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检索条件"任意字段=12th International Conference on Computing and Pattern Recognition, ICCPR 2023"

共 284 条记录，以下是41-50 订阅

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A Multi-Sensor Algorithm for Activity and Workflow recognition in an Industrial Setting 19

A Multi-Sensor Algorithm for Activity and Workflow Recogniti...

引用

12th ACM international conference on PErvasive Technologies Related to Assistive Environments (PETRA)

作者： thomay, Christian Gollan, Benedikt Haslgruebler, Michael Ferscha, Alois Heftberger, Josef Res Studios Austria Pervas Comp Applicat Vienna Austria Johannes Kepler Univ Linz Inst Pervas Comp Linz Austria Fischer Sports GmbH Ried Austria

ISBN: (纸本)9781450362320

In the recent revival of human labour in industry, and the subsequent push to optimally combine the strengths of man and machine in industrial processes, there is an increased need for methods allowing machines to understand and interpret the actions of their users. An important aspect of this is the understanding and evaluation of the progress of the workflows that are to be executed. Methods for this require both an appropriate choice of sensors, as well as algorithms capable of quickly and efficiently evaluating activity and workflow progress. In this paper we present such an algorithm, which provides activity and workflow recognition using both depth and RGB cameras as input. the algorithm's main purpose is to be used in an industrial training station, allowing novice workers to learn the necessary steps in assembling nordic ski products without the need for human supervision. We will describe how the algorithm recognizes predefined workflows in the sensor data, and present a comprehensive evaluation of the algorithm's performance on a real data recording of operators performing their work in an industrial setting. We will show that the algorithm fulfills the necessary requirements and is ready to be implemented in the training station application.

关键词： Industrial assistance systems activity recognition pattern recognition pervasive computing sensor fusion

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the research of Intelligent Fault Diagnosis System Based on Internet of things 12

The research of Intelligent Fault Diagnosis System Based on ...

引用

IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of things (ithings) / 5th IEEE Int Conf on Smart Data

作者： Wen, Yingli Zhu, Zhiliang Dai, Yuxing Hu, Jing Chen, Changan Wenzhou Univ Coll Math & Elect Informat Engn Wenzhou Peoples R China Zhejiang Ind & Trade Vocat & Tech Coll Wenzhou Peoples R China YaLong Intelligent Equipment Grp Wenzhou Peoples R China

ISBN: (纸本)9781728129808

In order to obtain accurate real-time system state recognition under the supervision of the universal mobile internet of things management platform, an intelligent fault diagnosis system is proposed. And the intelligent fault diagnosis system is presentation based on ZigBee technology and particle filter in this paper. Real-time collection of multi-variable data is realized by adopting ZigBee wireless sensor network, and the accurate estimation of the state of monitored objects and intelligent prediction of faults are based on the particle filtering algorithm. the proposed approach is implemented on a circuit fault identification system. the application results show that the proposed approach can achieve the real-time remote monitoring, accurate state estimation and fault diagnosis, meanwhile, effectively enhance applicable scope and diagnostic level of the fault diagnosis system.

关键词： Fault diagnosis Particle filter Zigbee pattern recognition Internet of things

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Engineering Applications of Neural Networks 2011

引用

丛书名： IFIP Advances in Information and Communication Technology

2011年

作者： Lazaros S. Iliadis Chrisina Jayne

ISBN: (数字)9783642239571

ISBN: (纸本)9783642239564

the two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. the 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. the first volume includes the papers that were accepted for presentation at the EANN 2011 conference. they are organized in topical sections on computer vision and robotics, self organizing maps, classification/pattern recognition, financial and management applications of AI, fuzzy systems, support vector machines, learning and novel algorithms, reinforcement and radial basis function ANN, machine learning, evolutionary genetic algorithms optimization, Web applications of ANN, spiking ANN, feature extraction minimization, medical applications of AI, environmental and earth applications of AI, multi layer ANN, and bioinformatics. the volume also contains the accepted papers from the Workshop on Applications of Soft computing to Telecommunication (ASCOTE 2011), the Workshop on Computational Intelligence Applications in Bioinformatics (CIAB 2011), and the Second Workshop on Informatics and Intelligent Systems Applications for Quality of Life Information Services (ISQLIS 2011).

