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LSTM-Based Joint Distribution Optimal Transport for Wind Power Forecasting

LSTM-Based Joint Distribution Optimal Transport for Wind Pow...

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2023 international conference on Power System Technology, PowerCon 2023

作者： Sun, Yun Zhou, Mingqin Guodian Nanjing Automation Co. Ltd Digital Platform Department Nanjing China

ISBN: (纸本)9798350300222

Wind power is a kind of clean energy and it has many beneficial effects on the environment and people's life. Governments all over the world are devoting to expanding the scale of wind power with policy support. There are many challenges to overcome in expanding wind power scale such as making new turbines connected in the whole power grid and building specific models for every turbine in a certain farm which have hundreds of turbines. An approach named LSTM- based Joint Distribution Optimal transport(LSTN-JDOT) with the help of LSTM and optimal transport is proposed to forecast wind power to overcome those problems in this article, which uses the thought of transfer learning. LSTM-JDOT combines the advantages of the LSTM in extracting features for time series and the ability of optimal transport in aligning the distribution among different turbines. By this way, model of a certain turbines could be used to forecast others' power without large amount of data. Experiments between proposed method and other methods prove that LSTM-JDOT have higher performance and have the ability to forecasting wind power in short term with the help of transfer learning. © 2023 IEEE.

关键词： Wind power

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RBFNN-based Control Design Methodology for a DC Servo Motor with a Lyapunov Stability

RBFNN-based Control Design Methodology for a DC Servo Motor ...

引用

international conference on Control Systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： N.A. Bezzubov S.V. Feofilov O.V. Goryachev Dept. of Automatic Control Systems Tula State University Tula Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper presents a methodology for the design of adaptive control using radial basis functions neural networks to compensate for the influence of changes in plant parameters and disturbances. The stability of a closed loop system with a neural network adapter is achieved by choosing the appropriate Lyapunov function. A DC servo motor model that takes into account viscous friction is considered as a plant.

关键词： Adaptation models Motor drives Uncertainty Robust stability Friction Radial basis function networks Stability analysis Mathematical models Servomotors Lyapunov methods

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Analysis of Intelligent Force Control methods for Red-Meat Gripping Applications 10

Analysis of Intelligent Force Control Methods for Red-Meat G...

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10th IEEE Jubilee international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Takács, Kristóf Takács, Bence Tarsoly, Sándor Garamvölgyi, Tivadar Galambos, Péter Rudas, Imre Haidegger, Tamás Óbuda Universtiy Antal Bejczy Center for Intelligent Robotics Budapest Hungary Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Hungary Óbuda University Budapest Hungary

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Slaughterhouse automation is a challenging but important effort that could potentially improve the efficiency and hygiene of meat-processing while replacing human operators with machines and robots in dangerous and unpleasant jobs. Automated handling of carcasses require high level of intelligence, autonomy and adaptability, thus AI-driven systems, various sensors and intelligent tools are necessary parts of such systems. This paper presents novel force control methods implemented in a smart gripper intended for automated pig cutting. While the gripper is capable of handling high payloads (whole legs and 15-20 kg of intestines), the force control algorithm also allows delicate grasping on soft tissues. The gripping force is estimated separately for the two fingers of the gripper based on novel timing belt strain measurement methods. The elongation of the belts are measured by strain-gauges, and by magnetic encoders measuring the rotations of axes. The force sensors were tested with a tensile testing machine, and the functioning force-controlled gripper was tested with real pig carcasses too. The sensors have also proved useful for giving other feedback during automated pig cutting too, such as warning about inadequate gripper positioning relative to the target, and error signal generation when the target object slipped out of the gripper. © 2022 IEEE.

关键词： Grippers

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Development and Integration of Operator Behavior models for the Evaluation of Autonomous Plants

Development and Integration of Operator Behavior Models for ...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Artan Markaj Florian Pelzer Nils Richter Alexander Fay Mehmet Mercangöz Institute of Automation Technology Helmut-Schmidt-University Hamburg Hamburg Germany Automation Technology BASF SE Ludwigshafen Germany Chair of Automation Ruhr University Bochum Bochum Germany Autonomous Industrial Systems Lab Imperial College London London United Kingdom

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Process plants are becoming increasingly autonomous to compensate for the lack of experienced plant operators. However, certain activities still need to be carried out by humans. To move from a low to a high level of autonomy, an evaluation of operator and plant behavior is necessary. This provides insights into suitable automation measures. Our contribution presents two methods for the creation, integration and evaluation of operator behavior in plant simulations. This will enable the identification and learning of suitable control actions for different levels of plant autonomy at an early stage in plant engineering.

