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检索条件"任意字段=Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition"

共 740 条记录，以下是41-50 订阅

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CircleSense: A Pervasive computing System for Recognizing Social Activities

CircleSense: A Pervasive Computing System for Recognizing So...

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11th Annual IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Liang, Guanqing Cao, Jiannong Zhu, Weiping Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467345750;9781467345736

Social activities have great impact for human beings in psychological health and social relationship. the recognition of social activities can unobtrusively recognize and record users' daily social activities, enabling users to better manage their life. Existing work of social activity recognition focus on recognizing a limited set of social activities and are mainly based on the patterns of individual user such as location pattern, vocal pattern, or others. However, social activities inherently exhibit the patterns with respect to multiple users rather individual user. In this paper, we introduce the concept of social circle, to extract social patterns associated with multiple users in a generic set of social activities. A social circle refers to a set of users frequently gathering to conduct certain social activities. Based on social circle, we design a system called CircleSense that supports accurate recognition of a generic set of social activities. We validate the effectiveness of CircleSense through the real trace collected by 10 volunteers. the result shows that CircleSense outperforms existing methods in terms of accuracy of social activity recognition.

关键词： Pervasive computing Social Activity recognition Social Circle

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Home safety handwriting pattern recognition system

Home safety handwriting pattern recognition system

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11th IEEE international conference on Cognitive Informatics and Cognitive computing, ICCI*CC 2012

作者： Lian, Kuang-Yow Hsiao, Sung-Jung Sung, Wen-Tsai Department of Electrical Engineering National Taipei University of Technology Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taiwan

ISBN: (纸本)9781467327930

the intelligent home security control, and has gradually become an indispensable basic equipment in our daily lives. In this study, using the handwriting recognition technology to confirm user identity and manage security of the door, this is an innovative approach. this system is by smart phones to do the actions of management and control;it is assumed that users from outside the house to go into the house, this system will use two stages to do the work of identity confirmation. the first phase, the user must input the username and password on a smart phone, and then wait for the successful recognition of identity. In the second phase of implementation, the user must use a handwritten way, to write the text or pattern of previous settings on smart phones. After the success of system identification in the second stage, the user will get permission to enter the door and control the electronic lock. Our system can also be modified to use only the second phase of the handwriting recognition to do the identity confirmation. Because some elderly or disabled to input the username and password to the system by a phone keypad, the action of input is very inconvenient. therefore, our system will be able to improve the above-mentioned deficiencies and strengthen the work of the home security. Users enter a handwriting on the phone, identity confirmation after the success of the door will be opened immediately, which is how easy the system. In this paper, we also use the control circuit module to replace the traditional computer RS-232 interface, and the space occupied is very small. Finally, our research Android and Java programming language, development platform, writing smartphones and Server recognition program, and to implement successful demonstration of a home security system we have developed. Intelligent home security control, Handwriting recognition, Android, Java © 2012 IEEE.

关键词： Java programming language

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Implementing a Robust IoT Solution to Monitor Prone Position using Machine Learning Techniques 11

Implementing a Robust IoT Solution to Monitor Prone Position...

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11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Belay, Semagn Shifere Prince, T. Beyene, Tizazu Bayih Marew, Walelegne Yirdew Kebede, Andebet Dessiewu Woldia University School of Computing Woldia Institute of Technology Ethiopia College of Science and Arts University Department of Computer Science United Arab Emirates

ISBN: (纸本)9781665480383

It is essential to monitor sleeping patterns and position in order to learn about one's health state in order to enhance it and minimize sleep apnea. the use of a bed-mounted seismometer device to track the heart and respiratory patterns, as well as physical movement and attitude, while sleeping is discussed in this paper. the Internet of things (IoT) and its gadgets and systems are becoming a part of everyday life. they are primarily used to track everyday tasks, particularly those that are relevant to individual health and wellness. As a matter of fact, and it's becoming increasingly important throughout the COVID-19 outbreak. A intelligent tracking and warning Internet of things system known "NEF"was updated in this work to detect on-bed movement patterns, such as the prone position, using a variety of neural network models. On-bed movement patterns were recorded from seven different participants. the models derived using deep network were the most accurate when just the prone and supine postures were included. Random forest, on the other hand, produced the greatest total proportion of properly analysis refers to a set. Beddings such as pillows and blankets are expected to be investigated in greater depth in the future. © 2022 IEEE.

关键词： pattern recognition

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Investigating the use of multiple languages for crisp and fuzzy speaker identification 11

Investigating the use of multiple languages for crisp and fu...

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11th international conference of pattern recognition Systems, ICPRS 2021

作者： de Lima, thales Aguiar da Costa-Abreu, Márjory Center of Earth and Exact Sciences Federal University of Rio Grande do Norte Natal Brazil Department of Computing Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9781839534300

the use of speech for system identification is an important and relevant topic. there are several ways of doing it, but most are dependent on the language the user speaks. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, we must take into account that people can and will speak different languages and have different accents. thus, this research evaluates speaker identification systems on a multilingual setup. Our experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. Initial tests indicated the systems have certain robustness on multiple languages. Results with more languages decreases our accuracy, but our investigation suggests these impacts are related to the number of classes. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： Loudspeakers

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From Serverful to Serverless: A Spectrum of patterns for Hosting Application Components 11

From Serverful to Serverless: A Spectrum of Patterns for Hos...