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Research on Power Voice Based on WaveNet+LSTM Improvement 12

Research on Power Voice Based on WaveNet+LSTM Improvement

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Wang, HanJun Zhu, MingYun Wang, Qun University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350332919

With the rapid development of speech recognition [1] in recent years, the WaveNet model has also been discovered by more people and applied to speech recognition. In the open source LJSpeech-1.0, AISHELL and thch-30 and other general speech datasets, the model has achieved relatively good recognition results, but in the face of a large number of power voice data, because of the uniqueness and complexity of industry speech, the model is generally performed, so in this paper we have made some improvements to the model, aiming to improve the recognition of power-related speech data. First of all, in view of the shortcomings of the WaveNet network model in power speech recognition, we have improved and optimized it. the output gate structure of the LSTM network model is used to increase the nonlinearity of the model, so as to optimize the structural composition of the new model. We then introduced the core structure of the Reduced Convolution in WaveNet. According to the length of speech in the electric speech dataset, the receptive field of this structure is adjusted, and the sensory field is theoretically analyzed in order to better analyze the speech data. Finally, we experimentally verify the feasibility of applying the improved WaveNet model on the thch-30 dataset and the power-related dataset, and evaluate the trained model. © 2023 IEEE.

关键词： Speech recognition

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Predicate Clustering-Based Entity-Centered Graph pattern recognition for Query Extension on the LOD 12th

Predicate Clustering-Based Entity-Centered Graph Pattern Rec...

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12th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Kim, Jongmo Kong, Junsik Park, Daeun Sohn, Mye Sungkyunkwan Univ Suwon South Korea

ISBN: (纸本)9783319935546;9783319935539

In this paper, we propose a method to reduce the difficulties of query caused by lack of information about graph patterns even though the graph pattern is one of the important characteristics of the LOD. To do so, we apply the clustering methodology to find the RDF predicates that have similar patterns. In addition, we identify representative graph patterns that imply its characteristics each cluster. the representative graph patterns are used to extend the users' query graphs. To show the difficulties of the query on the LOD, we developed an illustrative example. We propose the novel framework to support query extension using predicate clustering-based entity-centered graph patterns. through the implementation of this framework, the user can easily query the LOD and at the same time collect appropriate query results.

关键词： pattern recognition

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ApproxCT: Approximate Clustering Techniques for Energy Efficient Computer Vision in Cyber-Physical Systems 12

ApproxCT: Approximate Clustering Techniques for Energy Effic...

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12th international conference on Open Source Systems and Technologies (ICOSST)

作者： Javed, Raja Haseeb Siddique, Ayesha Hafiz, Rehan Hasan, Osman Shafique, Muhammad Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan ITU Lahore Pakistan Vienna Univ Technol Inst Comp Engn Vienna Austria

ISBN: (纸本)9781538695647

the emerging trends in miniaturization of Internet of things (IoT) have highly empowered the Cyber-Physical Systems (CPS) for many social applications especially, medical imaging in healthcare. the medical imaging usually involves big data processing and it is expedient to realize its clustering after data acquisition. However, the state-of-the-art clustering techniques are compute intensive and tend to reduce the processing capability of battery-driven or energy harvested IoT based embedded devices (e.g., edge and fogs). thus, there is a desire to perform energy efficient implementation of the machine learning based clustering techniques. Since, the clustering techniques are inherently resilient to noise and thus, their resilience can be exploited for energy efficiency using approximate computing. In this paper, we proposed approximate versions of the widely used K-Means and Mean Shift clustering techniques using the state-of-the-art low power approximate adders (IMPACT). the trade-off between power consumption and the output quality is exploited using five well-known pattern recognition datasets. the experiments reveal that K-Means algorithm exhibits more error resilience towards approximation with a maximum of 10% - 25% power savings.

