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International Conference on Electrical Machines and Systems (ICEMS)

作者： Yoshihiro Miyama Kan Akatsu Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki Japan Department of Faculty of Engineering Yokohama National University Yokohama Japan

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

A carrier harmonic iron loss reduction technique for the phase-shift windings dual three-phase motors is proposed in this manuscript. By controlling two three-phase current phase-shifts from 30 degrees to -30 degrees, the voltage phase-shift is controlled from 30 degrees to 19 degrees, and the first and third carrier harmonic iron loss is reduced to a 42.9% maximum. Finite element analysis (FEA) confirms that the carrier harmonic current and carrier harmonic iron loss is reduced related to carrier harmonic impedance.

关键词： Torque Windings Harmonic analysis Motors Iron Finite element analysis Impedance Copper Voltage control

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Novel Temporary Bonding/debonding System Enabling advanced Packaging Process

Novel Temporary Bonding/Debonding System Enabling Advanced P...

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International Conference on Electronics Packaging (ICEP)

作者： Emi Miyazawa Tetsuya Enomoto Yuta Akasu Shogo Sobue Yuki Nakamura Saeko Ogawa Takashi Kawamori High Performance Materials Department Packaging Materials Group Institute for Advanced Integrated Technology Resonac Corporation Packaging Materials R&D Department Film R&D Group Electronics R&D Center Electronics Business Headquarters Resonac Corporation High Performance Materials Department Bonding Materials Group Institute for Advanced Integrated Technology Resonac Corporation

Novel temporary bonding/debonding system including high heat resistant temporary bonding adhesive and debonding methods with newly developed photonic release by Xe flash light irradiation will be introduced. New temporary bonding adhesive is endurable up to 300 °C and can be easily removed by peeling off after debonding from the support carrier. By adopting the dual layer structure with adhesive layer and metal layer on glass carrier, one-shot Xe flash light irradiation within 5 ms enables debonding cleanly, indicating the increased throughput at debonding step. Process demonstration results of wafer thinning down to 50 μm, fan-out wafer level packaging (FOWLP) will also be shown.

关键词： resistance radiation effects Xenon Packaging Throughput Wafer scale integration reliability

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Analysis of tool wear in micromilling Ti6Al4V and its impact on generated surface integrity

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Wear 2025年 570卷

作者： Gonçalves, Maria Clara Coimbra Alsters, rob Curtis, david Bushlya, Volodymyr M'Saoubi, rachid Ghadbeigi, Hassan University of Sheffield Mechanical Engineering Department Mappin Street Sheffield United Kingdom R&D Materials and Technology Development Seco Tools AB Zandterweg 14 Lottum5973 RC Netherlands Advanced Manufacturing Research Centre University of Sheffield RotherhamS60 5TZ United Kingdom Lund University Ole Römers väg 1 Lund221 00 Sweden R&D Materials and Technology Development Seco Tools AB Fagersta737 82 Sweden

Tool wear is one of the dominant factors that impacts the surface integrity of machined materials. Understanding the impact of tool wear on surface integrity is crucial in micromilling, due to the size of the features being machined and the cutting tools. Therefore, this work aims to analyse the impact of tool wear on surface integrity during the micromilling of Ti6Al4V. In this context, microslots were manufactured on a Ti6Al4V workpiece using both coated and uncoated 1 mm flat end mills at constant cutting parameters with varying cutting lengths in order to analyse the progression of tool wear with machined length and its impact on surface integrity. The microtools were investigated using both Scanning Electron Microscope (SEM) and infinite focus optical imaging (Alicona) to determine the tool wear evolution. Surface integrity was assessed by analysing surface roughness areal parameters, surface microscope images, and subsurface microstructure and microhardness perpendicular to the cutting direction. The results show that surface quality was not affected by the evolution of tool wear, with surface topography, including surface roughness parameters, remaining within a similar range until the catastrophic failure of the tool. Analysis of the machined surface revealed small chips adhered to it, which affected the surface texture height measurements, leading to a predominance of atypical peaks on the surface. Subsurface analysis of the machined material showed that the microstructure and microhardness remained consistent with the bulk material characteristics, indicating no evidence of severe plastic deformation in the machined subsurface. However, once the tool failed and began rubbing against the workpiece surface, swept grains due to material dragging and heat-affected zones were observed in the subsurface microstructure. Swept grains, caused by material extrusion, were also observed in the microstructure of the top burr formation regions throughout the experiments

关键词： Titanium alloys

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Novel Gender and Age Prediction from real Time Facial Images using Linear regression Algorithm 8

