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control System Response Improvement via Denoising Using Deep Neural Networks 10

Control System Response Improvement via Denoising Using Deep...

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ieee 10th Annual Ubiquitous Computing, Electronics and Mobile Communication conference (UEMCON)

作者： Fathi, Kiavash Mahdavi, Mehdi Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

ISBN: (纸本)9781728138855

Noise is an inseparable part of control systems. Every sensor reading used for determining the state of a control system is corrupted with noise, therefor increasing the signal to noise ratio of sensor readings can significantly improve the performance of systems. the proposed filter in this paper captures the underlying probability distribution of the noise-free input signal in the training stage and therefor is capable of refining the corrupted input signal regardless of the distribution of the added noise. In order to acquire better data-driven based results in the suggested approach, it has been decided to use different neural network structures and stack these layers to form a hybrid multilayer filter. the properties of the stacked neural network sublayers guarantee a robust and general solution for the intended purpose. the key elements of the proposed filter are two autoencoders, a dense neural network and a convolutional layer. Effects of every sublayer on the corrupted input signal, along with fine-tuning of these sublayers are discussed in detail. Afterwards in order to assess the generality and the robustness of the method, the proposed filter is exposed to a non-Gaussian noise. Finally, the proposed filter is tested on a linear and a nonlinear system. the comparison between systems' output to the reconstructed signal with that of the original noise-free signal, suggests the substantial improvement in the performance of the given systems. this improvement is in terms of systems' output resemblance to the reference noise-free output signal when the reconstructed signal is applied to the systems.

关键词： Deep learning Auto-encoder Dense neural network control system Sensor reading Signal Processing Denoising

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Towards an Inline Quality Monitoring for Crimping Processes Utilizing Machine learning Techniques 10

Towards an Inline Quality Monitoring for Crimping Processes ...

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10th International Electric Drives Production conference (EDPC)

作者： Meiners, Moritz Mayr, Andreas Kuhn, Marlene Raab, Bernhard Franke, Joerg Friedrich Alexander Univ Erlangen Nuremberg FAU Inst Factory Automat & Prod Syst FAPS Nurnberg Germany

ISBN: (纸本)9781728184586

With the increasing demand for electric vehicles, automobile manufacturers and suppliers need to adjust their value chains to meet future requirements. In wire harness assembly, which is part of the production of vehicle electrical systems, the joining of wires and connecting elements, usually realized by crimping, is one of the most complex and quality-critical processes. For quality assessment, the crimp height and pull-out force are measured as primary quality criteria. In the context of the increased safety requirements of autonomous driving, monitoring the crimp connections should be automated, holistic, and in real-time for each wire-crimp connection during production. However, the high complexity of internal and external influences as well as the variety of crimp connections complicates the operation of conventional process monitoring systems based on hard-coded criteria. In this context, data-driven approaches using the methods of artificial intelligence are moving into focus. the emerging development of information technology opens up new perspectives for process monitoring systems with inherent intelligence. To provide a proof of concept for an intelligent process monitoring, in this paper experiments are carried out on a crimping station, provoking different types of error conditions. the resulting dataset is then analyzed using deep learning. Finally, a comparison of the approaches shown is carried out, followed by an outlook on future research activities.

关键词： artificial intelligence machine learning deep learning crimping quality control process curve autoencoder

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A wind turbine fault identification using machine learning approach based on pigeon inspired optimizer 10

A wind turbine fault identification using machine learning a...

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10th ieee International conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Gad, Ibrahim Hassanien, Aboul Ella Tanta University Faculty of Science Tanta Egypt Egypt Faculty of Computers and Ai Cairo University Cairo Egypt

ISBN: (纸本)9781665440769

Wind turbines (WT) need regular maintenance, which should be carried out as soon as possible after installation. Additionally, due to unscheduled maintenance, wind farms may lose valuable time, resulting in a loss of revenue. Since the health of the turbine is constantly monitored, it is an easy task to discover faults early and schedule regular maintenance as soon as it becomes necessary. the supervisory control and data acquisition (SCADA) data used in this study was generated by an Irish wind farm. It is a well-known dataset that contains information on the performance of the turbines. the data is categorized into three main types: operational data, status data, and warning data. Feature selection is essential in developing machine learning methods. Irrelevant features in datasets have a negative impact on the algorithm's performance, and they also increase the durationof time required to train the machine learning model before it can be used. this work presents a feature selection technique based on pigeon inspired optimizer for the widely used SCADA data. the experimental findings demonstrate that the LSTM algorithmproduces the best results, with accuracy, precision, recall and F-score values of 98%, 95%, 98%, and 97%, respectively. As a result, the proposed model can be utilized to detect defects in wind turbines in the field. © 2021 ieee.

关键词： Wind turbines

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Cooperative Multi-Agent Deep Reinforcement learning for Forex Algorithmic Trading using Proximal Policy Optimization 10

Cooperative Multi-Agent Deep Reinforcement Learning for Fore...

