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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-20页

作者： Islam, Md Mahamodul Das, Pobithra Rahman, Md Mahbubur Naz, Fasiha Kashem, Abul Nishat, Mosaraf Hosan Tabassum, Nujhat Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Leading University Sylhet Bangladesh Department of Industrial and Systems Engineering Lamar University Beaumont United States Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Civil and Environmental Engineering Shahjalal University of Science & amp Technology Sylhet Bangladesh Department of Civil and Environmental Engineering Islamic University Of Technology -IUT(OIC) Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Forecasting the compressive strength of high-performance concrete (HPC) is crucial for its practical applications. However, conducting experimental tests for this purpose demands significant resources and time. In recent times, data-driven modeling, particularly machine learning, has become prominent in this field. Thus, this study employed machine learning methods, including Gradient Boosting, Random Forest, and CatBoost algorithms, to predict the compressive strength of high-performance concrete (HPC). Furthermore, interpretable algorithms like SHapley Additive exPlanations (SHAP) were utilized in this study to assess the contribution of input variables to predictions. The dataset includes parameters such as cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, with compressive strength as the output variable for models development. The CatBoost model performance was superior to GBM and RF models, with R² values of 0.979 for the training stage and 0.959 for the testing stage. Moreover, it demonstrated minimal mean absolute errors (MAE) of 2.37 MPa and 3 MPa for compressive strength prediction in the training and testing stages, respectively. Furthermore, the SHAP analysis revealed that age, cement, and superplasticizer are the primary influencers in determining compressive strength. The machine learning models presented in this study exhibit promising potential for practical implementation in different engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Compressive strength High-performance concrete Machine learning SHAP analysis

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A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images

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Journal of Rock Mechanics and Geotechnical engineering 2023年第12期15卷 3105-3117页

作者： Shuai Zhao Guokai Zhang Dongming Zhang Daoyuan Tan Hongwei Huang Department of Civil and Environmental Engineering The Hong Kong Polytechnic University999077Hong KongChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai200092China

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

关键词： Crack segmentation Crack disjoint problem U-net Channel attention Position attention

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Design and Modeling of a Two-stage PV Inverter for Single Phase and Three Phase Applications

Design and Modeling of a Two-stage PV Inverter for Single Ph...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Shen, Yidi Ghaderloo, Reza Asrar Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

ISBN: (纸本)9798350316445

This paper presents the modeling and design of a 1kW two-stage photovoltaic (PV) inverter compatible with both single phase and three phase grid. The topology consists of a cascade of dual-active-bridge (DAB) based dc-dc stage and a two-level voltage source inverter (VSI) stage. Two-level VSI based dc-ac stage is designed and the modulation schemes are compared considering operation for both single phase and three phase output connection. A 3 leg interleaved hybrid pulse width modulation (3 leg-IHPWM) is proposed for single phase operation to reduce power loss and current total harmonic distortion (THD) with full utilization of the third switching leg. Sizing of dc link capacitance is analyzed for both single phase power decoupling and three phase load unbalance. Circuit design and component selection are conducted including dc link voltage selection, output filter sizing, DAB dc-dc stage transformer sizing and modulation optimization for zero-voltage-switching (ZVS) ability and control flexibility. The proposed design is verified through hardware prototype and experimental testing. © 2023 IEEE.

关键词： Single Phase Three Phase Two-stage Inverter Voltage Source Inverter

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Revolutionizing Personalized Health: The Frontier of Wearable Biomolecule Sensors Through 3D Printing Innovation

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Biomedical Materials and Devices 2024年 1-17页

作者： Rajendran, Jerome Esfandyarpour, Rahim Department of Electrical Engineering and Computer Science University of California Irvine 92697 CA United States Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of Engineering University of California Irvine 92697 CA United States

This review article delves into the innovative intersection of 3D-printed technologies and wearable chemical sensors, highlighting a forward-thinking approach to biomarker monitoring. It emphasizes the transformative role of additive manufacturing in the development of wearable devices tailored for the precise detection of chemical biomarkers, crucial for proactive disease management and health assessment. By offering a detailed exploration of how 3D printing of nanomaterials contributes to pioneering sensor designs, this review underscores the practicality of sensor wearability, ensuring comfort and efficacy for users. We address the challenges of material resilience, sensor durability, and efficient data communication, while also charting the significant trends and future directions that promise to redefine the landscape of flexible and wearable chemical sensors. Through a comprehensive analysis, this article aims to showcase the pivotal advancements and ongoing innovations in the field, emphasizing the critical impact of 3D printing on enhancing personalized healthcare and wearable diagnostics. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： 3D printing Biomarker monitoring Chemical sensors Flexible Personalized healthcare Wearable

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Reduced Sensor Control Approach for Active Power Decoupling Circuit in PV Microinverter Application

