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data Science and Information System (ICdSIS), IEEE International Conference on

作者： K M Monica Parameswaran T J. Swapna S. Prabu S. Kaliappan School of Computer Scienceand engineering VIT University Chennai India Department of Computer science and Engineering School Of Engineering and Technology CMR University lakeside campus Bangalore India Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of ECE Mahendra Institute of Technology Namakkal India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

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.

关键词： Cloud computing Ciphers databases Software algorithms Software Encryption Software measurement

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review of life cycle assessments (LCA) for mobility powertrains

Transportation Engineering

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Transportation Engineering 2022年 10卷

作者： Sarathy, S. Mani Nagaraja, Shashank S. Singh, Eshan Cenker, Emre Amer, Amer Clean Combustion Research Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Saudi Aramco Research and Development Center Transport Technologies R&D Division Dhahran31311 Saudi Arabia

Continued economic growth is accelerating the demand for transport energy in the road, aviation, and marine sectors. The environmental impacts of technologies across these sectors need significant attention if society is to meet its climate change mitigation targets. Current policies focusing entirely on electrification are shown to be insufficient in mitigating environmental impacts. Powertrains equipped with combustion engines will still be operational 2040 onwards, especially in the developing countries and currently underdeveloped countries. In addition, the environmental impacts of electrified systems are not negligible, and lifecycle environmental impacts need attention. This paper presents a detailed summary of fuel-powertrain options that society will be dependent on, for next several decades. Environmental impacts using lifecycle assessment (LCA) are presented for various technologies. This review highlights the current inadequacies in application of LCA methods for transport systems, and the need to improve LCA methodologies to drive effective policy and decision making. © 2022

关键词： Life cycle

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Underwater Object detection using Image Enhancement and deep Learning Models

Underwater Object Detection using Image Enhancement and Deep...

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European Workshop on Visual Information Processing (EUVIP)

作者： Adane Nega Tarekegn Faouzi Alaya Cheikh Mohib Ullah Erik Tobias Sollesnes Cornelia Alexandru Saeed Nourizadeh Azar Erdeniz Erol George Suciu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway USEA Ocean Data Oslo Norway Research & Development Department BEIA Consult International Bucharest Romania R&D and AI Department OBSS Technology Istanbul Turkey Elkon Elektrik San. Tic. A.S Istanbul Turkey

Autonomous underwater vehicles (AUVs) are efficient robotic tools, offering a wide range of applications in ocean exploration and research, such as oceanographic mapping, environmental monitoring, and archaeology. Incorporating an automatic object detection system with AUVs can substantially improve their ability to perceive and recognize objects in a complicated and often hazardous environment. Currently, detecting underwater objects relied on a man-in-the-loop approach, where AUVs captured vast amounts of data and saved them in memory for offline processing. This study investigates the use of deep learning for automatic image preprocessing and object detection, evaluating and comparing three state-of-the-art YOLO (You Only Look Once) models, including YOLOv8, YOLOv7, and YOLOv5. Extensive experiments were conducted using publicly available underwater image datasets, revealing that the pre-trained models attain superior performance on the Brackish dataset. YOLOv5 and YOLOv8 achieved the highest mean average precision (mAP) with a score of 99%, while YOLOv7 scored 89%. Furthermore, an underwater image enhancement algorithm is employed on the UrPC2021 dataset, significantly improving the detection accuracy with a 3% increase in mAP across all three models. In terms of inference speed, YOLOv5 demonstrated the highest frames per second (FPS), while maintaining comparable performance in mAP and recall.

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Stability Control Strategy of Photovoltaic dC Microgrid System based on distributed Hydrogen Energy Storage 6

Stability Control Strategy of Photovoltaic DC Microgrid Syst...