关键词： Automata Manufacturing automation

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Component-based Development Method for Intelligent Robot Systems 23

Component-based Development Method for Intelligent Robot Sys...

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7th international conference on Computer Science and Application Engineering, CSAE 2023

作者： Zhang, Jiachuang Cai, Mingxin Cheng, Lianglun Wang, Tao Wu, Heng School of Automation Guangdong University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400700590

With the advent of the intelligent manufacturing era, the demand for industrial robots has increased, and the scenarios and functional requirements of industrial robots have become increasingly complex. Developers need to design robot control systems based on specific functions, environments, and tasks, which often suffer from poor flexibility, high development costs, and long development cycles. To address the issues of low system development efficiency, poor reusability, and limited scalability in traditional code-based robot control system development methods, this paper proposes a component-based development method for robot control systems. By adopting a component-based modeling and integration approach, the robot control system is built based on component models, providing a fast, efficient, and scalable solution for robot control system development. © 2023 ACM.

关键词： Industrial robots

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CoIn: A Lightweight and Effective Framework for Story Visualization and Continuation 24

CoIn: A Lightweight and Effective Framework for Story Visual...

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32nd ACM international conference on Multimedia, MM 2024

作者： Tao, Ming Bao, Bing-Kun Tang, Hao Wang, Yaowei Xu, Changsheng Nanjing University of Posts and Telecommunications Nanjing China Peng Cheng Laboratory Shenzhen China Peking University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

Story visualization aims to generate realistic and coherent images based on multi-sentence stories. However, current methods face challenges in achieving high-quality image generation while maintaining lightweight models and a fast generation speed. The main issue lies in the two existing frameworks. The independent framework prioritizes speed but sacrifices image quality with the non-collaborative image generation process and basic GAN-based learning. The autoregressive framework modifies the large pretrained text-to-image model in an auto-regressive manner with additional history modules, leading to large model size, resource-intensive requirements, and slow generation speed. To address these issues, we propose a lightweight and effective framework, namely CoIn. Specifically, we introduce a Context-aware Story Generator to predict shared context semantics for each image generator. Additionally, we propose an Intra-Story Interchange module that allows each image generator to exchange visual information with other image generators. Furthermore, we incorporate DINOv2 into the story and image discriminators to assess the story image quality more accurately. Extensive experiments show that our CoIn keeps the model size and generation speed of the independent framework, while achieving promising story image quality. © 2024 ACM.

关键词： Semantics

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Deep Learning for Semantic Segmentation of Football Match Image

Deep Learning for Semantic Segmentation of Football Match Im...

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Innovations and Development of Information Technologies and robotics (IDITR), international conference on

作者： Yutian Wu Wuqi Zhao Chen Huang Yaming Xi Qing Li Heng Wang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Physical Education Beijing International Studies University Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

As one of the most popular sports, football has been a subject to growth and advancements in technology. The combination of football and artificial intelligence is expected to be used for intelligent football analysis. Image semantic segmentation is an important basis for image analysis and understanding. This paper proposes a deep learning-based image segmentation model for pixel-level classification of the video recordings frames of football matches. Every pixel of football video frame is classified into one of the 10 classes, e.g., players, ball, goal bar and several background scenes. In this paper, we first test a variety of CNN architectures and pre-trained models and select the MobileNet-UNet architecture as our baseline. We note the severe unbalanced data distribution in football scene segmentation. To solve this problem, the weighted multi-class cross-entropy loss is adopted in training of MobileNet-UNet to redistribute the weights of classification loss, focusing on smaller foreground object classes and improving segmentation accuracy. We also propose to use image transformations and a random mixture sampling technique for training data augmentation to reduce model overfitting. The model is trained and validated in the well-annotated Football Semantic Segmentation Open Dataset. The proposed best model achieves 0.96 frequency weighted IoU and 0.90 mean IoU segmentation accuracy on validation set.

