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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11091 条记录，以下是881-890 订阅

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MAML-TSC: Model-Agnostic Meta-Learning for Time Series Classification 7

MAML-TSC: Model-Agnostic Meta-Learning for Time Series Class...

引用

7th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Wang, Jiahao Sun, Zutong Yan, Hang Chen, Hao School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Intelligent Manufacturing and Information Technology Yaan Polytechnic College Yaan625000 China

ISBN: (纸本)9798350350890

User portrait technology, involves analyzing historical usage records of household electronic devices, is evolving quickly. Traditional methods using LSTM or CNN for time series classification face challenges in achieving high accuracy with limited data. To address this, our study introduces MAML-TSC, which combines a dual-channel Transformer with model-agnostic meta-learning (MAML). this model features a dual-channel Transformer that leverages a multi-head self-attention mechanism to extract information from both time and channel axes effectively. By incorporating MAML, the model harnesses knowledge transfer to optimize initialization parameters for diverse training tasks, enhancing performance with fewer data samples. Experimental outcomes show MAML-TSC achieving 90% accuracy in the meta-learning phase and maintaining over 80% after 10 iterations, outperforming the DeepConvLSTM baseline by 12.5% and surpassing most current deep-learning approaches. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning based Object Detection Methods: Current Status and Future Prospects 7

Deep Learning based Object Detection Methods: Current Status...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Wasid, Mohammed Ahmad, Shahnawaz Hussain, Shahadat Ansari, Mohd Saqib Bennett University School of Computer Science Engineering & Technology Uttar Pradesh Greater Noida India Aligarh Muslim University Department of Electronics Engineering Uttar Pradesh Aligarh India

ISBN: (纸本)9798350350067

Object Detection has evolved significantly since its inception in the early 2000s when Object Localization was regarded as a challenging task. the transition from Localization to object detection has seen rapid advancements in state-of- the-art techniques. the evolution of object detection can be broadly categorized into Traditional Methods and Deep Learning (DL) Methods. In this paper, we examine both approaches, highlighting the rise of YOLO (You Only Look Once) as a dominant force in Object Detection. We provide a comprehensive survey of all YOLO versions, alongside an overview of pre- YOLO object detection methods. Our discussion also explores optimizing object detection through architectural improvements and data enhancements, while considering future opportunities for increased efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Monitoring of Partial Discharge Inside High-Voltage Power Transformer Using UHF Sensors 7

Monitoring of Partial Discharge Inside High-Voltage Power Tr...

引用

7th IEEE international conference on Condition Assessment Techniques in Electrical Systems, CATCON 2024

作者： Dhara, Sourav Koley, Chiranjib Chakravorti, Sivaji Department of Electrical Engineering National Institute of Technology Durgapur Durgapur India Department of Electrical Engineering Jadavpur University Kolkata India

ISBN: (纸本)9798331530662

High-voltage power transformers at electrical substations play crucial roles in delivering electric power to consumers. Unattended partial discharge (PD) activities at components inside power transformer tanks cause transformer failures in most cases. UHF sensors have potential applications at power transformers for online non-contact monitoring of PD events inside transformer tank. A PD monitoring system detects active PD sources, keeps continuous track of PD locations, and provides early alarms. this paper presents studies on PD monitoring inside an HV power transformer using multiple disc-type UHF sensors mounted on its tank outer walls. the paper includes simulation and experimental investigations that show the merits and challenges of UHF sensors-based online PD monitoring within HV power transformer tanks. ©2024 IEEE.

关键词： UHF devices

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Construction of Generalized λμ-Ball Curves of Degree n 7

Construction of Generalized λμ-Ball Curves of Degree n

引用

7th international conference on Image, Vision and computing, ICIVC 2022

作者： Du, Bo Yuan, Xinguang Zhong, Jingyu Hu, Gang Xi'an University of Technology School of Science Xi'an China Xi'an University of Technology International Engineering College Xi'an China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

In Computer Aided Geometric Design (CAGD), the representation of curves and surfaces is a research focus. Benefiting from the fast computing speed and excellent properties of the basic functions, the Ball curves and surfaces have been widely used and deeply studied. In this paper, a generalized Ball curve with two parameters is constructed and discussed, called λμ-Ball curves. Firstly, based on the cubic Ball basic function with shape parameters, we derive the λμ-Ball basis functions of degree n by using the recursive algorithm. then, the properties of λμ-Ball basis functions are discussed. Finally, the G1 continuity conditions of the generalized λμ-Ball curve are given. the results show that λμ-Ball curve can be adjusted more flexibly by modifying the shape parameters and describing the complex geometry under the G1 continuity conditions. © 2022 IEEE.

关键词： Parameter estimation

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Competitive and Collaborative Learning Accelerates the Convergence of Deep Convolutional Neural Networks 7

Competitive and Collaborative Learning Accelerates the Conve...

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7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Dang, Yanbin Yang, Yuliang Chen, Yueyun Zhu, Mengyu Yin, Dehui School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Shunde China

ISBN: (纸本)9781665487115

In the training of convolutional neural networks (CNNs), the layer-by-layer learning based on the backpropagation (BP) algorithm causes that in each round of weights update, the learning of the latter layer determines the learning of the former layer, while the former layer cannot directly affect the latter layer. this means that the flow of error information is unidirectional, causing non-cooperative learning between layers, thereby reducing the convergence speed of the networks. In this work, we propose a network structure that evaluates the relative contribution of each layer in the CNNs to the final output error. During training, it indirectly realizes the bidirectional flow of information between layers, achieving the purpose of cross-layer collaborative learning. Our algorithm also fuses features at different scales on the detection networks, which we call the flexible feature fusion network(FFN). On public datasets, we have conducted rich experiments. With the help of FFN, the convergence speed of the object detection model is greatly improved. Without pre-training weight initialization, the convergence speed of the model is approximately doubled. © 2022 IEEE.

