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Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Intelligent Automation & Soft Computing 2023年第3期35卷 2755-2771页

作者： T.Manikandan S.Karthikeyan J.Jai Jaganath Babu G.Babu Department of Electronics and Communication Engineering Rajalakshmi Engineering CollegeChennai602105India Department of Electronics and Communication Engineering Velammal Institute of TechnologyChennai601204India Center for System Design Chennai Institute of TechnologyChennai600069India Department of Bio-Medical Engineering Easwari Engineering CollegeChennai600089India

Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate *** a result,it needs an efﬁcient despeckling method for ultrasound images in clinical practice and *** article proposes a novel adaptive fuzzyﬁlter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy ***ﬁltering method comprises detection andﬁltering ***,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy ***,the system adaptively selects the better-suitedﬁlter for the speciﬁc image region,resulting in signiﬁcant speckle noise suppression and retention of detailed *** suggested approach uses a weighted averageﬁlter to distinguish between noise and edges at theﬁltering *** addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound *** proposed methodology shows that the proposed fuzzy adaptiveﬁlter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

关键词： Image processing fuzzy logic directional differences classiﬁcation ultrasound technology

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Design of a Comparator with Improved Noise, Delay Time and PSRR for a Single-Slope ADC

Design of a Comparator with Improved Noise, Delay Time and P...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Jang, Heon-Bin Kim, Yeong-Seok Cheon, Jimin Kumoh National Institute of Technology Department of Semiconductor System Engineering Korea Republic of

ISBN: (纸本)9798350371888

In this paper, we propose a comparator structure with improved noise, delay time, and power supply rejection ratio (PSRR) of a single-slope ADC used in CMOS image sensor (CIS). To improve the noise characteristics, a capacitor is inserted to reduce the bandwidth. However, the delay time and PSRR characteristics are worse. The proposed comparator uses a spilt-length transistor at the input of the comparator to improve the PSRR, and the PSRR is improved by isolating the capacitor from the power supply voltage and ground. Simulation results show that the proposed structure has improved PSRR and noise compared to the existing structures, and the delay time and layout area of the comparator are improved due to the smaller size of the capacitor. The proposed comparator is designed using a 0.18-μm CMOS process. © 2024 IEEE.

关键词： Comparators (optical)

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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

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Improved Artificial Neural Networks Based Whale Optimization Algorithm

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2023年第3期4卷 195-202页

作者： Ibrahem Alhayali, Royida A. Aladamey, Mostafa Khaled Abd Alrahman Subhi, Mohammed Rashid Mohammed, Mostafa Abdulghafoor Amir, Amiza Abdalkareem, Zahraa A. Department of Architecture Engineering College of Engineering University of Diyala Diyala Iraq Alimam Aladham University College Baghdad Iraq Department of Petroleum System Control Engineering College of Petroleum Processes Engineering Tikrit University Iraq Faculty of Engineering & Electronic Engineering Technology University Malaysia Malaysia

Owing to the increasing interest in artificial neural networks (ANNs) across various fields of study, many studies have focused on enhancing their performance through the utilisation of different learning algorithms. This study examines the use of the Whale Optimization Algorithm (WOA) as a training algorithm to improve the classification accuracy of ANNs. To achieve a high level of classification accuracy with ANN models, it is imperative to ensure that the model is appropriately designed in terms of the employed structure, training algorithm and activation function. In this work, WOA was adopted to train ANN models using 10 well-known datasets sourced from the UCI machine learning repository. The classification accuracy of a WOA-trained ANN was compared with that of a backpropagation-trained ANN, and the results showed that the WOA-trained ANN exhibited superior performance. © 2023 College of Education, Al-Iraqia University. All rights reserved

关键词： Neural networks

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Enhancing tensile properties of pulsed CMT–MIG welded high strength AA2014-T6 alloy joints: Effect of post weld heat treatment

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International Journal of Lightweight Materials and Manufacture 2024年第2期7卷 344-352页

