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A Sports Training System Based on Kinect intelligent Wearable Device 4

A Sports Training System Based on Kinect Intelligent Wearabl...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Gao, Lan Geng, Nannan Wang, Chaoqun Shandong Youth University of Political Science Jinan250103 China

ISBN: (纸本)9798350333558

Combined with the research of somatosensory technology and human motion recognition, this paper proposes an intelligent motion training system based on somatosensory recognition. Firstly, this paper filters the bone data collected by Kinect through jitter elimination and double exponential smoothing, and then extracts the feature vector of bone data by using space vector method to build a finite state machine model. the DTW algorithm combined with the weighted Euclidean distance is used to match the motion template to achieve the accurate and low delay motion recognition target. this paper first analyzes the design requirements of the system, designs the hardware architecture and software architecture of the system according to the requirements, and makes a detailed description of the specific implementation of motion recognition in the software. the experimental results show that after 198 tests, the motion recognition rate of the pull-up is 100%, and the accuracy rate is 99%. After 200 tests, the motion recognition rate of the squat is 99%, and the accuracy rate is 97.5%. After 594 tests, the motion recognition rate of the standing long jump is 97.8%, and the accuracy rate is 87.9%. It is proved that the recognition rate of the system for motion is very high and the motion recognition function is very reliable, but the accuracy rate can reach the level of consumer grade products except for standing long jump. Result finding that although the system still has some drawbacks in the uninterrupted long jump performance, the success of the overall bow motion recognition algorithm indicates good motion recognition performance without rapid movement, can accurately identify the position and motion-decomposing motions. © 2023 IEEE.

关键词： Vector spaces

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Transformers partial discharge pattern recognition based on Vision Transform 4

Transformers partial discharge pattern recognition based on ...

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2024 4th international conference on Power System and Energy Internet, PoSEI 2024

作者： He, Shifang Baihetan Hydropower Plant China Yangtze Power Co. Ltd Hubei Yichang443000 China

the secure and stable operation of modern electricity systems depends on the reliable performance of transformers. Partial discharge (PD), as a common fault phenomenon in transformers, has a significant impact on its performance and lifetime. Traditional PD identification methods rely on manual feature extraction and lack generalization capability. In addition, this paper proposes a partial discharge recognition method based on Vision Transformer (ViT), which utilizes the self-attention mechanism of the visual transformer to accurately recognize partial discharges in transformers. through the training and testing of the transformer partial discharge mapping dataset, the results show that the method in the present paper outperforms the traditional method in terms of recognition accuracy and stability, and the accuracy is maintained between 96.24% and 97.08%, which has a good prospect for engineering applications. © Published under licence by IOP Publishing Ltd.

关键词： Distribution transformers

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Study on Adaptive Cruise Control systems of Passenger Vehicles and Solutions Based on V2X Technologies 4

Study on Adaptive Cruise Control Systems of Passenger Vehicl...

引用

2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Luo, Tiancheng Beijing University of Technology College of Metropolitan Transportation Beijing100124 China

ISBN: (纸本)9798350308877

Adaptive Cruise Control (ACC) systems play a crucial role in enhancing automotive safety. However, ACC systems suffer from limitations such as inadequate single-vehicle perception. Current research predominantly focuses on target recognition algorithms, overlooking the technological pathways for enhancing ACC system performance through vehicle-to-vehicle and vehicle-to-infrastructure cooperative technologies. this paper provides a comprehensive review of ACC technology's development and current state, encompassing ACC products from automotive manufacturers, relevant standards, regulations, and associated research theories while summarizing the existing issues in contemporary ACC technology. Furthermore, this paper explores ACC technology solutions based on these cooperative and networked approaches by analyzing the current status and advantages of vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technology (V2X). the study reveals that by integrating vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technologies, it is possible to cost-effectively improve the reliability and safety of ACC systems in scenarios such as cornering, driving on wet or slippery surfaces, and encountering stationary obstacles. this research provides theoretical support for the development of ACC systems, further enhancing road traffic safety and operational efficiency. © 2023 IEEE.

关键词： Adaptive cruise control

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Design and Development of An Onboard intelligent Device based on Jetson NX 4

Design and Development of An Onboard Intelligent Device base...

