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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bansal, Saloni Reddy, R. Venkateswara Aggarwal, Saurabh Adnan, Myasar Mundher Kumar, Devender Angadi, Sanjeevkumar Gla University Department of Computer Engineering and Applications Mathura India Cmr College of Engineering & Technology Department of Cse India Uttaranchal Institute of Technology Uttaranchal University Department of Mechanical Engineering Dehradun248007 India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Nutan College of Engineering and Research Computer Science and Engineering Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

Wi-Fi Sensor Networks (WSNs) have emerged as a transformative era for several applications, from environmental monitoring to protection systems, due to improvements in wi-fi verbal exchange and sensor technologies. The deployment of Mobile Wireless Sensor Networks (MWSNs) introduces the task of maximizing community lifetime at the equal time as making sure green facts series and processing. This test proposes a novel technique to extend the lifetime of MWSNs by using way of leveraging Evolutionary Computing (EC) techniques. Through a comparative assessment of five EC algorithms-Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Neighborhood Field Optimization (NFO)-the research highlights the strengths and limitations of every method in optimizing the energy performance of MWSNs. The results show the functionality of EC strategies in enhancing the sustainability and operational basic overall performance of WSNs, presenting treasured insights for destiny studies and realistic programs in the subject. © 2024 IEEE.

关键词： Genetic algorithms

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Vehicle Overtaking in a Bidirectional Mixed-Traffic Setting

Vehicle Overtaking in a Bidirectional Mixed-Traffic Setting

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2022 American Control Conference, ACC 2022

作者： Tariq, Faizan M. Suriyarachchi, Nilesh Mavridis, Christos Baras, John S. The Institute for Systems Research University of Maryland Electrical and Computer Engineering Department College ParkMD United States

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

With the advent of autonomous vehicles on public roads imminent in the near future, special emphasis needs to be placed on addressing scenarios pertaining to mixed-traffic settings, comprised of human-driven and autonomous vehicles. In this paper, we address the problem of autonomous vehicle overtaking in a bidirectional mixed-traffic setting. We design a mixed-integer model predictive controller that maximizes the ego vehicle's velocity while prioritizing safety and accounting for driver comfort. The proposed approach: (i) operates in a limited sensing range while accounting for occlusion;(ii) is able to retract the overtake decision through a receding horizon approach;(iii) is robust to the variations in sensory input and driving behaviors of external agents due to behavior-dependent safety margins;and (iv) reduces to a mixed-integer optimization problem with linear constraints, yielding low computational complexity. We demonstrate the behavior of the proposed approach in a realistic traffic simulation environment. © 2022 American Automatic Control Council.

关键词： Autonomous vehicles

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Real-Time Monitoring and Control of Drones Using IoT-Blockchain Integration

Real-Time Monitoring and Control of Drones Using IoT-Blockch...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Mehta, Shiva Aneja, Aseem Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering U.P Greater Noida India

ISBN: (纸本)9798350378092

This work presents an IoT-Blockchain integration solution to enhance the typical Drone systems to acquire optimal organizational performance. Several questions that are associated with modern drone operations are solved by the suggested system, and these questions are the following: data security, efficiency of work, and system scalability. Concerning the experimental results, the boost in the realism of the data transfer rates can also be seen - the mean rate is increased from 500 to 1200 KB/s, thus enabling real-time control. Another effect of Smart Contracts was reducing the time it took to carry out operational activities to which the contracts were applied. For instance, the time taken to route a flight to save time was brought down from 10 minutes to 2 minutes, the time to allocate resources for the flight was brought down from 15 minutes to 3 minutes, and the time to schedule maintenance of the flight was brought down from 20 minutes to 5 minutes. It also presented the framework's performance metrics, such as a 0% data breach incidence rate different from the annual average of 12 in centralized traditional systems. This goes well with Blockchain's ability to house information in a record that can never be altered, providing data security and credibility. However, the system also proved scalability as the number of drones changed from 10 to 100, and the throughput changed from 100 to 920 TPS. Also, the system latency remained below 70 ms during this process, which is critical for responsive trading applications. © 2024 IEEE.

关键词： Scalability

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs 37

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Ve...

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

ISBN: (纸本)9781713899921

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27, 714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： computer aided logic design

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Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

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Food Science and Human Wellness 2024年第1期13卷 166-172页

作者： Changge Guan Zhenglin Tan Shucheng Li Yi Wang Naoyuki Yamamoto Chong Zhang Songjun Wang Junjie Chen Xinhui Xing Key Laboratory for Industrial Biocatalysis Ministry of Education of ChinaDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China School of Life Science and Technology Tokyo Institute of TechnologyYokohama 226-0026Japan Sensing System Research Center National Institute of Advanced Industrial Science and TechnologyTsukuba 305-8565Japan Center for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Henan Giant Salamander Protection and Development Association Luoyang 471000China Intelligent Computing Research Center Harbin Institute of TechnologyShenzhen 518071China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolShenzhen 518055China Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhen 518067China

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in ***,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence *** this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA *** functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to *** have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide ***-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is *** study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

关键词： Chinese giant salamander Transcriptomic analysis Bioactive components Functional peptides mining

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Software defined network (SDN) based data server computing system

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International Journal of Information Technology (Singapore) 2025年第1期17卷 607-613页

作者： Madhura, K. Sekhar, Gillala Chandra Sahu, Amaresh Karthikeyan, M.P. Khurana, Saniya Shukla, Meenu Vashisht, Nitish Information and Communication Technology Manipal Institute of Technology Mahe Bengaluru India Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Ajay Binay Institute of Technology Odisha India Department of Computer Science and Information Technology Jain (Deemed to be University) Karnataka Jain Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Rajpura India

