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Research on intelligent Sorting System 22

Research on Intelligent Sorting System

引用

22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Mao, Yanghong Chen, Chuixin Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Guangzhou Institute of Science and Technology Department of Intelligent Manufacturing and Electrical Engineering Guangzhou China

ISBN: (纸本)9781665470674

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. The acquired image is enhanced and edge detected. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. and sent to STM32 through the serial port. Then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Manipulators

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Multi-purpose Recommender Platform using Perceiver IO 22

Multi-purpose Recommender Platform using Perceiver IO

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22nd IEEE international conference on data Mining (ICDM)

作者： Cevahir, Ali Kanada, Kentaro Stockmark Inc Tokyo Japan

ISBN: (纸本)9798350346091

Web services usually require many different types of recommender systems using large amount of user log and content data, in order to provide personalized content to their customers. Different recommenders may share the same customer-base or cross-use models/data. It is challenging to design different models for each recommendation task. In this work, we propose a general-purpose framework for various recommendation tasks based on Perceiver IO model. Perceiver IO is a general machine learning architecture based on transformer-style attention modules, which helps eliminating feature engineering for various tasks. Different type of recommenders can be developed with minimal modifications and models can be transferred among different tasks. Our experiments with a variety of recommendation scenarios confirm that our framework is able to handle those tasks while achieving state-of-the-art accuracy.

关键词： recommender systems personalization transformer networks Perceiver IO transfer learning news recommendation

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Optimal Economic Gas Turbine Dispatch with Deep Reinforcement learning 22

Optimal Economic Gas Turbine Dispatch with Deep Reinforcemen...

引用

22nd World Congress of the international Federation of Automatic Control (IFAC)

作者： Sage, Manuel Staniszewski, Martin Zhao, Yaoyao Fiona McGill Univ Dept Mech Engn Montreal PQ Canada Siemens Energy Canada Ltd Montreal PQ Canada

ISBN: (纸本)9781713872344

Dispatching strategies for gas turbines (GTs) are changing in modern electricity grids. A growing incorporation of intermittent renewable energy requires GTs to operate more but shorter cycles and more frequently on partial loads. Deep reinforcement learning (DRL) has recently emerged as a tool that can cope with this development and dispatch GTs economically. The key advantages of DRL are a model-free optimization and the ability to handle uncertainties, such as those introduced by varying loads or renewable energy production. In this study, three popular DRL algorithms are implemented for an economic GT dispatch problem on a case study in Alberta, Canada. We highlight the benefits of DRL by incorporating an existing thermodynamic software provided by Siemens Energy into the environment model and by simulating uncertainty via varying electricity prices, loads, and ambient conditions. Among the tested algorithms and baseline methods, Deep Q-Networks (DQN) obtained the highest rewards while Proximal Policy Optimization (PPO) was the most sample efficient. We further propose and implement a method to assign GT operation and maintenance cost dynamically based on operating hours and cycles. Compared to existing methods, our approach better approximates the true cost of modern GT dispatch and hence leads to more realistic policies. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0/)

关键词： reinforcement learning data-based control intelligent control of power systems optimal operation and control of power systems

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Study of the Intensity of Feedback of Vibration Information from Different Parts of the Human Body 22nd

Study of the Intensity of Feedback of Vibration Information ...

引用

22nd international conference on Man-Machine-Environment System engineering, MMESE 2022

作者： Zhang, Jianyi Du, Shan Dong, Haochen Li, Wei Sui, Haolin Zhao, Xinyang School of Mechanical Power Engineering Harbin University of Science and Technology Heilongjiang Harbin150080 China University of Washington Seattle98105 United States

ISBN: (纸本)9789811947858

The physiological feedback data and subjective evaluation results of the subjects’ human bodies at different positions and different vibration intensities were obtained by designing human vibrotactile experiments using personal questionnaire methods and electrical skin data. Statistical and variance tests were used to compare and analyse the subjects’ comfort at different position intensities. The author’s research explores the subjects’ subjective feelings and electrical skin responses in different vibration intensities on different human body parts to provide a physiological basis for natural human-computer interaction research and haptic feedback device development. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Physiological models

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Chimera: A Hybrid Machine learning-Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis 22nd

Chimera: A Hybrid Machine Learning-Driven Multi-Objective De...

引用

22nd international conference on intelligent data engineering and automated learning

作者： Yu, Mang Huang, Sitao Chen, Deming Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9783030916077;9783030916084

In recent years, hardware accelerators based on field programmable gate arrays (FPGA) have been widely applied and the high-level synthesis (HLS) tools were created to facilitate the design of these accelerators. However, achieving high performance with HLS is still time-consuming and requires expert knowledge. Therefore, we present Chimera, an automated design space exploration tool for applying HLS optimization directives. It utilizes a novel multi-objective exploration method that seamlessly integrates active learning, evolutionary algorithm, and Thompson sampling, which enables it to find a set of optimized designs on a Pareto curve by only evaluating a small number of design points. On the Rosetta benchmark suite, Chimera explored design points that have the same or superior performance compared to highly optimized hand-tuned designs created by expert HLS users in less than 24 h. Moreover, it explores a Pareto frontier, where the elbow point can save up to 26% of flip-flop resource with negligible performance overhead.

