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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode Control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

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International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Predictive EQCi-Optimized Load Scheduling for Heterogeneous IoT-Data in Fog computing Environments

Predictive EQCi-Optimized Load Scheduling for Heterogeneous ...

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International Conference on Image Information Processing

作者： Sridhar Madasamy R Vikkram A Basi Reddy T. Nandhini Shipra Gupta A Nagamani Cognizant Technology Solution 500 Frank W. Burr Boulevard Teaneck NJ USA Department of Information Technology Karpagam College of Engineering Coimbatore Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Department of information technology KSR Institute for Engineering and technology Tiruchengode TamilNadu India School of Management Graphic Era Hill University Dehradun Uttarakhand Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

This paper presents a pioneering predictive EQCi-optimized load scheduling technique tailored to the complexities of heterogeneous IoT data within fog computing environments. The proposed method strategically assigns tasks to fog nodes by considering Load Scheduling Policies (LSPs) and the computing capabilities of fog nodes. Incorporating an EQCi-technique, the model enhances load allocation precision for various LSPs, facilitated by representing LSPs as quantum bits (qubits). This representation enables efficient assessment of probabilistic Execution Index values and Node Availability Index (NAI) for fog nodes, facilitating real-time task allocation. Furthermore, the introduction of EQCi-NN introduces a predictive model that anticipates the optimal fog node for specific tasks with given LSPs. This predictive capability minimizes execution delays and enhances throughput within the fog computing environment. The proposed technique’s performance is rigorously validated through simulations using the iFogSim toolkit, and its efficacy is demonstrated against existing load scheduling methods. Comparative analysis with other predictive techniques highlights the precision and efficiency of the proposed prediction model. The research underscores the presented system’s effectiveness in predicting optimal computational nodes within fog computing platforms, particularly in scenarios necessitating real-time analytical outcomes. By synergizing quantum-inspired representation and predictive modeling, this study advances load scheduling methodologies in fog computing, optimizing resource utilization and bolstering the responsiveness of IoT-driven applications.

关键词： Processor scheduling Computational modeling Predictive models Throughput Real-time systems Resource management Indexes

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A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 833-842页

作者： Hussain, Tassadaq Wang, Wei-Chien Gogate, Mandar Dashtipour, Kia Tsao, Yu Lu, Xugang Ahsan, Adeel Hussain, Amir Edinburgh Napier University School of Computing Edinburgh EH11 4BN United Kingdom National Cheng Kung University Institute of Computer and Communication Engineering Tainan 701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei 11529 Taiwan Chung Yuan Christian University Department of Electrical Engineering Taoyuan 32023 Taiwan National Institute of Information and Communications Technology Tokyo 187-0021 Japan University of Wolverhampton School of Mathematics and Computer Science Wolverhampton WV1 1LY United Kingdom

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms have been successfully applied in target signal enhancement in acoustic applications. As most target signals carry semantic information encoded in a hierarchal structure in short-and long-term contexts, noise may distort such structures nonuniformly. In most deep neural network-based algorithms, such local and global effects are not explicitly considered in a modeling architecture for signal enhancement. In this article, we propose a temporal attentive pooling (TAP) mechanism combined with a conventional convolutional recurrent neural network (CRNN) model, called TAP-CRNN, which explicitly considers both global and local information for acoustic signal enhancement (ASE). In the TAP-CRNN model, we first use a convolution layer to extract local information from acoustic signals and a recurrent neural network (RNN) architecture to characterize temporal contextual information. Second, we exploit a novel attention mechanism to contextually process salient regions of noisy signals. We evaluate the proposed ASE system using an infant cry dataset. The experimental results confirm the effectiveness of the proposed TAP-CRNN, compared with related deep neural network models, and demonstrate that the proposed TAP-CRNN can more effectively reduce noise components from infant cry signals with unseen background noises at different signal-to-noise levels. We further tested the TAP-CRNN ASE system on a downstream infant cry detection (ICD) system, which determines whether a sound segment is involved in an infant cry event. Experimental results show that TAP-CRNN ASE can effectively reduce the noise components, thereby improving the performance of ICD under noisy conditions. © 2020 IEEE.

关键词： Acoustic signal enhancement (ASE) bidirectional long-short term memory convolu-tional neural networks recurrent neural networks (RNN)

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Optimised with Secure Approach in Detecting and Isolation of Malicious Nodes in MANET

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Wireless Personal Communications 2021年第1期119卷 21-35页

作者： Thiagarajan, R. Ganesan, R. Anbarasu, V. Baskar, M. Arthi, K. Ramkumar, J. Department of Computer Science and Engineering Prathyusha Engineering College Chennai India Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai India

To keep the process of routing and data transmission operations, it’s needed for a proper layer of security to keep it safe from malicious attacks. So in this paper, we proposed a framework that helps in detecting the malicious nodes at each of the destinations. After the detection process, it is isolated and discarded in while path establishment through various techniques. The development of an algorithm that supports Multipath Reliable Routing helps in determining paths for a set of disjoint nodes. Based on the index of reliability the paths are reordered. Now the process of information in form of data are been dispersed from source to destination. The main primary path is set to transfer the information. The final destination in case of mismatch of information transferred from a source when received at destination then feedback is given in negative manner stating that there is a mismatch of data along with the information related to the transmission and its path. Hence due to this, the destination will be able to recover since the information is checked each time for reliability. The final results show that the proposed approach results and have a greater impact when compared to the existing methods. Further, it reduces delay in packet transmission and overhead parallel it increases the delivery of packets.

