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Pipeline Manager: A Flexible Semi-automatic dataflow Analysis Framework 22

Pipeline Manager: A Flexible Semi-automatic Dataflow Analysi...

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22nd IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Chen, Cheng-Hui Hong, Huai-Che Hong, Yu-Shiang Wang, Hsiao Yu Yu, Shyr-Shen Inst Informat Ind Reg Ind Serv Div Nantou Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan

ISBN: (纸本)9781665404037

Industrial big data analysis has received a bunch of attentions in recent decades. There are several famous machine learning or deep learning frameworks used in different scenarios. However, we lack a stable and easy-to-operate pipeline framework. In this paper, the purpose is to propose an algorithm pipeline integration framework to help industrial AI systems deal with loads, scheduling and automatic operations.

关键词： data Pipeline Machine learning deep learning RESTful API

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AcneAI+: Revolutionizing Dermatology Through Advanced Machine learning

AcneAI+: Revolutionizing Dermatology Through Advanced Machin...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Yashu Vinay Kukreja Prateek Srivastava Ashish Garg Shanmugasundaram Hariharan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents a resilient model for classifying skin acne using sophisticated machine-learning methodologies. Within dermatological diagnostics, the prevalence of acne highlights the need for precise and effective categorization techniques. The model that has been suggested exhibits a high level of accuracy, with a recorded value of 89.41%. Additionally, it displays a precision of 89.59% and an F1 score of 89.41%. These results prove the model's effectiveness in accurately categorizing various forms of acne. The thorough assessment of specific kinds of lesions regularly demonstrates high accuracy and recall, confirming the model's capacity to provide correct predictions for a wide range of skin disorders. The use of micro and macro average measures serves to highlight the model's equitable performance. To refine the model further, it is recommended to include more extensive and varied datasets, providing more excellent generalization capabilities. The integration of sophisticated deep learning architectures and supplementary characteristics can enhance the model's precision, establishing it as a relevant instrument for dermatologists in clinical environments. The present study is a valuable contribution to automated dermatological diagnosis, providing a viable route for accurate and fast categorization of skin conditions.

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Real-Time Human Pose Estimation as a Cost-Effective Solution for the Teleoporation of a 6-Axis Cobot Arm 22

Real-Time Human Pose Estimation as a Cost-Effective Solution...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Henderson, Benn Coleman, Sonya Kerr, Dermot Quinn, Justin Harrigan, Shane School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798331527471

This paper explores the application of BlazePose, a monocular human pose estimation (HPE) model, within a teleoperation framework for a UR5 six-axis robot. Achieving teleoperation with only a single RGB camera and a device without a powerful GPU will improve accessibility and cost effectiveness of teleoperation solutions. This study evaluates the 2D pose estimation capabilities of BlazePose for robotic teleoperation tasks. Given the necessity of manipulating the UR5 in three-dimensional space, we implement a 2D-based controller that translates the teleoperator’s 2D right hand position within a configurable hand workspace to the corresponding position of the robot’s Tool Centre Point (TCP) within the robot’s available workspace along two dimensions. The left hand is then utilised for controlling the robot’s motion along the third dimension and operating the attached OnRobot RG2 gripper during the pick- and-place task. Additionally, we explore an alternative control paradigm utilising the 3D pose estimation of BlazePose for a more intuitive controller. Two experiments are conducted: the pick-and-place task to assess the 2D-based controller in common robotic tasks, and a hold position task. The hold position task aims to assess the amount of excess movement attributable to the HPE model when utilising the 3D-based controller. The results reveal that while the 2D pose estimation capabilities enable effective teleoperation, the utilisation of 3D estimation results in poor translation to robot control and significant excess motion. These findings underscore the importance of accurate depth estimation in 3D HPE models for precise and reliable teleoperation. © 2024 IEEE.

