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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Kandregula, Venkat Reddy Zaharis, Zaharias D. Ahmed, Qasim Zeeshan Khan, Faheem A. Hafeez, Maryam Loh, Tian Hong Chochliouros, Ioannis P. Lazaridis, Pavlos I. School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki541 24 Greece Electromagnetic & Electrochemical Technologies Department National Physical Laboratory Middlesex TeddingtonTW11 0LW United Kingdom Marousi151 24 Greece

ISBN: (纸本)9783031494123

With continuous improvements in wireless products, there is an increasing demand for radio frequency (RF) measurement equipment. Wireless communication relies heavily on antennas, and measuring their performance when deployed in the field is essential to understand the coverage provided. To analyze the performance of the antenna when deployed in the field, the radiation patterns of the antenna were measured. Measuring the radiation patterns of an antenna involves measuring the strength of the electric field in the far-field Fraunhofer region. Spectrum analyzers are widely used to measure the strength of an electric field. Standard spectrum analyzers are heavy and bulky. On the other hand, we propose portable and lightweight spectrum analyzers, such as the Tiny SA Ultra, and the Signal Hound SA44B. The performance of both these small size spectrum analyzers was validated using a signal generator. The performance of SA44B was validated by measuring the radiation patterns of a Printed Log-Periodic Antenna (PLPDA). Measurements were performed at ground level in a sports hall at 800 MHz while maintaining a far-field distance of 30 m. The radiation patterns of the PLPDA were measured with a good accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Log periodic antennas

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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On Shaping Gain of Multidimensional Constellation in Linear and Nonlinear Optical Fiber Channel

arXiv

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arXiv 2024年

作者： Chen, Bin Liang, Zhiwei Lei, Yi Deng, JingXin Li, Shen Liga, Gabriele School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province China Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering Université Laval Québec Canada Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating their performance in arbitrary transmission systems. In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system. In order to extend the applicability of this method to a broader set of modulation formats, we extend the established NLI model to take the 4D joint distribution into account and thus able to analyze the complex interactions of non-iid signaling in DP systems. With the help of the NLI model, we conduct a comprehensive analysis of the state-of-the-art modulation formats and investigate their actual shaping gains in two types of optical fiber communication scenarios (multi-span and single-span). The numerical simulation shows that for arbitrary modulation formats, the NLI power and relative shaping gains in terms of signal-to-noise ratio can be more accurately estimated by capturing the statistics of MD symbols. Furthermore, the proposed method further validates the effectiveness of the reported NLI-tolerant modulation format in the literature, which reveals that the linear shaping gains and modulation-dependent NLI should be jointly considered for nonlinearity mitigation. © 2024, CC BY-NC-SA.

关键词： Optical fibers

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Disturbance Rejection Control for PMSGs Based on Novel Sliding Mode Control Law and Unknown System Disturbance Estimator

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IEEE Transactions on Energy Conversion 2025年

作者： Wei, Chun Yin, Tianqi Qiao, Wei Zhu, Ying Zhejiang University of Technology College of Information Engineering Zhejiang Hangzhou310023 China University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588 United States Hohai University School of Electrical and Power Engineering Nanjing211100 China

This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities. © 1986-2012 IEEE.

关键词： Sliding mode control

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

引用

arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance

Employing Feature Engineering for River Stage Forecasting to...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： Measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

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An Artificial Intelligence Approach for Virtual Prototyping of Functional Situational Awareness systems for Hybrid Military Vehicles

An Artificial Intelligence Approach for Virtual Prototyping ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Deborah George Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Situational awareness in complex systems is important for their reliable and efficient operations. Complex systems consist of components/nodes and subsystems. Functional situational awareness (FSA) is focused on providing qualitative and quantitative functional capabilities of components, subsystems, and the system over time. Safe-/mission-critical systems require FSA in real-time. The development of an artificial intelligence (AI)-based FSA system inferencing tool, herein, referred to as an FSA inference system development engine (FIDE), tailored to hybrid military ground vehicles (HMGVs) is presented. FIDE can be utilized to generate preliminary designs, virtual prototyping of FSA and its evaluation on actual HMGVs as well virtual vehicle testbeds. FIDE consists of four modules, namely, i) intelligent knowledge acquisition, ii) FSA topology model, iii) FSA engine and iv) human-computer interface (HCI). The development and operation of these modules is presented. FIDE generated FSA systems can ensure reliable and efficient operation of Vehicle Power systems (VPSs). Case studies are presented for virtual prototypes of an FSA engines and HCIs using FIDE.

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Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

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International Journal of Mining Science and Technology 2022年第2期32卷 309-322页

作者： Jiayao Chen Hongwei Huang Anthony G.Cohn Dongming Zhang Mingliang Zhou Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai 200092China School of Computing University of LeedsLS29JT LeedsUnited Kingdom Department of Computer Science and Technology Tongji UniversityShanghai 211985China School of Civil Engineering Shandong UniversityJinan 250061China Luzhong Institute of Safety Environmental Protection Engineering and MaterialsQingdao University of Science and TechnologyZibo 255000China School of Mechanical and Electrical Engineering Qingdao University of Science and TechnologyQingdao 260061China

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image *** data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace *** is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:***,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance *** results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace ***,discussions on feature importance,generalization ability and classification error are conducted for the proposed *** experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity ***,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification *** proposed method provides a new alternative approach for the identification of 3D rock trace.

关键词： Tunnel face Rock discontinuity trace Machine learning Gradient boosting tree Generalization ability

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