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Performance Study and Comparative Analysis of Routing Protocols in Wireless sensor Networks 4th

Performance Study and Comparative Analysis of Routing Protoc...

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4th International conference on advanced Computational and Communication Paradigms, ICACCP 2023

作者： Kumar, Mandeep Ali, Jahid Research Scholar Computer Science and Engineering I.K. Gujral Punjab Technical University Jalandhar – Kapurthala Highway VPO – Ibban Kapurthala144603 India Professor Computer Applications Sri Sai Iqbal College of Management and Information Technology VPO Badhani Tehsil Punjab Pathankot145001 India

ISBN: (纸本)9789819942831

Wireless sensor Network (WSN) is usually prolonged into huge geographical regions through inexpensive sensor devices that can tolerate themselves with very less power usage. The networking ability enables the sensor nodes for integrating, coordinating, and collaborating with each other, which is a basic alternation in network area and it distinguishes sensor network nodes from other networks, namely Ad-Hoc, Internet (IP) datagram and etc. At present, the routing protocol in a wireless sensor network experiences various issues, including coverage packet loss, video real time and real-time audio streaming, new scalability, energy restraints, severe weather environments and so on. Moreover, network routing is termed as the incorporation of a routing approach and routing protocol. The intent of the routing system is to afford consistent assessment of network nodes topology when the routing model offers intelligence with regards to optimal path computation. Thus, in this paper, a study is made with several routing systems in WSN and the performance is evaluated. Here, the performance evaluation is done by the existing routing methods along with the Taylor SailFish Optimizer (Taylor SFO) approach. Additionally, the performance of the routing technique is analyzed with throughput, distance, energy, end to end delay as well as Packet Delivery ratio (PDR). The Taylor SFO routing model achieved minimal distance and delay of 195.44 m, and 15.25 ms as well as maximum throughput, PDR, and residual energy of 119.98 kbps, 94.87%, and 221.65 J. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Routing protocols

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Online Parameter Identification of Lithium Battery Model Based on Bias Compensated Least Square

Online Parameter Identification of Lithium Battery Model Bas...

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UNIfied conference of International Workshop on Defence applications of Multi-Agent systems, DAMAS 2023, International conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Liu, Jiahao Kong, Jinzhen Feng, Yuxing Zhen, Dong Zhang, Hao Feng, Guojin School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Advanced Equipment Research Institute Co. Ltd. of HEBUT Tianjin300401 China

ISBN: (纸本)9783031494123

Accurate lithium-ion battery modeling affects accuracy of battery state estimation, which will affect the safe operation of the electric vehicle. However, due to complex operating environments and influence of sensor noises, measured signals inevitably contain random noises, which hence influences model estimation accuracy. To solve the problem, this paper introduces a variable error model to estimate the average weighted variance of random noises and uses an augmented parameter vector to calculate a bias term, which can be compensated to estimation results of the battery model parameters. Finally, unbiased estimations of model parameters are obtained to improve accuracy of the established model. Results show that compared with traditional least square algorithm, the forgetting factor least square method based on bias compensation proposed in this paper can get higher precise. Under the Urban Dynamometer Driving Schedule (UDDS) and Hybrid Pulse Power Characterization (HPPC) test conditions, the mean absolute errors are reduced by 25 and 28%, and the root mean square errors are reduced by 25.1 and 42.7%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Battery management systems

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sensor-Based Multi-Waypoint Autonomous Robot Navigation with Graph-Based Models

Sensor-Based Multi-Waypoint Autonomous Robot Navigation with...

