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检索条件"任意字段=Proceedings of the 3rd International Conference on Parallel and Distributed Information Systems"

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Applying information and WSN Technologies for Optimizing Complex Farming Ecosystem 3rd

Applying Information and WSN Technologies for Optimizing Com...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： Kapil, Anil Venugopal, Anita Athavale, Vijay Anant Surajmal University Udhamsingh Nagar Uttarakhand Kichha India Department of FP IT Unit Dhofar University Dhofar Oman Walchand Institute of Technology Maharashtra Solapur India

ISBN: (纸本)9789811979811

Today, network communication holds a crucial importance of all other types of networks. Of all the advanced network structures, distributed networking system has huge demand. Smart dust network which is one of the categories of distributed networks is the future of distributed networks and has its applications in crucial fields. It is envisioned to blend the features such as ability to sense, compute, and communicate wirelessly. These micro devices can be sprinkled to form a dense network that can monitor real-life processes with high precision results. In this work, we present the characteristics, applications and possibility of using artificial technologies in Smart Dust networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Ecosystems

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Dynamic Fitting Multi-Task Network for Multi-PAR in Smart City 3

Dynamic Fitting Multi-Task Network for Multi-PAR in Smart Ci...

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3rd IEEE international conference on Digital Twins and parallel Intelligence, DTPI 2023

作者： Sun, Haoyun Yu, Zepei Zhao, Hongwei Zhang, Weishan Xu, Liang Zhou, Jiehan School of Computer Science and Technology Qingdao China Beijing University of Science and Technology School of Computer Science and Technology Beijing China University of Oulu School of Network Information Systems Oulu Finland

ISBN: (纸本)9798350318470

Multi-pedestrian attribute recognition (MPAR) is an important task for smart city surveillance. Most existing works focus on the scenario with simple background, lacking performance and accuracy for practical deployment. There needs a comprehensive solution for multi-pedestrian attributes recognition which can perform multi-pedestrian detection, pedestrian attribute recognition, pedestrian re-identification in a well-designed inference network to meet both accuracy and performance requirements. This paper proposes a novel end-to-end Dynamic Fitting Multi-Task Network (DFMTN) for multi-Pedestrian attribute recognition (DFMTN4PAR). DFMTN can perform pedestrian detection, pedestrian attribute recognition and pedestrian re-identification in one shot in a single neural network by adopting a multi-task learning strategy, boosted with the proposed dynamic fitting method to prevent gradient explosions. We have extensively evaluated the proposed DFMTN4PAR approach for its accuracy and performance, and compared with the state of art work, which shows that DFMTN4PAR can significantly improve the detection efficiency while ensuring accuracy, and compared with DeepMAR, the DFMTN4PAR approach improves the pedestrian attributes recognition accuracy by 5%-20%, and the detection speed by 30%-60%. The DFMTN4PAR approach has been successfully deployed to 10 smart residential areas in Qingdao, China and runs stably for over a year. © 2023 IEEE.

关键词： Smart city

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3D Single Target Tracking Algorithm Based on Dynamic Search Center 29

3D Single Target Tracking Algorithm Based on Dynamic Search ...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhu, Yanping Chen, Qi Jiang, Aimin Zhu, Chenyang Liu, Chang Chen, Wenlong Changzhou University College of Microelectronics and Control Eng. Changzhou China Changzhou University College of Computer Sci. and Ai Changzhou China Hohai University College of Information Sci. and Eng. Changzhou China

ISBN: (纸本)9798350330717

In the realm of 3D single object tracking (SOT), Siamese network-based algorithms have shown remarkable performance. However, they can lose track of targets in complex environments. To enhance tracking accuracy and continuity, in this paper we introduce a new re-detection mechanism with a dynamic anchor relocation strategy. This mechanism includes: (i) dynamic anchor generation: When tracking fails, dynamic anchors are generated and adjusted until the target is re-detected. The most confident anchor prediction becomes the tracking result;(ii) model aggregation: A re-detected module is integrated into the PTT-Net framework, significantly improving tracking accuracy compared to other algorithms;(iii) reliability assessment: An assessment criterion for tracking reliability in PTT-Net is established to balance accuracy and speed. © 2023 IEEE.

关键词： dynamic anchor object tracking re-detection mechanism Siamese network tracking reliability

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parallel distributed implementation of neuroevolution of augmenting topologies in continuous control tasks 3

Parallel distributed implementation of neuroevolution of aug...

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3rd IEEE international conference on Advanced Trends in information Theory, ATIT 2021

作者： Achour, illia Doroshenko, Anatoliy information Systems and Technologies Department National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic institute" Kyiv Ukraine

ISBN: (纸本)9781665438452

This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes batch genome evaluation for the purpose of performance optimization, fair, and even computational resources usage. The benchmarking shows that the generated neural networks evaluation process can give orders of magnitude increase of efficiency on the demonstrated continuous control task and computational environment. © 2021 iEEE.

