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共 37018 条记录，以下是1711-1720 订阅

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The Digital Twin Model of Chemical Production systems in Smart Factories: A Case Study 23

The Digital Twin Model of Chemical Production Systems in Sma...

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： Li, Heng Liu, Zheng Yuan, Wei Chen, Guiheng Chen, Xiaolong Yang, Yingze Peng, Jun School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Nowadays, more and more information technology such as internet-of-things (IoT) and artificial intelligence (AI) are applied to all walks of life. Facing the pressure of technological innovation, traditional manufacturers have paid attention to digital twin to improve production lines. However, most of the research on digital twin have not implement the concept of digital twin into actual production lines. To fill this gap, this paper demonstrates a digital twin application case study with whole process analysis based on machine learning. More specifically, data preprocessing, clustering, classification and regression are utilized to build a digital twin model. Moreover, we deploy digital twin model to cloud server and develop web application with real-time diagnosis function which has user-friendly interactive interface. Furthermore, the digital twin model of chemical production system has already been implemented in a physical chemical process plant. So the results of the experiment can prove the effectiveness of our proposed model building approaches. © 2021 ieee.

关键词： Production systems Technological innovation Smart cities Buildings real-time systems Digital twins Servers

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Digital Twin-Based Transfer Learning for Collaborative Robot systems: A Proof of Concept 38

Digital Twin-Based Transfer Learning for Collaborative Robot...

引用

38th international conference on Information Networking (ICOIN)

作者： Roongpraiwan, Supat Li, Zongdian Pourghasemian, Mohsen Gacanin, Haris Sakaguchi, Kei Tokyo Inst Technol Dept Elect & Elect Engn Tokyo Japan Rhein Westfal TH Aachen Chair Distributed Signal Proc Aachen Germany

ISBN: (纸本)9798350330946;9798350330953

Nowadays, the increasing trend toward digitalization has driven the extensive adoption of collaborative robotic automation across industries, yet a significant limitation is the robots' adaptability to unexpected and dynamic environments. This research introduces a Digital Twin (DT)-based Transfer Learning (TL) approach that combines DTs and Machine Learning (ML) to enhance adaptability in collaborative robot systems. The proposed system uses DT cyberspace for pre-training ML algorithms and leverages TL to apply this knowledge to real-world applications. This innovative approach efficiently trains state-of-the-art ML models, delivering exceptional performance while reducing the required time and data resources. The proof-of-concept experiments, employing the proposed DT-based TL to control soccer robots, demonstrate a remarkable 96% reduction in training time while maintaining a high level of adaptability, achieving a 70% goal accuracy rate in dynamic scenarios.

关键词： digital twin collaborative robot system machine learning transfer learning proof of concept

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Cyber Physical systems: Security Technologies, Application and Defense 9

Cyber Physical Systems: Security Technologies, Application a...

引用

9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Malarkodi, K.P. Sugumar, R. Baswaraj, D. Hasan, Arif Kousalya, A. Nirmala, S. Sri Krishna Arts and Science College Department of Computer Applications Coimbatore India Vasavi College of Engineering Hyderabad India Amity University ABS Madhya Pradesh India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350397376

New security threats have surfaced in response to the meteoric rise in the prevalence of Cyber-Physical systems (CPS). The latest iteration of CPS has a wide range of flaws, dangers, attacks, and safeguards. However, there hasn't been any comprehensive research done on CPS security problems. In particular, it has been challenging to examine the issue using a single generalized model due to the variability of CPS elements and the diversity of CPS systems. In this study, we use a holistic security model to examine CPS safety. We examine the technologies at the perceptual, network, and application levels. Smart grids, transportation, healthcare, and residential applications, along with the most common problems and solutions. Since many studies did not focus on a particular use case, we categorize them into broad categories, such as ICSs, cloud servers, and embedded devices. © 2023 ieee.

关键词： Cyber Physical System

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Implementation of Plant Data Security using embedded Hardware 5

Implementation of Plant Data Security using Embedded Hardwar...

