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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 36882 条记录，以下是481-490 订阅

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management

Research on Optimization Algorithm and Simulation Model of O...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yinquan Hu Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. The algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. These experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications.

关键词： PI control Heuristic algorithms Simulation Battery management systems Deep reinforcement learning Energy efficiency Batteries Safety PD control Optimization

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Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bidirectional GRU-LSTM

Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bi...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Hitesh Ninama Jagdish Raikwal Institute of Engineering and Technology Devi Ahilya University Indore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Automatic gauge reading plays a vital role in industrial automation, enabling efficient and accurate monitoring of parameters such as pressure, temperature, and flow. Traditional image processing struggles with diverse gauge designs, inconsistent lighting, and environmental factors. This study introduces a hybrid deep learning approach, combining Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, to enhance accuracy and robustness. Five hybrid models, including CNN-LSTM, CNN-GRU, and their bidirectional versions, were evaluated using synthetic and real-world *** best performing model, CNN with SpatialDropout2D and Bidirectional GRU-LSTM (Model E), achieved a Mean Absolute Error (MAE) of 0.030, Root Mean Squared Error (RMSE) of 0.041, and an R 2 score of 0.991. It maintained high accuracy in various conditions, with an average inference time of 25 milliseconds per image, demonstrating suitability for real-time applications.

关键词： Deep learning Training Technological innovation Accuracy Temperature Robustness real-time systems Convolutional neural networks Monitoring Long short term memory

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Intelli-Helmet for Motorcycles Using Internet Of Things

Intelli-Helmet for Motorcycles Using Internet Of Things

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Dharshan Rajendran Sangeetha M Gowtham Balasubbiramani Kavin Sekar Department of Information Technology K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Transport may be an essential requirement for the society. It makes human life simple and comfortable. It causes the passing of people and damages any part of the body. To avoid the particular action, we attempt to actualize a framework that is a Mishap discovery and informing framework utilizing Bluetooth-based Portable Application. In this framework, the vibration sensor is utilized as an input to the framework and the microcontroller analyzes the reaction by comparing it. In the event that mishap happens, sensors perusing exceed the limit, and it takes the suitable activity. The system sends an SMS alert to specialists, ensuring quick assistance for accident victims. It also monitors driver activity, detecting signs of intoxication before starting the vehicle or while driving. The proposed embedded approach delivers promising results in enhancing road safety.

关键词： Vibrations Microcontrollers Motorcycles Emergency services Road safety real-time systems Internet of Things Monitoring Accidents Vehicles

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Sign Language Detection System Using CNN and HOG: Bridging the Communication Gap for Deaf and Hearing Communities

Sign Language Detection System Using CNN and HOG: Bridging t...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Aditi Shegokar Tejas Kale Lalit Patil Poonam Gupta G H Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In order to effectively extract features, this research proposes a novel real-time sign language detection system that combines Convolutional Neural Networks (CNN) with Histogram of Orientated Gradients (HOG). The device bridges the communication gap between the hearing and deaf communities by successfully recognizing both static and dynamic gestures in real time using a typical web camera. This technology, which is intended to be both economical and easy to use, removes the need for pricey hardware, allowing for wider application. Testing demonstrates that the system can accurately and consistently identify a large variety of hand gestures, increasing its applicability for everyday use in a variety of contexts. The technology has potential applications in public areas, educational institutions, and businesses where effective communication between sign language users and non-users is crucial since it fosters social inclusion.

关键词： Human computer interaction Sign language Histograms Technological innovation Image recognition Auditory system Feature extraction real-time systems Convolutional neural networks Testing

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Ambient Energy Capture from Human Footsteps using Piezoelectric Sensors

Ambient Energy Capture from Human Footsteps using Piezoelect...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Arish P Poli Gowtham Kumar Reddy Sankarapu Ramcharan Vijendra Babu D School of Electronics Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Embedded Technology School of Electronics Engineering Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

This research work explores footstep energy harvesting as a sustainable solution for urban energy needs. Using piezoelectric materials and electromagnetic induction, it converts pedestrian movement into electricity, ideal for high-traffic areas like train stations. Inspired by real-world applications in London and Tokyo, the study evaluates efficiency, economic benefits, and environmental impact. While individual steps generate minimal power, large-scale implementation can reduce fossil fuel reliance and enhance smart city energy systems. If one could further laminate the code for collecting energy under the assemblies of hybrid panels integrating solar and wind energy, it would surely boost this one towards greener and efficient urban spaces. The proposed prototype finds a sustainable solution for energy harvesting effectively.

