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61st International Scientific Conference on Experimental Stress Analysis, EAN 2023

作者： Nieslony, Adam Czekaj, Karol Mazurek, Bartosz Opole University of Technology Faculty of Mechanical Engineering Department of Mechanics and Machine Design Prószkowska Opole Poland ZF CV Systems Poland Ostrowskiego 34 Wroclaw Poland Science and Technology Park in Opole Technologiczna 2 Opole Poland

ISBN: (纸本)9788055336770

Fatigue failure remains a major concern in the design and performance evaluation of machine components and structures as it accounts for a significant proportion of mechanical failures. This article presents a fatigue evaluation methodology based on SN (stress-cycles to failure) curves to understand and predict the fatigue behavior of components under various loading conditions with widely varying device geometry and dynamics. In order to accurately interpret and utilize the SN curves, the paper outlines key factors influencing material fatigue, including stress amplitude, mean stress, stress concentration, environmental effects, and surface finish. The integration of these factors into the SN curve-based assessment is discussed to tailor fatigue evaluations to specific machine components and structures. To demonstrate the practical application of SN curves in fatigue assessment two case studies of machine components and structures are presented. The paper ends with a summary and conclusions, the most important of which is that the greatest impact on the design fatigue life has accurately estimated stresses resulting from the load conditions and the dynamics of the structure. © 2024 EAN 2023 - 61st Conference on Experimental Stress Analysis, Proceedings of Full Papers. All rights reserved.

关键词： Stress analysis

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Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

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Computer systems science & engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Saurabh Suman Utkarsha Pacharaney Vishal Kumar Choudhary Rahul Raushan Abhishek Kumar Gulshan Kumar Department of Computer Science and Design Faculty of Engineering and Technology Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Wardha India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed.

关键词： Industries Education Medical services Data processing Data models Robustness Splines (mathematics) Optimization Hyperspectral imaging Image classification

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Development of Machine Learning-Based Assessment system for Laparoscopic Surgical Skills Using Motion-Capture

Development of Machine Learning-Based Assessment System for ...

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2024 IEEE/SICE International Symposium on system Integration, SII 2024

作者： Ebina, Koki Abe, Takashige Yan, Lingbo Hotta, Kiyohiko Highuchi, Madoka Iwahara, Naoya Furumido, Jun Kon, Masafumi Murai, Sachiyo Kurashima, Yo Komizunai, Shunsuke Tsujita, Teppei Sase, Kazuya Chen, Xiaoshuai Senoo, Taku Shinohara, Nobuo Konno, Atsushi Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Hokkaido University Faculty of Medicine Department of Renal and Genitourinary Surgery Sapporo Japan Hokkaido University Clinical Simulation Center Faculty of Medicine Sapporo Japan Kagawa University Faculty of Engineering and Design Takamatsu Japan National Defense Academy of Japan Department of Mechanical Engineering Yokosuka Japan Tohoku Gakuin University Department of Mechanical Engineering and Intelligent Systems Sendai Japan Graduate School of Science and Technology Hirosaki University Hirosaki Japan

ISBN: (纸本)9798350312072

Laparoscopic surgery is a widely used surgical technique, on the other hand, its high degree of difficulty makes it difficult for beginners to learn the technique efficiently. In addition, recent working hour restrictions and shortages of surgeons have resulted in insufficient training time, and establishing the efficiently training methods is becoming urgent needs. Therefore, to promote the skill proficiency of novice surgeons, machine learning-based assessment system for laparo-scopic surgical skills was developed. A measurement system with a simple configuration was introduced so that trainees can easily use it alone. Alternatively, the indices related to the opening ratio and the rotation angle of surgical instruments, which were measured in the authors' previous study were no longer available. Therefore, comparative experiments were conducted to verify the effect of the lack of indices related to these data on the accuracy of skill evaluation. Based on the measurement data of 104 wet lab trainings measured in the previous study, a machine learning model that evaluates surgeon's skill at 3 levels based on the number of surgical experiences, and global operative assessment of laparoscopic skills (GOALS) which is a type of surgical skill evaluation index were established. By using the explainable AI method, this system can present the skill evaluation result including its basis to the trainee. Since the developed system can be easily operated by a GUI-based program, trainees can confirm the quantitative evaluation result on-site immediately after the training. © 2024 IEEE.

关键词： Laparoscopy

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Two-dimensional CuO nanosheets-induced MOF composites and derivatives for dendrite-free zinc-ion batteries

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纳米研究（英文版） 2023年第5期16卷 6881-6889页

作者： Guoqiang Yuan Yang-Yi Liu Jun Xia Yichun Su Wenxian Wei YinBo Zhu Yang An HengAn Wu Qiang Xu Huan Pang School of Chemistry and Chemical Engineering Yangzhou UniversityYangzhou 225002China School of Chemistry and Chemical Engineering Yangzhou UniversityYangzhou 225002China School of Electrical Engineering Engineering Technology Research Center of Optoelectronic Technology ApplianceTongling UniversityTangling 244061China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern MechanicsCAS Center for Excellence in Complex System MechanicsUniversity of Science and Technology of ChinaHefei 230027China Department of Materials Science and Engineering SUSTech Academy for Advanced Interdisciplinary StudiesSouthern University of Science and TechnologyShenzhen 518055China

