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

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Pathfinder: A Fog Assisted Vision-Based System for Optimal Path Selection of Service Robots 9

Pathfinder: A Fog Assisted Vision-Based System for Optimal P...

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Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and pattern Recognition (ICIVPR)

作者： Irtisam, Niloy Ahmed, Riad Akash, Mohammad Moniruzzaman Abdullah, Raiyaan Sarker, Sujan Rahman, Sejuti Jamal, Lafifa Univ Dhaka Dept Robot & Mechatron Engn Dhaka 1000 Bangladesh

ISBN: (纸本)9781728193311

Service delivery application involving robots relies on the success of the navigation process. To ensure maximum performance, the navigation process should generate optimal path avoiding collisions with dynamic obstacles. Selecting such a path by analyzing the dynamic environment condition while putting minimal overhead on robots is a challenging problem in service robot navigation. In this work, we develop a Fog assisted service robot navigation system that puts most of the computing tasks to a central Fog server. The server employs a vision-based monitoring system to locate the robots and obstacles. The optimal path is generated by analyzing the available information and the robots are instructed accordingly. We implement a test-bed system to evaluate the performance of the proposed system. The results depict that the proposed fog-based system can achieve as low as 23.81% of average distance covered, 22.05% of average service delivery time, and 22.72% of average energy consumption compared to the without fog systems. Contribution-A fog-based system for service robot navigation is proposed that uses computer vision to generate optimal obstacle free path.

关键词： Service Robot Path Selection Path Planning Fog robotics Robot Vision Internet of Things

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An Auto-adjusting Weight Model for Imbalanced Wafer Defects Recognition

An Auto-adjusting Weight Model for Imbalanced Wafer Defects ...

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Sensing, Measurement & Data Analytics in the era of Artificial intelligence (ICSMD), international conference on

作者： Yu Chen Xinjia Zhao Meng Zhao Jing Ji School of Mechano-Electronic Enginnering Xidian University Xi'an China School of Economics and Management China University of Geosciences Wuhan China

Wafer defect recognition is popular research in the semiconductor industry. Generally, each defect pattern is related to a specific manufacturing problem. By identifying defect patterns correctly, manufacturing problems can be recognized and fixed in time, which improves the quality and production yield of wafers. However, due to the location, light and the increasing number of wafers, traditional recognition methods achieve unsatisfactory performance. Currently, convolutional neural network (CNN) based methods outperform traditional methods in accuracy and speed, but fail when training with imbalanced target classes. To address the imbalanced problem, a CNN-based knowledge distillation (KD) method is proposed. To improve the identification of different types of defects, a multi-head attention layer is applied to the proposed CNN model, which enriches local and global information of features. Besides, when training the CNN model, target features are constrained with Distillation Loss and Focal Loss, reducing the effect of dataset imbalance. Experiments on the public dataset WM-811K are conducted to verify the proposed methods, and experimental results showed that the accuracy, precision, recall, specificity, and F1 score of our method reached 97.7%, 96.9%, 97.2%, 99.7% and 97.0% respectively, and the classification accuracy of each class was above 93.0%, which indicates the proposed method was reasonable and effective on large-scale imbalanced wafer defect datasets.

关键词： Semiconductor device modeling Training Data analysis Electronics industry Production Data models Manufacturing

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Opinion Dynamics Driven Under Leadership in Cooperation-Competition Social Networks 16

Opinion Dynamics Driven Under Leadership in Cooperation-Comp...

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16th IEEE international conference on Control and Automation (ICCA)

作者： Bai, Libing Shi, Lei Chen, Hongjian Shao, Jinliang Cheng, Yuhua Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Shen Zhen Inst Artifcial Intelligence & Robot Soc Shenzhen 518054 Peoples R China Univ Elect Sci & Technol China Glasgow Coll Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781728190938

Social psychological research shows that although the opinions of most agents (followers) tend to prevail, sometimes a firm and influential agent (leader) can influence or even overturn the followers' preferences. Inspired by this fact, this paper designs a Degreet-model-based opinion evolution rule in cooperation-competition social networks, and uses the relevant properties of the super-stochastic matrix to conduct theoretical analysis on how the leader influence the formation of followers' opinions. Finally, algebraic conditions for achieving leader-driven opinion consensus and opinion polarization are established, respectively. Moreover, the correctness of the theoretical results is verified by numerical simulations.

