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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

共 696 条记录，以下是121-130 订阅

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Texture of Activities: Exploiting Local Binary patterns for Accelerometer Data Analysis 12

Texture of Activities: Exploiting Local Binary Patterns for ...

引用

12th international conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Asuroglu, Tunc Acici, Koray Erdas, Cagatay Berke Ogul, Hasan Baskent Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781509056989

recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. the problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary pattern (LBP) approach, which is frequently used in identifying textures in images, in one-dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. the experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.

关键词： activity recognition accerelometer data wearable sensor classification local binary pattern

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Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

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12th IEEE international conference on Information Reuse and Integration (IRI)/1st international Workshop on Issues and Challenges in Social computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

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BTI Aging Influence and Mitigation in Neural Networks Oriented In-Memory computing SRAMs 12

BTI Aging Influence and Mitigation in Neural Networks Orient...

引用

12th international conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Dilopoulou, Christina Tsiatouhas, Yiorgos University of Ioannina Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350321074

Static random access memories (SRAMs) are commonly used in In-Memory computing (IMC) architectures that perform neural network related computations directly in the memory unit, overcoming the data transfer constrains of traditional Von-Neumann architectures. However, Bias Temperature Instability (BTI) aging is a growing reliability concern for nanometer technology SRAMs. In this paper, we examine the BTI aging influence on SRAM based IMC architectures and propose an aging mitigation method to enhance their reliability and lifetime. According to our simulation results, BTI aging can affect the computation reliability of IMC cells. Aging mitigation is achieved by periodically alternating active with inactive transistors and vice-versa in these cells. © 2023 IEEE.

关键词： Reliability

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Complex Activity recognition Using Polyphonic Sound Event Detection 12th

Complex Activity Recognition Using Polyphonic Sound Event De...

引用

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

作者： Kang, Jaewoong Kim, Jooyeong Kim, Kunyoung Sohn, Mye Sungkyunkwan Univ Dept Ind Engn Suwon South Korea

ISBN: (纸本)9783319935546;9783319935539

In this paper, we propose a method for recognizing the complex activity using audio sensors and the machine learning techniques. To do so, we will look for the patterns of combined monophonic sounds to recognize complex activity. At this time, we use only audio sensors and the machine learning techniques like Deep Neural Network (DNN) and Support Vector Machine (SVM) to recognize complex activities. And, we develop the novel framework to support overall procedures. through the implementation of this framework, the user can support to increase quality of life of elders'.

关键词： Complex activity pattern recognition Polyphonic sound event detection Machine learning

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XGBoost-based alignment determination of rotation axis under leakage field 12

XGBoost-based alignment determination of rotation axis under...

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

作者： Li, DongMei Bo, ZunYan Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350332919

Rotating shaft alignment detection is an important part of equipment inspection for large mechanical equipment. In view of the characteristics of the mechanical structure of rotating shaft alignment and the shortcomings of existing detection methods, this paper proposes a method for determining rotating shaft alignment under leakage magnetic field based on the XGBoost method. Specifically, the leakage magnetic field is formed at the rotating shaft connection by using leakage magnetic detection technology, and the position and distance of misalignment are predicted by picking up the leakage magnetic field information and handing it over to the model established by XGBoost method for judgment. the experiments show that the XGBoost method has good accuracy and superiority in judging the rotation axis alignment. © 2023 IEEE.

关键词： Magnetic leakage

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A Dynamic Spatio-temporal Network with Self-attention for Multi-station Passenger Flow Prediction 12

A Dynamic Spatio-temporal Network with Self-attention for Mu...

