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

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Fine-grained Affective Processing Capabilities Emerging from Large Language Models 11

Fine-grained Affective Processing Capabilities Emerging from...

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11th international conference on Affective Computing and Intelligent Interaction (ACIIW)

作者： Broekens, Joost Hilpert, Bernhard Verberne, Suzan Baraka, Kim Gebhard, Patrick Plaat, Aske Leiden Univ LIACS Leiden Netherlands Free Univ Amsterdam Dept Comp Sci Amsterdam Netherlands German Res Ctr Artificial Intelligence DFKI Saarbrucken Germany

ISBN: (纸本)9798350327434

Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics.

关键词： ChatGPT Large Language Models sentiment analysis emotion representation computational modeling of emotion emotion elicitation

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Pear tree leaves based on image analysis Research on disease identification methods 4

Pear tree leaves based on image analysis Research on disease...

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4th international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2024

作者： Guo, Shuxi Xu, Zhijun Chen, Yingchun Zhang, Xiuqing Hebei Baisha Tobacco Co. Ltd. Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China

ISBN: (纸本)9798331531225

Pear trees are susceptible to black spot, brown spot, rust, black star, ring rot and other diseases in the planting process, resulting in large-scale pear fruit production and pear fruit quality decline. In this study, the identification and classification of pear brown spot and rust were realized based on image analysis combined with SVM algorithm, and the disease recognition model was established by MATLAB software, requiring the identification rate of pear diseases and pests to reach more than 90%. The main research contents and conclusions are as follows: (1) disease image preprocessing. The color image is transformed into gray image, image denoising, histogram equalization and other operations are carried out to enhance the image. In the aspect of image segmentation, HSV color space and RGB color space are analyzed and compared, and based on HSV color space, the threshold method is used to segment the color image spots. (2) Image feature value extraction. The color feature, texture feature and shape feature of the image are introduced and analyzed, and the texture feature is extracted by LBP operator. (3) Disease recognition model. Based on the principle of neural network, the disease recognition model is constructed with the GUI interface of MATLAB. The image to be tested is uploaded to the model and the result of disease identification is output. The disease recognition rate is above 95%. © 2024 IEEE.

关键词： MATLAB

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A Three-Degree-of-Freedom Large-Stroke Flexible Tip-Tilt-Piston Stage Driven by Voice Coil Motor 17th

A Three-Degree-of-Freedom Large-Stroke Flexible Tip-Tilt-Pis...

引用

17th international conference on Intelligent robotics and Applications

作者： Sima, Jinfu Chen, Yunzhuang Xu, Zhihao Lai, Leijie Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Addressing the limitation of traditional piezoelectric-driven tip-tilt-piston stages in accommodating millimeter-scale large-stroke applications due to their restricted stroke, a three-degree-of-freedom large-stroke flexible tip-tilt-piston stage actuated by a voice coil motor has been conceptualized. This stage undergoes compliance modeling and performance evaluation. Initially, the structural design of this large-stroke stage is outlined, encompassing three sets of vertically aligned driving chains positioned at 120. intervals. To achieve motion decoupling among chains and linear guidance for the motor rotor, flexible spherical joints and parallelogram mechanisms are employed. The kinematic equation for the platform is formulated based on the geometric arrangement of each chain. Subsequently, the compliance matrix approach is utilized to establish an analytical model for assessing the flexibility of the spherical joints and the entire tip-tilt-piston stage. Finally, a finite element analysis is conducted on the static and dynamic characteristics of the platform to validate the accuracy of the analytical model. This platform finds applicability in diverse scenarios that necessitate spatial positioning over significant stroke lengths.

关键词： Tip-tilt-piston stage Large stroke Compliance Voice coil motor Spherical joint Finite element analysis

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Experiment analysis of Separation in Circling Pigeon Flocks

Experiment Analysis of Separation in Circling Pigeon Flocks

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： You, Lingchen Duan, Haibin Deng, Yimin Tong, Bingda School of Automation Science and Electrical Engineering Beihang University Beijing100083 China

ISBN: (纸本)9781665481090

Pigeons are often observed flying in a circular motion around their loft. Pigeons sometimes experience separation from a larger group and fly in a different direction from the other pigeons. Based on the high-frequency data collected by GPS devices, the study uncovers the pattern of the separation behavior of circling pigeons. The findings reveal that during the separation process, the individual pigeon departs from the group with a velocity deviation that is nearly perpendicular to the group average velocity. The finding suggests that during the separation process of the flock, the separated pigeon mainly separates from the flock in a velocity perpendicular to the velocity of the flock, instead of separating from the flock in random directions. © 2024 IEEE.

