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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

引用

Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： R. Arul Jose P. Ebby Darney R. Santhana Krishnan J. Relin Francis Raj D. Jansi Rani P. Sundaravadivel Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bengaluru India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency.

关键词： Deep learning Training Computational modeling Battery management systems real-time systems Data models Batteries State of charge Optimization Long short term memory

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Automatic Playlist Generation: A Comprehensive Crux of a Methodology for Technology Trend

Automatic Playlist Generation: A Comprehensive Crux of a Met...

引用

ieee international conference on Consumer Electronics (ICCE)

作者： Min-Kyung Sung Chae-Woon Kim Sanghyuck Lee Jaesung Lee Dept. Artificial Intelligence Chung-Ang University Seoul South Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The rapid expansion of large-scale music data on streaming platforms has significantly fueled the development of music recommendation systems, which aim to enhance music exploration by accurately reflecting user preferences. A central challenge in this domain is improving the accuracy of Automatic Playlist Generation, which involves generating new playlists that align with user preferences while maintaining the musical characteristics of previously listened tracks. Various Automatic Playlist Generation methods have been proposed, utilizing different data types and algorithms. This paper provides an classification of existing Automatic Playlist Generation approaches based on neural network architectures, model principles, characteristics, and applications. Additionally, it discusses the evaluation methodologies used to assess Automatic Playlist Generation performance and compare the quality of generated playlists. The paper also explores real-world applications of Automatic Playlist Generation and the potential for integrating these systems across different platforms and devices, emphasizing the importance of addressing the remaining challenges in future research.

关键词： Surveys Performance evaluation Accuracy Video on demand Reviews Neural networks real-time systems User experience Web sites Recommender systems

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Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned using a Many-Task-Based Approach 24

Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned...

引用

Proceedings of the SC '24 Workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Torben Kalkhof Andreas Koch Embedded Systems and Applications Group Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9798350355543

Current programming models face challenges in dealing with modern supercomputers' growing parallelism and heterogeneity. Emerging programming models, like the task-based programming model found in the asynchronous many-task HPX programming framework, offer new ways to express parallelism, enhance scalability, and mask synchronization and communication latency on multi-core and distributed *** high-performance computing benchmarks are often unsuitable for comparing different programming models due to their limited code complexity. However, real-world scientific applications are usually too complex. As a middle ground, proxy applications model the behavior of actual scientific problems, while reducing code *** our research on using HPX to program machines with heterogeneous compute units (e.g., GPU and FPGA/AI Engines), we have also substantially optimized a pure HPX-based software baseline of the LULESH proxy application. This paper discusses the techniques we applied yielding single-node speed-ups of 1.33x to 2.25x for different problem sizes relative to the LULESH OpenMP reference implementation.

关键词： HPC

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Advances in Computational Epilepsy Diagnostics: Machine Learning at the Forefront

Advances in Computational Epilepsy Diagnostics: Machine Lear...

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Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Anmol Rattan Singh Gurjinder Singh Nagendar Yamsani Saif Obbayed Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Epilepsy is a prevalent neurological condition marked by spontaneous, recurrent seizures, significantly affecting quality of life and increasing mortality risk. This research paper delves into advanced computational methodologies for the detection and classification of epileptic seizures. Leveraging the power of machine learning and deep learning, this study aims to overcome the limitations of traditional diagnostic methods, which typically involve invasive monitoring and subjective analysis of EEG data. By introducing sophisticated machine learning models and employing non-linear classification techniques through kernel methods and support vector clustering, this research enhances the precision and speed of epilepsy diagnosis. The study not only explores the integration of these models into non-invasive, real-time monitoring systems but also discusses their impact on clinical practice and healthcare systems.

关键词： Deep learning Accuracy Computational modeling Epilepsy Medical services Brain modeling Electroencephalography real-time systems Kernel Monitoring

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TrustMesh: A Blockchain-Enabled Trusted Distributed computing Framework for Open Heterogeneous IoT Environments

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

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Working ieee/IFIP conference on Software Architecture

作者： Murtaza Rangwala Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh’s ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh’s effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks.