引用

5th ieee international conference on Advances in Science and Technology, ICAST 2022

作者： Prasath, J.S. Kannan, G. Narayana Engineering College Department of Ece Gudur India Amrita College of Engineering and Technology Department of Ece Nagercoil India

ISBN: (纸本)9781665492638

embedded systems are utilized in variety of fields. embedded chips are used in smart mobiles, home appliances, processing the real-time information etc. embedded systems are susceptible to different attacks, energy constraints and greater complexness. The objective of this proposed system is to preserve the plant data from intruders in real-time environment. The process parameters are detected by the sensors and performs encoding using the embedded system. The decoding is done to obtain the information in readable text. The Graphical User Interface (GUI) is utilized to monitor the parameters. The methodology is required for securing the plant parameters and to prevent the alteration of data over internet from attackers. This proposed system is applicable for protecting the parameters across wireless medium. © 2022 ieee.

关键词： embedded systems

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2024 ieee international conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

2024 IEEE International Conference on Intelligent Signal Pro...

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2024 ieee international conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

The proceedings contain 157 papers. The topics discussed include: a reversible data hiding scheme for ECG signals using CNN-PEE;design and development of in-memory-compute SRAM cell using 45nm technology;stand alone or non-standalone 5G tactical edge network architecture for military and use case scenarios;secrecy capacity optimization in RF/FSO systems: impact of mixed Rayleigh and log-normal fading with atmospheric turbulence;real-time eye-tracking mouse control system using OpenCV and facial landmark detection;a time-efficient path navigating landmine detection robot;optimizing GPS positioning: a deep learning approach to improve accuracy;a novel structure of on-chip multilayered half-turn inductor for RF applications;and low profile wideband 3 element parasitic hexagonal patch antenna.

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Traffic Sign Recognition System with Hybrid CNN and Random Forest Models for Autonomous Driving applications 1

Traffic Sign Recognition System with Hybrid CNN and Random F...

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1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Vaidya, Akhil Tulsiyan, Darsh Surve, Omkar Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350352931

Accurate traffic sign recognition is crucial for driver assistance systems, yet challenges persist due to environmental factors and camera distortions. This study introduces a novel framework for real-time traffic sign detection and classification using vehicle dash-cams. The system employs a fine-tuned YOLOv8 model for detection, achieving mAP50 of 99.348%. Classification utilizes a two-stage approach: VGG16-based image embedding generation followed by Random Forest classification, resulting in an F 1 score of 98.61%. The model is trained on a diverse dataset combining GTSRB and web-scraped images, with extensive augmentation to enhance robustness. Additionally, Microsoft's TrOCR extracts numerical values from speed limit signs. This hybrid approach merges the strengths of convolutional neural networks and Random Forests, producing a robust solution even with a very limited amount of training data. The system's high accuracy and real-time performance significantly enhance driver safety by ensuring reliable traffic sign recognition across various conditions. © 2024 ieee.

关键词： Convolutional neural networks

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5G Network: Architecture, Protocols, Challenges and Opportunities 3

5G Network: Architecture, Protocols, Challenges and Opportun...

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3rd ieee international conference on computing, Communication, and Intelligent systems, ICCCIS 2022

作者： Javid, Iqra Khara, Sibaram Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665462006

The 5th Generation system is being established to give unmatched connectivity that will connect anything anywhere. 5G networks are designed to deliver high-speed, reduced latency for increased mobile broadband, large machine-to-machine communication and real-time applications. High data rates, high coverage, high reliability, cost-effectiveness are all advantages of 5G. This article discusses architecture, protocols, enabling technologies like mmWave, massive multi-input-multi-output, heterogeneous network, application areas of 5G and challenges in the 5G network. © 2022 ieee.

关键词： 5G mobile communication systems

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Edge Based Skin Cancer Decision Support System Using Machine Learning Algorithms

Edge Based Skin Cancer Decision Support System Using Machine...