关键词： Birds

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SYNERGY OF robotics AND IOT MONITORING IN INDUSTRIES USING DEEP RESNETS

SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING D...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Gobinath Kumarasamy, M. Kongu Kumar, M. Anbarasu, L. Padmavathi, N. Kumar, S. Sathish Ramya, P. Dept. of Computer Science and Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India College of Engineering Tamilnadu Karur India Engineering College Tamilnadu Perundurai India Department of Electronics and Communication Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India Department of EEE Erode Sengunthar Engineering College Tamilnadu Perundurai India General Engineering- Electrical RMD Engineering College Chennai India Department of EEE M. Kumarasamy College of Engineering Karur Tamilnadu India Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305708

This paper explores the synergistic integration of robotics and Internet of Things (IoT) monitoring within industrial settings, employing Deep Residual Networks (Deep ResNets). The convergence of robotics and IoT has the potential to revolutionize various industries by enabling efficient data collection, real-time analysis, and informed decision-making. Deep ResNets, known for their exceptional feature extraction capabilities, further enhance the fusion of these technologies. In this study, we delve into the collaborative framework of robotics and IoT, elucidating how Deep ResNets bolster the process. We discuss the benefits of this integration, including optimized resource allocation, predictive maintenance, and adaptable production systems. Through empirical analysis, we demonstrate the efficacy of Deep ResNets in handling complex data from integrated robotics and IoT sources. Ultimately, this research sheds light on the transformative impact of amalgamating robotics, IoT monitoring, and Deep ResNets, paving the way for intelligent and responsive industries. © 2023 IEEE.

关键词： robotics

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Thermal-Structural analysis of a Deployable Metamorphic Robotic Grasper with Finite Element Method 17th

Thermal-Structural Analysis of a Deployable Metamorphic Robo...

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17th international conference on Intelligent robotics and Applications

作者： Gao, Changqing Fan, Ruikai Wang, Hanlin Zhao, Yongjie Liu, Fei Du, Xuelin Shantou Univ Coll Engn Dept Mech Engn Shantou 515063 Peoples R China Shenzhen Polytech Univ Ind Training Ctr Shenzhen 518055 Peoples R China Hunan Inst Technol Sch Intelligent Mfg & Mech Engn Hengyang 421002 Peoples R China

ISBN: (纸本)9789819607709;9789819607716

This paper takes a deployable metamorphic robotic grasper as the studied object and its thermal-structural analysis with finite element method is carried out, by which the thermal deformation of the robotic grasper can be obtained and the optimization can be performed. As a result, the mechanical performance of the robotic grasper can be greatly enhanced, which is of great significance for the on-orbit operation of space spacecraft. Firstly, the robotic grasper is sample introduced and a theoretical model of the space temperature field is established, which offers a theoretical basis for finite element simulation. In addition, by finite element analysis software, an automatic mesh partition is applied and the finite element model of the robotic grasper is then established. By loading the temperature field, the thermal deformation can be shown, which provides a clear idea for structural optimization. Finally, through a large amount of simulation data, the most thermal deformation of the robotic grasper can be optimized, and then further simulation of the optimized model is carried out, the results show that the thermal deformation effectively decreases and the theoretical model is feasible.

关键词： metamorphic robotic grasper thermal-structural analysis finite element method thermal deformation

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Artificial intelligence System for Classification of Diabetic Retinopathy 2

Artificial Intelligence System for Classification of Diabeti...

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Harikrishna, Dharsinala Kumar, Naluguru Udaya Marri Laxman Reddy Institute of Technology and Managament Department of ECE Telangana Dundigal Hyderabad India

ISBN: (纸本)9781665460477

Diabetic Retinopathy (DR) becomes the crucial disease in different disease groups and millions of people suffering with it every year rapidly. However, the conventional methods are failed to classify the DR in early stage due to complex architecture of eye fundus image. Therefore, this article is focused on implementation of deep learning convolutional neural network (DLCNN) based artificial intelligence approach for classifying multiple stages of DR. Initially, the hybrid features are extracted from IDRID dataset by using Local Binary pattern (LBP), Local Gaussian Difference Extrema pattern (LGDEP), and Histogram of Oriented Gradient (HOG) descriptors. Further, Linear Discriminant analysis (LDA) is used to select the inter disease and intra disease dependent based optimal features. Then, DLCNN model is trained with these features for classification of DR grades for each test retinal image. The simulation results show that proposed DR classification results shows better subjective and object performance as compared to conventional machine learning and deep learning methods. © 2022 IEEE.