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10th International conference on control, Instrumentation and Automation, ICCIA 2024

作者： Farooghi, Kian Khaloozadeh, Hamid K. N. Toosi University of Technology Department of Systems and Control Tehran Iran

ISBN: (纸本)9798331509972

this paper presents a novel multi-Agent deep reinforcement learning (DRL) approach for automated trading in the foreign exchange (Forex) market, utilizing Proximal Policy Optimization (PPO). the proposed system employs two cooperative agents: one specializing in position entry and another in position exit. Both agents leverage Long Short-Term Memory (LSTM) neural networks to process temporal market data and technical indicators. the reward function is designed to encourage collaborative behavior, with rewards shared between agents upon trade closure. Furthermore, the implementation of Generalized Advantage Estimation (GAE) method enables the agents to balance immediate rewards with long-Term consequences, enhancing the stability and efficiency of the learning process. this research contributes to the growing field of AI-driven Forex trading strategies, offering valuable insights into applying multi-Agent reinforcement learning in dynamic financial markets. the proposed methodology shows efficiency in developing lucrative trading systems capable of navigating the complexities of the Forex market. Experiments conducted on EUR/USD data demonstrate the effectiveness of this approach. Across 150 runs on the test dataset, the framework achieved profits ranging from 3.5% to 6.9%, with a median return of 5.8%. © 2024 ieee.

关键词： Deep reinforcement learning

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Estimating the Probability Density Function of New Fabrics for Fabric Anomaly Detection 10th

Estimating the Probability Density Function of New Fabrics f...

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10th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Rippel, Oliver Mueller, Maximilian Muenkel, Andreas Gries, thomas Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Inst Text Tech Aachen Germany

ISBN: (纸本)9789897584862

Image-based quality control aims at detecting anomalies (i.e. defects) in products. Supervised, data driven approaches have greatly improved Anomaly Detection (AD) performance, but suffer from a major drawback: they require large amounts of annotated training data, limiting their economic viability. In this work, we challenge and overcome this limitation for complex patterned fabrics. Investigating the structure of deep feature representations learned on a large-scale fabric dataset, we find that fabrics form clusters according to their fabric type, whereas anomalies form a cluster on their own. We leverage this clustering behavior to estimate the Probability Density Function (PDF) of new, previously unseen fabrics, in the deep feature representations directly. Using this approach, we outperform supervised and semi-supervised AD approaches trained on new fabrics, requiring only defect-free data for PDF-estimation.

关键词： Anomaly Detection Quality control Fabric Inspection Transfer learning Probability Density Estimation

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Optimal Scheduling for Dual-ESS Considering Life-Cycle Degradation 10

Optimal Scheduling for Dual-ESS Considering Life-Cycle Degra...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Jeong, Taekyeong Park, Jaemin Sim, Min K. Seoul Natl Univ Sci & Tech Dept Ind Engn Seoul South Korea Seoul Natl Univ Sci & Tech Dept Data Sci Seoul South Korea Seoul Natl Univ Sci & Tech Fac Dept Ind Engn Seoul South Korea

ISBN: (纸本)9798350373981;9798350373974

In this research, we develop an optimal operation strategy for dual Energy Storage systems (ESS) using reuse batteries to minimize both electricity cost and battery degradation. Our method employs a two-stage optimization: first, leveraging Reinforcement learning (RL) to determine optimal charging and discharging amounts, then followed by Quadratic Programming (QP) to allocate these amounts across dual batteries efficiently. this approach demonstrates the potential for substantial economic savings using reuse batteries for ESS. the results indicate the ESS with reuse batteries can enhance operational efficiency and achieve 4.3% - 10% cost savings compared to using ESS using new batteries.

关键词： Energy Storage System (ESS) reuse battery Battery Degradation Dual-ESS Quadratic Programming

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Machine learning-driven Traffic Flow Prediction Using Cloud control Big data Analysis 10

Machine Learning-Driven Traffic Flow Prediction Using Cloud ...

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10th International conference on Virtual Reality, ICVR 2024

作者： Jalil, Khurrum Chen, Qian Zahid, Muhammad Noaman Jalil, Fazila University of Shanghai for Science and Technology Department of Traffic Engineering Shanghai200093 China Hunan University of Humanities Science and Technology School of Information Loudi417000 China University of Science and Technology Beijing School of Economics and Management Beijing100083 China

ISBN: (纸本)9798350364231

Due to the increasing number of intelligent vehicles (IVs) on existing roads, IVs present several challenges, such as congestion, CO2 emissions, idling, and travel delays. Transportation departments rely on forecasting methods using traffic flow data to address issues before implementing new plans for smooth traffic flow. To achieve this, managing the vast amounts of intelligent transportation system (ITS) data necessitates the adoption of cloud computing for data storage capabilities and machine learning methods to facilitate efficient traffic prediction analysis. In addition, real-time data analysis is crucial for evaluating the effectiveness of the on-going implementation of the ITS plans and identifying gaps for improvement. In this paper, the proposed method integrates a machine learning-based traffic flow prediction model that uses cloud computing for traffic data analytics. then, the efficiency ratio of the proposed method is evaluated. through experimentation, we demonstrate the effectiveness of our method, achieving a prediction accuracy of up to 90%. this result suggests that the proposed approach is highly competitive compared to existing solutions. © 2024 ieee.