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12th international conference on Awareness Science and Technology, iCAST 2023

作者： Wang, Fengzhi Lv, Qinzhi Liu, Lijuan College of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9798350324693

Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the temporal features without considering any spatial features. Constructing a passenger flow prediction model for multiple stations, or even a whole network, is more valuable for practical applications. therefore, we develop a dynamic spatio-temporal network (DSTNet) with a self-attention (SA) mechanism for multi-station passenger flow prediction. A dynamic graph convolutional network (DGCN) is applied for the spatial feature extraction, and gated recurrent unit (GRU) is combined to learn the temporal features. SA is applied to further assign the weights for the extracted spatio-temporal features. the Experiment has been conducted on the passenger flow in the Xiamen bus rapid transit (BRT). the results demonstrate that the proposed DSTNet with SA (SA-DSTNet) outperforms the baselines in the multi-station passenger flow prediction task. © 2023 IEEE.

关键词： Forecasting

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Road Traffic Accident pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

引用

12th international conference on Awareness Science and Technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

the discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. this work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. this work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. the Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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Trusted Charity System Based on Blockchain Access Control 12

Trusted Charity System Based on Blockchain Access Control

引用

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

作者： Yu, Jingang Chen, Weifan Shenyang Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350332919

At present, there are widespread problems in traditional centralized charity systems such as unclear information about the flow of funds, centralized data easily tampered with, and data security not guaranteed, so public charity systems need a trusted and decentralized solution. Blockchain has the property of self-trust because of its tamper-proof and traceable nature. It enables mutual trust among stakeholders without the need for third-party proof. the adoption of blockchain technology can solve the trust problem in charity systems and improve the transparency and trust of charitable organizations. To this end, this paper proposes a trusted charity system based on blockchain access control by combining the traceable and tamper-evident characteristics of blockchain with the charity field. © 2023 IEEE.

关键词： Smart contract

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A Finger Knuckle Print Classification System Using SVM for Different LBP Variants 1

引用

12th international conference on Robotics Vision Signal Processing and Power Applications

作者： Riaz, Imran Ali, Ahmad Nazri Huqqani, Ilyas Ahmad Univ Sci Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia Mirpur Univ Sci & Technol Mirpur Inst Technol MUST Mirpur Azad Kashmir Pakistan

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

Finger knuckle print is one of the most important biometric traits and plays a vital role in a secure identification system. In this paper, performance evaluation of local binary pattern (LBP) and its variants center symmetric local binary pattern (CS-LBP) and median local binary pattern (MLBP) are investigated. After feature extraction, a support vector machine (SVM) with the linear kernel is used for the performance evaluation of two different datasets named the Poly-U FKP dataset and the USM-FKP dataset. the experimental results show that CS-LBP performs better for the USM-FKP dataset with an accuracy of 86.2% which demonstrates the potential of the FKP classification system.

关键词： Finger knuckle print Local binary pattern Support vector machine

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Generalized query by transduction for online active learning

Generalized query by transduction for online active learning

引用

2009 IEEE 12th international conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Balasubramanian, Vineeth Chakraborty, Shayok Panchanathan, Sethuraman School of Computing and Informatics Arizona State University Tempe AZ 85281 United States

ISBN: (纸本)9781424444427

Transductive inference has gained popularity in recent years as a means to develop pattern classification approaches that address the specific issue of predicting the class label of a given data point, instead of the more general problem of inferring the ideal classifier function. In this paper, we propose a Generalized Query by Transduction (GQBT) approach for active learning in the online setting. this approach is based on the theory of conformal predictions, which has recently been proposed based on principles of algorithmic randomness, transductive inference and hypothesis testing. the proposed GQBT approach can be used along with any existing pattern classification algorithm, and can also be used to combine multiple criteria in selecting an unlabeled example appropriately in the active learning process. the results of our experiments with different datasets from the UCI Machine Learning repository demonstrate high promise in the proposed approach, with significantly lower label complexities than other existing online active learning approaches. the GQBT approach was also evaluated on face recognition using videos from the VidTIMIT dataset, and the observed superior performance supports the potential of applicability of the proposed approach in real-world problems. ©2009 IEEE.

关键词： Face recognition

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