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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhou, Hui Wang, Zhiyuan Han, Zhongyang Wang, Linqing Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. Then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. The experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise. © 2024 ieee.

关键词： Coal mines

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Two-Dimensional Statistical Pattern Analysis for Effective Modeling and Monitoring of Limited Batch Process data in Food Industry 13

Two-Dimensional Statistical Pattern Analysis for Effective M...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, Jinlin Zhang, Zheng Jiangnan University State Key Laboratory of Food Science and Resources Wuxi214122 China School of Food Science and Technology Jiangnan University Wuxi214122 China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering 999077 Hong Kong

ISBN: (纸本)9798350361674

This study presents a novel two-dimensional statistical pattern analysis (2D-SPA) method, specially designed to address the challenges inherent in monitoring and modeling processes within low-volume, high-quality food manufacturing settings. By ingeniously integrating robust statistical techniques within a two-dimensional sliding window framework, this approach significantly expands the analytical dataset by operating across both batch and time dimensions, effectively overcoming the constraints posed by limited batch sizes. This innovative strategy not only augments the detection and diagnosis capabilities for subtle process deviations and emerging trends but also capitalizes on the dynamic statistical variances observed across batches and over time, thus offering a comprehensive view of the process variability. The application of 2D-SPA is meticulously validated through an industrial case study focused on lactic acid bacteria fermentation, where it demonstrates marked improvements in accuracy and sensitivity for process monitoring. The results underscore the method's potential to significantly enhance quality assurance measures in food batch production. © 2024 ieee.

关键词： Food industry

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Reinforcement learning Based Dispatch of Batteries 22

Reinforcement Learning Based Dispatch of Batteries

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ieee 22nd Mediterranean Electrotechnical conference (MELECON)

作者： Benedicto, Pedro Silva, Ricardo Gouveia, Clara INESC TEC Inst Syst & Comp Engn Porto Portugal

ISBN: (纸本)9798350387032;9798350387025

Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

关键词： Reinforcement learning battery microgrid control optimization

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data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes

Data-driven Force Observer for Human-Robot Interaction with ...

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2024 International conference on Intelligent Robots and systems

作者： Tesfazgi, Samuel Kessler, Markus Trigili, Emilio Lederer, Armin Hirche, Sandra Tech Univ Munich TUM Sch Computat Informat & Technol Chair Informat Oriented Control ITR D-80333 Munich Germany Scuola Super Sant Anna BioRobot Inst I-56025 Pontedera Italy Swiss Fed Inst Technol Dept Comp Sci Learning & Adapt Syst Grp CH-8092 Zurich Switzerland

ISBN: (纸本)9798350377712;9798350377705

Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic components. While observer-based methods have been shown to improve these estimates, they rely on accurate models of the system, which are challenging to obtain in complex operating environments. In this work, we overcome this issue by learning the unknown dynamics components using Gaussian process (GP) regression. By employing the learned model in a Bayesian filtering framework, we improve the estimation accuracy and additionally obtain an observer that explicitly considers local model uncertainty in the confidence measure of the state estimate. Furthermore, we derive guaranteed estimation error bounds, thus, facilitating the use in safety-critical applications. We demonstrate the effectiveness of the proposed approach experimentally in a human-exoskeleton interaction scenario.

关键词： Human robot interaction

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Audio Keyframe Extraction of Belt Conveyor Inspection Robot Based on Frequency Band Entropy and Pixel Significance 13

Audio Keyframe Extraction of Belt Conveyor Inspection Robot ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Yang, Xiaoyu Luo, Minghua Fang, Chongquan She, Ying Chongqing Research Institute of China Coal Science and Technology Corporation Chongqing400039 China State Key Laboratory of Coal Mine Disaster Prevention and Control Chongqing400037 China

ISBN: (纸本)9798350361674

As the main transportation equipment in coal mines, the safety and stability of belt conveyors are of great significance for the production efficiency and safety of coal mines. The traditional manual inspection method has problems such as low efficiency, poor real-time performance, and potential safety hazards. Fixed sensors have problems such as difficult deployment, difficult maintenance, and high cost. The emergence of belt conveyor inspection robots has provided a new mode for inspection methods. Unlike a fixed sensor with a single fixed mode of data, the data collected by the inspection robot during its movement is dynamic and time-varying. The traditional fixed sensor fault diagnosis methods often have poor results when directly applied to inspection robots. This article proposes an audio frame extraction technique based on frequency band entropy and pixel significance, based on the dynamic audio signals collected by a belt inspection robot and the comprehensive utilization of audio and image processing techniques. Frequency band entropy can extract faults or abnormal noise frames with concentrated audio energy, and image significance can extract faults or abnormal noise frames caused by slight continuous collisions. Through experimental verification, this method can achieve the extraction of abnormal audio frames dynamically collected by inspection robots, and can extract audio near faulty rollers such as breakage, wear, rotor blockage, and bearing damage. The extraction of audio keyframes can provide basic data for subsequent roller fault localization and recognition, and provide research ideas for roller fault diagnosis methods based on inspection robot audio. © 2024 ieee.

关键词： Belt conveyors

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data-driven Approaches for Distribution Transformer Health Monitoring: A Review

Data-Driven Approaches for Distribution Transformer Health M...