关键词： Stochastic systems

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A technical framework for human-like motion generation with autonomous anthropomorphic redundant manipulators

A technical framework for human-like motion generation with ...

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IEEE international conference on robotics and Automation (ICRA)

作者： Averta, Giuseppe Caporale, Danilo Della Santina, Cosimo Bicchi, Antonio Bianchi, Matteo Univ Pisa Ctr Ric Enrico Piaggio Largo Lucio Lazzarino 1 I-56126 Pisa Italy Fdn Ist Italiano Tecnol Solt Robot Human Cooperat & Rehabil Via Morego 30 I-16163 Genoa Italy Univ Pisa Dipartimento Ingn Informaz Largo Lucio Lazzarino 1 I-56126 Pisa Italy MIT MIT Comp Sci Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728173955

The need for users' safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance.

关键词： Kinematics Manipulators Trajectory Computer architecture Cost function

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Students' Debugging Behavior analysis in Game-Based Learning 2021

Students' Debugging Behavior Analysis in Game-Based Learning

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7th international conference on Computing and Artificial intelligence, ICCAI 2021

作者： Yang, Fan Dong, Zhenghong Wu, Zhongwang Space Engineering University Beijing101416 China

ISBN: (纸本)9781450389501

As programing became more and more important, people are taking a large amount of work to help students to learn programming skills effectively. This paper applies a programming learning game called May's Journey to fit 5 debugging types including syntax, logical, structure, reasoning, and undefined debugging errors into programming levels. Then we can find out the reason why students make mistakes, and which debugging type would cause the mistakes of other debugging types. And we have 6 findings, (1) This paper proposes a student debugging model to describe how students make debugging errors, which is used for further analysis on student debugging behaviors. (2) This paper proposes to use group mean and with-in group variance based on student debugging model, which finds out the common debugging errors and personal debugging errors. (3) This paper proposes to extract student debugging patterns using Random forest, which identifies student debugging behaviors, so that students who have the same debugging pattern can be trained together. (4) This paper also proposes to use student debugging model-based SVM to extract student performance patterns, which identifies student performance changing over programming levels in terms of a specific debugging type. (5) This paper proposes to apply mean decrease accuracy and mean decrease Gini to identify the effectiveness of debugging types;and (6) this paper proposes to use a classification-based LSTM algorithm to predict debugging errors, which improves the predication accuracy a lot. Experiments and results are also provided to prove that our methods are valid and better. © 2021 ACM.

关键词： Students

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Enhancement of upper limb movement classification based on wiener filtering technique 22

Enhancement of upper limb movement classification based on w...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Jarrah, Yazan Ali Grace, Asogbon Mojisola Samuel, Oluwarotimi Williams Zhu, Mingxing Wang, Xin Delis, Alberto Lopez Huang, Weimin Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology UCAS Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Medical Biophysics Center Oriente University Cuba Shenzhen Children's Hospital Department of Neonatology Shenzhen Guangdong518038 China

ISBN: (纸本)9781728162676

Electromyogram pattern recognition (EMG-PR) is considered a potential method for upper limb prosthesis control. In principle, the feature extraction technique has been ranked the most influential factor that affect the EMG-PR method's performance. Despite the progress made thus far, there are inevitable interferences that could not be handled by the usual signal filtering approaches that are applied to enhance the extracted features. To address this issue, this study proposed a technique based on Wiener filtering for the preprocessing of EMG signals towards increasing the classification performance of EMG-PR systems. The performance of the proposed approach was investigated with recordings of high-density surface EMG which was obtained from four transhumeral amputees who performed five classes of limb movements. Then, features of five time-domain were analyzed in terms of their decoding accuracy, sensitive, and F1-score, with and without the application of the proposed for linear discriminant analysis and support vector machine classifiers techniques. Experimental results showed that by applying the proposed technique to the different feature sets, significant improvements in classification accuracy, sensitivity, and F1-score were observed across all subjects and classifiers. The proposed method improved the average classification accuracy by an increase of approximately 6.24% compared with the conventional method, while an increment as high as 16.77% was recorded for individual classes of movements. The outcomes of this study indicate that Wiener filtering may potentially boost the performance of EMG-PR systems in practical applications. © 2021 IEEE.

