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

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Reconfiguring Multidimensional PV Arrays for Optimizing Output Power Under PSC

Reconfiguring Multidimensional PV Arrays for Optimizing Outp...

引用

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

作者： Asit Kumar Parida Alivarani Mohapatra Rudra Narayan Dash Suvendu Sekhar Sabat Padarbinda Samal Sarita Samal School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India AGM(OPTCL) Engineering and Quality Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

A solar photovoltaic (PV) power system consists of multiple modules connected in parallel and series to achieve a desired range of output voltage and current. However, these modules are highly vulnerable to partial shading condition(PSC), a common issue that can cause considerable power loss, hotspots, reduced system performance, and even permanent damage to the modules. To address these challenges, reconfiguration techniques are employed to redistribute shading intensity across the modules, reducing current mismatch and enhancing the system's power output. Despite their benefits, these techniques have a major limitation: they are predominantly designed for symmetrical or square arrays, which are uncommon in real-world applications. To increase system output power under different partial shading scenarios, this paper presents a Multidimensional Array Reconfiguration(MAR) technique for modules in asymmetrical arrays. The suggested methodology uses a one-time module arrangement and is simple, affordable, and easy to implement. In situations involving partial shading, it achieves lower power losses and improved system dependability.

关键词： Performance evaluation Radiation effects Fill factor (solar cell) System performance Voltage Power systems

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Fog Vision for Roads and Rails

Fog Vision for Roads and Rails

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Surat Pyari Atti Pranitha A Prakash Raj M Department of Electrical and Electronics Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Reduced visibility due to fog creates hazardous conditions in sectors like transportation, aviation, and outdoor navigation. This research introduces a dual-purpose monitoring system that combines fog detection with real-time object recognition, designed using a Raspberry Pi 3 Model B+ paired with an OV5647 5MP IR cut camera sensor and QuickDetect, a real-time object detection module utilizing Python and the Ultralytics YOLO v8 model [1] [2]. Our system processes video frames to assess fog presence through contrast and blurriness indicators via OpenCV while simultaneously detecting objects with YOLO v8, enabling a comprehensive environmental assessment. The unified design of fog detection and object recognition on a single, affordable platform allows the system to evaluate visibility and detect relevant obstacles, enhancing situational awareness. Preliminary testing has indicated promising results, with efficient detection of fog and accurate identification of objects. This project contributes a practical and accessible solution for safety improvements in low-visibility environments, with potential applications in smart transportation, environmental monitoring, and autonomous navigation.

关键词： YOLO Accuracy Navigation Streaming media Cameras real-time systems Object recognition Environmental monitoring Detection algorithms Testing

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Design Of Stepper Motor Based Dosing System to Enhanced Accuracy & Efficiency For Chemical Industry

Design Of Stepper Motor Based Dosing System to Enhanced Accu...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Onkar Ghige Darshan Gunjal Poonam Daunde Department Of Electrical Engineering Sanjivani College of Engineering Kopargaon India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This research paper presents the design and implementation of a stepper motor based dosing system aimed at improving accuracy and efficiency in the chemical industry. Because liquid dosing precision is becoming more and more important, especially in the manufacturing of pharmaceuticals, chemicals, and packaging, this system uses a hybrid NEMA 17 stepper motor that offers accurate control with a 0.9 degree step angle. To ensure precise fluid distribution and preserve operational integrity, the dosing mechanism makes use of sophisticated control algorithms in conjunction with a dependable auger system and non-return valves. Thorough testing was done at different volume settings (5, 10, 15, and 20 mL) to assess the accuracy and repeatability of the system, showing that it can greatly reduce errors. By reducing manual intervention, the system's automated architecture improves both operational efficiency and product quality. The study also highlights how crucial real time monitoring and user friendly interfaces are for simple supervision and adjustment. This study addresses important issues in liquid dosing processes across a range of industries by demonstrating its adaptability for various industrial applications, advancing precision engineering and industrial automation.

关键词： Chemical industry Liquids Accuracy Automation Motors Valves real-time systems Quality assessment Chemicals Testing

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Application of Deep Learning in Autonomous Mobile Robot Control: An Overview

Application of Deep Learning in Autonomous Mobile Robot Cont...

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Annual ieee systems conference

作者： Minh Nguyen Rickey Dubay Department of Mechanical Engineering Intelligent Controls Laboratory University of New Brunswick Fredericton New Brunswick Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Autonomous mobile robots (AMRs) are reshaping industries by automating tasks across diverse sectors, including logistics, healthcare, agriculture, and manufacturing. Recent advancements in deep learning (DL) have enhanced traditional control systems, empowering AMRs to process high-dimensional sensor data, improve perception, and navigate complex, dynamic environments. These techniques enable AMRs to perform a wide array of sophisticated tasks, including real-time navigation in crowded and cluttered spaces, dynamic obstacle avoidance, and accurate object recognition in settings like warehouses and agricultural fields. Beyond industrial applications, AMRs are also making significant strides in healthcare, particularly in elderly care, where they provide personalized assistance, help with mobility, and monitor health metrics through advanced perception and control mechanisms. This paper provides an overview of DL techniques in AMR systems, examining the roles of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL). CNNs are explored for visual perception tasks such as object detection, scene understanding, and localization. RNNs are utilized for processing sequential and time series data, such as inertial measurement units or force/torque sensors, to enhance scene perception. Finally, RLs are applied to decision-making and path planning in uncertain and dynamic environments. The paper also addresses the growing role of DL in overcoming key challenges in AMR systems, including enhancing robustness to environmental variations, enabling scalability across diverse operational scenarios, and improving autonomous decision-making capabilities.

