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17th Dallas Circuits and systems conference (DCAS)

作者： Akin, Vehbi Mete, Mutlu McMillen High Sch Murphy TX 75094 USA Texas A&M Univ Commerce Dept Comp Sci & Informat Syst Commerce TX USA

ISBN: (纸本)9798350349535;9798350349542

Signal resolution is crucial for diverse applications, notably impacting accuracy, signal-to-noise ratio, and system performance. Today, microcontrollers (MCUs) designed for industrial purposes provide analog-to-digital converter (ADC) resolutions ranging from 10 to 16 bits. Therefore, evaluating the ADC resolution of machine learning algorithms is crucial to enhance the system's performance and cost-effectiveness. This study explores the impact of signal resolutions of 10, 11, 12, and 16-bit sensor data, emphasizing their significance in machine learning classification tasks specifically aimed at motor fault detection. By conducting controlled experiments, this study utilizes a comprehensive sensor dataset obtained from two industrial motors to assess the effects on signal quality and information retrieval. Since AC drives account for over 50% of global electricity consumption and find widespread use in various industrial applications, the findings shed light on the trade-offs associated with different resolution levels in real-world applications, particularly industrial settings. The research contributes to signal processing understanding, aiding the selection of resolutions for specific contexts, and facilitating informed decisions in motor-driven systems and machine learning classification.

关键词： signal resolution machine learning classification ADC electric motors bearing faults

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Driver Identification Using Deep Generative Model With Limited data

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第5期24卷 5159-5171页

作者： Hu, Hongyu Liu, Jiarui Chen, Guoying Zhao, Yuting Gao, Zhenhai Zheng, Rencheng Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Tianjin Univ Sch Mech Engn Tianjin 300354 Peoples R China

The scarcity of driving data constrains the accuracy of deep learning (DL)-based driver identification methods in practical application scenarios. To address this issue, this study proposes a novel unsupervised deep generative model called the convolution condition variant autoencoder (CCVAE) for driving data augmentation. In CCVAE, aided by driver identification information, the condition variant autoencoder can learn the real driving data distribution of each driver through an unsupervised learning paradigm;and aiming for better feature representation ability, convolutional neural network and deconvolution are leveraged, respectively. Therefore, a large number of synthetic samples can be generated by the generative part of the CCVAE. We demonstrate the effectiveness of the CCVAE through extensive experimental analysis using a real dataset collected from a vehicular CAN bus;the improvement of the DL-based driver identification results is demonstrated using synthetic samples. For instance, when only using 2% of the original data, approximately 20% improvement is achieved in terms of four evaluation indicators for two commonly used DL-based driver identification methods, namely, 1-D CNN and LSTM. Furthermore, several comparable experiments with state-of-the-art deep generative methods reveal the superior performance of the proposed CCVAE with respect to identification results, synthetic data quality, and model computation time. Therefore, the proposed model accomplishes a breakthrough in driver identification with limited data and shows great potential in data-driven applications of intelligent vehicles.

关键词： Vehicles data models Feature extraction Convolutional neural networks Computational modeling Unsupervised learning Training data CAN bus data driver identification deep generative model data augmentation deep learning

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Machine learning-Based control of Dual-Sourcing Inventory systems 34

Machine Learning-Based Control of Dual-Sourcing Inventory Sy...

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34th Annual International conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Neghab, Davood Pirayesh Li, Shijie Cevik, Mucahit Wahab, M.I.M. Toronto Metropolitan University Department of Mechanical and Industrial Engineering 50 Victoria Street TorontoONM5B 2K3 Canada

