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International Middle-East Power System Conference, MEPCON

作者： Asmaa Mohamed El-Saieed Hanan A. Mosallam Dept. of Computers and Systems Mansoura High Institute of Engineering and Technology Mansoura Egypt Department of Electrical Engineering Mansoura Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Accurate forecasting of renewable energy output is vital for enhancing the efficiency and stability of integrated power systems. This study introduces a novel deep learning approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to predict energy production from solar, wind and hydropower sources. The hybrid CNN-LSTM model utilizes historical meteorological data, including solar irradiance, cloud cover, temperature, wind speed, and precipitation, to forecast future energy generation with high precision. The CNN component extracts spatial features from the weather data, while the LSTM component captures temporal dependencies and long-term trends. For solar energy forecasting, the model estimates solar radiation based on factors like temperature and humidity, while wind energy predictions rely on wind speed variations, and hydropower output is forecasted using rainfall and watershed information. This comprehensive approach enables accurate and reliable predictions of renewable energy production. The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R 2 ), etc. Results demonstrate that the hybrid model significantly improves the accuracy and reliability of renewable energy forecasts. This advanced tool provides valuable insights for energy managers, utility companies, and policymakers, aiding in optimizing resource allocation and enhancing the sustainability and resilience of energy grids.

关键词： Accuracy Hydroelectric power generation Predictive models Power system stability Hybrid power systems Data models Power system reliability Convolutional neural networks Wind forecasting Forecasting

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Optimized Rewinding of Oversized Induction Motors

Optimized Rewinding of Oversized Induction Motors

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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Fernando J. T. E. Ferreira Dept. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

When an oversized and/or faulty three-phase squirrel-cage induction motor (SCIM) needs to be rewound, there is an opportunity to redesign its stator winding as a function of its actual load cycle, aiming at improving its efficiency, power factor, and reliability. This paper proposes a systematic methodology to optimize the stator winding of in-operation, line-operated, fixed-speed SCIMs, with two goals, namely, (i) optimization of the fundamental component of magnetizing flux to the actual load cycle, reducing the original core saturation level and nominal power of the motor, and (ii) optimization of the pitch and turns of the coils to minimize the winding copper losses and the airgap magnetomotive force harmonic content. Hybrid computing approaches using the first principles and nondeterministic methods can be used for winding optimization. The proposed methodology has been applied to three oversized 5.5 kW, 4-pole SCIMs, driving industrial ceramic paste mixers, and led to a 30-45% reduction in the line current, a 10-25% reduction in the input active power, and a 20-35% increase in the power factor, enabling an upgrade of the efficiency class from "IE0"/IE1 to IE3/IE4. The decrease of the motor line current reduces the Joule losses in the upstream cables and transformers, freeing power transmission capacity in the power supply network. The proposed strategy is a cost-effective alternative to the replacement of old oversized motors with new well-sized motors, avoiding mechanical readaptation of the fixing and coupling systems and allowing for higher efficiency and power factor levels than those of new well-sized motors. It also fosters copper recycling, the electric motor circular economy, and the local economic activity associated with the motor repair/rewinding sector.

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Application of Machine Learning Techniques for Predicting False Data Injection Attacks in Smart Grid 25

Application of Machine Learning Techniques for Predicting Fa...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Sakr, Hesham A. Eladl, Abdelfattah A. Ibrahim, Hegazi M. El-Afifi, Magda I. Elsewedy University of Technology Faculty of Engineering Technology Cairo Egypt Nile Higher Institute for Eng. and Technology Dept. of Elect. and Comm.Eng Mansoura Egypt Mansoura University Faculty of Engineering Electrical Engineering Department El-Mansoura35516 Egypt Egyptian Chinese University Faculty of Computers and Information Systems Cairo Egypt