作者： Rajendran, C. Sonar, Tushar Ivanov, Mikhail Sandeep, Ch. Shanthi, C. Gurajala, Naveen Kumar Balachandar, K. Xu, Jinyang Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore641008 India Chelyabinsk454080 Russia Department of Mechanical Engineering Institute of Aeronautical Engineering Telangana Hyderabad500043 India Department of Physics Sona College of Technology Tamil Nadu Salem636005 India Mechanical Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Department of Mechatronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

The main objective of this investigation is to study the effect of post weld heat treatments (PWHTs) on tensile properties, hardness, and microstructure of pulsed CMT–MIG (cold metal transfer arc–metal inert gas) welded AA20214-T6 aluminum alloy joints. The welded joints were subjected to PWHT of artificial aging (AA), solution annealing treatment (ST) and ST + aging (STA). The tensile properties and microhardness of joints were evaluated. The microstructure of joints was studied using optical microscope (OM) and transmission electron microscope (TEM). The fractured surface of tensile specimens was analyzed using scanning electron microscope (SEM). Results showed that the tensile properties and hardness of as welded and PWHT joints are inferior compared to base metal (BM). This mainly refers to the microstructural heterogeneity in different regions of joints and softening of heat affected zone (HAZ) induced by the weld thermal cycle. The PWHTs of AA and ST did not show significant effect on tensile strength and hardness of AW joints. However, the slight reduction in elongation was observed in AA and ST joints. The STA joints showed higher joint efficiency of 71.6%, than other joints by compromising on the elongation. This refers to the greater precipitation of hardening precipitates in STA joints compared to AA and ST joints. It exhibited the higher tensile and yield strength of 326 MPa and 266 MPa and the lowest elongation of 3.8%. The STA joints showed 28.35%, 38.28% and 57.77% reduction in tensile strength, yield strength and elongation compared to BM respectively. All the tensile specimens of joints failed in HAZ owing to the lower hardness. This refers to dissolution of precipitates in HAZ. However, the HAZ softening is less severe in STA joints than AW, AA, and ST joints. © 2023 The Authors

关键词： Microstructure

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A Two-Stage Approach for Electric Vehicle Routing Problem with Time Windows and Heterogeneous Recharging Stations

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Tsinghua Science and technology 2024年第5期29卷 1300-1322页

作者： Tianyu Luo Yong Heng Lining Xing Teng Ren Qi Li Hu Qin Yizhi Hou Kesheng Wang School of Electronic Engineering Xidian UniversityXi’an 71007China Beijing Institute of Electronic System Engineering Beijing 100854China School of Logistics and Transportation Central South University of Forestry and TechnologyChangsha 410004China School of Management Huazhong University of Science and TechnologyWuhan 430074China School of River and Ocean Engineering Chongqing Jiaotong UniversityChongqing 400074China Department of Mechanical and Industrial Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

An Electric Vehicle(EV)is an appropriate substitution for traditional transportation means for diminishing greenhouse gas ***,decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging *** resolve the former issue,several transportation companies increases the travel distance of the EV by establishing recharging stations in various *** proposed Electric Vehicle-Routing Problem with Time Windows(E-VRPTW)and recharging stations are constructed in this context;it augments the VRPTW by reinforcing battery capacity ***,super-recharging stations are gradually emerging in the *** can decrease the recharging time for an EV but consume more energy than regular *** this paper,we first extend the E-VRPRTW by adding the elements of super-recharging *** then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging ***,we compare the experimental results of this approach with other algorithms on several sets of benchmark ***,we analyze the impact of super-recharging stations on the total cost of the logistic plan.