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6th International Conference on Power and Energy technology, ICPET 2024

作者： Zhou, Honglian Hu, Wenyu Zhang, Lei Li, Feng Li, Chunshuang Liu, Yin Song, Lingjun State Grid Xinjiang Power Co. Economic and Technical Research Institute Xinjiang China Tsinghua University School of Vehicle and Mobility Beijing China Xinjiang Huadian Reed Lake Liang New Energy Co. New Energy Power Generation Department Xinjiang China TBEA Technology Investment Co. Power Electronics R&D Department Tianjin China Huaneng Xinjiang Energy Development Co. Toshkent River Hydropower Branch Xinjiang China

ISBN: (纸本)9798350375855

In response to the issue of energy supply instability that arises from the widespread adoption of distributed renewable energy resources at the user-end in the future, a dC microgrid system and control method based on distributed hydrogen energy storage are presented. Firstly, the mathematical models of photovoltaic power generation, electrochemical energy storage, and electrolyzer-hydrogen storage tank-fuel cell hydrogen energy storage system are established. Secondly, the components are coupled to establish an integrated dC microgrid system. Finally, based on the control objective of dC bus voltage stabilization, the corresponding control strategy is proposed. According to different fluctuation situations, the effects on the system and the effectiveness of the control strategy are analyzed when the input or load fluctuate. © 2024 IEEE.

关键词： bus voltage control dC microgrids distributed hydrogen energy storage renewable energy

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Optimization of 2.2d Underfill Process by Novel Methodology and direct Observation of Capillary Underfill Process

Optimization of 2.2D Underfill Process by Novel Methodology ...

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Electronic Components and technology Conference (ECTC)

作者： Chia-Peng Sun Yu-En Liang dyi-Chung Hu Er-Hao Chen Jeffrey ChangBing Lee Srikar Vallury Product R&D Division CoreTech System (Moldex3D) Zhubei City Taiwan R.O.C. CEO SiPlus Co. Hsinchu Taiwan R.O.C. CEO Department SiPlus Co. Hsinchu Taiwan R.O.C. Global Engineering Development Division Integrated Service Technology Inc Hsinchu Taiwan R.O.C. Moldex3D North America Inc. Detroit United States

The process of capillary underfill encapsulation encounters productivity issue of long filling time that draws additional manufacturing costs. Since the chip size becomes larger, multiple passes are required for fill and fillet passes to fill up the cavity and create the sealing joint. Traditionally, to obtain an adequate dispensing design (pass route and order) for the capillary process to fill up the package without obvious voids, a long waiting time between passes is set to ensure that the underfill moving front for each pass to halts dispensing before the next pass. Based on our direct underfill observation experiments, the underfill dispensing waiting time is unnecessarily too long. In this work, to reduce the cycle time of underfill process without extensive and tedious trial and error, a novel methodology for optimizing dispensing strategy is proposed. A well-validated model-equivalent bump group (EBG) model of 3d CAE modeling for capillary underfill process is applied to predict the optimal waiting time between passes for dispensing design. Moreover, in-situ observation of underfill process experiment is employed to verify the methodology. It shows that when the optimized waiting time for each pass is applied, the propagation of the underfill moving front by EBG model is like that of the experiment with a long waiting time for each pass. This methodology can optimize the underfill process to reduce cost and speed up production time compared to the conventional try and error underfill process.

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Heavy metal (Pb, Cd and Cr) contamination and human health risk assessment of groundwater in Kuakata, southern coastal region of Bangladesh

Geosystems and Geoenvironment

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Geosystems and Geoenvironment 2025年第1期4卷

作者： Chowdhury, Ali Newaz Naher, Samsun Likhon, Md. Nur Alam Hassan, Jubairul Fariha, Zannati Nur Hasan, Md. raisul Apon, Tanha dewan Bhuiyan, Md. Anwar Hossain Bhuiyan, Md Mesbah Uddin Department of Oceanography Noakhali Science and Technology University Noakhali 3814 Bangladesh Pulp and Paper Research Division Bangladesh Council of Scientific and Industrial Research Laboratories Dhaka 1205 Bangladesh Research and Development (R&D) GeoCore Research Center (GCRC) Dhaka 1214 Bangladesh Department of Geology University of Dhaka Dhaka 1000 Bangladesh