关键词： Reinforcement learning

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Evaluating the Efficacy of Machine Learning Methods in Phishing Detection: A Comparative Analysis 3

Evaluating the Efficacy of Machine Learning Methods in Phish...

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3rd IEEE international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Saraswathi, P. Keerthi, S. Reshmika, K.S. Gourav Pritham, G.R. Prabha, M. Dhiyanesh, B. Velammal College of Engineering and Technology Department of Information Technology Madurai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

In the field of cybersecurity, the ongoing issue of phishing attacks is still of utmost concern because it puts both individuals and companies at serious risk. In order to prevent these fraudulent efforts and protect sensitive data, it is essential to implement effective detection and mitigation mechanisms. This research undertakes a thorough comparative evaluation of various machine learning models designed for phishing detection. Through a review of the literature on phishing detection techniques, we highlight the need for a methodical comparison analysis to identify the most effective strategies. In order to close this gap, we carefully assess the effectiveness of well-known machine learning methods, including Neural Networks, Gradient Boosting, XGBoost, Decision Trees, Random Forests, Support Vector Machines (SVM), and Logistic Regression. Using datasets from reliable sources, such as the PhishTank open-source service and the University of New Brunswick's dataset, we create a balanced dataset with 5000 authentic URLs and the same amount of phishing URLs. Our approach includes thorough feature extraction and preprocessing steps to guarantee the accuracy and applicability of the dataset. To evaluate each model's effectiveness, key performance indicators such F1-score, precision and recall are used. Our findings highlight subtle variations in the models' functionality and provide insight into their advantages and disadvantages for phishing detection. Additionally, we use visualisation approaches to explain each model's outputs, which offer deeper insights into the operational dynamics of each model. Our results reveal subtle differences in the performance of the models, explaining their various advantages and disadvantages in the field of phishing detection. The resulting comparative study provides useful information about the effectiveness of machine learning techniques, which helps decision-makers in practical deployment settings. © 2024 IEEE.

关键词： Phishing

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3rd international Symposium on New Energy and Electrical Technology, ISNEET 2022

3rd International Symposium on New Energy and Electrical Tec...

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3rd international Symposium on New Energy and Electrical Technology, ISNEET 2022

ISBN: (纸本)9789819905522

The proceedings contain 102 papers. The special focus in this conference is on New Energy and Electrical Technology. The topics include: Novel Design Technique of Fuzzy Adaptive PI Regulator for Permanent Magnet Synchronous Motor;multi-port Energy Router for Virtual Motor Control;bidirectional Communication Between parallel Buck Converters;identification of Key State information of Substation Equipment Based on Text Mining and Semantic Analysis Technology;a Simulation Method for the Supply-Demand Conditions of Credits Under Corporate Average Fuel Consumption and New Energy Vehicles Credit Policy;Price Discovery and Volatility Modelling in the EU ETS: Evidence from Phase III;research on Intelligent Energy Management System for Differential Pressure Power Generation;distributed Cooperative Control for DC Microgrid Clusters Interconnected by Multi-port Converter;Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network;real-Time Electromagnetic Transient Simulation for Regional Power Grid Based on Cloudpss;IP Core Design of Phase-Locked Loop for Grid-Connected Photovoltaic Inverter Based on FPGA;Prediction of Minimum Miscibility Pressure (MMP) of CO2-Crude Oil System Based on GWO-RBF Neural Network;Research on Operation Optimization of Crude Oil Pipeline Based on PPSO Algorithm;research on Life Prediction Method of Motor Bearings;research and Application of Power Distribution Monitoring System Based on Edge Computing;research on Transformer Inter-turn Fault Protection Method Based on Innovation Graph;research on Relay Protection of Active Distribution Network Based on Innovation Graph;optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm;Decoupling Control Strategy for Multi-active Bridge DC/DC Converters Based on Dichotomy;preface.

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Augmenting Radar Data via Sampling from Learned Latent Space 3

Augmenting Radar Data via Sampling from Learned Latent Space

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3rd IEEE international conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Scholz, Daniel Kreutz, Felix Gerhards, Pascal Huang, Jiaxin Hauer, Florian Knobloch, Klaus Mayr, Christian Infineon Technologies Dresden Technische Universit ät Dresden Dresden Germany Infineon Technologies München Munich Germany Technische Universit ät Dresden Highly-Parallel Vlsi Systems and Neuro-Microelectronics Dresden Germany

ISBN: (纸本)9798350335262

Data augmentation is a widely used technique to regularize deep learning models. It is especially famous in computer vision due to its simplicity to apply. Literature suggests numerous ways of transforming images without changing the characteristic semantics. However, for data coming from sensors such as radar these approaches are not applicable leading to data augmentation being not commonly performed. To solve this problem and close the gap we investigate how a Variational Autoencoder (VAE) can be trained on radar data to sample from the learned latent space and use the resulting data to regularize the training of a classifier. We run our experiments on two radar gesture datasets and show that the introduction of generated data can increase generalization. We investigate whether the learned embedded space is sufficient and propose how to sample from the latent space while preserving labels for successful supervised training. © 2023 IEEE.