关键词： Cyber-Physical Systems Internet of things Approximate computing Clustering Low Power Approximate Adders Energy Consumption Computer Vision

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Research on Defect Segmentation Algorithm of Industrial Products Based on Transformer 12

Research on Defect Segmentation Algorithm of Industrial Prod...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Yu, Bo Yan, Zijian Wang, Wei Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Sciences and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350332919

For detecting surface defects of industrial products, especially on recognition accuracy and more detailed boundary recognition, an improved semantic segmentation model based on SegFormer network is put forward in this paper. In the decoder part of the model, the bi-directional feature pyramid network (BiFPN) structure is used to receive the multi-scale feature maps output from encoder, and semantic information between feature maps of different sizes are integrated. then, the integrated semantic features are input to Boundary Refinement (BR) module to enhance Boundary information and enhance the final segmentation effect, so that the improved Segformer structure is better applicable to surface defect detection of industrial products. © 2023 IEEE.

关键词： Semantics

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Algorithms of the Cluster and Morphological Analysis for Mineral Rocks recognition in the Mining Industry 12th

Algorithms of the Cluster and Morphological Analysis for Min...

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12th international conference on Intelligent computing (ICIC)

作者： Baklanova, Olga E. Baklanov, Mikhail A. D Serikbayev East Kazakhstan State Tech Univ Ust Kamenogorsk Kazakhstan Tomsk State Univ Tomsk Russia

ISBN: (纸本)9783319422947;9783319422930

this paper describes an algorithm for automatic segmentation of color images of various ore types, using the methods of morphological and cluster analysis. there are some examples illustrating the usage of the algorithm to solve mineral recognition problems. the effectiveness of the proposed method lies in the area of automatic objects of interest identification inside the image, tuning the parameters of the amount allocated to the segments. this paper contains short description of morphological and cluster analysis algorithms for the mineral recognition in the mining industry.

关键词： pattern recognition Segmentation of color images Cluster analysis Morphological analysis Mineral rocks Mining industry

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Hybrid spintronic/straintronics: A super energy efficient computing scheme based on interacting multiferroic nanomagnets

Hybrid spintronic/straintronics: A super energy efficient co...

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12th IEEE international conference on Nanotechnology (IEEE-NANO)

作者： Atulasimha, Jayasimha Bandyopadhyay, Supriyo Virginia Commonwealth Univ Dept Mech & Nucl Engn Med Coll Virginia Campus Richmond VA 23284 USA Virginia Commonwealth Univ Dept Elect & Comp Engn Med Coll Virginia Campus Richmond VA 23284 USA

ISBN: (纸本)9781467322003

We have theoretically shown that multiferroic nanomagnets (consisting of a piezoelectric and a magnetostrictive layer) could be used to perform computing while dissipating similar to few 100 kT/bit at clock rates of similar to 1GHz [1,2,3]. they can act as memory elements [2], binary logic gates [3, 4] and associative memory for four state logic [5, 6]. the latter enables signal processing functions such as ultrafast image reconstruction and pattern recognition [7]. this talk will provide an overview of our research in modeling stress induced nanoscale magnetization dynamics, its application to ultra low energy hybrid spintronic/straintronics memory and information processing, and discuss preliminary experimental work in fabrication and experimental demonstration of these devices.

关键词： straintronics spintronics multiferroic nanomagnetic logic (NML)

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Driver recognition based on dynamic handgrip pattern on steeling wheel

Driver recognition based on dynamic handgrip pattern on stee...

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2011 12th ACIS international conference on Software Engineering, Artificial Intelligence Networking and Parallel Distributed computing, SNPD 2011

作者： Chen, Ronghua She, Mary Fenghua Sun, Xiangping Kong, Lingxue Wu, Yuchuan Institute for Technology Research and Innovation Deakin University Geelong VIC Australia School of Mechanical Engineering and Automation Wuhan Textile University Wuhan China

ISBN: (纸本)9780769544755

this paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. the experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design. © 2011 IEEE.

关键词： Automobile steering equipment

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