引用

2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Yu, Xiaoyong Qin, Liwen Chen, Xin Wu, Lifang Zhang, Biyun Electric Power Research Institute of Guangxi Power Grid Co. Ltd Nanning China Shantou Power Supply Bureau of Guangdong Power Grid Co. Ltd Shantou China

ISBN: (纸本)9798350308877

this paper presents the design and development of an onboard intelligent device based on Jetson Xavier NX. the device utilizes the computational and recognition capabilities of AI chips to analyze safety signs and provide feedback, assisting in inspections. the technological components and design of the onboard intelligent device are discussed, including the structural framework of embedded systems built with AI chips, as well as the components and applications of embedded systems. the DJI Maverick series models, M30 and M300, are selected and their applications and performance in the power grid industry are introduced. the unmanned aircraft and onboard intelligent device are connected through the DJI Maverick series interface for expansion and control. Edge computing is implemented using a training and computing separation model, and a development roadmap is provided. the designed onboard intelligent device is installed on DJI drones to real-time recognize video streams captured by the drones, which includes modules for data collection, computational analysis, wireless communication, storage, input/output communication, and power interfaces. through this design, intelligent identification and real-time monitoring of public safety in the power sector can be achieved. © 2023 IEEE.

关键词： Edge computing

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Diffusion Probabilistic Model Based End-To-End Latent Fingerprint Synthesis 4

Diffusion Probabilistic Model Based End-To-End Latent Finger...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Li, Kejian Yang, Xiao Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China

ISBN: (纸本)9798350324303

Fingerprints have been crucial evidence for law enforcement agencies for a long time. though the rapidly developing deep learning has dramatically improved the performance of the latent fingerprint recognition algorithm, a fully automated latent fingerprint identification system is still far from meeting actual needs. One major issue is the lack of publicly available latent fingerprint databases. Recently, diffusion probabilistic models have emerged as state-of-the-Art deep generative methods for image synthesis. these models have better distribution coverage and less mode collapse than the popular Generative Adversarial Networks. In this paper, we propose an end-To-end latent fingerprint synthetic approach based on the improved denoising diffusion probabilistic model. the proposed approach could simultaneously generate latent, rolled, and plain fingerprints of high visual realism. Several primary degradation factors, such as various background textures, limited area of ridge patterns, and structural noise, can be directly generated without any postprocessing, unlike existing methods. We conduct NFIQ2 and perceptual analysis in the experiments to evaluate the proposed approach. the results indicate that the quality and visual realism of the proposed synthetic fingerprints is similar to the natural ones, demonstrating the effectiveness of our approach. © 2023 IEEE.

关键词： Deep neural networks

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Advancements In intelligent Robotics: Exploring Facial recognition Technologies For Human-Machine Interaction 4

Advancements In Intelligent Robotics: Exploring Facial Recog...

引用

4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Vamsi, B. Karthik, H.B. Navi, Mahantesh Bhanuteja, G. Vidyasagar, K.N. Reva University School Of Ece Bengaluru India

ISBN: (纸本)9798350384628

this paper clarifies applications of humanoid robots in AI-lab auxiliary environments. Some are available on Pepper which is used most. Pepper robot is mainly used as the digital consultant in Labs. there are different functionalities that differs having been a caretaker for certain visitors to serving as an AI instructor and provides answers to queries in numerous project-specific domains. the integrated speech and image recognition systems use our in-house AI technologies. In the context of enhanced face recognition, this abstract investigates the synergy of artificial intelligence (AI), convolution neural networks (CNN), neural networks (NN), and the humanoid robot Pepper. the creation of complex facial recognition systems has been accelerated by the integration of AI techniques, especially CNN and NN architectures. this research explores the real-world applications and interactions made possible by the merging of cutting-edge robotics and AI, making use of Pepper's humanoid capabilities. the paper explores the technical elements of using CNN and NN for reliable face recognition, highlighting Pepper's function as a platform for putting these algorithms to use and improving them in real-world settings. Applications include scenarios including human-robot contact, security, and personalized help. © 2023 IEEE.

关键词： Anthropomorphic robots

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intelligent target recognition method for infrared/laser composite fuze 4

Intelligent target recognition method for infrared/laser com...