ISBN: (纸本)9798350379648

Attacks that involve False Data Injection (FDI) provide a substantial risk to the safety and dependability of contemporary power systems. The weaknesses that are present inside the system's infrastructure are exploited by these attacks, which frequently circumvent the conventional measures that detect bad data. Because of the sophistication of these attacks, which are characterized by their ability to circumvent conventional protections and their targeted nature, it is necessary to develop novel techniques to detection and mitigation. For the purpose of identifying, preventing, and predicting FDI threats, this research studies the application of machine learning approaches. In order to improve the security posture of power systems, the strategy that has been developed intends to make use of the potential that data-driven models possess. According to preliminary findings, the approach that was developed demonstrates a high level of accuracy in identifying anomalies within the data of the system, which suggests that it has the potential to be an effective countermeasure against FDI threats. In particular, Extra Trees and XGBClassifier are quite efficient when it comes to identifying attacks that include the injection of bogus data into power systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Modular Cyber-Physical System for Smart Industry: A case study on energy load disaggregation

Modular Cyber-Physical System for Smart Industry: A case stu...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Alves, Jorge Sousa, Pedro Matos, Barbara Mendes, Jerome Souza, Francisco Matias, Tiago University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Coimbra3000-108 Portugal Radboud University Dept. Analytical Chemistry & Chemometrics AJ Nijmegen6525 Netherlands

ISBN: (纸本)9798350347074

Constant changes have marked the industry, and cyber-physical systems have offered great potential in Industry 4.0 on production processes, allowing the integration of computational intelligence and real-time processing. In this context, this paper proposes a modular hardware system for industrial applications according to a plug & play concept. The proposed system comprises a processing module (mPP) and specific expansion modules (mEXs). The mPP is responsible for administrating the network/communication. It collects data from the mEXs, extracts features, processes the features through artificial intelligence, and interacts with the production process. The mEXs comprise sensors, conditioning circuits, and an analog/digital converter. They are responsible for acquiring field signals and their transmission to the mPP. The proposed system is designed for operating with electrical protection, allowing reliable operation of the mPP and mEXs. For evaluating and demonstrating the proposed system in a real scenario, we applied it in a case study for non-intrusive energy load disaggregation. © 2023 IEEE.

关键词： Cyber Physical System

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices

Benchmarking Convolutional Neural Network Inference on Low-P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Oscar Ferraz Helder Araujo Vitor Silva Gabriel Falcao Instituto de Telecomunicações Dept. of Electrical and Computer Engineering University of Coimbra Portugal Institute of Systems and Robotics

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s.

关键词： Performance evaluation Benchmark testing Signal processing Throughput Hardware Convolutional neural networks Internet of Things

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PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture

arXiv

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arXiv 2024年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dept. of Aeronautical and Automotive Engineering LUCAS Lab Loughborough University United Kingdom

3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adjacent rows, leading to descriptor ambiguity and, consequently, compromised retrieval performance. In this work, we address the challenges of 3D LiDAR place recognition in horticultural environments, particularly focusing on inter-row ambiguity by introducing three key contributions: (i) a novel model, PointNetPGAP, which combines the outputs of two statistically-inspired aggregators into a single descriptor;(ii) a Segment-Level Consistency (SLC) model, used exclusively during training to enhance descriptor robustness;and (iii) the HORTO-3DLM dataset, comprising LiDAR sequences from orchards and strawberry fields. Experimental evaluations conducted on the HORTO-3DLM and KITTI Odometry datasets demonstrate that PointNetPGAP outperforms state-of-the-art models, including OverlapTransformer and PointNetVLAD, particularly when the SLC model is applied. These results underscore the model’s superiority, especially in horticultural environments, by significantly improving retrieval performance in segments with higher ambiguity. The dataset and the code will be made publicly available at https://***/Cybonic/*** © 2024, CC BY.

关键词： Deep learning

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory*

Movement-Based Interaction: An Approach to Visual and Audito...

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Experiment@ International Conference (exp.at)

作者： Maria Rita Nogueira Paulo Menezes Institute of Systems and Robotics University of Coimbra Coimbra Portugal Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention.

