segments of the piecewise max-pooling output on the relation classification, the gate mechanism is introduced to assign different weights to the three segments and highlight important segments. After a series of experiments, it is proven that the model we presented is better than the previous approaches on each evaluation criterion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Temporal Feature Matters: A Framework for Diffusion Model Quantization

arXiv

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arXiv 2024年

作者： Huang, Yushi Gong, Ruihao Liu, Xianglong Liu, Jing Li, Yuhang Lu, Jiwen Tao, Dacheng Electrical and Computer Engineering Department Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Software Development Environment Beihang University China Department of Data Science and AI Faculty of IT Monash University Australia Electrical Engineering Department Yale University United States Department of Automation Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore

The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. Efficient Post-Training Quantization (PTQ) is crucial to address these issues. However, unlike traditional models, diffusion models critically rely on the time-step for the multi-round denoising. Typically, each time-step is encoded into a hypersensitive temporal feature by several modules. Despite this, existing PTQ methods do not optimize these modules individually. Instead, they employ unsuitable reconstruction objectives and complex calibration methods, leading to significant disturbances in the temporal feature and denoising trajectory, as well as reduced compression efficiency. To address these challenges, we introduce a novel quantization framework that includes three strategies: 1) TIB-based Maintenance: Based on our innovative Temporal Information Block (TIB) definition, Temporal Information-aware Reconstruction (TIAR) and Finite Set Calibration (FSC) are developed to efficiently align original temporal features. 2) Cache-based Maintenance: Instead of indirect and complex optimization for the related modules, pre-computing and caching quantized counterparts of temporal features are developed to minimize errors. 3) Disturbance-aware Selection: Employ temporal feature errors to guide a fine-grained selection between the two maintenance strategies for further disturbance reduction. This framework preserves most of the temporal information and ensures high-quality end-to-end generation. Extensive testing on various datasets, diffusion models and hardware confirms our superior performance and acceleration. © 2024, CC BY.

关键词： Spatio-temporal data

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Optimal Design to Dual-Scale Channel Estimation for Sensing-Assisted Communication Systems

arXiv

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arXiv 2024年

作者： Bai, Zhiyue Hou, Fen Cai, Lin X. Shan, Hangguan State Key Laboratory of IOTSC Department of ECE Faculty of Science and Technology University of Macau China Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States

Sensing-assisted communication is critical to enhance the system efficiency in integrated sensing and communication (ISAC) systems. However, most existing literature focuses on large-scale channel sensing, without considering the impacts of small-scale channel aging. In this paper, we investigate a dual-scale channel estimation framework for sensing-assisted communication, where both large-scale channel sensing and small-scale channel aging are considered. By modeling the channel aging effect with block fading and incorporating CRB (Cramér-Rao bound)-based sensing errors, we optimize both the time duration of large-scale detection and the frequency of small-scale update within each subframe to maximize the achievable rate while satisfying sensing requirements. Since the formulated optimization problem is non-convex, we propose a two-dimensional search-based optimization algorithm to obtain the optimal solution. Simulation results demonstrate the superiority of our proposed optimal design over three counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Instantaneous Creatinine Testing in Urine Using a Modified Bifilar Helix Antenna

Instantaneous Creatinine Testing in Urine Using a Modified B...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Rayan AlSayed Ali Joseph Costantinc Youssef Tawk Rouwaida Kanj Fatima Asadallah Joanna Ghaddar Assaad A. Eid Nassim Fares Dania Chelala Electrical and Computer Engineering Department Maroun Semaan Faculty of Engineering and Architecture American University of Beirut Beirut Lebanon Department of Anatomy Cell Biology and Physiological Sciences Faculty of Medicine American University of Beirut Physiology and Pathophysiology Research Laboratory Faculty of Medicine Saint Joseph University Beirut Lebanon Department of Nephrology Faculty of Medicine Saint Joseph University Beirut Lebanon

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

In this paper, we propose a highly sensitive, chemical-free bifilar helix antenna that is dedicated to the instantaneous assay of creatinine in urine. At its core, the proposed antenna structure leverages the behavior of the transmitted and reflected electromagnetic waves propagating through a sample under test and captures the changes in the scattering parameters caused by variations in the composition of the loading sample. To accentuate the sensitivity of the bifilar helix towards changes in the creatinine levels, a slotted cylinder surrounding the sample holding a plastic polyactic acid (PLA) tube is added. The recorded changes in the scattering parameters are subjected to machine learning and data analytics techniques to translate these changes into creatinine level estimations. The proposed resulting sensor is validated on artificial urine as well as animal and human urine samples. Testing conducted on urine samples collected from rats demonstrated high sensitivity towards changes in the creatinine composition. Moreover, clinical measurements on human urine samples reported high accuracy of 93%. These results reveal the outstanding capability of the sensor to provide instantaneous creatinine screening.

关键词： Helical antennas Sensitivity Data analysis Accuracy Split ring resonators Transmitting antennas Machine learning

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Minute Level Ultra-Rapid and Thousand Copies Level Ultra-Sensitive Pathogen Nucleic Acid Identification Based on Contactless Impedance Detection Microsensor

SSRN

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SSRN 2024年

作者： Zhang, Zhikang Yuan, Haojun Yin, Jiawen Li, Min Cao, Xinyi Gao, Wanlei Jin, Qinghui The Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo315211 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Nucleic acid testing (NAT) is a valuable method for keeping pathogens from spreading. However, the long detection time and large size of the instruments involved significantly limit the efficiency of detection. This work describes an integrated NAT micro-sensor that facilitates rapid and extremely sensitive detection based on nucleic acid amplification (NAA) on a chip. The biochip consists of two layers incorporating a heater, a thermometer, an interdigital electrode (IDE) and a reaction chamber. The heater and thermometer are utilized to maintain a specific temperature for the sample in the chamber. The thermometer exhibits a good linear correlation with a sensitivity of 9.36 Ω/°C and the heater achieves a heating efficiency of approximately 6.5 °C/s. IDE is employed for real-time monitoring of the in-chip reaction system impedance and NAA process. The IDE demonstrates strong linearity with a sensitivity of -1.20 kΩ/mM. Specific nucleic acids from two pathogens (SARS-CoV-2, Vibrio vulnificus) are detected with this micro-sensor. The samples are qualitatively analyzed on microchip within 3 minutes, with a limit of detection (LOD) of 103 copies/μL. The proposed sensor presents several advantages, including reduced NAT time and increased sensitivity. Consequently, it reveals significantly potential in the field of epidemic prevention. © 2024, The Authors. All rights reserved.

关键词： Nucleic acids

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