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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 ieee.

关键词： Machine learning

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MATE: Masked Autoencoders are Online 3D Test-time Learners

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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ieee/CVF international conference on Computer Vision (ICCV)

作者： Mirza, M. Jehanzeb Shin, Inkyu Lin, Wei Schriebl, Andreas Sun, Kunyang Choe, Jaesung Kozinski, Mateusz Possegger, Horst Kweon, In So Yoon, Kuk-Jin Bischof, Horst Graz Univ Technol Inst Comp Graph & Vision Graz Austria Christian Doppler Lab Embedded Machine Learning Vienna Austria Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9798350307184

Our MATE is the first Test-time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test- time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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Application and Performance Analysis of Physics-Informed Neural Networks in Solving Complex Physical systems 2

Application and Performance Analysis of Physics-Informed Neu...

引用

2nd ieee international conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Chen, Xuyang University of California Letters and Science DavisCA United States

ISBN: (纸本)9798350368208

This study introduces a quantum-accelerated framework that integrates quantum optimization with physics-informed neural networks (PINNs) to solve PDEs more efficiently. By leveraging the Quantum Approximate Optimization Algorithm (QAOA), the proposed framework enhances the initialization and boundary parameter optimization of PINNs, addressing key challenges such as slow convergence and high computational cost. The hybrid approach combines the data efficiency and physical constraint embedding capabilities of PINNs with the computational advantages of quantum optimization, enabling faster and more accurate solutions for complex PDEs. The framework was validated using the Shuhao Cao PDE dataset, demonstrating significant improvements in training efficiency and accuracy compared to traditional numerical solvers and classical PINNs. Experimental results highlight that quantum optimization effectively accelerates convergence by optimizing initial conditions and boundary constraints, reducing the computational overhead associated with classical training processes. Despite its reliance on emerging quantum hardware and the sensitivity of PINNs to hyperparameter selection, this approach offers a scalable and robust solution for solving high-dimensional PDEs. The findings underscore the potential of hybrid quantum-classical methods in advancing scientific computing, providing a foundation for future research in stochastic PDEs, multi-physics problems, and other real-world applications. © 2024 ieee.

关键词： Stochastic systems

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Hands-On Gaming: real-time Gesture Detection in Rock-Paper-Scissors Using Mediapipe and CNN

Hands-On Gaming: Real-Time Gesture Detection in Rock-Paper-S...

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2025 international conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Darmawan, Irfan Meldiawati, Aulia Melda Rizal, Randi Rahmatulloh, Alam Haerani, Erna Gunawan, Rohmat Telkom University Department of Information Systems Faculty of Industrial Engineering Bandung Indonesia Siliwangi University Faculty of Engineering Departement of Informatics Tasikmalaya Indonesia Siliwangi University Faculty of Engineering Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

real-time hand gesture detection enables intuitive non-verbal communication between humans and devices. In gaming, using hand gestures as a control mechanism enhances user engagement and provides a more immersive experience. This research implements a real-time hand gesture detection system for the Rock-Paper-Scissors game by combining Mediapipe for feature extraction and a Convolutional Neural Network (CNN) for gesture classification. Mediapipe detects and extracts key hand landmarks in real-time, while the CNN classifies the gestures into three categories: rock, paper, and scissors. The proposed system demonstrates high performance, achieving an impressive classification accuracy of 98.12% after 500 training epochs. Furthermore, it ensures adequate detection speed for seamless real-time interaction in gameplay. Future improvements to this system include utilizing a larger and more diverse dataset to enhance the model's ability to recognize a broader range of hand gesture patterns. This approach could improve robustness across different lighting conditions, hand sizes, and variations in gesture execution. Additionally, refining the CNN model architecture could lead to further performance gains in both accuracy and speed. The findings of this research highlight the potential for hand gesture detection to enhance interactivity in gaming and beyond. applications such as augmented reality (AR), virtual reality (VR), and other interactive systems could benefit from this technology. This study demonstrates that integrating Mediapipe and CNNs can provide an accurate, responsive, and engaging user experience, paving the way for further advancements in gesture-based interaction technologies. © 2025 ieee.