This study investigates the quality of groundwater in Kuakata, a coastal area in southwestern Bangladesh, by analyzing the levels of lead (Pb), cadmium (Cd), and chromium (Cr) in 50 groundwater samples. The concentrations of these heavy metals were determined using atomic absorption spectrometry. The results indicated that while cadmium was not detected, significant levels of lead and chromium exceeded recommended safety thresholds. The contamination was evaluated using several indices, including the heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and degree of contamination (C_d). Health risk assessments for adults and children revealed potential non-carcinogenic and carcinogenic risks, particularly from lead. This study provides essential data to guide groundwater management and improve public health in the region. The groundwater samples contain traces of additional heavy metals including lead and chromium. The lead concentration ranges from 0.0003 to 0.1049 mg/L, while chromium ranges from 0.00074 to 0.1828 mg/L. The levels of certain substances in groundwater exceeded the maximum limit set by international and local health authorities for safe drinking water. Heavy metal evaluation index (HEI), along with the degree of contamination (C_d) and heavy metal pollution index (HPI), were used to assess the pollution load. The results of HEI and HPI revealed that a significant portion of the samples in the study area, specifically 34% and 38%, were found to be strongly and seriously affected, whereas 38% samples were within the highest values (>4) of C_d. Based on the hazard index (HI) and incremental lifetime cancer risk (ICLr) values, the deep groundwater in the coastal area presents a notable health risk to the local population, particularly children. This study provides essential data that can be used as a foundation for developing effective groundwater management strategies in the coastal region, with the ultim

关键词： Cancer risk Coastal area Groundwater Heavy metals Pollution indexes

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Industry-Scale Orchestrated Federated Learning for drug discovery 37

Industry-Scale Orchestrated Federated Learning for Drug Disc...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Oldenhof, Martijn Ács, Gergely Pejó, Balázs Schuffenhauer, Ansgar Holway, Nicholas Sturm, Noé dieckmann, Arne Fortmeier, Oliver Boniface, Eric Mayer, Clément Gohier, Arnaud Schmidtke, Peter Niwayama, ritsuya Kopecky, dieter Mervin, Lewis rathi, Prakash Chandra Friedrich, Lukas Formanek, András Antal, Peter rahaman, Jordon Zalewski, Adam Heyndrickx, Wouter Oluoch, Ezron Stößel, Manuel Vančo, Michal Endico, david Gelus, Fabien de Boisfossé, Thaïs darbier, Adrien Nicollet, Ashley Blottière, Matthieu Telenczuk, Maria Nguyen, Van Tien Martinez, Thibaud Boillet, Camille Moutet, Kelvin Picosson, Alexandre Gasser, Aurélien djafar, Inal Simon, Antoine Arany, Ádám Simm, Jaak Moreau, Yves Engkvist, Ola Ceulemans, Hugo Marini, Camille Galtier, Mathieu KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 Heverlee3001 Belgium BME-HIT CrySyS Lab Budapest Hungary BME-MIT Budapest Hungary Novartis Institutes for BioMedical Research Novartis Campus BaselCH-4002 Switzerland Bayer AG Leverkusen51373 Germany Substra Foundation - Labelia Labs Nantes France Discngine 79 Avenue Ledru Rollin Paris75012 France Institut de Recherches Servier 125 chemin de ronde Croissy-sur-Seine78290 France Boehringer Ingelheim RCV GmbH & Co KG Vienna Austria Molecular AI Discovery Sciences R&D AstraZeneca Cambridge United Kingdom R&D IT AstraZeneca Cambridge United Kingdom Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Merck KGaA Global Research & Development Frankfurter Strasse 250 Darmstadt64293 Germany GmbH Staffelseestraße 2 Munich81477 Germany Pillar Biosciences Inc. 9 Strathmore Rd NatickMA01760 United States Janssen Pharmaceutica NV Turnhoutseweg 30 Beerse2340 Belgium Kubermatic Hamburg Germany Owkin Paris France