The growing clientele and broadband demand lead to several technical restrictions in ensuring stable connections between data centers. The data on existing network devices such as switches, routers, and gateways must be routed to some particular point commonly known as software-defined network (SDN). SDN is defined as a networking approach that makes use of software-based controllers or application programming interfaces (API’s) to communicate with existing hardware and provide paths to traffic in the network. In order to overcome technical restrictions in the network, the given paper introduces an application-based enabled open flow information center network layout to accomplish transport load allocation and higher efficiency. In order to mitigate noise in the network, the proposed approach employs a data centre network (DCN) and implements it using SDN controller such as OpenDaylight (ODL). ODL is an open-source platform for automating networks of any size and scale. It allocates traffic requests among the linked servers, thereby ensuing higher throughput, optimization of load balancing, and versatility in SDN’s. Lastly, the performance of the proposed approach is validated by assessing the throughput rate (Mbps) and average delay (ms) prior to load balancing (BLD) and after load balancing (ALD). The results show that the proposed approach leads to higher throughput after load balancing optimization using ODL. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： DCN Load balancing ODL SDN Throughput

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INCLUSION OF MANUFACTURABILITY CONSIDERATIONS ON DESIGN OPTIMIZATION OF A COMPOSITE ROTOR BLADE

INCLUSION OF MANUFACTURABILITY CONSIDERATIONS ON DESIGN OPTI...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Wall, Emily S. Haehnel, Robert B. Sharma, Gehendra S. Dalton, Larry McCall, Tonya G. Dettwiller, Ian Huberty, Wayne Center for Advanced Vehicular Systems Mississippi State University StarkvilleMS United States US Army Engineer Research and Development Center HanoverNH United States Institute for Systems Engineering Research Mississippi State University StarkvilleMS United States Center for Advanced Vehicular Systems Extension Mississippi State University CantonMS United States US Army Engineer Research and Development Center VicksburgMS United States Advanced Composites Institute Mississippi State University StarkvilleMS United States

ISBN: (纸本)9780791888599

The optimization of rotor blade aerodynamic and structural design is pivotal for enhancing overall rotorcraft performance in terms of, e.g., reduced power requirements and energy consumption, and increased range and cargo capacity. Use of composite blades are increasing attractive owing to improved fatigue characteristic, reparability, etc., however, not all proposed composite structure designs are practical to manufacture. This research effort explores the intricate relationship between composite blade design and manufacturability, the potential utility of optimization software (e.g., iVABS) to improve the overall manufacturability of a given structural design. Integrated VABS (iVABS) is a software tool developed to aid in the design optimization of composite slender structures. Design parameters can be defined in iVABS to control various aspects of the internal structure such as spar web spacing, web curvature, number of plies, fiber orientation, and materials, many of which have a direct effect on the overall manufacturability of the blade. The team delves into the critical aspects of manufacturability that directly influence the manufacturing process. The overarching goal is to establish rules that will guide a manufacturability assessment that can be integrated into the initial design phase, guiding iVABS towards creating rotor blade designs that not only meet stress, strain and aerodynamic requirements, but are also inherently more manufacturable. The integration of manufacturability considerations into the optimization process, as demonstrated by these efforts, represents a proactive approach to ensuring that optimal structural designs align with the capabilities and constraints of the given manufacturability processes, thereby streamlining the manufacturing process. The research includes the consideration of the manufacturability constraints that could be applied to the identified design parameters and demonstrates how this can be applied via a case study sh

关键词： Design for manufacturability

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AI Processing of Wearable IMU Data for Exoskeleton Gait Analysis 8

AI Processing of Wearable IMU Data for Exoskeleton Gait Anal...

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8th IEEE International Forum on research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Vittoria Guerra, Bruna Maria Sozzi, Stefania Pizzocaro, Serena De Nunzio, Alessandro Marco Schmid, Micaela University of Pavia Dept. of Computer Electrical and Biomedical Engineering Pavia Italy Lunex International University of Health Exercise and Sports Department of Research and Development Luxembourg Luxembourg Lunex International University of Health Exercise and Sports Dept. of Research and Development Luxembourg Luxembourg Luxembourg Health & Sport Sciences Research Institute Asbl Luxembourg Luxembourg

ISBN: (纸本)9798350362138

Walking involves multiple gait cycles comprising stance and swing phases, crucial for stability and propulsion. Recovery of walking is a primary goal in neurological rehabilitation, with robotic exoskeletons emerging as promising tools. These devices assist individuals with lower extremity weakness, utilizing various signal acquisition technologies like inertial sensors (IMUs) to interpret human body movements. Accurately estimating gait phases, particularly for online exoskeleton control, presents a significant challenge. Deep Learning (DL) algorithms offer solutions by analysing IMU signals to predict gait events. A proposed DL model, based on a hybrid solution of one-dimensional Convolutional Neural Network and Bidirectional Long Short-Term Memory Neural Network, utilizes kinematic data from hip, knee and ankle to identify gait phases during exoskeleton-assisted walking, achieving 98.64% accuracy in stance and swing identification. Moreover, the study evaluates the time taken for the prediction of the gait phases, crucial for effective exoskeleton control, estimating the time spent on acquisition, elaboration, and classification procedures. The results show a processing time of 57 milliseconds for a 1-second segment of IMU signal. Evaluated on 26 healthy subjects, the model demonstrates potential for online gait phase identification. Overall, the proposed DL model shows promise for improving exoskeleton-assisted rehabilitation, offering tailored support, and enhancing the effectiveness of gait training interventions. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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