关键词： Convolutional neural networks

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Predicting time Series of the Lorenz Chaotic System Using Random Forest Regression 8

Predicting Time Series of the Lorenz Chaotic System Using Ra...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Li, Yuxuan University of Jinan School of Mathematical Sciences Shandong Jinan China

ISBN: (纸本)9798350302455

This paper presents a novel approach for predicting Lorenz chaotic sequences using machine learning algorithms. The proposed research has broad applications in studying the chaotic properties of nonlinear sequences, predicting financial market trends, and has significant academic and commercial value. Chaotic sequences are highly unpredictable nonlinear sequences, and their behavior is extremely sensitive to initial value changes. Chaotic maps have found applications in various fields, such as physics, encrypted communication, and financial market prediction. The Lorenz chaotic system is a classical chaotic system that has been widely used in science and engineering. In this study, we utilized the random forest regression technique in machine learning to predict the time series produced by the Lorenz system. Our experimental results demonstrate that the machine learning algorithm can accurately predict the chaotic sequences, which enhances our understanding of these nonlinear sequences and enables us to apply them to chaotic systems more effectively. This research provides valuable insights into the dynamics of the Lorenz chaotic system, and the proposed approach has the potential to be extended to other chaotic systems. © 2023 ieee.

关键词： Chaotic systems

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Predicting Fouling Progress in Water Cooled Chillers Using Bidirectional LSTM Neural Networks and Synthetic Data 1

Predicting Fouling Progress in Water Cooled Chillers Using B...

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1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Hasan, Mohd Faizan Thirumalvalavan, S. Raj, S. Solomon Iftekhar Hussain, Beporam Khan, Maaz Allah Kolekar, Aniket B. Integral University Department of Mechanical Engineering U.P Lucknow India Arunai Engineering College Department of Mechanical Engineering TiruvannamalaiPin - 606 603 India Department of Mechanical Engineering Telangana Hyderabad-75 India Bapatla Engineering College Department of Mechanical Engineering Andhra Pradesh Bapatla522102 India Department of Civil Engineering Uiet Uttar Pradesh Lucknow India D y Patil International University Department of Mechanical Engineering Maharashtra Pune India

ISBN: (纸本)9798350367720

The primary challenge in developing a practical predictive model for equipment cleaning is determining the variables the neural network should calculate, focusing on a cost-benefit analysis. Given the impracticality of using real installation data due to the slow development of deposits, synthetic data were generated using a theoretical model in Engineering Equation Solver, considering variations in external wet bulb temperature and chiller parameters. These simulations, improving upon previous studies, trained neural networks to recognize fouling patterns in new data. The final model, suitable for real applications, was created using Python and executed in Google Colab Notebook, leveraging TensorFlow, Keras, NumPy, Pandas, and scikit-learn libraries. A Bidirectional Long Short-Term Memory (LSTM) architecture with the "tanh"activation function and Adam optimization was adopted, balancing accurate modeling and avoiding overfitting. The model was trained with 10,000 data points, using time, wet bulb temperature, condensation temperature, conductance, pump pressure, and tube length as inputs, and Rf as the output. The results highlighted fouling progression and its impact on chiller performance, demonstrating the viability of this approach. Future work includes testing in real installations, adapting the model using transfer learning, and exploring different architectures and additional physical parameters. © 2024 ieee.

关键词： Water cooling systems

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Applicability of Deep Learning Model Trainings on embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean conference on embedded computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 ieee.

