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YOLO-CCA: An Encoder-Decoder Framework Vehicle Detector Based on Channel Attention 5

YOLO-CCA: An Encoder-Decoder Framework Vehicle Detector Base...

引用

5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Liu, Xuyang Liu, Lingyun Liu, Tianle School of Civil and Transportation Engineering Hebei University of Technology Tianjin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Aeronautics Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350333442

Vehicle detection (VD), aiming at detecting the position of vehicles, can largely promote the development of connected and autonomous vehicles and intelligent transportation systems. Most VD algorithms cannot meet the demand of real-time road monitoring, due to some unsolved problems: (i) low accuracy for small vehicle targets that is demanding for real-time vehicle monitoring;(ii) high computational cost that is hard to deploy in edge devices. To solve these problems, this paper introduces a novel VD algorithm called YOLO-CCA inspired by channel attention technique. Efficient Channel Attention (ECA) and Encoder-Decoder modules are adopted into YOLO-CCA algorithm for VD. With the introduction of the Complete-IoU (CIoU) loss function, the convergence rate is also accelerated. YOLO-CCA algorithm is capable of capturing cross-channel information and maximizing it, which leads to high accuracy for vehicle detection, especially for small vehicle targets detection. Extensive experiments based on the UA-DETRAC public dataset demonstrate that YOLO-CCA outperforms the other four baseline algorithms, and improve 5.6% to the best baseline algorithm in small vehicle targets detection. © 2023 IEEE.

关键词： Autonomous vehicles

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3DFed: A Secure Federated Learning-based System for Fault Detection in 3D Printer Industry 13

3DFed: A Secure Federated Learning-based System for Fault De...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Paramartha Putra, Made Adi Verana, Mark Sampedro, Gabriel Avelino Kim, Dong-Seong Lee, Jae-Min Informatic Enginering Stmik Primakara Denpasar Indonesia Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

This paper proposes a secure federated learning (FL) approach for 3D printer fault detection, namely 3DFed. Most current 3D fault detection systems were developed with a centralized learning approach, which is less efficient for large-scale deployment due to limited data for training. The FL-based system could be exploited to further increase fault detection accuracy while maintaining high performance by using the FedAvg algorithm. The 2D convolutional neural network (CNN) was used to extract data features from an image array. To further improve security in the simulation work, a certificate authority (CA) was added to maintain secure communication between the FL server and clients. The suggested 3DFed with the proposed CNN-based model can deliver high classification accuracy while preserving minimum time-cost, according to a thorough performance evaluation. Also covered in depth is how total client variance affects the learning process. © 2022 IEEE.

关键词： 3D printers

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An Improved Slime Mould Algorithm with Quasi Reflection-based Learning for Global Optimization Problems 23

An Improved Slime Mould Algorithm with Quasi Reflection-base...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Qingxin Qi, Qi Jia, Heming Li, Ni School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Intelligence Education Ministry of Education Haikou China

ISBN: (纸本)9781665494571

The Slime Mould Algorithm (SMA) is a swarm-based method inspired by hunting behaviors of slime mould. However, SMA often suffers from slow convergence and local minima. In this paper, we propose an improved variant of SMA, coined as QRSMA, which is enhanced with the quasi reflection-based learning approach. The new algorithm involves two major components. First, the tent map is used to increase the population diversity in the initial phase. Second, the quasi reflection-based learning is introduced to increase the ability of exploitation. Finally, the performance of QRSMA is evaluated through assorted tests including thirteen benchmark functions and one engineering design problem against other four well-known optimization algorithms. The experimental results demonstrate better performances of the proposed QRSMA in exploring capability and convergence quality than other algorithms. © 2021 IEEE.

关键词： Image segmentation Sociology Benchmark testing Behavioral sciences Statistics Optimization convergence

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Routing Algorithm Based on High Latency Communication Node Awareness in Dynamic Networks 1

Routing Algorithm Based on High Latency Communication Node A...

