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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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A System to Identify Walking pattern Using Machine Learning for a Load Reduction Exoskeleton Robot 18

A System to Identify Walking Pattern Using Machine Learning ...

引用

IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhao, Hanqing Nambo, Hidetaka Kanazawa Univ Grad Sch Nat Sci & Technol Artificial Intelligence Lab Elect Engn & Comp Sci Kanazawa Ishikawa Japan

ISBN: (纸本)9781665441018

In this study, to realize the gait recognition and gait prediction of Load reduction exoskeleton robot in the walking process. Firstly, we designed a plantar pressure measurement device. The information about walking posture of human legs, feet, and the whole body can be reflected by the magnitude and distribution of plantar pressure. The plantar pressure measurement device is used to collect data and generate the required training datasets. Gait recognition and gait prediction analysis are obtained by using deep neural networks and recurrent neural networks for gait recognition and gait prediction. Eventually, it can be applied to the control system of exoskeleton walking and abnormal gait prediction.

关键词： Robotic exoskeleton systems Deep Learning IoT man-machine intelligent system Walk Assist

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Identification of e-Nose Wine Varieties Based on Feature Extraction and Classification Modeling 3rd

Identification of e-Nose Wine Varieties Based on Feature Ext...

引用

3rd international conference on Wireless Communications and Applications,ICWCA 2019

作者： Qiao, Miao Zhang, Lei College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789811556968

A new method for rapid identification of wine varieties was established by using transducer electronic nose detection system. System performance: firstly, the electronic nose with ten different sensors is used to collect the information of seven kinds of grape wine, then the sensor information of each kind of grape wine is extracted by polynomial curve fitting method, then the qualitative analysis of different kinds of grape wine is realized by principal component analysis, and finally, the classification algorithm of grape wine is combined with lightGBM algorithm. At the same time, Bayesian optimization is used to optimize the super parameters of the model. Through the classification model in the validation set, the accuracy of the lightGBM algorithm reached 95.24%, achieving the best classification effect, and verifying the effectiveness of the new pattern recognition method, which provides a new idea for wine classification and identification. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Transducers

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Human Gait analysis Method Based on Kinect Sensor 13th

Human Gait Analysis Method Based on Kinect Sensor

引用

13th international conference on Intelligent robotics and Applications (ICIRA)

作者： Wang, Nianfeng Lin, Guifeng Zhang, Xianmin South China Univ Technol Guangdong Prov Key Lab Precis Equipment & Mfg Tec Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9783030666453;9783030666446

In this paper, a new method for human gait analysis based on the Kinect Sensor is introduced. Such method based on Kinect sensor can be divided into three steps: data acquisition, pre-processing and gait parameter calculation. First, a GUI (Graphical User Interface) was designed to control the Kinect sensor and get the required raw gait data. In the pre-processing, abnormal frames are removed first. Afterwards,the influence of Kinect's installation error is eliminated by coordinate system transformation. What's more,the noise is eliminated by using moving average filtering and median filtering. Finally, gait parameters are obtained by the designed algorithm which composed of gait cycle detection, gait parameter calculation, and gait phase extraction. The validity of the gait analysis method based on Kinect v2 was verified by experiments.

关键词： Gait analysis Kinect Motion capture

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High Resolution Soft Tactile Interface for Physical Human-Robot Interaction

High Resolution Soft Tactile Interface for Physical Human-Ro...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Huang, Isabella Bajcsy, Ruzena Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94270 USA

ISBN: (纸本)9781728173955

If robots and humans are to coexist and cooperate in society, it would be useful for robots to be able to engage in tactile interactions. Touch is an intuitive communication tool as well as a fundamental method by which we assist each other physically. Tactile abilities are challenging to engineer in robots, since both mechanical safety and sensory intelligence are imperative. Existing work reveals a trade-off between these principles- tactile interfaces that are high in resolution are not easily adapted to human-sized geometries, nor are they generally compliant enough to guarantee safety. On the other hand, soft tactile interfaces deliver intrinsically safe mechanical properties, but their non-linear characteristics render them difficult for use in timely sensing and control. We propose a robotic system that is equipped with a completely soft and therefore safe tactile interface that is large enough to interact with human upper limbs, while producing high resolution tactile sensory readings via depth camera imaging of the soft interface. We present and validate a data-driven model that maps point cloud data to contact forces, and verify its efficacy by demonstrating two real-world applications. In particular, the robot is able to react to a human finger's pokes and change its pose based on the tactile input. In addition, we also demonstrate that the robot can act as an assistive device that dynamically supports and follows a human forearm from underneath.