关键词： Deep learning Scalability Decision making Medical services Robot sensing systems real-time systems Safety Mobile robots Older adults Autonomous robots

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Comparative Methods for Flexibility Estimation and Load/Loss Analysis in TSO/DSO Coordination with DERs

Comparative Methods for Flexibility Estimation and Load/Loss...

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Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： T Koteswara Rao Neeraj Singh Ankur Singh Rana Department of EEE National Institute of Technology Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The modern power system is undergoing vast transformation due to the growing penetration of of Distributed Energy Resources (DERs) such as Solar, Wind and other DGs within distribution networks. This paradigm shift asks for coordinated efforts between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) to maintain grid stability and resilience. This paper compares the Linear DistFlow and Forward-Backward Sweep methods for estimating flexibility at the Transmission System Operator - Distribution System Operator (TSO-DSO) interface in an ieee 33-bus distribution system. Using Monte Carlo simulations over 10,000 scenarios, both methods evaluate flexibility potential with distributed energy resources (DERs) such as Diesel Generators (DGs), Wind, and Photovoltaics (PV). Results indicate distinct performances in terms of load and loss reduction, flexibility range, and computational efficiency, offering insights into each method's effectiveness in modern distribution systems.

关键词： Wind energy generation Wind Accuracy Monte Carlo methods Estimation Power system stability real-time systems Distributed power generation Planning Computational efficiency

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Spiking Generative Models Based on Variational Autoencoder and Adversarial Training

Spiking Generative Models Based on Variational Autoencoder a...

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

作者： Wenchuan Zhang Jie Zhang Ricky Yuen-Tan Hou Weifeng Su Wentao Fan Department of Computer Science Guangdong Provincial/Zhuhai Key Laboratory IRADS Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Department of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking Neural Networks (SNNs) offer significant potential for energy-efficient computing due to their binary, event-driven properties. However, existing deep generative SNNs often struggle to produce high-quality low-dimensional representations in latent space, adversely affecting the quality of their generated samples. To address this limitation, we introduce a novel generative model that integrates the principles of variational autoencoders (VAEs) with adversarial training techniques. Our model consists of a generative module based entirely on an SNN-structured VAE and a discriminator employing artificial neural networks. This discriminator enhances the training process of our SNN-based generative model by applying adversarial principles similar to those used in Generative Adversarial Networks (GANs). Additionally, we have developed a conditional generation capability within our model, enabling the controlled production of specific images based on label inputs. Experimental evaluations on multiple datasets demonstrate that our model achieves superior image quality compared to existing top-performing SNN-based generative models. The source code of our model is accessible at https://***/zxj8806/SGM-VaGAN.

关键词： Training Adaptation models Image synthesis Source coding Autoencoders Spiking neural networks Production Signal processing real-time systems Speech processing

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Optimizing Network Intrusion Detection systems for IoT: Insights from Ensemble and Neural Network Models

Optimizing Network Intrusion Detection Systems for IoT: Insi...

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

作者： Chintakunta Pranay Teja P Murali Karthik Kalam Santhosh Reddy Saroja Kumar Rout Kottu Santosh Kumar Srisaiteja Thullimilli Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The rapid increase in IoT devices has resulted in a significant rise in network traffic, creating new challenges in maintaining strong cybersecurity. To effectively combat both known and unknown cyber threats, such as DoS attacks, advanced network intrusion detection systems (NIDS) in IoT environments must ensure real-time and thorough detection. This study investigates the effectiveness of various machine learning and deep learning models in identifying DoS attacks using the TON_IoT dataset, specifically focusing on its network traffic component. The employed models included ensemble classifiers like the Extra Trees Classifier and Gradient Boosting Classifier, as well as various neural network architectures, including the Multi-Layer Perceptron (MLP) Classifier, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Artificial Neural Networks (ANN). The experimental findings reveal that the Gradient Boosting Classifier achieved the highest performance with a flawless accuracy of 100%, while both the Extra Trees Classifier and MLP Classifier obtained commendable outcomes with accuracies of 90%. The CNN also performed well, achieving an accuracy of 95% due to its high precision and recall metrics. Conversely, both the DNN and ANN struggled with the complexity of the dataset, resulting in lower accuracies of 68% and 38%, respectively. In summary, the findings indicate that ensemble learning techniques, particularly the Gradient Boosting Classifier, are effective in detecting DoS attacks and contribute to the development of robust NIDS tailored for IoT contexts. Thus, this research adds valuable perspectives on creating efficient intrusion detection systems to address the distinctive security challenges commonly faced by IoT networks.