ISBN: (纸本)9798331504830

We examine a data-driven approach to dual-sourcing inventory systems under periodic review with uncertain demand, utilizing historical data. Different from conventional fixed-ordering policies, we advocate for a machine learning methodology to capture the dynamic behaviour of demand. We train machine learning models to predict demand for single-period and multi-period horizons. Subsequently, we utilize these predictions to manage and mitigate supply chain risk by ordering from two suppliers. One supplier, identified as the regular supplier, offers lower unit prices but longer delivery times. The other is the emergency supplier, which provides same-day delivery at a higher unit price. Through an extensive numerical study utilizing actual data from Rossmann drug stores and various machine learning models, we systematically assess the performance of prediction models. Further, we enhance the learning process of the machine learning models by incorporating inventory cost parameters into the training objective function. Specifically, we customize the learning objective, switching from mean squared error (i.e., non-guided models) to a single-period inventory cost function (i.e., guided models). This modification results in significant cost reduction in the dual-sourcing inventory system. Guiding the models offers robust data-driven solutions for complex sequential systems where fully integrating learning and decision-making is not feasible. © 2024 ieee.

关键词： Adversarial machine learning

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data-driven Safety Filter: An Input-Output Perspective

Data-Driven Safety Filter: An Input-Output Perspective

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American control conference (ACC)

作者： Bajelani, Mohammad van Heusden, Klaske Univ British Columbia Sch Engn 3333 Univ Way Kelowna BC V1V IV7 Canada

ISBN: (纸本)9798350382662;9798350382655

Implementation of learning-based control remains challenging due to the absence of safety guarantees. Safe control methods have turned to model-based safety filters to address these challenges, but this is paradoxical when the ultimate goal is a model-free, data-driven control solution. Addressing the core question of "Can we ensure the safety of any learningbased algorithm without explicit prediction models and state estimation?" this paper proposes a data-driven Safety Filter (DDSF) grounded in Behavioral System Theory (BST). The proposed method needs only a single system trajectory available in an offline dataset to modify unsafe learning inputs to safe inputs. This contribution addresses safe control in the input-output framework and therefore does not require full state measurements or explicit state estimation. Since no explicit model is required, the proposed safe control solution is not affected by unmodeled dynamics and unstructured uncertainty and can provide a safe solution for deterministic Linear Time-Invariant (LTI) systems with unknown time delays. The effectiveness of the proposed DDSF is illustrated in simulation for a high-order six-degree-of-freedom aerial robot and a time-delay adaptive cruise control system.

关键词： Adaptive cruise control

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A Machine learning Approach of MHD Stokes Flow in a Lid-driven Cavity 10

A Machine Learning Approach of MHD Stokes Flow in a Lid-Driv...

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

作者： Gurbuz-Caldag, Merve Pekmen, Bengisen TED Univ Dept Math TR-06420 Ankara Turkiye

ISBN: (纸本)9798350373981;9798350373974

In this study, the trilayer neural network (TNN) is generated for the prediction of some chosen problem variables of Stokes flow in a lid-driven cavity subjected to the uniform magnetic field with an inclination angle for the first time. The data set to develop TNN model of magnetohydrodynamics (MHD) Stokes flow is gathered from the numerical results. The method of approximate particular solution (MAPS) is employed to the nondimensional governing equations which are comprised of the Navier-Stokes equations and Maxwell equations neglecting the convective terms. The numerical outcomes are obtained for several values of Hartmann number in [0,100] and the inclination angle in [0,pi]. In TNN model, the input variables are Hartmann number and inclination angle of magnetic field, and the output variables are the norm of stream function and v velocity component, the minimum value of u velocity and the norm of vorticity. It is shown that TNN model gives the efficient predictions of velocity indicators of Stokes flow.

关键词： Navier Stokes equations

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GOSMART: A data-driven Web Platform for Personalized Study Recommendations and Academic Performance Improvement 12

GOSMART: A Data-Driven Web Platform for Personalized Study R...