关键词： Adversarial machine learning

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AI for Smart Battery State Estimation: A Perspective 10

AI for Smart Battery State Estimation: A Perspective

引用

10th ieee International Power Electronics and Motion control conference, IPEMC 2024 ECCE Asia

作者： Sui, X. Che, Y. Zheng, Y. Weinreich, N. Andre He, S. Teodorescu, R. Aalborg University Department of Energy Denmark

ISBN: (纸本)9798350351330

In battery management systems (BMSs), state estimation stands as a pivotal element yet encounters significant challenges. these include the poor observability inherent in fixed configuration battery packs, limited generalizability of pre-trained machine learning models, and the deficiency of higher-level management strategies. To address these obstacles, we propose a forward-looking perspective on the future BMS state estimation, introducing the concept of a "Smart Battery". Battery digital twin enables synthetic data generation and physics-informed AI development. this approach integrates battery digital twin to generate synthetic data, which is then used for data augmentation and physics-informed AI development. Additionally, it incorporates advanced data cleaning and selection techniques to preserve essential information and augment data management efficiency. Leveraging cutting-edge AI algorithms, such as transfer learning and meta-learning, aims to mitigate issues of model generalization and feature invalidation under various operating conditions. Furthermore, this paper emphasizes the importance of multi-task learning for batteries, enabling comprehensive health assessments. By fully utilizing both short-term estimations and long-term predictions, the proposed framework contributes to the advancement of higher-level health and thermal management designs. We aim to furnish pioneering insights for state estimation in future intelligent BMSs. © 2024 ieee.

关键词： Battery management systems

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Mitigating Online Microgrid controller Deployment Issues with learning Techniques: An Approximate MPC Approach 10

Mitigating Online Microgrid Controller Deployment Issues wit...

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10th International conference on Optimization and Applications, ICOA 2024

作者： Taïeb, Zinédinc Sandou, Guillaume Vasset, Nicolas Gaoua, Yacine Pichenot, Grégoire CentraleSupélec Liten Cea University of Paris-Saclay L2S Grenoble Gif-sur-Yvette France CentraleSupélec University of Paris-Saclay L2S Gif-sur-Yvette France Liten Cea University of Grenoble Alpes Grenoble France

ISBN: (纸本)9798350387353

Due to the increasing complexity of energy systems, standard rule-based controllers may become inadequate for Energy Management System (EMS) supervision. Advanced optimal techniques as Model Predictive control (MPC) can be too intensive in computational resources, especially for small agents with low-performance environment. therefore, control issues about reducing implementation complexity gains interest in literature. In particular, surrogate models integrating learning techniques come within this scope by mitigating online implementation issues with offline data-driven approximations. However, there is a lack of works dealing with the analysis of performance robustness of such approximate controllers, when any uncertainties occur in online control. In this paper, we propose a freestanding approximate explicit MPC method, mobilizing learning techniques that approximate an analytical control law derived from a formal multiparametric programming. We propose to characterize the performance of the so-called reduced controller on simulation of a PV-storage microgrid, with historical data and forecast mismatch. We found numerically that the reduced controller has very similar performances compared to the baseline MPC when the forecast error is approximatively limited to 20%, and globally presents a relative change of economical loss less than 4% in average on simulation tests. © 2024 ieee.

关键词： Predictive control systems

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Federated learning Privacy-preserving Method Based on Bregman Optimization 10

Federated Learning Privacy-preserving Method Based on Bregma...

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10th ieee International conference on Cyber Security and Cloud Computing and 9th ieee International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Gengming Zhang, Jiyong Zhang, Shaobo Yin, Yijie Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

Federated learning has received a lot of attention for its ability to solve the data silo problem, but it is also limited by the problem of data heterogeneity and privacy. Non-Independent Identical Distribution (Non-I.I.D) data leads to performance degradation of federation models, and privacy problem have been studied as a hot topic in the field of federated learning. However, current research rarely considers non-I.I.D data and privacy simultaneously. In this paper, we propose a federated learning scheme based on Bregman and differential privacy (FLBDP). Our approach adopts Bregman distance for personalized model training, which aims to control the difference between local model and global model in a limited range, the FLBDP can reduce the model difference to improve the model performance by Bregman optimization. In addition, we use a Gaussian mechanism to perturb the personalized model and update the local model by the perturbed personalized model, which enables the model parameters to satisfy differential privacy in the uplink channel to enhance user privacy protection. © 2023 ieee.

关键词： learning systems

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