关键词： Visualization Pediatrics Aging Older adults

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Forewarning Crossing Intention of Pedestrians Using Multimodal Deep Learning Approach 7

Forewarning Crossing Intention of Pedestrians Using Multimod...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Nguyen, Chi-Tam Duong, Minh-Thien Phan, Thanh-Danh Nguyen, Van-Binh Le, My-Ha Hcmc University of Technology and Education Intelligent System Laboratory Ho Chi Minh City Viet Nam Soongsil University Dept. of Information and Telecommunication Engineering Seoul Korea Republic of Chungbuk National University Dept. of Intelligent Systems and Robotics Cheongju Korea Republic of Institute of Engineering-Technology Thu Dau Mot University Thu Dau Mot City Viet Nam Hcmc University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

Nowadays, traffic accidents not only come from public transportation but also from pedestrians. Therefore, designing a solution to protect pedestrian safety is essential. This article proposes a system for forewarning the crossing intentions of pedestrians, comprising the tasks of detection, tracking, and intention prediction. Firstly, our proposed system performs the detection and pose estimation of pedestrians using the YOLOv7-Pose model. Subsequently, the OC-SORT algorithm is employed for the tracking task. Ultimately, an efficient architecture based on the VoVNet backbone and Temporal Shift Module is provided to forewarn whether pedestrians will cross the road or not. The input of the proposed network in multimodality includes RGB image or semantic image map, keypoints, and vehicle speed. In addition, the bag-of-freebies technique is applied to enhance the model's overall performance and address the issue of biased model creation caused by imbalanced data. Comprehensive experiments performed on the JAAD benchmark dataset demonstrate that our proposed method performs better than existing benchmark classification models. © 2024 IEEE.

关键词： Mass transportation

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ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards

arXiv

引用

arXiv 2023年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal The Dept. of Aeronautical and Automotive Engineering LUCAS Lab. Loughborough University United Kingdom

Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to structural similarity across the entire field. In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness. Hence, we propose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans to global descriptors. Specifically, this work proposes a new global feature aggregation approach, which fuses multiple aggregation methods into a robust global descriptor. ORCHNet is evaluated on real-world data collected in orchards, comprising data from the summer and autumn seasons. To assess the robustness, We compare ORCHNet with state-of-the-art aggregation approaches on data from the same season and across seasons. Moreover, we additionally evaluate the proposed approach as part of a localization framework, where ORCHNet is used as a loop closure detector. The empirical results indicate that, on the place recognition task, ORCHNet outperforms the remaining approaches, and is also more robust across seasons. As for the localization, the edge cases where the path goes through the trees are solved when integrating ORCHNet as a loop detector, showing the potential applicability of the proposed approach in this task. The code will be publicly available at:https://***/Cybonic/***. © 2023, CC BY.

关键词： Orchards

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From Conventional Non-Conductive Foams to Soft Piezoresistive Pressure Sensors: A Low-Cost Approach to Large-Area Pressure-Mapping

From Conventional Non-Conductive Foams to Soft Piezoresistiv...

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IEEE SENSORS

作者： Manuel Reis Carneiro Luis Rosa Mahmoud Tavakoli Dept. of Electrical and Comp. Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal Dept. of Mechanical Engineering Soft Machines Lab Carnegie Mellon University Pittsburgh Pennsylvania United States

This article presents a low-cost, and scalable method for synthesizing large sheets of functionalized foam for soft resistive pressure sensing that is compatible with any commercially available non-conductive foam. The process is illustrated by soaking Pu (Polyurethane) foam in a carbon-based polymeric mixture resulting in a functional foam with large pressure detection range up to 500 kPa, while retaining its deformability and recoverability after cyclic compression. The foam also shows unprecedentedly fast response time of ~120 ms. The foam was integrated in a wearable respiration sensor and in a large multitouch sensing mat that allows for precise data acquisition on the position and intensity of various simultaneous touches without the occurrence of ghost touches.

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