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Autonomous systems: sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

作者： Rogers iii, James H. Sellers, Timothy Lei, Tingjun Carruth, Daniel W. Luo, Chaomin Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States Center for Advanced Vehicular Systems Mississippi State University StarkvilleMS39759 United States

ISBN: (数字)9781510661974

ISBN: (纸本)9781510661967

Multi-waypoint navigation for autonomous robot is in high demand in real-world robotics applications including search and rescue, disaster response, and environment exploration. In this paper, a sensor-based methodology is proposed for validation of autonomous robot multi-waypoint navigation utilizing graph-based models with adjacent node selection. In addition to time and distance efficiency, the proposed graph-based models incorporate safety in relation to the robot's environment as a driving feature. These models provide path planning for autonomous robot experiments. This methodology is implemented in a real-world environment with simulated dynamic obstacles utilizing a Clearpath Jackal unmanned ground vehicle (UGV) featuring four-wheel drive, GPS, odometry, and a Velodyne VLP-16 LiDAR as the experimental robot configuration. The LiDAR unit utilized has a one hundred meter range with a thirty degree vertical field of view, as the primary navigation sensor. It is utilized to map the environment prior to performing autonomous navigation experiments. This robot configuration is also presented in a simulated environment for more thorough evaluation. The environments to be examined are controlled with obstacles and environment features within the expected view of the LiDAR. The proposed graphbased model is evaluated in terms of planning efficiency from simulated to real-world environments including dynamic obstacle avoidance. © 2023 SPIE. All rights reserved.

关键词： Navigation

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Proceedings - 17th International conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

Proceedings - 17th International Conference on Signal-Image ...

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17th International conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

ISBN: (纸本)9798350370911

The proceedings contain 68 papers. The topics discussed include: advanced IoT edge architecture for smart city;foreground-preserving background modification: a deep learning approach;basketball players identification and tracking using a single fixed camera;augmenting collaborative practices: the Hololiver case study;deceiving deep learning-based fraud SMS detection models through adversarial attacks;AI-RIDE: a multi-camera system for the evaluation of motorcycle driving test;data catalog: approaches, trends, and future directions;smart soft sensor for froth flotation quality monitoring using pre-trained feature extractor and vision transformer ViT;energy performance profiling of a GPU-based CPM implementation;and an overview of the approaches for generating test data in the context of the quality assurance of big data applications.

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Implementation options of a fully SiC Buck-CSI for advanced motor drive application 24

Implementation options of a fully SiC Buck-CSI for advanced ...

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24th European conference on Power Electronics and applications (EPE ECCE Europe)

作者： Lee, Yonghwa Castellazzi, Alberto Kyoto Univ Adv Sci Solid State Power Proc SP2 Lab Ukyo Ward 18 Gotanda Cho Kyoto 6158577 Japan

ISBN: (纸本)9789075815399

This paper discusses the design option for a current source inverter for a 15kW high-speed machine with a wide-band-gap technology-based electric motor drive. Both control and modulation options are considered, as well as the integration of silicon carbide (SiC) bi-directional switches to yield highperformance bi-directional switches, as required by the topology. Recently, by taking advantage of the high-speed switching performance of SiC, a simple and robust control design method without voltage sensing and the Two-Third Modulation (TTM) method, which has the benefit of high efficiency, have been proposed. Based on this recent work, this paper proposes the simplified current-sensor-less controller of the CSI-fed PMSM. Also, this paper compared the characteristics of the TTM with the conventional PWM methods by simulation models.

关键词： Current Source Inverter (CSI) Silicon Carbide (SiC) High-speed drive High frequency power converter High power density systems

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Time Machine: Generative Real-Time Model For Failure (and Lead Time) Prediction in HPC systems 53

Time Machine: Generative Real-Time Model For Failure (and Le...

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53rd Annual IEEE/IFIP International conference on Dependable systems and Networks (DSN)

作者： Alharthi, Khalid Ayed Jhumka, Arshad Di, Sheng Gui, Lin Cappello, Franck McIntosh-Smith, Simon Univ Warwick Coventry England Univ Chicago Argonne Natl Lab Chicago IL USA Kings Coll London London England Univ Bristol Bristol England Univ Bisha Bisha Saudi Arabia Alan Turing Inst London England Univ Illinois Champaign IL USA