关键词： Local binary pattern

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Monte Carlo-based Measurement Uncertainty Propagation on a Virtual Sensor for Real- Time Human Balance Exergame analysis 3

Monte Carlo-based Measurement Uncertainty Propagation on a V...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Giulietti, Nicola Fabiocchi, Davide Giberti, Hermes Carnevale, Marco Università degli studi di Pavia Dip. Ingegneria Industriale e dell'Informazione Pavia Italy

ISBN: (纸本)9798350378009

Nowadays, increasingly advanced Artificial intelligence techniques are progressively being integrated with design methodologies. The use of advanced inferential models trained on real-time data is making it possible to analyze the dynamic behaviors of very complex systems simply by observing them through vision-based measurement techniques, offering an innovative approach to indirect measurement of forces at play without the need for additional bulky and difficult-to-install sensors. However, it is not always possible to estimate the reliability of these systems because they depend on variables that are poorly controlled and difficult to assess. In addition, the assessment of measurement uncertainty is critical for the adoption of data-driven artificial intelligence models in commercial products and services. This paper explores the possibility of using cameras for the development of a virtual sensor for the dynamic analysis of human balance in a balance platform system. The developed virtual sensor is characterized by estimating the propagation of the measurement uncertainty through the use of the Monte Carlo method. The results obtained make it possible to evaluate the metrological performance of the proposed method. © 2024 IEEE.

关键词： Intelligent systems

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A Design and Development of Web Application Vulnerability Detection Using Graph-based Modeling analysis Over CAPEC 21

A Design and Development of Web Application Vulnerability De...

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21st international Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Bongkodmalee, Teeramaet Vongvipaporn, Supasake Siripanich, Preravitch Fugkeaw, Somchart Vorakulpipat, Chalee Thammasat Univ Sirindhorn Int Inst Technol Sch ICT Pathum Thani Thailand Natl Elect & Comp Technol Ctr Informat Secur Res Team Pathum Thani Thailand

ISBN: (纸本)9798350381771;9798350381764

This paper presents a new approach to web application vulnerability detection by integrating the Common Attack pattern Enumeration and Classification (CAPEC) framework with our proposed graph analysis techniques. The proposed model aims at detecting the vulnerabilities of multiple web applications based on the examination of their attack patterns found in web server logs. We introduced a graph-based detection model to identify and classify attack patterns. This approach enhances the efficiency of the vulnerability discovery as well as improves the insightful analysis of the correlation between vulnerabilities and attack patterns. To demonstrate the functionality and efficiency of the proposed technique, we conducted the experiments to show how the system detects the vulnerabilities from the generated log data and provided the processing time of the system when it deals with multiple log files and attack patterns. The experimental results highlight the potential of our proposed system to advance web application security in the face of evolving cyber threats.

关键词： Web Application Security Vulnerability Detection Graph-Based analysis Cyber Threats Common Attack pattern Enumeration and Classification (CAPEC) Log analysis

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international conference on robotics, Control, Automation and Artificial intelligence, RCAAI 2023

International Conference on Robotics, Control, Automation an...

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international conference on robotics, Control, Automation and Artificial intelligence, RCAAI 2023

ISBN: (纸本)9789819746491

The proceedings contain 46 papers. The special focus in this conference is on robotics, Control, Automation and Artificial intelligence . The topics include: Wildlife Intrusion Detection and Prevention in Farm Fields Using IoT Technology;predictive Maintenance of Gearbox: A Cost-Effective IoT Approach for Remaining Useful Life Estimation;Ergonomic Knee Exoskeleton System with Real-World Perception and Actuation Using Computer Vision, Image Processing and LiDAR Data Fusion;dynamic Voltage and Frequency Scaling Implementation for System-Level Power Reduction;Development of CNC Gantry Drilling Machine;a Comprehensive Survey on Robo-Ethics;semi-automated Inkjet Marking System with Lead Screw Actuation for Aerospace Components;effect of Nano-coolant Blends on the Performance of Compact Heat Exchanger—A Review;Identification of Deity Images Using CNN and Transfer Learning Models;reinventing Supermarkets: Enhanced Customer Convenience Through Deep Learning Technology;model Predictive Controller Based Virtual Inertia Controller for Enhancing the Frequency Stability of Microgrid;GUI-Enabled Wearable Solution for Tremor Detection and Fall Prevention;design and Development of Electrical System for Retrofitting a Turning Centre;an Elucidative analysis of Quadruped Robot’s Locomotion and Design;comparative Assessment of Pathfinding Algorithms for Efficient Maze Navigation in Industrial Applications;automated Bundy Tube Metrology with Deep Learning;an Advanced Surveillance Motorized Car for Real-Time Inspection with Sensing Capabilities;cybersecurity Threats, Forensics, and Challenges;Tea Leaf Disease Detection and Classification Using CNN, SVM and Dense Net;Enhancing Beach Cleaning Efficiency: ML-Based Litter Detection by an Autonomous Robot;ALIVE: A Low-Cost Interactive Vaccine Storage Environment Module Ensuring Easy Portability and Remote Tracking of Operational Logistics to the Last-Mile.

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