关键词： Remote control

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Fuzzy Q-learning Control for Temperature Systems 22

Fuzzy Q-learning Control for Temperature Systems

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22nd IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Chen, Yeong-Chin Hung, Lon-Chen Syamsudin, Mariana Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Lunghwa Univ Sci & Technol Dept Elect Engn Taoyuan Taiwan

ISBN: (纸本)9781665404037

In this paper, the reinforcement learning algorithm applied to temperature control of the internet of things (IoT), which aims to develop a multi-purpose intelligent micro-power control switch to achieve advanced temperature control research. This paper is based on the fuzzy Q-learning PID control algorithm based on reinforcement learning, with LinkIt Smart 7688 Duo platform. The error value between the set temperature and the actual sensed temperature is exposed to the reinforcement learning PID control operation. Specifically, a temperature sensor will provide temperature feedback to the LinkIt Smart 7688 Duo in order to achieve the stated temperature control. Finally, the suggested control approach will be compared to PID control to illustrate its efficacy and performance.

关键词： reinforcement learning IoT fuzzy q-learning multi-agent temperature

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Privacy-Preserved Neural Graph Similarity learning 22

Privacy-Preserved Neural Graph Similarity Learning

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22nd IEEE international conference on data Mining (ICDM)

作者： Hou, Yupeng Zhao, Wayne Xin Li, Yaliang Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Minist Educ Engn Res Ctr Next Generat Intelligent Search & Re Beijing Peoples R China

ISBN: (纸本)9781665450997

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://***/RUCAIBox/PPGM.

关键词： graph similarity learning privacy-preserving graph neural networks

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Joint Global and Local Feature learning Based on Facial StO2 for Stress Recognition 19

Joint Global and Local Feature Learning Based on Facial StO2...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Dong Liu, Xinyu Chen, Tong Peng, Dairong Wang, Jiaxiu Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9798350346558

Stress is an emotional state that is inevitable in social life, it is of great importance to accurately identify different types of stress. In this paper, we use human facial tissue oxygen saturation (StO2) to identify four states of an individual's baseline, emotional stress, high-intensity physical stress and low-intensity physical stress. For the stress classification, we proposed a network called GLNet that combines global and local StO2 features. Specifically, GLNet learns local features from facial regions that provide the effective stress information and global features from the whole face. Then, the two features are fused at decision-level and classified. In addition, a new data augmentation method was proposed to address the data imbalance, which generates new data by constructing a mixed network called MixNet that fuses the depth features of multiple data. Experimental results show that MixNet can effectively alleviate the problem of data imbalance and GLNet combined with the data expanded by MixNet achieved the best performance compared to previous methods of StO2 stress recognition. © 2022 IEEE.

关键词： Face recognition

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A Hierarchical Nonparametric Bayesian Model Based on Scaled Dirichlet Distribution 22

A Hierarchical Nonparametric Bayesian Model Based on Scaled ...

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22nd IEEE international conference on Information Reuse and Integration for data Science (IRI)

作者： Manouchehri, Narges Baghdadi, Ali Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781665438759

data clustering is one of the principle unsupervised learning methods in various domains of science. In this paper, we propose a new clustering approach based on hierarchical Dirichlet processes of scaled Dirichlet distribution. Our motivation is flexibility of this distribution which provides a good potential to fit non-Gaussian data. Also, some issues such as the high costs of labeling medical data and sensitivity in this domain encouraged us to construct an unsupervised learning algorithm. We applied batch and online variational inference to learn our model as both methods can estimate model parameters and complexity, simultaneously. To demonstrate the capability of our proposed model, we tested our framework on two applications related to Internet of Health Things (IoHT) and computer-assisted diagnosis (CAD). Our proposed model demonstrates comparable results to similar alternatives.

