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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2959 条记录，以下是951-960 订阅

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Smart System To Monitor and Control Transformer Health Condition in Sub-Station

Smart System To Monitor and Control Transformer Health Condi...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Sagor Dash Sanjoy Das MD. Baki Billah Balaram Das Istiaque Ahamed Abir Ahmed Department of Computer Engineering (CoE) American International University - Bangladesh (AIUB) Dhaka Bangladesh Department of Electrical and Electronics Engineering (EEE) American International University - Bangladesh (AIUB) Dhaka Bangladesh

A substation is the most important component of transporting or distributing electricity in any region or industry. To control and monitor the substation, different automation structures are developed in our country and around the world. The proposed system is to develop an IoT-based smart solution for monitoring and controlling the transformer in a substation. The control unit serves as the hub for all the system equipment and activities. Using an ultrasonic sensor and the DHT11, this smart solution can monitor transformer oil and temperatures. When the temperature of the transformer climbs over the specified value, the cooling fan turns on and provides sufficient air to lower the temperature. The voltage and current sensors' collected data determine whether the circuit is open or closed. This eliminates the expense at the substation by minimizing operating costs. As a result, both observational and operational effectiveness will undoubtedly improve.

关键词： Temperature sensors Temperature measurement Substations Prototypes Switches Oil insulation signal processing

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Tuna Optimization Algorithm-Based data Placement and Scheduling in Edge Computing Environments 3rd

Tuna Optimization Algorithm-Based Data Placement and Schedul...

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3rd international conference on Machine Learning, Internet of Things and Big data, ICMIB 2023

作者： Jayalakshmi, P. Subashka Ramesh, S.S. Department of CSE SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai India

ISBN: (纸本)9789819939312

Mobile edge computing (MEC) represents the promising technology that targets at facilitating different resources for processing and storing near the edge of mobile devices. Nevertheless, limited availability of resources in MECs possesses a necessity for adopting an appropriate management for preventing wastage of resources. In specific, the scheduling of workflow indicates a process that attempts in the mapping of tasks to the most suitable set of resources available in MECs based on the satisfaction of the objectives. In this paper, Tuna Optimization Algorithm-based data Placement and Scheduling (TOA-DPS) is proposed with improved convergence by preventing the problems of local optima to map tasks to most suitable resources. It adopted a method of task prioritization for determining the order in which the tasks in the scientific workflows are executed. Further, Tuna Optimization Algorithm (TOA) is utilized for attaining data-demanding workflow scheduling along with data placement using Dynamic Voltage and Frequency Scaling (DVFS) in MEC environments. The performance evaluation of proposed TOA-DPS-based scheduling mechanism is conducted using extensive simulations carried out over renowned scientific workflows of various sizes. The outcomes of the proposed TOA-DPS-based scheduling scheme confirmed better performance of data access by 21.38%, and minimized energy consumption by 19.84%, better than the baseline approaches used for investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile edge computing

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A Blockchain-Based Distributed Storage Network to Manage Growing data Storage Needs 3

A Blockchain-Based Distributed Storage Network to Manage Gro...

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3rd international conference on signal processing and Communication (ICPSC)

作者： Kanade, Vijay A. Pune India

ISBN: (纸本)9781665428644

IoT industry has come to the fore in this modern techno-frenzy age. This has caused digital data explosion and has kept the data storage industry on its toes. The paper proposes a novel blockchain-based data storage model where users contribute the storage space of their personal electronic devices to meet the growing data storage needs. The paper dives into a decentralized system design, along with an equitable compensation model for all the storage space contributing users. The decentralized framework provides a common platform for interaction between the storage space contributors and buyers. The proposed solution provides an alternative to cloud storage which relies heavily on servers.

关键词： Blockchain model Decentralized storage space Compensation model Contributing users IoT devices Cloud storage Blockchain hashing

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Comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy data 3rd

Comparative Analysis of Classification Methods to Predict Di...

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3rd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2021

作者： Jyothi, Uppalapati Padma Dabbiru, Madhavi Bonthu, Sridevi Dayal, Abhinav Kandula, Narasimha Rao Vishnu Institute of Technology A.P. Bhimavaram India Dr. L. Bullaya College of Engineering A.P. Vishakapatnam India

ISBN: (纸本)9789811958670

Diabetes mellitus is the century’s epidemic. It is a metabolic disease and causes high blood sugar. Diabetes prediction is usually performed through collecting a blood sample and testing with some conditions. It is difficult to immediately predict the diabetic condition for the ICU admitted patients from the past history. In this work, we propose an ML technique that suits this data. The proposed approach was evaluated in the context of WIDS 2021 challenge using different classification techniques like logistic regression, random forest, K-nearest neighbors, and ensemble models like XGBoost and LightGBM, achieving one of the best results in the challenge. Obtained results are verified using receiver operating characteristic (ROC) curves in a systematic manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Intelligent electrical fire warning system design 3

Intelligent electrical fire warning system design

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3rd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Yu, Lan Yang, Cai-Wei Jia, Zhen-Guo You, Guang-Hao Liu, Zi-Qi Zhao, Huan-Huan Changchun Institute of Technology Jilin Changchun China Jilin Building Energy Supply and Indoor Environment Control Engineering Research Center Jilin Changchun China

ISBN: (纸本)9781510663473

At present, Chinese electrical fire prevention and control is still in the development stage. The existing electrical fire monitoring products have technical defects, the false alarm rate is high, and the monitoring effect on electrical fire is limited. Because of these situations, the microcontroller STM32WLE5CCU6 and the sharp micro power quality chip RN8302B is used as the core, combined with the LSTM neural network, an intelligent front-end device with real-time monitoring of electrical fire data and data depth processing functions are designed, which realizes the monitoring and prediction of an electrical fire. And composed an intelligent electrical fire warning system capable of wireless communication and information interaction with intelligent front-end equipment, LoRa wireless communication, RS485 communication, and cloud platform. © 2023 SPIE.