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Enhancing Thermal Infrared Tracking with Natural Language Modeling and Coordinate Sequence Generation

arXiv

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arXiv 2024年

作者： Yan, Miao Zhang, Ping Zhang, Haofei Hao, Ruqian Liu, Juanxiu Wang, Xiaoyang Liu, Lin School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Optoelectronic Science and Engineering Shenzhen Institute for Advanced Study the Yibin Institute University of Electronic Science and Technology of China Chengdu611731 China The Innovation Centre Office K1 Department of Computer Science University of Exeter Streatham Campus ExeterEX4 4RN United Kingdom Research Institute of China Ordnance Industries NO.208 Beijing102200 China

Thermal infrared tracking is an essential topic in computer vision tasks because of its advantage of all-weather imaging. However, most conventional methods utilize only hand-crafted features, while deep learning-based correlation filtering methods are limited by simple correlation operations. Transformer-based methods ignore temporal and coordinate information, which is critical for TIR tracking that lacks texture and color information. In this paper, to address these issues, we apply natural language modeling to TIR tracking and propose a novel model called NLMTrack, which enhances the utilization of coordinate and temporal information. NLMTrack applies an encoder that unifies feature extraction and feature fusion, which simplifies the TIR tracking pipeline. To address the challenge of low detail and low contrast in TIR images, on the one hand, we design a multi-level progressive fusion module that enhances the semantic representation and incorporates multiscale features. On the other hand, the decoder combines the TIR features and the coordinate sequence features using a causal transformer to generate the target sequence step by step. Moreover, we explore an adaptive loss aimed at elevating tracking accuracy and a simple template update strategy to accommodate the target’s appearance variations. Experiments show that NLMTrack achieves state-of-the-art performance on multiple benchmarks. The Code is publicly available at https://***/ELOESZHANG/NLMTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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MedBench:A Comprehensive,Standardized,and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models

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Big Data Mining and Analytics 2024年第4期7卷 1116-1128页

作者： Mianxin Liu Weiguo Hu Jinru Ding Jie Xu Xiaoyang Li Lifeng Zhu Zhian Bai Xiaoming Shi Benyou Wang Haitao Song Pengfei Liu Xiaofan Zhang Shanshan Wang Kang Li Haofen Wang Tong Ruan Xuanjing Huang Xin Sun Shaoting Zhang Shanghai Artificial Intelligence Laboratory Shanghai 200232China Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 200025China Chinese University of Hong Kong Shenzhen 518172China Shanghai Artificial Intelligence Research Institute Shanghai 200240and also with Shanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Qing Yuan Research Institute Shanghai Jiao Tong UniversityShanghai 200240China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China West China Hospital Sichuan UniversityChengdu 610041China School of Design and Innovation Tongji UniversityShanghai 200092China Department of Computer Science and Technology East China University of Science and TechnologyShanghai 200237China School of Computer Science Fudan UniversityShanghai 200433China Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai 200092China

Ensuring the general efficacy and benefit for human beings from medical Large Language Models(LLM)before real-world deployment is ***,a widely accepted and accessible evaluation process for medical LLM,especially in the Chinese context,remains to be *** this work,we introduce“MedBench”,a comprehensive,standardized,and reliable benchmarking system for Chinese medical ***,MedBench assembles the currently largest evaluation dataset(300901 questions)to cover 43 clinical specialties,and performs multi-faceted evaluation on medical ***,MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure,with physical separations between question and ground ***,MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer *** MedBench to popular general and medical LLMs,we observe unbiased,reproducible evaluation results largely aligning with medical professionals’*** study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs.

关键词： Medical Large Language Model(MLLM) benchmark platform open-source

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AI-Powered Real-Time Big Data Analytics for ICU Resource Management

AI-Powered Real-Time Big Data Analytics for ICU Resource Man...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Kalaichelvi N. Mohankumar C. Uthayakumar Barkathulla A K.S. Manojee S. Murugan Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Tiruchengode Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Vivekanandha College of Engineering For Women Tiruchengode Namakkal Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Improving patient outcomes and operational efficiency in intensive care units (ICUs) relies heavily on efficient resource management. This work presents a novel LSTM-based methodology for real-time optimization of ICU resources, using big data to enhance decision-making. Patient vitals, equipment utilization, and staff activities are just a few examples of the real-time data gathered from the many ICU sensors and monitoring devices the system incorporates using LSTM networks. LSTM networks excel in processing time-series data, allowing the system to anticipate resource requirements, spot bottlenecks, and allocate resources optimally. The proposed system offers predicted insights together with practical suggestions by analyzing historical and real-time data streams. Simulations and case studies show that the system can increase operational efficiency and predict demand. Findings point to major gains in patient care, savings, and decision-making assistance in fast-paced ICUs. This method uses LSTM networks to tackle the intricacies of contemporary hospital environments, revolutionizing ICU management with innovative AI and big data analytics. LSTM networks improve real-time decision-making, resource management, and patient outcomes in ICUs.

关键词： Analytical models Accuracy Decision making Predictive models Big Data Real-time systems Data models Resource management Long short term memory Load modeling

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Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment

Enhanced Honey Badger Algorithm for Resource Allocation and ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： R Rajagopal AR Arunarani A Arivarasi Anup Ingle Ravichandran T R. Vijaya Prakash Department of Computer Science & Engineering Alliance College of Engineering & Design Alliance University Bengaluru Karnataka India Department of Compuational Intelligence School of Computing College of Engineering and Technology SRM Instutite of Science and technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Electronics and Telecommunications Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Karpagam College of engineering Coimbatore Tamilnadu India School of Computer Science and Artificial Intelligence SR University Warangal India

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources.

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