关键词： Unsupervised learning mixture models hierarchical Dirichlet process scaled Dirichlet distribution Internet of Health Things (IoHT) computer-assisted diagnosis (CAD) human activity recognition breast cancer

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Towards an Effective Tool Wear Monitoring System with an AI Model Management Platform

Towards an Effective Tool Wear Monitoring System with an AI ...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Jian Wen Chen Meng Shiun Tsai Che Lun Hung Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Mechanical Engineering National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

automated monitoring of tool wear is crucial for maintaining product quality. Furthermore, implementing AI techniques for real-time tool monitoring involves not only developing models but also managing their versions, avoiding the issue of models becoming less accurate as the properties of the machinery change over time. Consequently, this study develops a tool wear prediction system integrated with an artificial intelligent (AI) model management platform. First, this system uses various machine learning models to extract diverse signal features from sensor fusion, thereby boosting the accuracy of tool wear prediction. Secondly, the AI Models Management Platform comprises the C# programming language, Neural Networks Processing Unit (NPU) board, and Docker on both user and server sides, enhancing industrial processes and enabling real-time analysis of sensor data. According to these results, the Ensemble learning method within the machine learning model demonstrates superior performance, yielding an average root mean squared error (RMSE) of $\mathbf{0.000185}\ mm^learning$ . Additionally, AI model management platform efficiently handle various model versions and streamline data training processes, empowering users to select suitable models and thereby enhancing system robustness.

关键词： Training Accuracy Working environment noise Predictive models Sensor fusion Feature extraction data models Robustness Real-time systems Monitoring

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22nd international Stuttgart Symposium, ISSYM 2022

22nd International Stuttgart Symposium, ISSYM 2022

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22nd international Stuttgart Symposium, ISSYM 2022

ISBN: (纸本)9783658370107

The proceedings contain 35 papers. The special focus in this conference is on Stuttgart. The topics include: Well-to-Wheel Evaluation of Conventional and Alternative Powertrains for Municipal Refuse Collection Vehicles;The Development of a Heat Pump Based EV Thermal Management System;intelligent data Analytics with Artificial Intelligence for Hybrid Engine Restart;novel Approach for Vehicle-Self-Localization;virtual Validation of automated, Autonomous and Connected Mobility at the University Campus of Stuttgart;situation Awareness Management for Driver Take Over from Level 4;user-Oriented Development of Autonomous Vehicles using Immersive Visualization Tools;contribution of Fully Non-Magnetic Metal Materials to The Efficiency Enhancement of Electric Engines;calculation and Experimental Characterization of the Stiffness of Laminated Back Iron for Rotors of Axial Flux Machines;synergetic 1D–3D Reduced Order Modeling Techniques for Electric Motor Design Analysis;investigations into the Aerodynamic Influencof Trailers Towed by Battery Electric Passenger Cars;sensatorq, a New Approach for Measure the Forces at Wheels and Apply these to Vehicle Dynamics Control of the Future Mobility;on the Importance of Highly Resolved Wind Forecasts for Range Estimation;breaktor™ Battery Disconnect Unit Advanced Protection and Power Distribution for High Voltage Circuits in Electric Vehicles;a Modular Co-Simulation Framework with Open Source Software and Automotive Standards;The Use of Modern IT Architectures in Complex Test Scenarios of Systems engineering;method for the Automatic Generation of Vehicle-Specific Individual Test Sequence;virtual World Meets Reality – Validation of Advanced Driver Assistance Systems;empirical Temperature Modelling of the Diesel Oxidation Catalyst;auditory perceived quality of manual-mechanical control elements in cars.

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Multi-person Identification and Localization Algorithm Using RGB-D Image Segmentation 22

Multi-person Identification and Localization Algorithm Using...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Han, Xu Yan, Jun College of Communication & Information Engineering Nanjing University of Posts & Telecommunications Nanjing China

ISBN: (纸本)9781665470674

In recent years, with the development of smart phones and intelligent monitoring systems, visual object detection and localization methods have been widely used. Since RGB-D based person identification and localization technology have achieved better performances, an algorithm based on deep learning and image segmentation is proposed in this paper. First, the raw images are preprocessed. Then, image segmentations are performed according to the results of person detecting by YOLOv3 network. For multi-person identification, the Haar cascade classifier is used to determine regions of face on the segmented color images and the Eigen Face method is used for classification. For person localization, the convolutional neural network is preformed regression learning based on the depth images of a single person. Experiments results show that the proposed algorithm performs better than other existing approaches. © 2022 IEEE.

关键词： Image segmentation

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