关键词： Mechanical sensors Green energy Pedestrians Smart cities Wind energy Piezoelectric materials Prototypes real-time systems Energy harvesting Global Positioning System

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CrossSleep: Multi-Scale Attention with Cross-time Learning for Single Channel EEG-Based Sleep Staging

CrossSleep: Multi-Scale Attention with Cross-Time Learning f...

引用

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

作者： Jingchuan Lu Jinlong Yang School of Artificial Intelligence and Computer Science Jiangnan University WuXi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate sleep staging is essential for diagnosing sleep disorders and improving sleep health. While traditional methods rely on multichannel electroencephalogram (EEG) signals, single-channel EEG offers a more practical and non-intrusive alternative. However, the complexity of sleep dynamics across varying time scales presents a significant challenge for single-channel EEG-based staging. To address this, we propose a novel method combining Multi-Scale Attention and Cross-time Learning to capture both local and global temporal dependencies in EEG signals. The Multi-Scale Attention Module extracts features at multiple temporal scales, while the Cross-time Learning mechanism models long-term dependencies across time segments, improving classification performance. Our approach is evaluated on benchmark EEG sleep datasets, demonstrating superior accuracy and F1-scores, particularly in transition stages, compared to existing single-channel methods. The results suggest our method’s potential for real-world applications in portable and home-based sleep monitoring systems.

关键词： Accuracy Sleep Benchmark testing Signal processing Feature extraction Electroencephalography Robustness real-time systems Speech processing Monitoring

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Research on the Design of an Artificial Intelligence-Based Cybersecurity Automatic Early Warning System

Research on the Design of an Artificial Intelligence-Based C...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Zilong Zhang Setting: School of Computer Guangdong Business and Technology University Duanzhou Zhaoqing Guangdong China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the advent of the information age, cybersecurity issues have become increasingly severe. Traditional cybersecurity defense measures are no longer effective in addressing complex and dynamic security threats, making an artificial intelligence (AI)-based cybersecurity automatic early warning system a promising new research direction. This paper proposes the design and implementation of a cybersecurity automatic early warning system based on AI algorithms. The system collects network traffic data, analyzes potential security threats, and uses machine learning and deep learning algorithms to intelligently recognize and classify data, thereby detecting cyber attacks in real-time and issuing early warnings. This paper first reviews the current cybersecurity early warning technologies and, based on this, analyzes the applications of AI algorithms in cybersecurity. Subsequently, the functional requirements for a cybersecurity early warning system are discussed, followed by the design of system architecture and core algorithms. The proposed system's effectiveness and feasibility are validated through experiments and tests. Experimental results demonstrate that this system can accurately identify and issue warnings for various types of cyber attacks within a short timeframe, offering a high level of real-time performance and accuracy, thus providing effective technical support for cybersecurity.

关键词： Deep learning Adaptation models Accuracy Machine learning algorithms systems architecture Telecommunication traffic Alarm systems real-time systems Classification algorithms Cyberattack

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PIDNet-SLAM: A Multi-Resolution Semantic SLAM Algorithm for Dynamic Scenes

PIDNet-SLAM: A Multi-Resolution Semantic SLAM Algorithm for ...

引用

Annual ieee systems conference

作者： Siddharth Rajan Jason Gu Wei Liu Department of Electrical and Computer Engineering Dalhousie University Halifax Canada Southern University of Science and Technology China

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Simultaneous Localization and Mapping (SLAM) plays a vital role in the fields of Computer Vision and Robotics. Traditional SLAM frameworks often assume a static environment, which introduces significant errors during localization and mapping. To address this issue, researchers have incorporated various techniques to remove dynamic objects from the scene, but these methods fail to operate at real-time speeds. In this paper, we propose PIDNet-SLAM a semantic segmentation-based SLAM system optimized for dynamic environments. The system enhances the accuracy of ORB-SLAM3 by adding three parallel threads to the architecture: a fast semantic segmentation module using two branches of PIDNet for low and high resolutions separately, and a lightweight geometric module. We employed a novel training method to train the multi resolution PIDNet network, that utilizes the low-resolution branch to process non-keyframes and the high-resolution branch to process keyframes. This approach achieves high inference speeds while maintaining great accuracy. This multi-branch architecture significantly improves the performance of the semantic segmentation network, making it highly suitable for real-time applications. We evaluated our SLAM system on the TUM RGB-D dataset, comparing it with ORB-SLAM3, Dyna SLAM, and SOLO-SLAM. Our system achieved significant improvements, including enhanced localization accuracy in highly dynamic environments and an average processing speed of 47ms outperforming all other SLAM algorithms in handling dynamic scenes while operating in real-time.