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1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Meng, Xiaolei Zhao, Zhen Xie, Wenhua Su, Bengan Zhao, Shujian Liu, Yang Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao China Qingdao Zichai Boyang Diesel Engine Co. Ltd Qingdao266700 China

ISBN: (纸本)9798350323795

Aiming at the instability of communication links caused by environmental interference, dynamic change of node location, and long distance between nodes in the dynamic networking process of mobile clusters, this paper proposes an algorithm for high delay communication node perception and routing in dynamic networking, predicts the nodes that may generate high communication delay, and optimizes the path selection between high delay nodes to prevent network congestion. First of all, according to the communication status in the time series, the BiLSTM algorithm is used to predict the attenuation trend of the signal with the increase of distance, and calculate the distance between nodes that can establish efficient communication;Then, based on elbow method and K-means algorithm, group is divided according to the distance that can establish efficient communication, critical nodes are identified, and cost capacity diagram is constructed for critical nodes;Finally, according to the network flow optimization algorithm with maximum cost and maximum flow constraint, the best matching link is calculated and mapped to the communication path between critical nodes. By planning the path selection strategy between nodes with high latency communication in the cluster, the problem of unstable communication in the cluster is solved with less system overhead and faster convergence speed. © 2023 IEEE.

关键词： Network topology

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State Estimation of a Utility-Scale Hybrid AC/DC Grid using a Decentralized Approach 7

State Estimation of a Utility-Scale Hybrid AC/DC Grid using ...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Manikandan, K. Sampath Kumar, R. Vignesh, D. Department of EEE Sree Vidyanikethan Engineering College Tirupati517102 India Department of EEE KPR Institute of Engineering and Technology Coimbatore India Department of EEE Don Bosco Polytechnic College Chennai India

ISBN: (纸本)9781665464086

The main focus on the renewable energy resources to meet out the energy demand of the customers by integrating these generations with the AC grid on the Transmission end has necessitated to the maintenance of the voltage magnitude, phase angle and frequency within the specified limits. This challenge is aided by power system state estimation. The power system state estimation is the process of entrusting a value to an unknown system state variable based on measurement from that system according to system conditions. This paper presents a decentralized state estimation technique to estimate the state of a utility-scale hybrid AC/DC Network. The hybrid test system is formed by integrating the 5 bus DC system into IEEE 24 bus Reliability Test system which is taken as test case. The Weighted Least Square (WLS) method is used to perform the state estimation problem for the hybrid system in a decentralized manner. The state variables of hybrid system are estimated by using DIgSILENT software. The values obtained from the test case are compared with the true values which are taken from the conventional load flow measurements. The accuracy, convergence, normalized error and robustness of the test system are also analyzed. © 2023 IEEE.

关键词： State estimation

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CNN-Based Skin Lesion Classification for Melanoma Detection 7th

CNN-Based Skin Lesion Classification for Melanoma Detection

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Thiruvallur Chennai India

ISBN: (纸本)9783031699818

The significant worldwide health risk posed by skin cancer, especially melanoma, highlights the urgent need for sophisticated diagnostic techniques to facilitate early diagnosis. The goal of this research is to improve the efficiency and accuracy of melanoma diagnosis by doing a thorough analysis of dermatological pictures using CNNs. Two different CNN models are implemented: one uses the ResNet50 architecture for feature extraction, while the other is a specially created CNN for the classification of skin lesions. Thorough preparation of the data, including rotation, flipping, and sophisticated methods like loading, scaling, and normalizing of images, guarantees the best possible model performance. Treating the underlying imbalances in skin lesion datasets is one of the main goals. This is accomplished by computing and adding class weights during training, which is especially important for data that isn’t balanced. Properly calibrated learning rates, accuracy as the evaluation measure, categorical cross-entropy loss, and the Adam optimizer are all used in the model optimization process. One special feature of this project is the incorporation of a progress indicator based on TQDM, which offers real-time information about model convergence when training. Through the careful preparation of data, careful CNN designs, and efficient techniques for handling unbalanced data, this program seeks to substantially enhance the field of skin cancer diagnosis. As the fight against skin cancer never ends, the conclusive goal is to enhance patient outcomes through more precise and early melanoma detection. © IFIP international Federation for Information Processing 2024.

关键词： Oncology

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Leak Detection and Classification of Water Pipeline Data Using LSTM Auto-Encoder with Xavier Initialization 8

Leak Detection and Classification of Water Pipeline Data Usi...

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8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Lee, Daehee Choo, Hyunseung Jeong, Jongpil Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Smart Factory Convergence Suwon Korea Republic of

ISBN: (纸本)9798350373615

Modern cities operate extensive water supply networks. In Korea, the water supply network coverage reaches 98.9%, but more than 10% of water is still leaking every year. Previously, leaks in water pipelines were detected using flow meters and pressure meters, but these methods could only detect leaks after they occurred, necessitating on-site investigations to accurately pinpoint the location of the leak. In the United States, aging water and sewage facilities incur economic losses of hundreds of billions of dollars every year. To address this issue, this paper proposes an LSTM (Long Short-Term Memory) Auto-Encoder model, enhanced with Xavier Initialization, that can detect leaks using water pipeline data and automatically identify the location and extent of the leakage. This model achieved 93.7% performance by applying the Xavier Initialization technique to the LSTM Auto-Encoder model and achieved relatively high accuracy compared to the model using only LSTM Auto-Encoder. The results of this paper can serve as an effective predictive tool for monitoring water pipeline leaks. © 2023 IEEE.

