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Ensemble learning and Time-Domain Feature Mining based Appliance Power Consumption Pattern Recognition

Ensemble Learning and Time-Domain Feature Mining based Appli...

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learning and Technology conference (L&T)

作者： Saeed Mian Qaisar Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73.

关键词： Power demand Accuracy Sensitivity Feature extraction Smart meters Smart grids Ensemble learning Stakeholders Time factors Time-domain analysis

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State of Charge Estimation of Lithium-ion Batteries Using Long Short Term Memory Network in Electric Vehicle Applications

State of Charge Estimation of Lithium-ion Batteries Using Lo...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Marwa Gaich Sabeur Jemmali Bilal Manai LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisie Department of Electrical Engineering CEGEP de l'Outaouais Gatineau Québec Canada

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Precise State-of-Charge (SoC) estimation is crucial for accurate electric vehicle range prediction and efficient battery charge management. This paper explores a computationally efficient machine learning approach to address SoC-estimation-related issues. A Long Short-Term Memory (LSTM) network is utilized for predicting the SoC of lithiumion batteries, leveraging real-time data on temperature, voltage and current. The proposed LSTM model demonstrates remarkable performance, achieving a mean absolute error of 1.02%, as well as a 1.35% root mean squared error at 25°C, while ensuring low computational overhead, making it suitable for practical implementation in Battery Management Systems (BMSs). This work lays a solid foundation for future advancements in BMS design, offering significant improvements in SoC prediction accuracy, reliability and efficiency. The findings suggest that LSTM networks can effectively model the complex dynamics of battery behavior, even when having varied operational conditions.

关键词： Accuracy Computational modeling Estimation Battery management systems Machine learning Voltage Electric vehicles State of charge Vehicle dynamics Long short term memory

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Machine learning Operations: A Mapping Study 22nd

Machine Learning Operations: A Mapping Study

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22nd international conference on Software engineering Research and Practice, SERP 2024, and 23rd international conference on e-learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Chakraborty, Abhijit Das, Suddhasvatta Gary, Kevin Arizona State University TempeAZ85287 United States

ISBN: (纸本)9783031866432

Machine learning and AI have been recently embraced by many companies. Machine learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： DevOps

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Federated Split learning for Human Activity Recognition with Differential Privacy

Federated Split Learning for Human Activity Recognition with...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Josue ndeko Shaba Shaon Aubrey Beal Avimanyu Sahoo Dinh C. Nguyen Department of Electrical and Computer Engineering University of Alabama in Huntsville USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed comparison between traditional Federated learning (FL) and our FSL framework, showing that the FSL framework outperforms FL models in both accuracy and loss metrics. Additionally, we examine the privacy-performance trade-off under different data settings in the DP mechanism, highlighting the balance between privacy guarantees and model accuracy. The results also indicate that our FSL framework achieves faster communication times per training round compared to traditional FL, further emphasizing its efficiency and effectiveness. This work provides valuable insight and a novel framework which was tested on a real-life dataset.

关键词： Training Differential privacy Privacy Accuracy Simulation data models Human activity recognition Servers Protection Long short term memory

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Using Survey to Investigate the Integration of Artificial Intelligence in e-learning 22nd

Using Survey to Investigate the Integration of Artificial In...

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作者： Tao, Yi Yang, Zijiang Zhang, Ethan Boyi School of Information Technology York University Toronto Canada St. Robert Catholic High School Thornhill Canada

ISBN: (纸本)9783031866432

This paper focuses on analyzing data obtained from a survey conducted among 312 management undergraduate students in China. The aim of the survey is to explore their perceptions, inclinations, and attitudes towards the use of AI in e-learning. In addition, feature importance analysis is conducted to identify the most influential factors in determining students’ attitudes towards AI in e-learning. This analysis helps uncover the key factors that significantly impact students’ perceptions and evaluations of AI usage in the daily learning process. Overall, this paper provides a comprehensive and empirical analysis of the survey data. The findings contribute to a better understanding of the key factors influencing attitudes and provide valuable insights for educational practitioners, creators of AI technology, and policymakers involved in the implementation of AI technologies in the educational industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Use Digital Twins to Support Fault Diagnosis from System-Level Condition-Monitoring data

Use Digital Twins to Support Fault Diagnosis from System-Lev...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Killian Mc Court Xavier Mc Court Shijia Du Zhiguo Zeng CentraleSupélec Université Paris-Saclay Gif-sur-Yvette France Shenzhen Weimin Institute for Reliability Systems Engineering Shenzhen China Chair on Risk and Resilience of Complex Systems Laboratoie Genie Industriel CentraleSupélec Université Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Deep learning models have created great opportunities for data-driven fault diagnosis but they requires large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from 4 different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system.

关键词： Fault diagnosis Deep learning Training Performance evaluation Motors data models Digital twins Robots Predictive maintenance

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Heuristic data Placement in Distributed Hierarchical Storage Network Using Deep Reinforcement learning

Heuristic Data Placement in Distributed Hierarchical Storage...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Ravi Kaler Durga Toshniwal Computer Science and Engineering Department Indian Institute of Technology Roorkee Uttarakhand India

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces.

关键词： Performance evaluation Cloud computing Costs Markov decision processes Decision making Distributed databases Deep reinforcement learning Delays Time factors Optimization

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Towards Implementation of Privacy-Preserving Federated learning Aggregation Using Multi-key Homomorphic Encryption 25th

Towards Implementation of Privacy-Preserving Federated Lear...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Boudko, Svetlana Norwegian Computing Center Oslo Norway

ISBN: (纸本)9783031859229

Federated learning is a machine learning approach where a model is trained across multiple decentralized edge devices. Since the data are not uploaded to a server, this approach is particularly useful for data protection and efficient computation. Further, it can be combined with privacy-preserving technologies, e.g., homomorphic encryption for enhanced data protection. Considering all these elements, a practical solution will require an efficient multikey homomorphic encryption as well as an effective integration of federated model aggregation and multikey generation processes. The paper studies the related work for homomorphic encryption in the context of federated learning and outlines the rationale behind practical design of secure federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed computer systems

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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learning and Technology conference (L&T)

作者： Qazi Mazhar Ul Haq Leethar Yao Bo-Yu Lin Dept. of IBPI and Computer Science and Engineering Yuan Ze University Zhongli Taiwan Dept. of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method.

关键词： Adaptation models Smart cities Surveillance Education Generative adversarial networks data augmentation Adversarial machine learning Robustness Unsupervised learning Identification of persons

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intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Gloria Mollah Majumder Haider Danda B. Rawat Imtiaz Ahmed Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

关键词： Navigation Simulation Wireless networks Artificial neural networks Massive MIMO Reconfigurable intelligent surfaces Real-time systems Resource management Optimization intelligent control

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