Novel Gender and Age Prediction from Real Time Facial Images...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Nalini, N. Shahila, d. Ferlin deva rosi, A. Tamilselvi, M. Giri, Jayant Sherideh, Ala'a Al Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Saveetha University Saveetha School of Engineering Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350367904

The proposed novel algorithm utilizes linear regression in real-time facial image analysis for predicting both gender and age. By leveraging features extracted from facial data, the model aims to provide accurate and instantaneous predictions. The emergence of social media platforms has made the automatic classification of gender and age increasingly relevant. real-time frame processing is possible with OpenCV. The expected gender and age are provided as the output, and this frame is provided as the input. The capacity to autonomously ascertain age and gender from facial images because of its versatility in facial analysis applications. However, the current models are still behind the required accuracy level, which is required for these models to be used in real-world applications, because of the significant intraclass variance of face images (such as difference in lighting, position, scale, and occlusion). In this work, a deep learning framework uses an ensemble of residual and attentional convolutional networks to reliably determine the gender and age group of facial photographs. By employing an attention mechanism, this model can concentrate on the important and instructive aspects of the face, increasing its prediction accuracy to 90.15%. © 2024 IEEE.

关键词： Prediction models

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Enhanced bifunctional catalytic activities of N-doped graphene by Ni in a 3d trimodal nanoporous nanotubular network and its ultralong cycling performance in Zn-air batteries

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Journal of Energy Chemistry 2022年第3期31卷 466-473,I0013页

作者： Yanyi Zhang Xiang-Peng Kong Xiaorong Lin Kailong Hu Weiwei Zhao Guoqiang Xie Xi Lin Xingjun Liu Yoshikazu Ito Hua-Jun Qiu School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055GuangdongChina Shenzhen R&D Center for Al-based Hydrogen Hydrolysis Materials Shenzhen 518055GuangdongChina State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001HeilongjiangChina Blockchain Development and Research Institute Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Free-standing and fexible air electrodes with long-lasting bifunctional activities for both the oxygen reduction reaction(Orr)and the oxygen evolution reaction(OEr)are crucial to the development of wearable Zn-air rechargeable *** this work,we synthesize a fexible air electrode consisting of 3d nanoporous N-doped graphene with trimodal shells and Ni particles through repeated chemical vapor deposition(CVd)and acidic etching *** results indicate that such trimodal graphene morphology significantly enhances the active N-dopant sites and graphene-coated Ni surface,which consequentially boosts both the Orr and OEr activities,as well as catalytic ***-principles density functional theory(dFT)calculations reveal the synergetic effects between the Ni and the N-doped graphene;namely,the Ni nanoparticles boost the bifunctional activities of the coated N-doped graphene,and in turn the graphene-covering layers enhance the stability of *** to the better protection from the triple graphene shells,our trimodal N-doped graphene/Ni-based Zn-air battery can be stably discharged/recharged beyond 2500 h with low *** is reasonable to expect that such freestanding trimodal graphene/Ni would be promising in many fexible energy conversion/storage devices.

关键词： Triple shells Bifunctional Hierarchical Cycling durability Orr/OEr

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Fortifying Cloud data Security with the Maritime Search and rescue Algorithm 2

Fortifying Cloud Data Security with the Maritime Search and ...

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2nd International Conference on data Science and Information System, ICdSIS 2024

作者： Monica, K.M. Parameswaran, T. Swapna, J. Prabu, S. Kaliappan, S. Vit University School Of Computer Science And Engineering Chennai India Cmr University Lakeside Campus School Of Engineering And Technology Department Of Computer Science And Engineering Bangalore India Veltech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai India Mahendra Institute Of Technology Department Of Ece Namakkal India Lovely Professional University Division Of Research And Development Punjab Phagwara India

ISBN: (纸本)9798350381665

Cloud computing has fundamentally transformed the accessibility and payment models of services within distributed system environments. In response to heightened security concerns, cloud service providers offer diverse resources spanning Infrastructure, Software, Platform, and database services, supported by security measures such as Service Level Agreements (SLAs), access control rules, and cryptographic methods. This paper introduces a novel data encryption technique, termed data Security in Cloud Computing using Maritime Search and rescue Algorithm (dSCC-MSrA). By integrating an enhanced cypher block chaining algorithm with logical substitution and the Maritime Search and rescue Algorithm (MSrA) for key generation, this method provides a unique approach to encryption. Through rigorous optimization and analysis, it has undergone significant refinement, with comparisons against industry-standard algorithms showcasing its efficacy. The primary objective is to elevate encryption procedures and introduce complexity to cryptanalysis, ultimately enhancing data security in cloud computing. © 2024 IEEE.