关键词： data handling

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Energy-Efficient Virtual Machine Migration Algorithm in data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd international conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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FairdR: Ensuring Fairness in Mixed data of Fairly and Unfairly Treated Instances 1

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3rd CAAI international conference on Artificial Intelligence (CICAI)

作者： Liu, Yuxuan Kuang, Kun Zhang, Fengda Wu, Fei Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Fairness has emerged as a crucial topic in data mining and machine learning applications, driven by ethical and legal considerations. It is important to recognize that not all samples are treated unfairly, resulting in data heterogeneity in fairmachine learning. Existing fair models primarily focus on achieving fairness across all heterogeneous data, yet they often fall short in ensuring fairness within specific subgroups, such as fairly treated and unfairly treated data. This paper presents a novel problem of training a fair model on heterogeneous data, aiming to achieve fairness for both types of data, with a particular emphasis on the unfairly treated subset. To address this challenge, an effective approach is to recover the distribution of both fairly and unfairly treated data. In this study, we adopt the Structural Causal Model (SCM) to model the heterogeneous data as a mixture of causal structures. Leveraging the perspective of SCM, we propose a framework called FairdR, which utilizes the Hirschfeld-Gebelein-R ' enyi (HGR) correlation to accurately recover the distribution of both fairly and unfairly treated data. FairdR can serve as a pre-processing method for other fair machine learning models, providing protection for the unfairly treated members. Through empirical evaluation on synthetic and real-world datasets, we demonstrate that the presence of heterogeneous data can introduce unfairness in previous algorithms. However, FairdR successfully recovers the distribution of fairly and unfairly treated data, thus improving the fairness of downstream algorithms when dealing with heterogeneous data.

关键词： Fairness Causality Heterogeneity

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A Radar signal Sorting Algorithm Based on Improved FastICA

A Radar Signal Sorting Algorithm Based on Improved FastICA

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Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Yonghua He Xin Sang Yonggang Li Department of Electlonic and Optical Engineering University of Aerospace Engineering Beijing China

The FastICA algorithm is a novel iterative algorithm, also known as the fixed-point algorithm, proposed by Hyvrinen et al, at the University of Helsinki, Finland, unlike conventional neural network algorithms, FastICA uses a batch processing algorithm and requires larger data per iteration. In terms of both distribution and parallelism, the algorithm is still an artificial neural network-based algorithm. fastICA algorithms can be classified into three types: steepness, likelihood maxima, and negative entropy maxima. In this thesis, based on the FastICA algorithm based on negative entropy maximum, the algorithm has faster convergence speed and stronger robustness by introducing Newton iteration algorithm.

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Design and Implementation of IoT-Based Load Monitoring and Outage Management System

Design and Implementation of IoT-Based Load Monitoring and O...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Ahmed Muntasir Anwar Md. Rifat Hazari Mohammad Abdul Mannan Department of Electrical and Electronic Engineering American International University-Bangladesh Bangladesh

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the System collapses completely. This paper intents to utilize the idea to create a control system that will fulfill three objectives, monitoring of vital parameters controlling the power distribution, outage management by fault detection based on the variation of voltage, frequency, and current & protection of the circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The method used in this project can provide necessary safety from total system outages by adequately monitoring the instant data and historic data, managing the outage system by detecting faults, and cutting loads required to avoid a widespread blackout of a power system. Implementation of the proposed project can solve the problem of system blackout due to overload, under/over voltage, or under/over frequency. This developed system can supply necessary timestamped monitored data that can be accessed remotely and can also archive to create a proper load profile to ultimately help the modeling of Load Forecasting for a smooth and economic grid operation and can be used for developing the Smart Grid network.

关键词： Power distribution signal processing Predictive models Smart grids Safety Power system reliability Internet of Things

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Performance Analysis of Load Frequency Control for Power Plants Using Different Optimization Techniques

Performance Analysis of Load Frequency Control for Power Pla...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Niloy Goswami Abu Hena Md. Shatil Electrical and Electronics Engineering Department American International University-Bangladesh(AIUB) Dhaka Bangladesh

In this paper, several optimization techniques including the Particle Swarm Optimization (PSO) technique, the Genetic Algorithm (GA), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are applied to determine the most efficient output for load frequency control. These optimization techniques analyze the optimal level of system performance. The goal of this paper is to identify the most effective optimization technique for this sophisticated LFC system. In this research, three strategies (PSO, GA, ANFIS) are used in the LFC system to analyze frequency fluctuation and compare the load change rate. The model consists of the transfer function of the governor, turbine, rotating mass, and load. In this analysis, the ideal performance is examined across three separate case scenarios. The MATLAB/SIMULINK software simulates the performance analysis, which offers more realistic data and is generally preferred in this sort of optimization strategy work.

关键词： Fluctuations System performance Simulation Transfer functions Software Performance analysis Optimization

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