关键词： Fault tolerance Trusted computing Technological innovation Software architecture Scheduling algorithms Fault tolerant systems Trustless services Internet of Things Security Resource management

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Transparency of AI-XR systems: Insights from Experts

Transparency of AI-XR Systems: Insights from Experts

引用

ieee international conference on Artificial Intelligence and Virtual reality (AIVR)

作者： Clara Maathuis Dragos Datcu Open University of the Netherlands The Netherlands Independent Research The Netherlands

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Recent advancements in the field of Extended reality (XR) have increasingly integrated AI (Artificial Intelligence) technologies to enhance user experience and interactions. These are now crucial in XR applications in tasks like real-time object recognition, responsive environment generation, and adaptive intelligent behavior creation. Nevertheless, AI-XR systems need to adhere to social, legal, and ethical standards, norms, and values to ensure that their development, deployment, and use is done in a responsible and trustworthy manner. This assures building trust, preventing misusing sensitive information, and safeguarding user privacy and autonomy. To this end, transparency of AI-XR systems is fundamental for building and assuring trust. Accordingly, this research aims to understand and reflect on the perspectives that experts have in relation to building transparent XI-XR systems. To do so, a workshop with 14 field experts is conducted and the findings serve as a design framework with a rich palette of technical, socio-technical, and human-centered instruments that could be of use to researchers and practitioners in this domain.

关键词： Privacy Law Extended reality Instruments Buildings User experience real-time systems Object recognition Artificial intelligence Standards

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo Vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

引用

international Image Processing, applications and systems conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication systems architecture User experience real-time systems Stereo vision Safety

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Triboelectric Mat Multimodal System for Sleep Pose Estimation

Triboelectric Mat Multimodal System for Sleep Pose Estimatio...

引用

ieee international conference on Micro Electro Mechanical systems

作者： Jinlong Xu Xinge Guo Chengkuo Lee Department of Electrical & Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

In this manuscript, we proposed an unobtrusive infrared camera-enhanced triboelectric mat sensing system containing pixel-based area ratio mapping and multimodal sensory intelligence for the first time. The pixel-based area ratio mapping for the whole mat array is determined solely according to the integral values of triboelectric outputs. Meanwhile, multimodal sensory information and deep-learning fusion analytics can recognize different sleeping postures for different people to generate a comprehensive sleep behavior monitoring report. Finally, the proposed multimodal mat system presents a low-cost, noninvasive, and effective methodology as the functional interface for the applications of remote home healthcare monitoring.

关键词： Accuracy Smart homes Medical services Voltage real-time systems Thermal analysis Arrays Triboelectricity Monitoring Sports

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real time Implemenatation of Passive Cell Balancing and Verification of MIL and SIL in MATLAB

Real Time Implemenatation of Passive Cell Balancing and Veri...

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Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Rahul Ankushe Amruta Udapurkar Electrical Department RSCOE Pune India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

In modern day electric vehicle battery is heart, to improve the life of battery is challenge this can be done by different method like cell balancing, either by dissipating heat through resistors or giving charge from one cell to another cell. This paper gives exact topology and real time implementation of passive cell balancing and verification of cell balancing using model in loop (MIL) and software in loop (SIL) using MATLAB. This paper present passive cell balancing circuit and its implementation using MATLAB. How resistors are acting as reactive component in passive cell balancing that is discuss. Balancing takes one by one for each cell. Cell balancing is done during charging time and while doing cell balancing continuously current is monitor. Modern tool is used to verify the BMS i.e. MIL and SIL, once MIL & SIL is done hardware implement in hardware is very easy. In the present paper comparison of passive cell balancing and active cell balancing is done. The efficiency of passive cell balancing is 99%. In the present paper MOSFET is used as switch and to trigger MOSFET control technique is implemented.

关键词： Semiconductor device modeling Resistors MOSFET Codes real-time systems Mathematical models Hardware Batteries Topology MATLAB

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Complete Reconstruction of the Tongue Contour Through Acoustic to Articulatory Inversion Using real-time MRI Data

Complete Reconstruction of the Tongue Contour Through Acoust...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sofiane Azzouz Pierre-André Vuissoz Yves Laprie CNRS Inria Université de Lorraine Nancy France Inserm IADI U1254 Université de Lorraine Nancy France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acoustic articulatory inversion is a major processing challenge, with a wide range of applications from speech synthesis to feedback systems for language learning and *** recent years, deep learning methods have been applied to the inversion of less than a dozen geometrical positions corresponding to sensors glued to easily accessible articulators. It is therefore impossible to know the shape of the whole tongue from root to tip. In this work, we use high-quality real-time MRI data to track the contour of the tongue. The data used to drive the inversion are therefore the unstructured speech signal and the tongue contours. Several architectures relying on a Bi-LSTM including or not an autoencoder to reduce the dimensionality of the latent space, using or not the phonetic segmentation have been explored. The results show that the tongue contour can be recovered with a median accuracy of 2.21 mm (or 1.37 pixels) taking a context of 1 MFCC frame (static, delta and double-delta cepstral features).

关键词： Tongue Tracking Shape Magnetic resonance imaging Noise Natural languages Phonetics Sensor systems and applications real-time systems Speech synthesis

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