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Annual ieee Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Mogos, Aman Samson Liang, Xiaodong Chung, C. Y. Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK Canada Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9798350323979

Distribution transformers are the key components in distribution systems to maintain reliability of the system operation and reduce power outages. In this paper, a literature review is conducted on data-driven methods of the distribution transformer health monitoring by classifying the research streams and emphasizing advancements in machine learning, artificial intelligence and hybrid approaches in this area. The significance of data-driven methods is highlighted, demonstrating their ability to overcome traditional analytic limitations by providing real-time monitoring, prediction, and adaptability. As the distribution system continues to expand due to the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs), data-driven techniques emerge as a dependable and adaptable option for the effective transformer health monitoring.

关键词： data-driven approaches distribution transformer health index health monitoring machine learning

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Exploring AI-driven Drug Repurposing Strategies Targeting ErbB Signalling Pathway for Brain Cancer Therapy 11

Exploring AI-Driven Drug Repurposing Strategies Targeting Er...

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11th ieee International conference on Cybernetics and Intelligent systems (CIS) / 11th ieee International conference on Robotics, Automation and Mechatronics (RAM)

作者： Ng, Chi Kit Long, Jianqiao Tang, Muran Li, Jichun Quan, Mingming Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350364200;9798350364194

This study explores the application of machine learning methodologies in drug repurposing for brain cancer therapy, focusing on targeting the epidermal growth factor receptor (EGFR). Our approach involved the development of a predictive model to estimate the half-maximal inhibitory concentration (IC50) values of compounds against EGFR, leveraging existing biological activity data of known EGFR inhibitors and molecular structure descriptors. The constructed model exhibited efficacy in predicting the inhibitory activity of compounds against EGFR. Subsequent screening of a library of known drugs using the predictive model led to the identification of several compounds with low predicted IC50 values, indicating their potential as drug candidates for further investigation. This study underscores the utility of integrating machine learning techniques into drug repurposing endeavours, offering a pragmatic approach to identifying potential therapeutic options for brain cancer treatment.

关键词： Adversarial machine learning

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Online data-driven Adaptive control for Unknown Linear Time-Varying systems 62

Online Data-Driven Adaptive Control for Unknown Linear Time-...

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62nd ieee conference on Decision and control (CDC)

作者： Liu, Shenyu Chen, Kaiwen Eising, Jaap Beijing Inst Technol Sch Automat Beijing Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland

ISBN: (纸本)9798350301243

This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.

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ESO-Based Model-Free Adaptive Iterative learning Energy-Efficient control for Subway Train With Disturbances and Over-Speed Protection

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第8期24卷 8136-8148页

作者： Zheng, Jianmin Hou, Zhongsheng Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing 100044 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

An extended state observer based model-free adaptive iterative learning energy-efficient control (ESO-based MFAILEEC) scheme for subway train speed tracking with external disturbances and over-speed protection under the constraint on traction/braking force is proposed. Firstly, the continuous-time train motion dynamics is formulated into a discrete-time data model with consideration of external disturbances by applying the iterative dynamic linearization. Meanwhile, the external disturbances and the unknown nonlinear uncertainties of the train are transformed into a new state, which is estimated by an ESO designed in the iteration domain. Then, the ESO-based energy-efficient controller with learning ability is designed, which enables the train to achieve the purpose of energy efficiency by reducing the input force. Further, over-speed protection with trigger mechanism is developed to ensure the train operates within safe speed range. All the control strategies are designed under the constraint on traction/braking force by considering the practical limitation of the train system. No model information is involved in the whole design processes and it is a pure data-driven iterative learning approach. Rigorous mathematical analysis proves the feasibility and the robustness of the proposed method, which can guarantee the train operates safely and reliably. Finally, the simulation results further demonstrate the effectiveness of the proposed algorithm.

关键词： Public transportation Resistance Energy efficiency Aerodynamics Force Adaptation models Uncertainty Model-free adaptive iterative learning control energy-efficient control extended state observer subway train control disturbances over-speed protection

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Adversarial Weight Prediction Networks for Defense of Industrial FDC systems

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第11期20卷 13201-13211页

作者： Yin, Zhenqin Ye, Lingjian Ge, Zhiqiang Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Huzhou Univ Sch Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

In recent years, more and more open environment have led to confidential links and data exposure, which seriously threatens the security of industrial systems. Adversarial attacks can easily fool machine learning models by adding tiny perturbations to input data. Industrial fault detection and classification (FDC) system is an indispensable part of ensuring production safety, but it is also not immune to the impact of adversarial risks. Once it is under attack, the disastrous consequences that may be caused to the industrial system are unimaginable. Adversarial training is among the most effective defense methods to protect those data-driven intelligent systems. This article studies a novel reweighted adversarial training approach called adversarial weight prediction networks. By assigning more appropriate weights to different data samples, we can make better use of the limited model capacity of the industrial FDC system. Particularly, predicting weights through a synchronized network overcomes the limitations of insufficient information and nontransferability of existing statistical methods. Performance evaluation on three industrial cases containing structured and image data shows the superior generalization and stability of our proposed method.

关键词： Training Security Perturbation methods Robustness data models Fault detection Production Adversarial attack adversarial defense industrial fault detection and classification (FDC) reweighted adversarial training weight prediction network

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