引用

4th international conference on Defence Technology, ICDT 2024

作者： Yan, Jiangyi Duan, Rongxin Wang, Zhi Zheng, Gaoxiang Zhou, Peicheng Yang, Panxiong Wang, Keyan State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an China

In the context of complex environment, the intelligent target recognition method for infrared/laser composite imaging fuze encounters the following challenges: 1) the dearth of infrared/laser composite imaging fuze data hampers the advancement and implementation of deep learning technology in fuze target recognition;2) the extant infrared/laser composite imaging fuze target recognition methods exhibit suboptimal recognition accuracy in complex terrain and ground conditions, which predisposes them to early or false explotions. To address the aforementioned issues, we have constructed infrared/laser composite imaging target and background dataset in diverse scenes, terrains and features, which provides effective data support for the intelligent recognition of composite imaging fuze. Additionally, we have designed a cross-modal fusion attention mechanism and an invariant feature learning method. the method facilitates the fusion of multimodal complementary information and the intelligent recognition of multi-scale and multi-morphology targets in complex scenes. Furthermore, they enhance the accuracy of the composite imaging fuze, enabling it to adapt to complex terrains. this enhances the combat effectiveness of composite imaging fuze in complex environments, while also offering technical support for the intelligentization of target recognition systems in future ballistic fuze. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Deep learning

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Automatic detection of contextual laterality in Mammography Reports using Large Language Models 14

Automatic detection of contextual laterality in Mammography ...

引用

14th international conference on pattern recognition systems (ICPRS)

作者： Godoy, Eduardo de Ferrari, Joaquin Mellado, Diego Chabert, Steren Salas, Rodrigo Univ Valparaiso Escuela Ingn Informat Valparaiso Chile Millennium Inst Intelligent Healthcare Engn iHlth Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Tecn Federico Santa Maria Dept Comp Valparaiso Chile Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Valparaiso Escuela Ingn Civil Biomed Valparaiso Chile

ISBN: (纸本)9798350375657

this work explores the use of a Large Language Models (LLM) for the automatic identification of laterality spans in mammography reports. Specifically, we assess the performance of three GPT models accessible via OpenAI's API in categorizing sentences according to their entailment towards either the right or left breast. Our methodology involves employing a framework that takes natural language reports authored by radiologists as input, subsequently generating labels which are refined through straightforward rule-based post-processing methods. We evaluate three prompt types (zero-shot, one-shot and few-shot with 3 examples) across the GPT models. Our findings demonstrate that GPT-4o achieves the highest F1 score of 0.77 in the few-shot scenario. Furthermore, we observe notable performance differences and error rates between GPT-3.5-turbo and GPT-4 models, highlighting the superiority of the latter models. Additionally, we find that incorporating one or more task examples in the prompt reduces errors, enhances adherence to instructions and boosts overall performance of the models.

关键词： Large Language Models LLM Mammography Reports Natural Language Processing Transformers Clinical Text Mining

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Design of Integrated Robotic System for Object recognition and intelligent Grasping based on STM32 4

Design of Integrated Robotic System for Object Recognition a...

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4th IEEE international conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Gu, Zhihao Wang, Meng Peng, Huaibo School of Electrical Engineering and Automation Harbin Institute of Technology Institute of Technology Harbin China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the development of intelligent robots and the proposal of 'Industry 5.0', the intelligent robotic systems have been increasingly used in industrial scenarios. However, the current robotic grasping system is facing a series of problems, including low integration, low movability and limited application scenarios. To counter these problems, this paper designs an integrated robotic system for object recognition and intelligent grasping, consisting of an STM32F405RGT6 microcontroller board, path planning module, object recognition and intelligent grasping module, and environmental monitoring and communication module. this paper details the framework, the hardware and software design of the robotic system respectively. the results show that the robotic system achieves the design goal and realizes robust intelligent recognition and automatic grasping in a constantly moving process with high accuracy, stability and information-based degree. © 2022 IEEE.

关键词： Internet of things

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A Residual Neural Network-Based Incremental Learning Model for Driver State Perception 4

A Residual Neural Network-Based Incremental Learning Model f...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cai, Jie Zhang, Jingming Sun, Erxin Cai, Jianjun Company Ltd Research and Development Department Tianjin China

ISBN: (纸本)9798350314496

this paper presents a model for implementing active machine sensing of fatigue states in an intelligent cockpit. To make the model more robust and achieve higher accuracy, the model introduces residual modules in the convolutional neural network and incorporates input primitive attributes and contour information in the learning. In addition, new learning branches are added to the network. By learning the user's personalized performance data in the new branch, the model is more adaptable to different drivers. With reference to the user state by the model perceived, the cockpit switches between different cloud scenario modes. Finally, in comparative experiments, our model shows advantages over other models in driver state recognition. © 2023 IEEE.

关键词： Learning systems

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