关键词： High level synthesis

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DCGAN-based Augmentation for XGBoost Fault Modeling of On-board Train Control System 22

DCGAN-based Augmentation for XGBoost Fault Modeling of On-bo...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Wang, Jin-Lan Shen, Yan-Chun Cai, Bai-Gen Liu, Jiang College of Information Engineering Guangzhou Railway Polytechnic Guangzhou511300 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331527471

The on-board train control system is the core component in speed-interval control and safety assurance of the railway trains. The maintenance of the on-board train control system is of great significance to ensure the reliability and safe operation. However, the advanced condition-based maintenance mechanism has not been effectively applied, and the problem by imbalance sample data constrains the utilization of data-driven modeling methods to enable the advanced maintenance mode. To address these problems, this study proposes an enhanced fault modeling solution, which realizes the fault model using the XGBoost (eXtreme Gradient Boosting) method. Specifically, the DCGAN (Deep Convolutional Generative Adversarial Network) is adopted to achieve sample augmentation and enhance the capability of the XGFBoost-based fault model. Using practically collected fault log datasets from the on-board train control equipment, the performance of the proposed fault modeling solution with the sample augmentation capability is demonstrated, which reveals the potential in realizing the intelligent maintenance for practical operations. © 2024 IEEE.

关键词： Railroads

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Improving Maximum Likelihood Estimation Using Marginalization and Black-Box Variational Inference 22nd

Improving Maximum Likelihood Estimation Using Marginalizatio...

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22nd international conference on intelligent data engineering and automated learning

作者： Shalileh, Soroosh HSE Univ Lab Methods Big Data Anal LAMBDA Pokrovsky Blvd 11 Moscow Russia

ISBN: (纸本)9783030916077;9783030916084

Based upon Black Box Variational Inference, a new set of classification algorithms has recently emerged. The goals of this set of algorithms are twofold: 1) increasing generalization power;2) decreasing computational and implementation complexity. To this end, we assume a set of latent variables during the generation of data points. We subsequently marginalize the conventional classification likelihood objective function w.r.t this set of latent variables and then apply black-box variational inference to estimate the marginalized likelihood. We evaluate the performance of the proposed method by comparing the results obtained from the application of our method to real-world datasets with those obtained using several classification algorithms. The experimental results prove that our proposed method is competitive.

关键词： Black box variational inference classification Variational inference classification Variational inference Classification

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Comparative Analysis of Advanced Optimization Algorithms for Highway Photovoltaic System Layout 22

Comparative Analysis of Advanced Optimization Algorithms for...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Tao, Yanmeng Liu, Zhengke Ma, Wenxin Ma, Xiaolei School of Transportation Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Transportation Technology and System Ministry of Education Beijing100191 China

ISBN: (纸本)9798331527471

In the context of the current transition in energy structures and the development of renewable energy, optimizing the layout of solar energy systems along highways has become a significant research topic. This study employs five different advanced optimization algorithms, namely NSGA-II, Particle Swarm Optimization (PSO), Simulated Annealing (SA), Differential Evolution (DE), and a combination of NSGA-II and DE (NSGA-II-DE), to minimize total supply cost and maximize energy self-sufficiency. The comparative analysis reveals that the combination of NSGA-II and DE exhibits the best optimization performance. Through detailed experiment and data analysis, the performance and advantages of each algorithm in solving the layout problem of highway solar energy system are demonstrated, and specific implementation strategies are proposed for the optimal solution. In addition, sensitivity analysis is used to understand how changes in parameters such as module efficiency, energy demand fluctuations, and electricity price changes affect the optimization results, adding depth to the findings and making them more applicable to a variety of application scenarios. © 2024 IEEE.

关键词： Optimization algorithms

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Supervising Plant Growth in a Greenhouse 22

Supervising Plant Growth in a Greenhouse

引用

22nd international conference on Advances in ICT for Emerging Regions (ICTer)

作者： Alexander, D. L. Hathnapitiya, G. A. G. Gamage, Aruna, I Bandara, P. M. P. C. Giragama, G. W. M. N. U. I. B. Swamakantha, N. H. P. Ravi Supunya Sri Lanka Inst Informat Technol Fac Comp Malabe Sri Lanka

ISBN: (纸本)9798350346138

Since the beginning of civilization, agriculture has played a significant part in the economy of a nation. Currently, as the population continues to increase at a rapid rate, arable lands are dwindling alongside urbanization. Even though farmers devote a substantial amount of time and effort to farming, environmental factors such as seasonal shifts can have a significant impact on the crop. Smart agriculture is implemented to boost the production of high-quality goods and address the lack of control over the farming process. The intelligent greenhouse technology proposed here is called ''GSense," and it could boost plant productivity by managing the greenhouse's climate. In addition, this solution is useful for novices who are just beginning out in agriculture because it can make recommendations to its user. The execution of the solution is complemented by a mobile application and a desktop application via which the user may submit inputs and examine real-time sensor data

关键词： IoT Greenhouse Automation Machine learning NodeMCU RaspberryPi

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Online Deep Knowledge Tracing 22

Online Deep Knowledge Tracing

引用

22nd IEEE international conference on data Mining (ICDM)

作者： Zhang, Wenxin Zhang, Yupei Liu, Shuhui Shang, Xuequn Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China MIIT Key Lab Data Storage & Management Xian 710129 Shaanxi Peoples R China

ISBN: (纸本)9798350346091

This study focuses on solving the problem of knowledge tracing in a practical situation, where the responses from students come in a stream. Most current works of deep knowledge tracing are pursuing to integrate of more side information or data structure, but they often fail to make self-update in the dynamic learning situation. Towards this end, we here proposed an online deep knowledge tracing model, dubbed ODKT, by utilizing the online gradient descent algorithm to develop the traditional deep knowledge tracing (DKT) into online learning. Rather than learning a perfect model, the ODKT aims to train DKT in its using process step by step. Experiments were conducted on four public datasets for knowledge tracing. The results demonstrate that the ODKT model is effective and more suitable for practical applications.

关键词： Knowledge Tracing Online Machine learning RNN Educational data Mining intelligent Education

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