ISBN: (纸本)9781577358800

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLOddY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLOddY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLOddY platform generated new scientific discoveries which are described in a companion paper. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Industrial research

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Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer rewards in retail Marketing

Constructing a Blockchain-Based Loyalty Program Model for Se...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Ch. Anudeep Kathari Santosh M Sandeep Kumar Jahnavi M Sabareesh r B Kiran Bala GITAM school of business GITAM University Visakhapatnam A.P. Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore Tamil Nadu Department of Artificial Intelligence and Data Science K. Ramakrishnan college of Engineering Trichy TN

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5 .

关键词： Industries data privacy Communication systems Smart contracts Throughput Blockchains Fraud

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Simulation-based trajectory tracking coordination of intelligent vehicle with explicit model prediction

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International Journal of Simulation and Process Modelling 2020年第6期15卷 546-561页

作者： Xu, Xingyuan Xie, Liyang Zhang, Yin Northeastern University Wenhua Rd. 3-11 Shenyang110819 China Brilliance Auto R&D Center Economical and Technology Development Zone No. 12 8th Rd. Shenyang110141 China

In this paper a novel approach to trajectory tracking problem of intelligent vehicle is presented. A model-based optimisation algorithm for steering systems with multiple physical constraints is designed. A simplified linear vehicle model is designed in order to improve its real-time ability. Moreover, an offline solving method based on explicit model predictive control (EMPC) is presented to improve the computation speed. The simulation results show the offline solving method has the same tracking performance compared with the online solving problem by YALMIP toolbox. The computation speed is significantly increased. © 2020 Inderscience Enterprises Ltd.

关键词： Automobile steering equipment

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Optimization of Natural Language Processing Models for Multilingual Legal document Analysis

Optimization of Natural Language Processing Models for Multi...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： A. radhika Narinder Kumar Bhasin Sabareesh r Yallapragada ravi raju K.N.V. Satyanarayana I Infant raj Department of CSE SRK Institute of Technology Vijayawada lndia Amity University Zonal Head Indian Institute of Banking and Finance Delhi India Academic Training Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore India Department Of Computer Science &Technology Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of ECE S.R.K.R. Engineering College China amiram bhimavaram A.P India Department of computer science & Engineering K.Ramakrishnan college of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Multilingual legal document analysis poses unique challenges in the field of Natural Language Processing (NLP) due to the intricacies of legal language and the diverse linguistic landscape of legal texts across jurisdictions. This paper presents an optimization framework designed to enhance the performance of NLP models specifically tailored for multilingual legal document analysis. The proposed framework incorporates advanced techniques in pre-processing, feature engineering, and model architecture to address the complexities inherent in legal language. Leveraging multilingual embeddings and domain-specific knowledge, the model demonstrates improved accuracy in tasks such as named entity recognition, sentiment analysis, and document categorization across a range of languages. Additionally, the optimization framework emphasizes the importance of domain adaptation, acknowledging the nuances and variations in legal terminology across different legal systems. Through a combination of transfer learning and fine-tuning strategies, the model adapts to specific legal domains, ensuring robust performance in diverse legal contexts. Experimental results on a comprehensive dataset of multilingual legal documents validate the effectiveness of the proposed optimization framework. Comparative analyses with baseline models showcase significant improvements in precision, recall, and overall model performance. The findings underscore the potential of the optimized NLP model for applications in legal information retrieval, contract analysis, and legal knowledge management in a multilingual context. This research contributes to the growing body of knowledge in NLP and legal informatics, offering a valuable resource for researchers, practitioners, and developers working on multilingual legal document analysis. The optimized model presented in this paper has the potential to enhance the efficiency and accuracy of automated systems in handling legal texts across diverse linguistic enviro

关键词： Analytical models Adaptation models Text analysis Law Terminology Transfer learning Linguistics

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