关键词： Electric Vehicle(EV) vehicle routing Dynamic Programming(DP) two-stage algorithm

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Design of Flexible Fall Detection Wristband and Fall Notification system Considering Wearability for the Elderly

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Journal of Institute of Control, Robotics and systems 2023年第7期29卷 550-555页

作者： Hong, Jun-Ho Lim, Ji-Ho Park, Hong-Bum Shin, Dongwon Department of Mechanical Engineering Graduate School of Kumoh National Institute of Technology Korea Republic of Department of Mechanical System Engineering Kumoh National Institute of Technology Korea Republic of D-TRIPLE Co. Ltd

This paper proposes a wearable-based system that can respond quickly to possible elderly fall accidents in nursing homes. A wristband was used as a fall-detection device, and a design with improved user comfort and convenience was developed. Accordingly, a patient management system was designed to monitor patient condition in real-time, detent falls, and provide notifications for fast responses. The accuracy of fall detection was improved when acceleration and barometric pressure sensors were used. The performance of the wrist strap and patient management system was verified experimentally by confirming the fall detection and real-time monitoring results. © ICROS 2023.

关键词： Fall detection

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Formation Control and Collision Avoidance of Multiple USVs Based on Multi-agent Reinforcement Learning

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Journal of Institute of Control, Robotics and systems 2024年第12期30卷 1398-1405页

作者： Koo, Japyeong Kim, Kihun Jung, Jongdae Department of Naval Architecture and Ocean Engineering Chungnam National University Korea Republic of Ocean and Maritime Digital Technology Research Division Korea Research Institute of Ships and Ocean Engineering Korea Republic of Department of Autonomous Vehicle System Engineering Chungnam National University Korea Republic of

In this study, we developed a reinforcement learning-based approach for the cooperative navigation of unmanned surface vehicles (USVs) in marine environments. Specifically, we addressed the formation control and collision avoidance of USVs. To achieve this, we used a virtual leader to coordinate the reference position of each USV so as to maintain a specific formation shape. Additionally, we designed a dedicated reward function within a reinforcement learning framework to simultaneously maintain given formations and maximize collision avoidance. The models were then trained and tested in simulation environments with various obstacles. We evaluated three algorithms for formation control performance and obstacle avoidance: deep Q-network (DQN), multi-agent proximal policy optimization (MAPPO), and multi-agent deep deterministic policy gradient (MADDPG). The results showed that the MAPPO algorithm has higher success rates in collision avoidance and better formation maintenance compared with DQN and MADDPG. © ICROS 2024.

关键词： Unmanned surface vehicles

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Constructing a hybrid activation and parameter-fusion based CNN medical image classifier

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International Journal of Information technology (Singapore) 2024年第5期16卷 3265-3272页

作者： Maree, Mohammed Zanoon, Tareq Dababat, Alya’ Awwad, Mays Department of Information Technology Faculty of Information Technology Arab American University 13 Zababdeh Jenin Palestine Department of Computer System Engineering Faculty of Engineering Arab American University Jenin Palestine

In recent years, numerous deep learning models for medical image classification have emerged, with varying accuracies influenced by factors like image quality, content, and the convoluted low-level features. In this article, we aim to identify key features that enhance the precision of convolutional neural networks (CNNs), which are widely employed in this domain. We empirically demonstrate that parameters like activation functions, image augmentation, convolutional layers, filter sizes, and max pooling significantly impact precision, while others like hidden layers, epochs, and batch sizes affect quality and runtime negatively. To validate our proposal, we use a publicly-available dataset of colored Breast Cancer images which comprises 27,751 images distributed over three sub-datasets as follows: 4162 testing images (2980 no cancer and 1182 with cancer), 19,426 training images (13,911 no cancer and 5515 with cancer), and 4163 validation images (2981 no cancer and 1182 with cancer). Multiple experimental runs were conducted, and the outcomes of each run were recorded, taking into account various evaluation metrics and components of the CNN architecture. Results demonstrate that the highest CNN classifier’s accuracy was achieved when coupling RelU and Sigmoid activation functions using a 3 × 3 filter with a batch of size 32 and 20 epochs and 2 hidden layers. A Similar accuracy rate (83.55%) has also been achieved when coupling ReLU and Sigmoid activation functions using the DensNet-201 pre-trained CNN model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Breast cancer CNN Image classification Multi-level architecture Neural networks Parameter fusion

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