关键词： embedded systems

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The need for neuromorphic computing in industrial robotics

The need for neuromorphic computing in industrial robotics

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2023 international conference on Artificial Intelligence and Power Engineering, AIPE 2023

作者： Bhanja, Tapanta Nusbaum, Jonathan Abuibaid, Khalil Legler, Tatjana Wagner, Achim Ruskowski, Martin University of Kaiserslautern-Landau Machine Tools and Control Systems Department of Mechanical and Process Engineering Kaiserslautern Germany German Research Centre for Artificial Intelligence Innovative Factory Systems Kaiserslautern Germany

ISBN: (纸本)9798350310788

With Moore's law coming to its limits, the rate of increase in compute power available for processing applications is similarly coming to a halt. This implies that the compute intensive tasks, such as robotics, artificial intelligence, and high-performance space computing need innovative ways to cater their ever-increasing compute needs. One innovative way to solve computational bottlenecks is to bring compute and memory together, as opposed to the Von Neumann computational model, with greater degree of parallelism in an event-based, asynchronous computation paradigm. Neuromorphic computing is one such paradigm that draws its inspiration from the brain. Energy and computational efficiency, asynchronous and event-based processing being its salient features, neuromorphic computing is an area worth exploring for compute intensive tasks. In this paper, the authors explore the possibilities and benefits of neuromorphic computing in robotics, and establish possible research directions that could benefit the robotics domain. © 2023 ieee.

关键词： Robotics

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ASAP: Reconciling Asynchronous real-time Operations and Proofs of Execution in Simple embedded systems 22

ASAP: Reconciling Asynchronous Real-Time Operations and Proo...

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59th ACM/ieee Design Automation conference (DAC) - From Chips to systems - Learn Today, Create Tomorrow

作者： Caulfield, Adam Rattanavipanon, Norrathep Nunes, Ivan De Oliveira Rochester Inst Technol Rochester NY 14623 USA Prince Songkla Univ Hat Yai Thailand

ISBN: (纸本)9781450391429

embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices and sensor-alarm combinations), they are usually implemented under strict cost/energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. Motivated by this issue, recent work developed architectures capable of generating Proofs of Execution (PoX) for the correct/expected software in potentially compromised low-end MCUs. In practice, this capability can be leveraged to provide "integrity from birth" to sensor data, by binding the sensed results/outputs to an unforgeable cryptographic proof of execution of the expected sensing process. Despite this significant progress, current PoX schemes for low-end MCUs ignore the real-time needs of many applications. In particular, security of current PoX schemes precludes any interrupts during the execution being proved. We argue that lack of asynchronous capabilities (i.e., interrupts within PoX) can obscure PoX usefulness, as several applications require processing real-time and asynchronous events. To bridge this gap, we propose, implement, and evaluate an Architecture for Secure Asynchronous Processing in PoX (ASAP). ASAP is secure under full software compromise, enables asynchronous PoX, and incurs less hardware overhead than prior work.

关键词： embedded systems

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Multiagent Power Flow Control for Plug-and-Play Battery Energy Storage systems in DC Microgrids 58

Multiagent Power Flow Control for Plug-and-Play Battery Ener...

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58th international Universities Power Engineering conference, UPEC 2023

作者： Al-Saadi, Mudhafar Short, Michael School of Computing Engineering and Digital Technologies Teesside University Middelisborough United Kingdom

ISBN: (纸本)9798350316834

Multiagent reinforcement learning has proven remarkably effective at finding near-optimal solutions to complex non-linear control problems when compared to classical schemes. Such problems typically arise when considering power management problems related to advanced power distribution applications, such as micro/smart grids, smart buildings, electric vehicles, and vehicle-to-grid applications. The achievement of balanced synchronized charge/discharge of energy storage systems in real-time is often a critical factor in fulfilling optimized power flow and enhancing battery health and usable lifetime. It is also critical to reducing power losses, supporting energy/power balance, and integration of renewable/intermittent energy. This paper proposes a control adaptation to optimize the power flow of battery energy systems in a DC autonomous microgrid. Multiagent neighbor-to-neighbor information related to the variation in the load participation and measured state of charge is locally exploited to optimize the balance of power storage. The results confirm accurate synchronization of the charge/discharge and enhanced balanced output voltage under an excessive continuous load variation. In addition, for different expectations of real operation, regarding battery capacities, initial states of charge, environmental impacts, and degradation. Furthermore, the independence of the microgrid operation from the number of battery energy storage systems is verified through plug-and-play insertions and removals. © 2023 ieee.

关键词： Electric load flow

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