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12th ieee conference on systems, Process and control, ICSPC 2024

作者： Kasim, Nasrul Arif Bin Abu Khalip, Suhaila Binti Subbiah, Ahgalya Management and Science University Faculty of Information Science and Engineering Section 13 Selangor Shah Alam40100 Malaysia

ISBN: (纸本)9798350391398

Education has evolved significantly over the years, transitioning from traditional, one-size-fits-all models to more dynamic, personalized learning approaches. Today's students face unique challenges such as diverse learning styles, time management issues, and a need for high-quality resources. These factors impact academic performance and attrition rates, especially in rapidly growing undergraduate populations in Malaysia. This paper presents GOSmart, an innovative webbased system designed to enhance undergraduate students' study skills through personalized learning recommendations at Management and Science University. The primary objectives are to develop a system that provides personalized study recommendations, improves time management skills, and enhances overall academic performance. GOSmart integrates adaptive learning algorithms, progress tracking, and data visualization to provide tailored study recommendations. Developed using agile methodologies, the system includes a userfriendly front-end (HTML, CSS, JavaScript) and a robust back end (PHP, MySQL). A mixed-methods approach, involving structured questionnaires administered to 500 undergraduate students at Management and Science University, informed the design and feature set of the application. data analysis revealed that 75% of students experienced improved time management and study strategies, while overall academic performance increased by 25%. GOSmart not only enhances student learning experiences but also serves as a prototype for future research in personalized education technologies. © 2024 ieee.

关键词： Contrastive learning

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A Novel data-driven Self-Tuning SVC Additional Fractional-Order Sliding Mode controller for Transient Voltage Stability With Wind Generations

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ieee TRANSACTIONS ON POWER systems 2023年第6期38卷 5755-5767页

作者： Zhang, Guozhou Zhao, Junbo Hu, Weihao Cao, Di Tan, Bendong Huang, Qi Chen, Zhe Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 610054 Peoples R China Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Chengdu Univ Technol Coll Energy Chengdu 610059 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

This paper proposes a novel data-driven self-tuning additional sliding mode controller for power system transient voltage stability enhancement considering wind integration. We first develop a new additional fractional-order sliding mode controller (FOSMC) for the static var compensator (SVC). The tuning of FOSMC parameter settings is then reformulated as a Markov decision process (MDP) and solved by the deep reinforcement learning (DRL) algorithm. In addition, a data-driven estimation method is proposed for the identification of an equivalent transfer function that is further used to calculate reward during the training process, yielding the model-free training. After that, the well-trained agent allows us to tune the controller parameters considering system uncertainties and achieve robustness against various operating conditions. Comparative results with other state-of-art methods demonstrate that the proposed method can effectively suppress the chattering issue of the sliding mode controller and ensure transient voltage stability under different operating conditions.

关键词： Deep reinforcement learning fractional-order sliding mode controller static var compensator voltage control wind energy integrated system

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Temporal Hierarchical Clustering for Knowledge Aggregation in Connected Vehicular Networks with Federated Multi-Task learning 25

Temporal Hierarchical Clustering for Knowledge Aggregation i...

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ieee Wireless Communications and Networking conference (ieee WCNC)

作者： Nawaz, Muhammad Waqas Imran, Muhammad Ali Popoola, Olaoluwa Univ Glasgow James Watt Sch Engn Glasgow Scotland

ISBN: (纸本)9798350303582;9798350303599

The rise of connected vehicular networks (CVNs) holds promise for future intelligent transport systems, offering improvements in safety and road efficiency. CVNs face challenges due to data-driven perception and driving models, requiring extensive knowledge to navigate complex scenarios. In vehicular networks, federated learning (FL) is vital for privacy-preserving machine learning (ML). It allows collaborative training of a single ML model across edge devices while keeping data locally, preserving privacy. However, scalability remains a challenge, especially for large ML models, and can yield suboptimal results when local data distributions diverge. We present a robust and efficient Fed-aided multi-task temporal clustering (FeMTC) knowledge-sharing framework tailored to the demands of highly distributed vehicular networks. Our approach quantifies the temporal similarity between a pair of client vectors to group clients with higher similarity at the edge-base server and trains independently on single and multi-task cluster learning. Experiments show that FeMTC achieves faster convergence and up to 15% better performance than existing methods in some scenarios. It easily combines with other methods for improved performance and exhibits robust gains in various non-independent and identically distributed (non-IID) scenarios.