关键词： Accuracy Biological system modeling Network intrusion detection Telecommunication traffic Boosting Denial-of-service attack real-time systems Convolutional neural networks Internet of Things Random forests

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Controlling and Performance Evaluation of DC Microgrid Using Adaptive Control Integrated with Blue Whale Optimization

Controlling and Performance Evaluation of DC Microgrid Using...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Vijoy Kumar Peddiny Brajagopal Datta Abhik Banerjee Department of Electrical Engineering National institute of technology Arunachal Pradesh India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper introduces a new method for regulating and assessing DC microgrid performance: adaptive control combined with the Blue Whale Optimization (BWO) algorithm. Improving DC microgrid efficiency, stability, and dependability under a range of load and generation scenarios is the goal. The adaptive control technique responds to microgrid variations by dynamically adjusting control settings in real-time. These control settings are optimized using the BWO algorithm, which is based on the foraging behavior of blue whales and guarantees optimal system performance. Numerous simulations were run in order to evaluate the suggested system's efficacy. Comparing the results to conventional control approaches, it is clear that there have been significant gains in voltage regulation, power quality, and overall system efficiency. Adaptive control combined with BWO significantly reduces disturbances and increases the microgrid's resistance to errors and variations. This methodology exhibits exceptional flexibility and resilience in handling the varied and sporadic characteristics of sustainable energy resources inside the microgrid. By providing a bio-inspired optimization strategy, the findings expand DC microgrid control methodologies. This work highlights the possibility of using evolutionary algorithms in conjunction with adaptive control to attain the best possible operational performance in contemporary energy systems. The suggested approach paves the way for further study on intelligent control strategies in smart grid applications while also improving the operational reliability of DC microgrids.

关键词： Heuristic algorithms Microgrids Power system stability Whale optimization algorithms Stability analysis real-time systems time factors Adaptive control Voltage control Thermal stability

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Sequential Posterior Sampling with Diffusion Models

Sequential Posterior Sampling with Diffusion Models

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

作者： Tristan S.W. Stevens Oisín Nolan Jean-Luc Robert Ruud J.G. Van Sloun Dept. of Electrical Engineering Eindhoven University of Technology The Netherlands Philips Research North America Cambridge MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have quickly risen in popularity for their ability to model complex distributions and perform effective posterior sampling. Unfortunately, the iterative nature of these generative models makes them computationally expensive and unsuitable for real-time sequential inverse problems such as ultrasound imaging. Considering the strong temporal structure across sequences of frames, we propose a novel approach that models the transition dynamics to improve the efficiency of sequential diffusion posterior sampling in conditional image synthesis. Through modeling sequence data using a video vision transformer (ViViT) transition model based on previous diffusion outputs, we can initialize the reverse diffusion trajectory at a lower noise scale, greatly reducing the number of iterations required for convergence. We demonstrate the effectiveness of our approach on a real-world dataset of high frame rate cardiac ultrasound images and show that it achieves the same performance as a full diffusion trajectory while accelerating inference 25×, enabling real-time posterior sampling. Furthermore, we show that the addition of a transition model improves the PSNR up to 8% in cases with severe motion. Our method opens up new possibilities for real-time applications of diffusion models in imaging and other domains requiring real-time inference.

关键词： Computer vision Ultrasonic imaging Computational modeling Imaging Streaming media Diffusion models Transformers real-time systems Data models Trajectory

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Improved Multi-Output AI for Indoor Localization Application Based on real Data Measurements

Improved Multi-Output AI for Indoor Localization Application...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Chaima Ben Abdallah Rihab Souissi Salwa Sahnoun Ahmad Fakhfakh Faouzi Derbel Laboratory of Signals Systems Artificial Intelligence and Networks (SM@RTS) Digital Research Center of Sfax (CRNS) Sfax University Tunisia Smart Diagnostic and Online Monitoring Leipzig University of Applied Sciences Leipzig Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Indoor localization applications are increasingly used for tracking targets, particularly mobile ones, but tracking in non-line-of-sight (NLOS) conditions presents significant challenges, especially when relying on Received Signal Strength Indicator (RSSI) measurements. The variability of RSSI values is notably higher in NLOS environments. This study investigates multi-output AI system integration for real indoor localization implementation, focusing on optimizing model performance during the offline phase and assessing their real-world applicability. The Artificial Neural Network (ANN) and Random Forest (RF) models were tested in different environments, with results demonstrating their effectiveness in specific settings. The study area covers 193.25 m 2 , using a communication protocol that adjusts transmission power to track target mobility dynamically. Two phases of optimization were conducted: offline and online. Following optimization, RF was applied for tracking on the first floor, and ANN was selected for the second floor. On the second floor, ANN achieved an RMSE of 2.06 m, while RF outperformed on the first floor with an RMSE of 1.94 m. The offline phase played a crucial role in refining model accuracy, and once transitioned to real-time implementation, the optimized models demonstrated robust and efficient localization performance. These findings emphasize the importance of combining offline optimization with real-environment implementation to develop accurate and reliable indoor positioning systems.

关键词： Location awareness Radio frequency Wireless sensor networks Accuracy Target tracking Artificial neural networks Receivers Received signal strength indicator Floors Optimization

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