关键词： Leak detection

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Robust Facial Emotion Recognition System via De-Pooling Feature Enhancement and Weighted Exponential Moving Average

Robust Facial Emotion Recognition System via De-Pooling Feat...

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2024 IEEE Region 10 conference, TENCON 2024

作者： Win, Khin Cho Akhtar, Zahid Mohan, C. Krishna Indian Institute of Technology Dept. of Artificial Intelligence Hyderabad India State University of New York Polytechnic Institute Dept. of Network and Computer Security UticaNY United States Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350350821

Facial expression recognition (FER) in an uncontrolled environment presents a formidable challenge in affective computing and human-machine interaction domains. Existing FER models fail to generalize due to the innate expression nature of the intra-class separability and inter-class compactness. Although most state-of-the-art FER research focuses on more advanced frameworks and well-separated discriminative loss functions, further exploration is needed to obtain high-quality expression features. This paper proposes a robust feature enhancement approach for FER by integrating the De-pooling Feature Enhancement and Weighted Exponential Moving Average (WEMA) of Stochastic Gradient Descent (SGD). The proposed method utilizes De-Pooling Feature Enhancement to capture and enhance the high-quality expression features, while WEMA of SGD optimizes the training process for improved stability and convergence. Our extensive experimental analysis of benchmark datasets signifies that our proposed method prevails over state-of-the-art methods, achieving superior accuracy performances of 86.43% on FER2013, 94.97% on FERPlus, 94.71% on RAF-DB, as well as 80.62% and 72.90% on AffectNet, of 7 class and 8 class, respectively. © 2024 IEEE.

关键词： Stochastic systems

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Optimization of Power allocation coefficient for two-user NOMA using Nature-inspired optimization algorithms 14

Optimization of Power allocation coefficient for two-user NO...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ahuja, Shivam Chhabra, Hardik Jain, Aarti Netaji Subhas University of Technology Department of Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350335095

Power domain Non-Orthogonal Multiple Access (PD-NOMA) is an emerging name amongst the candidate technologies which are being considered for the deployment of fifth-generation networks. This medium access technique employs superposition of signals at the transmitter side for transmission and successive interference cancellation to retrieve the separate signals at individual receivers. For maximum utilization of PD-NOMA technique, the optimization of the power allocation coefficient values used for the superposition of the signals is crucial. This paper proposes nature inspired (NI) algorithms viz. Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Differential Evolution (DE), and Simulating Annealing (SA) for finding the optimum value of the power allocation coefficient for two user PD-NOMA. Numerical results show that by calculating power allocation coefficient using NI algorithms, the performance of the PD-NOMA has been enhanced in terms of Probability outage with respect to SINR. Moreover, simulations are performed to calculate power allocation coefficient by varying distance between two receiver nodes. Further, a comparison between different NI algorithms based power allocation coefficient calculations is compiled in terms of computational time and convergence and it is observed that the DE algorithm-based power allocation co-efficient calculations outperforms all other algorithms by 42.65%. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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eGaz: Enhanced Search Engine for Gazette Publications 7

eGaz: Enhanced Search Engine for Gazette Publications

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Mendis, M.C.P. Perera, M.S.M. Karunaratne, J.M.P.D. Silva, K.K.S. Haddela, Prasanna S. Gunarathne, Dimuth Adeepa Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9781665421683

A government gazette is a periodical distribution that has been approved to distribute public or legitimate takes note. Presently, governments gazettes are published in the official website with the facility of downloading the individual files in portable document format. Even though these gazettes are categorized into a few types, individual gazettes contain diverse amount of material from various sectors of the government making it stiffer to the reader to search for their preferred apprises without reading the complete file. To address this problem as a possible convenient solution, Enhanced Search Engine for Gazette Publications was developed. The system is able to scrape gazette files from the official website, which is followed by a progression of text extraction, summarizing and clustering the extracted data and hoard them in a distributed system. With the use of the Content Search Engine, user could loop through any material issued over gazettes which would bring up filtered results from the extracted data. Similarly, users can read summarized reports of advertisements on job vacancies, examination details and results of examinations and could even filter them by relevant departments. © 2022 IEEE.

关键词： Search engines

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