关键词： Semantics

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FPGA Based High Throughput LK Optical Flow Acceleration Design 3

FPGA Based High Throughput LK Optical Flow Acceleration Desi...

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3rd international conference on Electronic information Engineering and Data Processing, EIEDP 2024

作者： Liang, Wei Sun, Ting Xing, Fei Dong, Xinran Xiao, Heng Li, Lin School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China Department of Precision Instrument Tsinghua University Beijing100084 China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China

ISBN: (纸本)9781510680531

In the field of machine vision, algorithms for estimating optical flow in sequential images have been proposed for many years. However, due to the advancements in image resolution and video frame rates of sensor, the optical flow algorithm running on a CPU or a GPU may struggle to meet real-time processing and low power consumption requirements. This paper presents an FPGA-based accelerated method for high-speed LK optical flow estimation. We implemented a serial input and multi-window output buffer structure to enhance the algorithm's parallel processing capability and improve processing speed. Additionally, we designed a multi-clock convolution structure to reduce resource consumption. Tested on the Middlebury dataset, the FPGA-based LK optical flow algorithm developed in this paper can achieve a processing speed of 330 frames per second for image sequences with a resolution of 640x480. It also maintains a relatively high level of accuracy and has low power consumption. © 2024 SPIE.

关键词： Optical flows

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A Hybrid Prediction Method for Lithium-Ion Battery Degradation: SMA-ARIMA-LSTM Integration 3

A Hybrid Prediction Method for Lithium-Ion Battery Degradati...

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3rd international conference on Communications, information System, and Data Science, CISDS 2024

作者： Li, Haoran Bai, Yanhong Sun, Yongchao Zhi, Huixue Department of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China Department of Intelligent Manufacturing and Industry Shanxi University of Electronic Technology Linfen China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Accurately predicting the lifespan of lithium-ion batteries is crucial for reducing maintenance costs and advancing clean energy technologies. Traditional prediction methods often to accurately estimate the lifespan due to the diverse volatility characteristics of lithium-ion battery degradation. This paper proposes a Kurtosis-driven SMA-ARIMA-LSTM method to the lifespan of lithium-ion batteries. First, the data is decomposed into low and high volatility components using a moving average (SMA). Then, the I model is applied to the low volatility part, while the LSTM network handles the high volatility part. Finally, the parallel prediction results of these two parts are combined to the remaining lifespan of the lithium-ion battery. The model is validated using four sets of CS2 series lithium-ion battery degradation data provided by the CALCE center at University of Maryland. The results show that the hybrid model significantly improves prediction accuracy compared to standalone LSTM or ARIMA models, achieving near-perfect determination coefficients while significantly mean and root mean square errors. This effectively captures the overall degradation trend of the battery and the capacity regeneration phenomenon. The experimental results demonstrate that the proposed SMA-IMA-LSTM method achieves a fitting rate of over 95%, with MAE kept within 0.9% and RMSE kept within 1.4%, thus realizing precise prediction of the remaining lifespan of lithium-ion batteries. © 2025 SPIE.

关键词： autoregressive integrated moving average battery capacity prediction kurtosis optimized moving average lithium-ion battery long short-term memory network

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Verifcation of Academic Records Using Hyperledger Fabric and IPFS 3

Verifcation of Academic Records Using Hyperledger Fabric and...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Khaleelullah, Shaik Vangapalli, Sai Teja Gaddam, Malavika Hanumakonda, Vitesh Sai Goud Gangapuram, Uday Kiran Vignan Institute of Technology and Science Department of Information Technology Hyderabad India

ISBN: (纸本)9798350322842

Document forgery has become a significant concern recently due to the rapid development of modern technology and the simplicity of obtaining inexpensive, cutting-edge workplace supplies. Numerous cases of academic certificate fraud have been documented due to the lack of an effective anti-forgery system. Due to the lack of an effective anti-forgery system, incidents that result in the academic certificate being faked are frequently observed. As a result, methods for educational credential verification and authentication are becoming more and more necessary. Blockchain technology offers a promising solution for creating a secure, transparent, and immutable system for storing and verifying academic records. This paper proposes a permissioned blockchain-based system for verifying academic records using Hyperledger Fabric and InterPlanetary File System(IPFS). Hyperledger Fabric is a distributed ledger platform that supports smart contracts and private transactions among authorized participants. IPFS is a peer-to-peer file system that enables decentralized data storage and retrieval. The system allows academic institutions to issue digital certificates to their graduates and store them on IPFS. The hash of the certificates is then recorded on the Hyperledger Fabric blockchain. When a process is started to check if an academic certificate is real, the credentials are pulled from the off-chain database, and their hashes are generated. This hash is then compared to a hash that is already on the Blockchain. The suggested model completely satisfies all digital document verification system requirements by addressing the shortcomings and challenges in the current document verification methods. © 2023 IEEE.

关键词： Smart contract

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