关键词： Training Power demand Fuses Object detection Detectors Feature extraction Collaborative work

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

引用

7th international conference on Data Science and Information technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

the proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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Explainable Artificial Intelligence Techniques for Image Classification Models in Diverse Domains 7

Explainable Artificial Intelligence Techniques for Image Cla...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Joshi, Pranjali P. Bagade, Anant M. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India SCTR's Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350304268

Over the past few decades, Deep Learning (DL), which is a subpart of Machine Learning (ML), has gained recognition as the dominant technique in diverse domains such as Computer Vision, Natural Language Processing, and Image Recognition. However, despite the exceptional performance of DL models, they are often referred to as of 'Black Box' nature, due to their limited ability to explain the decisions. this has led to a need for explainability of DL models. therefore to address this issue of lack of explainability in DL models, Explainable Artificial Intelligence (XAI) techniques have been improving in recent era. Unlike DL, XAI has the ability to provide both decision-making and model explanations for data. this research review comprises articles that utilize XAI techniques for image classification tasks. the purpose of this paper is to showcase the utility of the most recent XAI techniques employed in Image Classification Models (ICM). this paper will assist researchers to develop a better intepretation of XAI techniques for image classification as well as related challenges and future research directions. © 2023 IEEE.

关键词： Deep Learning (DL) Explainable Artificial Intelligence (XAI) Image Classification Model (ICM) Machine Learning (ML)

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An Improved Design of Low Power High Speed Configurable Logic Block using 90nm CMOS technology 7

An Improved Design of Low Power High Speed Configurable Logi...

引用

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

作者： Daiyan, K.M. Mustakim, Ahmed Abi, Shaiokh Bin Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9781665464086

Configurable Logic Block (CLB) is the basic computational component of FPGA. the existing methods of creating CLB suffer from temperature sensitivity, higher static and switching power consumption, high clock latency from numerous clocks, increased propagation delay, incompatible process technology with state of the art etc. this paper aims to form a low power design of CLB with minimized propagation delay and clock latency, less layout area and more compatible process technology than the existing ones. the overall architecture is created using Verilog code in EDA playground as well as Cadence Virtuoso with 90nm process technology. three input majority function with the Boolean logic, F= A.B+B.C+A.C is implemented with the proposed CLB. the functionality is checked and the performance of the CLB is evaluated by different design matrices. Various performance parameters such as active energy, leakage energy, maximum operating frequency etc. have been measured to be 9.123fJ, 1.148fJ and 100MHz respectively. the maximum propagation delay (79.27ps) and the power efficiency (1.505µW) have been calculated by Virtuoso Wave calculator. Finally, the optimized physical layout is obtained, which renders minimum area of 233.91 µm2, resulting in highly compact integration of transistors. © 2023 IEEE.

关键词： Table lookup

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A Multiobjective Memetic Algorithm for Workflow Scheduling on Heterogeneous computing Systems 23

A Multiobjective Memetic Algorithm for Workflow Scheduling o...

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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： Zou, Chengming Shi, Mengyuan Liu, Xing Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things Wuhan China School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China Peng Cheng National Laboratory Shenzhen China

ISBN: (纸本)9781665494571

As commercial and scientific workflow applications are increasingly deployed on heterogeneous computing systems, the scheduling of these workflow tasks to appropriate heterogeneous processors and the consideration of multiple optimization objectives have emerged as significant challenges. this paper proposes an effective multiobjective memetic algorithm with customized neighborhood structures (CNS-MOMA) to optimize the makespan and energy consumption simultaneously. First, the proposed algorithm incorporates a problem-specific chromosome encoding approach and a well-designed initialization approach to generate an excellent initial population. Second, we employ a local search operator with time-aware and energy-aware neighborhood structures to help the algorithm yield a better Pareto front. Finally, extensive simulation experiments are conducted on various benchmarks to evaluate its performance, and the results show CNS-MOMA is superior to two other well-known multiobjective scheduling algorithms in terms of the following three main performance criteria: Pareto front, convergence, and uniformity. © 2021 IEEE.

关键词： Memetics Energy consumption Scheduling algorithms Sociology Scheduling Heterogeneous networks Encoding

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E-Safe: An E-waste Management and Awareness Application using YOLO Object Detection 7

E-Safe: An E-waste Management and Awareness Application usin...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Alfatmi, Khalid Shinde, Falguni Shashikant Shahade, Makarand Sharma, Shalaka Sanjay Aruja, Sejal Sanjay Chaudhari, Tanmay Yograj SVKM's Institute of Technology Department of Computer Engineering Dhule India

ISBN: (纸本)9798350397253

technology and related fields are advancing rapidly in today's era, which is leading to the increased usage of electronic equipment especially mobile phones. this rapid growth in the usage of technological equipment gives rise to E-waste generation since the advancement in technology forces the users to replace their existing equipment with the advanced ones. this throw on the scrapheap is increasing exponentially, leading to the immense generation of E-waste. the growing amount of waste must be properly handled and disposed off. E-waste mainly consists of chemicals and metals, which can be harmful or toxic in nature. Giving a blind eye to the handling of this waste can lead to great mishaps. Here, it mainly focuses on the technologies used for the handling of E-waste and provide a technical solution, which is an Android Application. this will take the image for processing and based on YOLO algorithms, it will identify the electronic device from image and will list out the components of identified device. the final output of the application will make the E-waste management process easier for managing data and will work as an awareness tool to make people aware about the E-Waste threats. © 2023 IEEE.

关键词： Object detection

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