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2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Nawaz, Anum Irfan, Muhammad Sadiqa, Hafza Ayesha Westerlund, Tomi School of Information Science and Technology Fudan University Shanghai China University of Turku Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology Turku Finland

ISBN: (纸本)9798350304602

Skin lesions has grown rapidly in recent years. Detecting and treating skin lesions in its early stages can be extremely beneficial to its recovery. Using Digital image processing (DIP) to extract skin diseases from lesions emerged rapidly due to in accessibility of dermatologist and laboratory facilities in remote areas specifically in under developed countries. Out of many deep learning based feature extraction algorithms, convolutional neural networks (CNN) outperform along with different classifiers. In this study, we focused on edge based classifiers for robust skin lesions classification. To choose the best combination of classifier with existing CNN based feature extraction methods we used HAM-10000 dataset, supervised by ISIC. Different mobile based classifiers such as QDA, KNN and SVM, along with their different hybrid versions were tested in same conditions. Experimental results shows that the cubic kernel function used in the cubic SVM approach allows for more complex decision boundaries to be created for melanoma detection, as the characteristics of melanoma lesions can vary greatly. The cubic kernel function enables the SVM model to capture these complex patterns and make accurate predictions using less computational resources as compared to other mobile based classifiers. Cubic SVM shows the accuracy of 98% while using only 20 seconds of time. © 2023 ieee.

关键词： Support vector machines

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Construction and Operation of a Generative Artificial Intelligence-Assisted Language Learning System 3

Construction and Operation of a Generative Artificial Intell...

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3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Cao, Yilu Lu, Haoyu Zhou, Yingping Gao, Jiahui Wang, Han Jiao, Yabei College Of Foreign Languages Chongqing University Of Technology Chongqing China College Of Computer Science And Engineering Chongqing University Of Technology Chongqing China School Of Accountancy Chongqing University Of Technology Chongqing China Liangjiang International College Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350318609

The generative artificial intelligence-assisted language learning system is a system that utilizes artificial intelligence technology. It provides users with more personalized and efficient language learning services through core modules such as generative pre-trained models and personalized learning engines, It is currently one of the significant applications of artificial intelligence technology in the field of language learning. This paper aims to introduce the design, construction, and operation of a generative artificial intelligence-assisted language learning system. In this system, the generative pre-trained model GPT -3.5 is employed, incorporating core modules such as natural language processing, generative adversarial networks, and Transformer models. This paper also conducts detailed research and analysis on aspects such as text generation quality in the system, response time testing of the model within the system, and feedback on system application scenarios. Through the research presented in this paper, valuable reference and guidance can be provided for the development and application of generative artificial intelligence-assisted language learning systems. © 2024 ieee.

关键词： Engines

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Periodic Stacked Transformer-based Framework for Travel time Prediction

Periodic Stacked Transformer-based Framework for Travel Time...

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international Joint conference on Neural Networks (IJCNN)

作者： Lin, Hui-Ting Dai, Hao Tseng, Vincent S. Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Informat Management Hsinchu Taiwan Natl Chung Hsing Univ Dept Management Informat Syst Taichung Taiwan

ISBN: (纸本)9798350359329;9798350359312

Travel time analysis and prediction play critical roles in developing Intelligent Transportation systems (ITS), which have attracted significant interests from the research community. Deep learning-based methodologies have proven to be powerful tools in utilizing big data for predicting travel times. However, while most studies have focused on shortterm predictions, predicting travel times over longer periods is equally important for wide applications like traffic management and route planning. Long-term prediction, which often receives less attention due to its complexity, remains a gap in current researches. To address this challenge, we propose the Periodic Stacked Transformer (PS-Transformer), a novel Transformer-based framework designed to enhance both short and long-term traffic predictions. PS-Transformer consists of two primary modules: the Segment Encoding Integration (SEI) and the Periodic Stacked Encoder-Decoder (PSED). SEI module extracts periodic patterns from traffic data, while PSED effectively captures shortterm and long-term dependencies from temporal attributes. Additionally, PSED tackles error accumulation, a common issue in extended prediction periods, through its non-autoregressive decoder design. Our PS-Transformer is validated through a series of experiments on a real-world dataset, demonstrating its capability in multi-step predictions that provide forecasts over an extended duration. Empirical evaluation results show that PS-Transformer outperforms state-of-the-art methods in both short and long-term travel time predictions across various metrics, including MAE, RMSE, and SMAPE.

关键词： Travel time prediction Intelligent Transportation systems Long-term prediction Periodic traffic data Transformer

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