Reduced Sensor Control Approach for Active Power Decoupling ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Shen, Yidi Zakzewski, Daniel Hasnain, Arafat Resalayyan, Rakesh Khaligh, Alireza University of Maryland Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

ISBN: (纸本)9798350316445

Active power decoupling (APD) circuit is of great importance in single-phase photovoltaic (PV) inverter systems for eliminating the fluctuating power drawn from the PV panel. The control of APD circuit is the key to the power decoupling performance and maximum power point tracking (MPPT) efficiency. This paper proposes a control approach for parallel boost type APD circuit with a single sensor, reducing the count of sensors compared to the conventional APD control approach. Operation of the APD circuit with control approach utilizing only the readings from the decoupling capacitor voltage sensor is analyzed in this work. Specifically, controller design for the APD circuit with reduced sensor applicable to a 40 V, 400 W microinverter is presented and experimentally verified with a GaN-based prototype. © 2023 IEEE.

关键词： Active Power Decoupling Microinverter PV Reduced Sensor

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Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches 5

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Sultan Bin Habib, Al Zadid Bin Syed, Md Asif Islam, Md. Ekramul Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798350394290

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate;they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy. © 2023 IEEE.

关键词： Adaptive boosting

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Research on dangerous flight weather prediction based on machine learning 4

Research on dangerous flight weather prediction based on mac...

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2024 4th International Conference on computer, Remote Sensing and Aerospace, CRSA 2024

作者： Liu, Haoxing Xie, Renjie Qin, Haoshen Li, Yizhou Flight Department Shanghai Jixiang Airlines Co. Ltd. Shanghai201315 China Department of Computer & Information Science & Engineering Herbert Wertheim College of Engineering University of Florida Gainesville32611 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland44106 United States

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions. © Published under licence by IOP Publishing Ltd.

关键词： Radial basis function networks

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Live Demonstration: A fully embedded adaptive real-time hand gesture classifier leveraging HD-sEMG and deep learning

Live Demonstration: A fully embedded adaptive real-time hand...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Isabel, Xavier Labbe, Thomas Chamberland, Felix Buteau, Etienne Campbell, Evan Cote-Allard, Ulysse Tam, Simon Fortier, Paul Campeau-Lecours, Alexandre Boukadoum, Mounir Scheme, Erik Gosselin, Benoit Laval University Electrical Engineering and Computer Engineering Department Canada University of New-Brunswick Institute of Biomedical Engineering Canada University of Oslo Department off Technology Systems Norway Laval University Mechanical Engineering Department Canada University of Quebec at Montreal Computer Science Department Canada

ISBN: (纸本)9798350300260

In a fully immersive virtual reality setting, visitors will actively participate in our demonstration, engaging in an interactive videogame utilizing our cutting-edge HD-sEMG Bracelet. Prior to this, they will undergo state-of-the-art training to personalize their machine learning gesture recognition model. The gameplay will be broadcast on a computer monitor, allowing onlookers to observe while the demonstrator explains the core concept of our work. By wearing the bracelet, the player gains control over their limb in a game inspired by Simon Says. Throughout the demonstration, visitors will gain firsthand experience and knowledge about the forthcoming advantages of HD-sEMG for control applications, such as prosthesis control. They will also discover how the EMaGer HD-sEMG Bracelet capitalizes on its uniform and high electrode density to enhance the reliability of hand gesture recognition models utilized in myoelectric prosthesis control. © 2023 IEEE.

关键词： Armband Control Convolution Deep Learning Edge Computing Electromyography Gesture HD-EMG Machine Learning Myoelectric Neural Network

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Low-Cost, High-Performance Algorithm and Hardware Architecture for SAO Mode Decision 2

Low-Cost, High-Performance Algorithm and Hardware Architectu...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Hou, Bingjing Liu, Chang Zhang, Yuejun Huang, Leilei Jing, Minge Fan, Yibo Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China East China Normal University Institute of Microelectronic Circuits and Systems Shanghai China

ISBN: (纸本)9798331515669

Sample adaptive offset (SAO) is applied for reducing sample distortion and attenuating ringing artifacts in both HEVC and VVC standards. The rate-distortion optimization process is used to select the best SAO parameter. It accounts for the majority of SAO complexity and determines the SAO filtering performance. Therefore, the rate-distortion cost calculation should be well handled to achieve both low cost and high coding performance. In this paper, we propose a simplified and optimized rate estimation algorithm based on the characteristics of SAO parameter syntax and entropy coding process. And we propose a hierarchical flow for rate-distortion cost calculation to further reduce the computational cost and storage cost for SAO mode decision hardware. The results show that the proposed algorithm achieves an average YUV loss of -0.10%, and the proposed SAO mode decision hardware architecture can be implemented by only 21.8k gates using TSMC 65nm process under 400 MHz. © 2024 IEEE.

关键词： Electric distortion

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