关键词： Location awareness Training Simultaneous localization and mapping Accuracy Heuristic algorithms Semantic segmentation Instruction sets Semantics Computer architecture real-time systems

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Target Recognition and Tracking Using You Only Look Once Version 5 with DeepSORT in Unmanned Aerial Vehicle Vision systems

Target Recognition and Tracking Using You Only Look Once Ver...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Shuhan Yu Hainan International College Communication University of China Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, the target recognition and tracking technology in Unmanned Aerial Vehicles (UAV) vision systems has gained considerable interest due to its promising implications for various applications. However, the existing Open Flow based Tracking (OFT) system faced challenges by factors such as varying lighting conditions and occlusions. Hence, this research proposes a You Only Look Once version 5 with Deep Learning based Simple Online real-time Tracking (YOLOv5-DeepSORT) is proposed to handle the occlusions. The proposed YOLOv5-DeepSORT detect and track objects across frames for accurate target detection and tracking in UAV vision systems. Initially, the input data is collected from Vision-based Drone (VisDrone) dataset and then, the data augmentation is performed to expand the dataset variability. After that, YOLOv5 is employed for object detection by incorporating Transformer based Multi-Head Attention (T-MHA) as backbone to extract features. Then, GhostNet is used as feature fusion technique to minimize the computational overhead of the network. Finally, DeepSORT is introduced to track the objects by predicting object motion and associating object across frames. From the results, the proposed YOLOv5-DeepSORT achieved better results in terms of recognition accuracy (99.5%), recognition speed (33.7 vehicles/sec), and manual time (1.4s) when compared with existing UAV-Vision Based Moving Target Detection (UAV-MTD).

关键词： YOLO Deep learning Target tracking Accuracy Target recognition Machine vision Autonomous aerial vehicles Feature extraction Transformers real-time systems

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Aquaponic Farming with Advanced Decision Support System: Practical Implementation and Evaluation

Aquaponic Farming with Advanced Decision Support System: Pra...

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international Image Processing, applications and systems conference (IPAS)

作者： Eleftheria Maria Pechlivani Georgios Gkogkos Panagiotis Christakakis Dimitrios Kapetas Ioannis Hadjigeorgiou Dimosthenis Ioannidis Dimitrios Tzovaras Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The adoption of Decision Support systems (DSS) by farmers faces significant challenges, including the need for user-friendly interfaces, comprehensive training, reliable support, and affordable pricing. Addressing these challenges is critical to advancing sustainable agriculture and meeting global food production demands. This paper introduces an advanced DSS tailored for aquaponics, leveraging cutting-edge technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain. These technologies are integrated into the DSS to optimize resource management, ensure cybersecurity, and simplify complex processes through an intuitive interface. real-time data is presented in an accessible format, enabling farmers of all skill levels to adopt the system with ease. The DSS features AI-driven robotic traps with 75% accuracy in real-time insect detection, autonomous robots achieving 94% precision in 3D spot spraying, and nutrient analyzers with over 92% accuracy in monitoring critical levels. A blockchain layer ensures secure data verification, traceability, and distributed AI model verification. Advanced hierarchical clustering algorithms analyze pest and nutrient dynamics, providing actionable insights for farm management. The system emphasizes interoperability and scalability, supporting seamless integration with various aquaponic setups. Field evaluations demonstrate the DSS’s capacity to reduce pesticide use by 50%, enhance crop yields, and lower sample analysis costs by 70%, highlighting its efficiency and sustainability. Data visualization latency remains below 430ms, enabling real-time responsiveness, while predictive models achieve 91% accuracy in forecasting pest population trends. These results solidify the DSS's role as a transformative tool in precision agriculture. By improving productivity, plant health, and the utilization of biopesticides and biofertilizers, this work bridges the gap between theoretical models and real-world applicat

关键词： Decision support systems Precision agriculture Accuracy Heuristic algorithms Scalability Data visualization Nutrients real-time systems Sustainable development Farming

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