ISBN: (纸本)9798350347937

High Performance Computing (HPC) systems generate a large amount of unstructured/alphanumeric log messages that capture the health state of their components. Due to their design complexity, HPC systems often undergo failures that halt applications (e.g., weather prediction, aerodynamics simulation) execution. However, existing failure prediction methods, which typically seek to extract some information theoretic features, fail to scale both in terms of accuracy and prediction speed, limiting their adoption in real-time production systems. In this paper, differently from existing work and inspired by current transformer-based neural networks which have revolutionized the sequential learning in the natural language processing (NLP) tasks, we propose a novel scalable log-based, self-supervised model (i.e., no need for manual labels), called Time Machine1, that predicts (i) forthcoming log events (ii) the upcoming failure and its location and (iii) the expected lead time to failure. Time Machine is designed by combining two stacks of transformer-decoders, each employing the self-attention mechanism. The first stack addresses the failure location by predicting the sequence of log events and then identifying if a failure event is part of that sequence. The lead time to predicted failure is addressed by the second stack. We evaluate Time Machine on four realworld HPC log datasets and compare it against three state-of-the-art failure prediction approaches. Results show that Time Machine significantly outperforms the related works on Bleu, Rouge, MCC, and F1-score in predicting forthcoming events, failure location, failure lead-time, with higher prediction speed.

关键词： Transformer decoder, LSTM, failure prediction, logs, HPC systems, deep learning, Time Machine, Generative Model, Lead Time Prediction, supercomputer, large scale systems, anomaly prediction

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A FLEX-FUEL sensor AND CONTROL STRATEGY FOR VARIABLE CONCENTRATIONS OF HYDROGEN-NATURAL GAS BLEND FUEL IN AN INTERNAL COMBUSTION ENGINE

A FLEX-FUEL SENSOR AND CONTROL STRATEGY FOR VARIABLE CONCENT...

引用

ASME International Design Engineering Technical conferences / 42nd Annual Computers and Information in Engineering conference (IDETC-CIE) / 25th International conference on advanced Vehicle Technologies (AVT)

作者： Dogal, Michael Anstrom, Joel Iyer, Suresh Penn State Univ Dept Mech Engn University Pk PA 16802 USA Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA

ISBN: (纸本)9780791887288

This paper proposes an ultrasonic sensor methodology to measure concentrations in a binary gaseous alternative fuel system of hydrogen and natural gas for automotive applications. Further, it proposes a sensing and control architecture that enables alternative fuel vehicles, in this case a series hybrid electric vehicle, to fuel and operate their conventional IC engines on variable concentrations of hydrogen and compressed natural gas (HCNG). This represents a new type of flex fueling capability namely Flex-HCNG. The proposed Flex-HCNG system includes the ability to refuel from any hydrogen or natural gas fueling station into a single high-pressure on-board storage tank and then to start and run under changing fuel concentrations. The benefits of Flex-HCNG will be greater demand for both natural gas and hydrogen by effectively increasing available fueling infrastructure and providing security through greater fuel choice. Increasing demand for hydrogen prior to widespread availability of fuel cell vehicles will facilitate faster transition to renewable hydrogen and natural gas economy.

关键词： Ultrasonic transducers Storage tanks Internal combustion engines Vehicles High pressure (Physics) Fuel systems Security Automotive industry Hybrid electric vehicles sensors Natural gas Fuels Fuel cell vehicles Hydrogen Economics Compressed natural gas

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Future Communication systems: Toward the Intelligent-based Optimization Approaches 10

Future Communication Systems: Toward the Intelligent-based O...

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10th International conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Kouhalvandi, Lida Alibakhshikenari, Mohammad Matekovits, Ladislau Shayea, Ibraheem Ozoguz, Serdar Dogus University Department of Electrical and Electronics Engineering Istanbul Turkey Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Madrid Leganés28911 Spain Politecnico di Torino Department of Electronics and Telecommunications Turin Italy Politehnica University Timisoara Department of Measurements and Optical Electronics Timisoara Romania Istituto di Elettronica e di Ingegneria Dell'Informazione e Delle Telecomunicazioni National Research Council Turin Italy Istanbul Technical University Department of Electronics and Communication Engineering Istanbul Turkey

ISBN: (纸本)9798350329674

The wireless communication systems are expecting to include the high-end service quality for the users and customers. In the last decade, the industry and academia are studying effectively on the sixth generation (6G) systems, since the high performance network connectivity will influence on the number of use cases and also varied applications. For this case, new challenging requirements with key specifications need to be considered deeply for fulfilling the technical drawbacks. In the very recently published papers, it is recognized that researchers focus on solving various problems appeared in the communication systems through advanced optimization methods based on the artificial intelligence (AI). This study devotes to summarize newly and recently published studies where they use optimization and AI-based approaches for tacking their problems related to the communication systems. This review will help readers to discover the suitable AI-based approach for their own challenges. © 2023 IEEE.