关键词： Robot sensing systems Cameras Safety Task analysis Visualization

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A Geometrical Criterion for the Synchronization of Dynamic Networks with Input Saturation 16

A Geometrical Criterion for the Synchronization of Dynamic N...

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16th IEEE international conference on Control, Automation, robotics and Vision (ICARCV)

作者： Ye, Juncen Que, Haoyi Ma, Longhua Xu, Ming Su, Hongye Zhe Jiang Univ Polytech Inst Ningbo Campus Ningbo 315000 Zhejiang Peoples R China Shenzhen Polytech Inst Intelligence Sci & Engn Shenzhen 518055 Guangdong Peoples R China Zhejiang Univ Ningbo Inst Technol Inst Automat & Elect Engn Ningbo 315100 Zhejiang Peoples R China Zhejiang Univ Inst Cyber Syst & Control Natl Lab Ind Control Technol Yuquan Campus Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781728177090

The paper investigated the global synchronization control problem of dynamic networks with discrete-time communications and input saturation. Based on a geometrical criterion of certain non-linear non-autonomous systems, we proposed a sufficient condition for stability of saturating linear control for a linear networked system. The stability could be ensured simply from a range of gains rather than a complex Lyapunov function. A numerical example is offered to show the effectiveness and the relative analysis.

关键词： Synchronization Input Saturation Complex Networks Circle Criterion

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Multimodal Corpus analysis of Autoblog 2020: Lecture Videos in Machine Learning 23rd

Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos ...

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23rd international conference on Speech and Computer, SPECOM 2021

作者： Hernandez, Abner Yang, Seung Hee Pattern Recognition Lab Friedrich Alexander University Erlangen-Nürnberg Erlangen Germany Department of Artificial Intelligence in Biomedical Engineering Friedrich Alexander University Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783030878016

This paper introduces a lecture video corpus, Autoblog 2020. With the increase of online learning in universities, there is a demand for a systematic toolchain development for lecture video processing. However, the existing lecture video corpus does not satisfy the requirement for such tasks, and lecture transcription and analyses are relatively unexplored areas in speech and natural language research. Autoblog 2020 Corpus is developed towards the end goal of free video-to-blog post conversion software that supports making video presentations more accessible. It will include automatic editing of disfluencies, automatic speech recognition (ASR), and spoken term extraction so that researchers can process and share their contents more efficiently. In this paper, we present a description of the corpus, linguistic analyses and preliminary experiment results regarding ASR, keyword extraction, and segmentation. The results will be used in future work to develop a video-to-blog post conversion. © 2021, Springer Nature Switzerland AG.

关键词： Speech recognition

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Robust Texture Features for Breast Density Classification in Mammograms 16

Robust Texture Features for Breast Density Classification in...

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16th IEEE international conference on Control, Automation, robotics and Vision (ICARCV)

作者： Li, Haipeng Mukundan, Ramakrishnan Boyd, Shelley Univ Canterbury Comp Sci & Software Engn Dept Christchurch 8041 New Zealand St Georges Med Ctr Canterbury Breastcare Christchurch 8014 New Zealand

ISBN: (纸本)9781728177090

This paper proposes an effective and robust texture feature descriptor to classify mammographic images into different breast density categories. Accurate breast density based categorization of images plays an important role in the risk assessment at early stages of breast cancer. Based on the commonly used local binary patterns (LBP), we investigate its variant method local quinary patterns (LQP) for considering more details of texture features. The rotation invariant approach with different concerned numbers of spatial transitions is used to extend LQP to rotation invariant LQP (RILQP). The proposed method recognizes more texture patterns and reduces the high dimensionality of its feature vector significantly, which make it a robust texture descriptor. In addition, in the breast density classification task, this paper also investigates the influence to classifying results by using resized mammogram images. Two mammogram datasets, INBreast and MIAS, are used in our experiments to test the proposed method. Comparing to state-of-the-art methods, competitive classifying results are observed using the RILQP method, with classification accuracies of 82.50% and 80.30% on INBreast and MIAS respectively. Comparative analysis also indicates that the proposed method outperforms other methods statistically.