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11th international conference on Cloud computing and Services Science (CLOSER)

作者： Yussupov, Vladimir Soldani, Jacopo Breitenbuecher, Uwe Brogi, Antonio Leymann, Frank Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9789897585104

the diversity of available cloud service models yields multiple hosting variants for application components. Moreover, the overall trend of reducing control over the infrastructure and scaling configuration makes it nontrivial to decide which hosting variant suits more a certain software component. In this work, we introduce a spectrum of component hosting patterns that covers various combinations of management responsibilities related to (i) the deployment stack required by a given component as well as (ii) required infrastructure resources and component's scaling rules. We validate the presented patterns by identifying and showing at least three real world occurrences of each pattern following the well-known Rule of three.

关键词： Application Deployment Hosting patterns Serverful Serverless Cloud computing

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Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions 11

Using neural and distance-based machine learning techniques ...

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11th international conference of pattern recognition Systems, ICPRS 2021

作者： Budke, Jaine Rannow da Costa-Abreu, Márjory Federal University of Rio Grande do Norte Natal Brazil Department of Computing Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9781839534300

Facial expressions are part of human non-verbal communication. Automatically discriminating between genuine and acted emotion can help psychologists, judges, human-machine interface, and so on. the problems for researchers starts when there are few real emotion facial datasets available, and thus, most of experimentation for evaluation is done by using fake emotions from actors. thus, this paper explores the problem of classifying emotions from facial expressions as genuine or acted. We propose to extract facial features from images and to classify using k-Means, k-Nearest Neighbor and Neural Network. the best results obtained presented a promising 98.6% of precision for happiness emotion and 92% for sadness emotion. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： Machine learning

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Online pattern recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble 11

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAACS)

作者： Pareek, Vishakha Chaudhury, Santanu Singh, Sanjay CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Indian Inst Technol Delhi Delhi 110016 India

ISBN: (纸本)9781665426053

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. the presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. the responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. the Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. the network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. the proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： 2D-CNN pattern recognition Ensemble Gas Sensor

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Determining the maximum power point location of the PV array under partial shading using the shading pattern recognition 11

Determining the maximum power point location of the PV array...

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11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Krstic, Nikola Tasic, Dragan Denic, Teodora University of Niš Faculty of Electronic Engineering Dept. of Power Engineering Niš Serbia

ISBN: (纸本)9798350386998

this paper proposes an approach for finding the maximum power point (MPP) location of the photovoltaic (PV) array in the case of partial shading based on the shading pattern recognition. Shading pattern recognition is achieved by determining the necessary solar irradiation for each part of the PV array surface to recreate the existing power-voltage (P-V) curve of the PV array. this is done using the metaheuristic particle swarm optimization (PSO) method in which criterion function is based on comparing the measured powers of the PV array (fitting power points) and the powers calculated using the PV array model for the same voltages. Model of the PV array is created from the PV cell model considering the topology and the partial shading of the PV array surface. the effect of partial shading is included by reducing the photocurrent of the PV cells in the shaded area of the PV array. When the shading pattern is known the location (voltage) of the MPP is found using the P-V curve obtained from the PV array model. Results generated for three different shading patterns show great precision of the proposed approach in determining the MPP location if the PV cell is accurately modeled, and enough number of fitting power points are used. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Coverage in biomimetic pattern recognition

Coverage in biomimetic pattern recognition

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11th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing (RSFDGrC 2007)

作者： Cao, Wenming Zhao, Guoliang Shenzhen Univ Intelligent Informat Proc Key Lab Beijing 518060 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China

ISBN: (纸本)9783540725299

Coverage is a kind of method to cover points of same class samples in feature space, which is based on Biomimetic pattern recognition. the mathematical description of coverage is given and the discriminant boundary of coverage is shown. Coverage is tested in face recognition on ORL database. Both the COVERAGE and SVM networks are used for covering. the results show that COVERAGE act better than SVM in generalization, especially for small sample set, which are consonant with the result of the applications of BPR.

关键词： Biomimetics

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A Machine Learning based Context-aware Prediction Framework for Edge computing Environments 11

A Machine Learning based Context-aware Prediction Framework ...

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11th international conference on Cloud computing and Services Science (CLOSER)

作者： Aljulayfi, Abdullah Fawaz Djemame, Karim Univ Leeds Sch Comp Leeds W Yorkshire England Prince Sattam Bin Abdulaziz Univ Riyadh Saudi Arabia

ISBN: (纸本)9789897585104

A Context-aware Prediction Framework (CAPF) can be provided through a Self-adaptive System (SAS) resource manager to support the autoscaling decision in Edge computing (EC) environments. However, EC dynamicity and workload fluctuation represent the main challenges to design a robust prediction framework. Machine Learning (ML) algorithms show a promising accuracy in workload forecasting problems which may vary according to the workload pattern. therefore, the accuracy of such algorithms needs to be evaluated and compared in order to select the most suitable algorithm for EC workload prediction. In this paper, a thorough comparison is conducted focusing on the most popular ML algorithms which are Linear Regression (LR), Support Vector Regression (SVR), and Neural Networks (NN) using real EC dataset. the experimental results show that a robust prediction framework can be supported by more than one algorithm considering the EC contextual behavior. the results also reveal that the NN outperforms LR and SVR in most cases.

关键词： Edge computing Self-adaptive Systems Machine Learning Prediction Framework Linear Regression Support Vector Regression Neural Networks Sliding Window

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