关键词： pattern recognition

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Aesthetic analysis of Mahogany Inlaid Silver pattern Based on Eye Movement Data

Aesthetic Analysis of Mahogany Inlaid Silver Pattern Based o...

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2020 IEEE international conference on Artificial intelligence and Computer Applications, ICAICA 2020

作者： Liu, Chunli Li, Junfeng School of Mechanical-electronical and Vehicle Engineering Weifang University Weifang Shandong China

ISBN: (纸本)9781728170046

From the perspective of user experience combined with computer vision technology to analyzed the preference of target user's in the inlaid silver pattern of mahogany, in order to promote the inheritance and development of this intangible cultural heritage. Firstly, using the morphological analysis method and the principle of semiotics to analysis and deconstruction of the pattern of mahogany inlaid silver products, Identify key features. Construct a 'mahogany inlaid silver pattern DNA model' and extract the core pattern as an experimental sample. Secondly, 40 target users of different ages and genders were selected as the subjects, using eye tracker to collect the eye movement data when the subjects observeing different patterns. Through the visual analysis of the data, discuss the relationship between the eye movement characteristics, the beauty of the pattern and the user's preference. Results show that the user's preference for patterns is greatly influenced by gender and less affected by age. Users of the same gender and different age have similar preferences for patterns. Based on the experimental results, the corresponding relationship between age, gender and pattern preference is given, which provides a reference for designers to conduct personalized design for different users and improve the success rate of products. © 2020 IEEE.

关键词： Computer vision

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Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements

Optimization-Based Visual-Inertial SLAM Tightly Coupled with...

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IEEE international conference on robotics and Automation (ICRA)

作者： Jinxu Liu Wei Gao Zhanyi Hu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and With School of Artificial Intelligence University of Chinese Academy of Sciences China

Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly minimized within a sliding window, in which the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are also addressed. Experimental results on public dataset in complex urban scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, including scenes mainly containing low-rise buildings and those containing urban canyons.

关键词： Couplings Doppler shift Global navigation satellite system Measurement errors Simultaneous localization and mapping conferences Measurement uncertainty

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12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series analysis;membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization;membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep Learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep Learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series pattern Discovery by Deep Learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep Learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard analysis Using FMEA: A DAM Case Study;Privacy Preserving Machine Learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs;bridging Semantic Web and Machine Learning: First Results of a Systematic Mapping Study.

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Large Scale Graph Based Network Forensics analysis 25th

Large Scale Graph Based Network Forensics Analysis

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25th international conference on pattern Recognition Workshops, ICPR 2020

作者： Di Rocco, Lorenzo Petrillo, Umberto Ferraro Palini, Francesco Dipartimento di Scienze Statistiche Università di Roma "La Sapienza" Roma Italy

ISBN: (纸本)9783030688202

In this paper we tackle the problem of performing graph based network forensics analysis at a large scale. To this end, we propose a novel distributed version of a popular network forensics analysis algorithm, the one by Wang and Daniels [18]. Our version of the Wang and Daniels algorithm has been formulated according to the MapReduce paradigm and implemented using the Apache Spark framework. The resulting code is able to analyze in a scalable way graphs of arbitrary size thanks to its distributed nature. We also present the results of an experimental study where we assessed both the time performance and the scalability of our algorithm when run on a distributed system of increasing size. © 2021, Springer Nature Switzerland AG.

关键词： Graphic methods

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