关键词： Local binary pattern

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1st international conference on Artificial intelligence and Sustainable Computing for Smart City, AIS2C2 2021

1st International Conference on Artificial Intelligence and ...

引用

1st international conference on Artificial intelligence and Sustainable Computing for Smart City, AIS2C2 2021

ISBN: (纸本)9783030823214

The proceedings contain 21 papers. The special focus in this conference is on Artificial intelligence and Sustainable Computing for Smart City. The topics include: Implementation of Touch-Less Input Recognition Using Convex Hull Segmentation and Bitwise AND Approach;virtually Interactive User Manual for Command and Control Systems Using Rule-Based Chatbot;wrapper-Based Best Feature Selection Approach for Lung Cancer Detection;application of Ensemble Techniques Based Sentiment analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners;A Hybrid Mathematical Model Using DWT and SVM for Epileptic Seizure Classification;multimodal Cyberbullying Detection Using Ensemble Learning;frequent Route pattern Mining Technique for Route Prediction in Transportation Network;student Clickstreams Activity Based Performance of Online Course;feature Selection with Random Forests Predicting Metagenome-Based Disease;Emotion Recognition Using Portable EEG Device;predicting the Default Borrowers in P2P Platform Using Machine Learning Models;human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods;classification of Extraversion and Introversion Personality Trait Using Electroencephalogram Signals;the Productivity Forecasting in the Sector of Non-market Services (Education and Health Care Sectors): A Case Study of Ukraine;An Emergent Role of Knowledge Graph and Summarization Methodology to Simplify Recruitment for the Indian IT Industry;knowledge and Innovation Management for Transforming the Field of Renewable Energy;a Research Perspective on Security in Fog Computing Through Blockchain Technology;ZKPAUTH: An Authentication Scheme Based Zero-Knowledge Proof for Software Defined Network;Efficiency Evaluation of Handover Management Techniques in LTE Heterogeneous Networks.

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Automatic Chain Line Segmentation in Historical Prints 25th

Automatic Chain Line Segmentation in Historical Prints

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25th international conference on pattern Recognition Workshops, ICPR 2020

作者： Biendl, Meike Sindel, Aline Klinke, Thomas Maier, Andreas Christlein, Vincent Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Germanisches Nationalmuseum Nuremberg Germany TH Köln Köln Germany

ISBN: (纸本)9783030687953

The analysis of chain line patterns in historical prints can provide valuable information about the origin of the paper. For this task, we propose a method to automatically detect chain lines in transmitted light images of prints from the 16th century. As motifs and writing on the paper partially occlude the paper structure, we utilize a convolutional neural network in combination with further postprocessing steps to segment and parametrize the chain lines. We compare the number of parametrized lines, as well as the distances between them, with reference lines and values. Our proposed method is an effective method showing a low error of less than 1 mm in comparison to the manually measured chain line distances. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

ISBN: (纸本)9783030691424

The proceedings contain 55 papers. The special focus in this conference is on Information and Communication Technology and Applications. The topics include: Application of Supervised Machine Learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems;anomaly Android Malware Detection: A Comparative analysis of Six Classifiers;Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF;A Conceptual Hybrid Model of Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) for Masquerade Attack Detection;An Automated Framework for Swift Lecture Evaluation Using Speech Recognition and NLP;DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal;hausa intelligence Chatbot System;an Empirical Study to Investigate Data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced Data;a Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value;application of Big Data Analytics for Improving Learning Process in Technical Vocational Education and Training;validation of Student Psychological Player Types for Game-Based Learning in University Math Lectures;outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score;an Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational intelligence;optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm;identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary pattern Features;feature Weighting and Classification Modeling for Network Intrusion Detection Using Machine Learning Algorithms;comparative Performance analysis of Anti-virus Software.

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