关键词： Connected Vehicular Networks (CVNs) Machine learning (ML) Federated learning (FL) Vehicle-2-infrastructure (V2I)

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Adaptive learning control for Smart Local Energy Community

Adaptive Learning Control for Smart Local Energy Community

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2024 ieee PES Innovative Smart Grid Technologies Europe conference, ISGT EUROPE 2024

作者： Brahmia, Ibrahim Klöckl, Bernd Vienna University of Technology Institute for Energy Systems and Electric Drives Vienna Austria

ISBN: (纸本)9789531842976

This paper presents a novel Adaptive learning control (ALC) approach for optimizing energy management in Smart Local Energy Communities (LECs). The framework combines advanced Reinforcement learning (RL) with Distributed Model Predictive control (DMPC), enabling real-time adaptation to fluctuations in energy demand, generation, and pricing. A key feature of this method is the use of data-driven algorithms to handle uncertainties in renewable energy sources, such as generation. While optimizing energy dispatch among prosumers. The energy management system employs an on-line trained neural network with stochastic weights that are continuously trained and updated in real-time using incoming data such as load, renewable output, and electricity prices, which are then used to optimize control actions. Additionally, a Hyperparameter Optimization (HPO) was integrated to ensure a balance between the rapid learning rate and the model stability. Decision making across LECs enhances overall system efficiency and resilience. Simulations and case studies demonstrate that this approach improves energy demand predictions and robust self learning, outperforming the conventional RL method. It maintains stable grid performance under dynamic, stochastic conditions and significantly advances self learning. A comparison between the robust prediction capabilities of ALC-EMS versus the conventional RL-EMS approach highlights the benefits of this innovative method. © 2024 ieee.

关键词： and Real-Time Computing Distributed Model Predictive control Energy Management Local Energy Communities Machine learning Reinforcement learning

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Enhancing Supply Chain Efficiency through Retrieve-Augmented Generation Approach in Large Language Models 10

Enhancing Supply Chain Efficiency through Retrieve-Augmented...

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ieee 10th International conference on Big data Computing Service and Machine learning Applications (ieee BigdataService)

作者： Zhu, Beilei Vuppalapati, Chandrasekar Intel Corp Global Supply Chain Hillsboro OR 97124 USA San Jose State Univ Comp Engn San Jose CA 95192 USA

ISBN: (纸本)9798350366396;9798350366389

This paper delves into the fascinating integration of Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) for optimizing supply chain management operations. RAG combines the robust retrieval capabilities of information retrieval systems with the generative prowess of neural language models to create a powerful tool that bolsters data protection while expanding the knowledge base to capture supply chain intricacies. This innovative methodology revolves around a dual-component system that employs a retrieval module to pinpoint relevant information from a knowledge base, while a generation module crafts contextualized responses using large language models. Through iterative retrieval strategies and tailored chunk optimization techniques, RAG enables contextualized analysis, predictive insights, and data-driven decision-making that streamlines processes from demand forecasting to inventory optimization. An experimental setup mimicking enterprise data classification assesses RAG's efficacy, employing recursive retrieval, multi-hop querying, and integration of generative and retrieval processes. Results showcase RAG's potential to revolutionize supply chain logistics, enhancing operational agility, minimizing disruptions, and fortifying data security. The impacts span improved forecasting accuracy, inventory level optimization, supplier risk assessment, and comprehensive supply chain reporting. However, RAG necessitates stringent ethical considerations and robust countermeasures against exploitation. Future work centers on system scalability, advanced evaluation metrics, and interdisciplinary collaboration between machine learning, retrieval systems, and supply chain domains. Overall, this paper presents a groundbreaking approach to optimizing supply chain management operations that could significantly impact the industry's future.

关键词： Deep learning RAG Supply Chain Operations Unstructured Big data LLM

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