Uncontrollable dendrite growth and side reactions resulting in short operating life and low Coulombic efficiency have severely hindered the further development of aqueous zinc-ion batteries(AZIBs).In this work,we designed to grow zeolitic imidazolate framework-8(ZIF-8)uniformly on CuO nanosheets(NSs)and prepared carbon-coated CuZn alloy NSs(CuZn@C NSs)by calcination under H2/Ar *** reflected by extended X-ray absorption fine structure(EXAFS),density functional theory(DFT),and in-situ Raman,the Cu-Zn and Zn-N bonds present in CuZn@C NSs act as zincophilic sites to uniformly absorb Zn ions and inhibit the formation of Zn *** the same time,CuZn@C NSs hinder the direct contact between zinc anode and electrolyte,preventing the occurrence of side *** impressively,the symmetric cells constructed with CuZn@C NSs anodes exhibited excellent zinc plating/exfoliation performance and long life cycle at different current densities with low voltage *** addition,low polarization,high capacity retention,and long cycle life over 1000 cycles at 5 A·g-1 were achieved when CuZn@C NSs were used as anodes for CuZn@C/V2O5 full cells.

关键词： two-dimensional CuO nanosheet metal-organic frameworks (MOFs)-based composites carbon-coated CuZn alloy nanosheets dendrite-free zinc-ion battery

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Morphology and gene expression of sex‐determining region Y‐box 9 and runt‐related transcription factor 2 in centrifugally compressed cell collagen‐combined constructs (C^(6))

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Biosurface and Biotribology 2023年第3期9卷 78-83页

作者： Xu Ye Masashi Yamazaki Wataru Ando Norimasa Nakamura Hiromichi Fujie Faculty of System Design Tokyo Metropolitan UniversityTokyoJapan Research Center for Medicine‐Engineering Collaboration TokyoJapan Department of Orthopaedic Surgery Kansai Rosai HospitalAmagasakiJapan Department of Orthopaedic Surgery Osaka University Graduate School of MedicineSuitaJapan Institute for Medical Science in Sports Osaka Health Science UniversityOsakaJapan Center for Advanced Medical Engineering and Informatics Osaka UniversitySuitaJapan

The authors previously developed a scaffold‐free tissue‐engineered construct(TEC)from mesenchymal stem cells(MSCs).Although the TEC exhibited even cell distribution and was successfully applied for cartilage repair in animal models,it is unsuitable for relatively large‐scale cartilage defects due to its small *** solve the problem,the authors recently developed a novel biomaterial,a centrifugally compressed cell‐collagen combined construct(C^(6))from a mixture of MSCs and atelocollagen,both of which are subjected to *** results of the previous study indicated that C^(6) exhibited high cell viability(70%)and sufficient cell distribution similar to that of the *** the present study,the morphology and gene expression of C^(6) were *** ex-amination indicated that C6 is six times thicker(approximately 1 mm)than the TEC after a 7‐day *** C^(6) remained unchanged in scale with increased cell density after a 21‐day *** electron microscopic observation indicated that C^(6) exhibited interconnected and porous microstructures,while the TEC had close‐knit *** transcriptase‐polymerase chain reaction analysis indicated that the expression of sex‐determining region Y‐box 9 and runt‐related transcription factor 2 was significantly higher in C^(6) than that in TEC.

关键词： biomaterials cartilage microstructures

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering design Problems

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Computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Remotely Estimating Strategy for Direction of Emitting Source of Gaseous Substance Using Angular Scanning of Ultrasound-Driven Straight Airflow

Remotely Estimating Strategy for Direction of Emitting Sourc...

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SICE Annual Conference

作者： Taihei Suda Masaya Takasaki Keisuke Hasegawa Department of Mechanical Engineering and System Design Faculty of Engineering Saitama University Saitama Japan Department of Mechanical Science Graduate School of Science and Engineering Saitama University Saitama Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

We propose a method for remotely estimating the direction of indoor gaseous sources based on the use of electronically steerable ultrasound-driven narrow airflows that create midair gas transportation paths to a spatially fixed sensor. Localization of gas sources have been investigated for several decades, where use of mobile robots equipped with sensors is a current dominant method. However, there are difficult cases to apply the moving robots, where workers are present or footing condition for the robots is bad, for example. Our proposed method employs narrow airflow paths that conveys the gas on the path to the sensor, hereby is free from those problems. In this paper, we experimentally assessed the possibility of the direction-estimation principle, where the sensor output is temporally related to the scanning angle of the ultrasound-driven airflows. It was confirmed that the sensor responded significantly when ultrasound-driven acoustic flow passed near the gaseous substance source. At the same time, significant sensor response was also observed occasionally for the case where the flow path was not close to the source. We give a brief discussion on the possible causes for this and propose several solutions.

关键词： Phased arrays Location awareness Visualization Ultrasonic imaging Transportation Robot sensing systems Acoustic arrays Acoustics Time measurement Time factors

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