关键词： Deep neural networks

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MCDPS: Enhancing Clinical Decision Support for Multiple Chronic Disease Prediction systems Using Ensemble Machine Learning Approaches 4th

MCDPS: Enhancing Clinical Decision Support for Multiple Chro...

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4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India Indian Inst Technol Technol Innovat Hub Patna 801103 Bihar India

ISBN: (纸本)9783031821554;9783031821561

People today suffer from various chronic diseases due to lifestyle choices and environmental conditions. Early prediction of these diseases is crucial to prevent them from worsening. However, it is often challenging for physicians to accurately diagnose these issues independently. A computational model based on big data analytics has numerous applications in the medical field. Advancements in machine learning (ML) and information technology have enabled more accurate recognition of diseases, health emergencies, and conditions. The advent of predictive algorithms has transformed disease diagnosis in the medical field. Previously, patient data records were meticulously examined to predict diseases and develop advanced models for trend analysis. However, with the rise of data analytics-driven intelligence, the evaluation of disease symptoms has become more accurate and efficient. These technologies enable the creation of predictive models capable of analyzing vast amounts of patient data to identify hidden patterns, aiding in early disease detection and diagnosis. This study explores the development of an ensemble ML-based multiple chronic disease prediction system (MCDPS) focused on three major diseases: chronic kidney disease (CKD), liver disease, and heart disease. The system utilizes ensemble ML models, including Decision Tree (DT), Gradient Boosting Classifier (GBC), Extreme Gradient Boosting (XGBoost), and Extra-Trees classifiers (ETC), to predict diseases. Its user-friendly and straightforward design makes it accessible to beginners. The system's performance in predicting the three chronic diseases has been evaluated and compared with other traditional models. The results show that the DT algorithm achieved an accuracy of 99%, 85%, and 83% for predicting CKD, heart disease, and liver disease, respectively.

关键词： Multiple Chronic Disease Prediction System Machine Learning Decision Tree Gradient Boosting Classifier Extreme Gradient Boosting Extra-Trees classifiers

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Heterotypic Current Transformer Design for Overcurrent Protection of SiC MOSFET 38

Heterotypic Current Transformer Design for Overcurrent Prote...

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IEEE Applied Power Electronics conference and Exposition (APEC)

作者： Du, Xia Du, Liyang Chen, Yuxiang Stratta, Andrea Wei, Yuqi Li, Xiaoling Mantooth, H. Alan Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Univ Nottingham Dept Elect & Elect Engn Nottingham England

ISBN: (纸本)9781665475396

Overcurrent protection of silicon carbide (SiC) MOSFETs plays a critical role in the medium voltage (MV) applications to avoid severe damage to the hardware, and there are more challenges because of the much higher insulation voltage required and the faster switching speed of SiC devices. To measure the power device switching current under these harsh conditions, highly integrated, compatible, and strongly insulated current sensors became the major technological breakthroughs to be solved. This paper presents a heterotypic current transformer design with strong compatibility and high insulation to realize overcurrent protection. A ferrite rod wound with copper wires is placed into a vertical bar Omega shape current conductor tube. This solution can be easily integrated into bus bars or power modules. The modeling analysis and design considerations of Omega-shape copper tube are discussed. The high insulation capability was realized by encapsulating the current transformer into the high dielectric strength silicone gel. Experimental Hi-Pot test proves the insulation capability up to 5 kV. Additionally, a double pulse test is carried out to evaluate the proposed current sensor performance, which can realize 72.3ns fast response including 16ns detection time and 56.3ns action time for overcurrent protection of SiC MOSFETs.

关键词： Heterotypic Current Tansformer Overcurrent Protection(OCP) Integrated Current sensor Silicon Carbide (SiC) MOSFET High Voltage Insulation Strong Capability Fast Response

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