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Active Learning for Condition-Based Maintenance of Industrial Machinery Using COMETH

Active Learning for Condition-Based Maintenance of Industria...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Franziska Zelba Gesa Benndorf Fraunhofer IOSB-INA Lemgo Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

We present a system for condition monitoring of industrial processes and machinery, called COMETH, which includes an active learning approach. The system is based on two complementary machine learning methods which are continuously updated through a feedback provided by the user of the system. The combination of two methods with active learning allows for the monitoring system to quickly adapt to changes in the boundary conditions of the application and to detect novel anomalies while keeping the amount of required feedback low. We illustrate the implementation and the usage of this active learning procedure for a conditioned-based maintenance of a towel picking machine. Additionally, we demonstrate the ability of the approach to correctly identify a detected anomaly. We show that the practicability for employing the proposed condition monitoring system can be further increased by using pre-trained models on similar machines in order to reduce the amount of required feedback and by providing a graphical user interface to facilitate the interaction with the technician.

关键词： Fault diagnosis Condition monitoring Learning systems Machine learning Data models Maintenance Machinery Monitoring Manufacturing automation Graphical user interfaces

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DEdgeNet: Extrinsic Calibration of Camera and LiDAR with Depth-discontinuous Edges

DEdgeNet: Extrinsic Calibration of Camera and LiDAR with Dep...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yiyang Hu Hui Ma Leiping Jie Hui Zhang Faculty of Science and Technology United International College BNU-HKBU Zhuhai China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Department of Computer Science Hong Kong Baptist University Hong Kong China

This paper addresses the problem of calibrating extrinsic parameter matrix between an RGB camera and a LiDAR. Multimodal sensing systems are essential for fully autonomous navigation platforms. A key pre-requisite for such a system is calibration between different sensors. As the two most widely equipped sensors, calibration between RGB cameras and LiDARs remains challenging. Existing methods address this problem without using explicit geometric priors. In this paper, we propose a novel real-time network that utilizes depth-discontinuous edges extracted from a single image to calibrate cameras and LiDARs. Our network consists of two key components: (1) a self-supervised edge extraction network named DEdgeNet, which detects depth-discontinuous edges from a single image and extracts corresponding features; (2) prediction of the extrinsic parameter matrix between the camera and the LiDAR by matching fixed features in RGB images and updating depth features in a coarse-to-fine frame. Specifically, considering that edges are rich and common in natural scenes, DEdgeNet simplifies RGB image encoding and extracts fixed edges for feature matching. We conducted extensive experiments on the KITTI-odometry dataset. The results show that our method achieves an average rotation error of 0.028° and an average translation error of 0.247 cm, which demonstrates the superiority of our method.

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A Siamese Network with Unlabeled Sample Enhancement for Remaining Useful Life Prediction 5

A Siamese Network with Unlabeled Sample Enhancement for Rema...

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5th international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Pan, Donghui Chen, Xin Liu, Yongbin School of Mathematical Science Anhui University Anhui China School of Big Data and Statistics Anhui University Anhui China School of Electrical Engineering and Automation Anhui University Anhui China

ISBN: (纸本)9798331529192

The remaining useful life (RUL) prediction method based on deep learning (DL) shows exceptional degradation feature extraction capabilities. A number of DL methods rely on large-scale labeled datasets to achieve effective supervised training. However, due to the rigorous maintenance schedules of equipment, the collected data predominantly consists of a limited number of labeled samples and a substantial volume of unlabeled samples. During the training phase of supervised DL models, the limited availability of labeled samples frequently leads to network overfitting, whereas the abundant unlabeled samples remain underutilized. This paper proposes a novel RUL prediction method based on unlabeled sample enhancement and contrastive learning, significantly enhancing the utilization of unlabeled samples. Initially, a Siamese network model undergoes pretraining using labeled data, utilizing an integration of graph convolutional network (GCN) and self-attention convolutional LSTM (ConvLSTM) network. Additionally, the contrastive learing with unlabeled samples enhancement is further utilized to learn degradation information through the incorporation of un-labeled samples to boost the performance of RUL prediction. The proposed framework is validated on turbofan engine datasets. Experimental results demonstrate that the performance of the proposed RUL prediction framework is substantially improved with the inclusion of unlabeled samples. © 2024 IEEE.

关键词： Contrastive Learning

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