关键词： Cryptography

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Exploring deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(dL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of dL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of dL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Implementation of Precision Education System Based on Machine Learning Model 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Center for Research & Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid21110 Jordan

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Precision Education (PE) is an approach that emphasizes the use of various forms of technology to personalize students’ educational opportunities according to their unique requirements. In order to facilitate effective learning that is suited to the needs of each individual, it is necessary to have complete information about the learning process. PE describes a personalized approach to classroom instruction. We propose dignified reinforcement Learning (drL) which includes all the essential elements required for PE, in order to determine the impact of developing multimodal innovations on individualized learning. The system aims to address issues with the standard model of education analysis, such as insufficient internal validity, predictive validity, timeliness, comparability, and understandability, in order to provide more precise instruction. Networking sites, the digital classroom, the intelligent agent, and the panel are the four fundamental aspects of an rL-based technology. Through model-based rL, the head attribute of the continuous educational environment gathers and processes a multimodal flow of student records from the various modules to create laws of appropriate behaviors that optimize the long-term benefits for both the individual and the computer. To demonstrate the usefulness of the proposed model, experimental evidence was presented using a subset of multimodal data. Practical and theoretical implications for future digital learning approaches and investigations may be drawn from rL systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Fuzzy Logic Technique based Fault diagnosis for Electrical distribution Network

Fuzzy Logic Technique based Fault Diagnosis for Electrical D...

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Innovation in technology (INOCON), IEEE International Conference for

作者： Saurabh G. rana Shefali Talati Manish J. desai Department of Electrical Engineering Faculty of Technology and Engineering The Maharaja Sayajirao University Vadodara India Department of R&D Electrical Research and Development Association Vadodara India

The power system needs to operate reliably and efficiently, with the distribution system being a critical component. distribution systems are prone to faults and malfunctions for several reasons. These issues can have a significant impact on the power grid, but their effects can be minimized quickly and accurately through fault diagnostics. Electric utilities face an ongoing challenge in promptly and precisely identifying and locating faults in the distribution system. AI tools can be an efficient method for accurately identifying these faults. There are several possible diagnostic methods, such as fuzzy logic, ANN (artificial neural network), ANFIS (adaptive neuro-fuzzy inference system), support vector machines, genetic algorithms, and matching approaches. Each technique has its own advantages and disadvantages. The paper has employed fuzzy logic for fault detection, identification, and location. This paper includes the study of multiple faults on different buses. Also, the identification and location of multiple faults are included. The simulation and algorithm development have been carried out in MATLAB/Simulink. Thus, fault detection, identification, and location have been done through a fuzzy logic algorithm.

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PepINVENT: Generative peptide design beyond the natural amino acids

arXiv

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arXiv 2024年

作者： Geylan, Gökçe Janet, Jon Paul Tibo, Alessandro He, Jiazhen Patronov, Atanas Kabeshov, Mikhail david, Florian Czechtizky, Werngard Engkvist, Ola de Maria, Leonardo BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Division of Systems and Synthetic Biology Department of Life Sciences Chalmers University of Technology Gothenburg Sweden BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Medicinal Chemistry Research and Early Development BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

Peptides play a crucial role in the drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the peptide properties from binding affinity, plasma stability to permeability. Incorporating novel NNAAs facilitates the design of more effective peptides with improved properties. The generative models used in the field, have focused on navigating the peptide sequence space. The sequence space is formed by combinations of a predefined set of amino acids. However, there is still a need for a tool to explore the peptide landscape beyond this enumerated space to unlock and effectively incorporate de novo design of new amino acids. To thoroughly explore the theoretical chemical space of the peptides, we present PepINVENT, a novel generative AI-based tool as an extension to the small molecule molecular design platform, rEINVENT. PepINVENT navigates the vast space of natural and non-natural amino acids to propose valid, novel, and diverse peptide designs. The generative model can serve as a central tool for peptide-related tasks, as it was not trained on peptides with specific properties or topologies. The prior was trained to understand the granularity of peptides and to design amino acids for filling the masked positions within a peptide. PepINVENT coupled with reinforcement learning enables the goal-oriented design of peptides using its chemistry-informed generative capabilities. This study demonstrates PepINVENT’s ability to explore the peptide space with unique and novel designs, and its capacity for property optimization in the context of therapeutically relevant peptides. Our tool can be employed for multi-parameter learning objectives, peptidomimetics, lead optimization, and variety of other tasks within the peptide domain. © 2024, CC BY.

关键词： Amino acids

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