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Determining the Fault Position in the Power System

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Determining the Fault Position in the Power System

International Conference on Smart Systems and Technologies (SST)

作者： Mario Šipoš Zvonimir Klaić Emmanuel Karlo Nyarko Krešimir Fekete Mario Primorac Damir Sljivac Croatian Armed Forces Zagreb Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Chair of Automation and Robotics Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781728197593

ISBN: (纸本)9781728197609

This paper describes the determination of the fault location in the power system. This problem is solved in two steps: by determining the line on which the fault occurred using the classification algorithm of the decision tree and determining the fault location on the line by linear regression. In the first step, the decision tree algorithm is used to classify the short circuit and to determine the faulted line based on the information about the residual voltages (voltage dips) on the buses due to the occurrence of a short circuit. In the second step, linear regression is used to find the fault location on the faulted line. The algorithm is tested using the 39-bus IEEE test system.

关键词： Decision trees Voltage fluctuations Fault location Voltage measurement Linear regression Meters Measurement uncertainty

Identification of a Class of Hybrid Dynamical Systems

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IFAC-PapersOnLine 2020年第2期53卷 875-882页

作者： Stefano Massaroli Federico Califano Angela Faragasso Mattia Risiglione Atsushi Yamashita Hajime Asama Department of Precision Engineering The University of Tokyo Faculty of Electrical Engineering Mathematics & Computer Science Robotics and Mechatronics University of Twente Department of Mechanical and Process Engineering - ETH Zürich

This paper presents a novel identification procedure for a class of hybrid dynamical systems. In particular, we consider hybrid dynamical systems which are single flowed and single jumped and whose flow and jump maps linearly depend on two sets of unknown parameters. A systematic way to determine whether the system is flowing or jumping is introduced and used to identify the unknown parameters by employing a linear recursive estimator. Simulations have been performed to prove the validity of the proposed methodology. Results proved the efficiency and accuracy of the developed identification procedure.

关键词： System Identification Hydrid Systems Linear Systems Recursive Estimation

EFaR 2023: Efficient Face Recognition Competition

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EFaR 2023: Efficient Face Recognition Competition

IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

关键词：

HIPA: Hierarchical Patch Transformer for Single Image Super Resolution

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arXiv 2022年

作者： Cai, Qing Qian, Yiming Li, Jinxing Lyu, Jun Yang, Yee-Hong Wu, Feng Zhang, David The Faculty of Information Science and Engineering Ocean University of China Shandong Qingdao266100 China The Department of Computer Science University of Manitoba WinnipegMBR3T 2N2 Canada The School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China The School of Nursing The Hong Kong Polytechnic University Hong Kong The Department of Computing Science University of Alberta EdmontonABT6G 2E9 Canada The School of Information Science and Technology University of Science and Technology of China Anhui Hefei230026 China The School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518000 China Linklogis Joint Laboratory of Computer Vision and Artificial Intelligence Guangdong Shenzhen518172 China

Transformer-based architectures start to emerge in single image super resolution (SISR) and have achieved promising performance. However, most existing vision Transformer-based SISR methods still have two shortcomings: (1) they divide images into the same number of patches with a fixed size, which may not be optimal for restoring patches with different levels of texture richness;and (2) their position encodings treat all input tokens equally and hence, neglect the dependencies among them. This paper presents a HIPA, which stands for a novel Transformer architecture that progressively recovers the high resolution image using a hierarchical patch partition. Specifically, we build a cascaded model that processes an input image in multiple stages, where we start with tokens with small patch sizes and gradually merge them to form the full resolution. Such a hierarchical patch mechanism not only explicitly enables feature aggregation at multiple resolutions but also adaptively learns patch-aware features for different image regions, e.g., using a smaller patch for areas with fine details and a larger patch for textureless regions. Meanwhile, a new attention-based position encoding scheme for Transformer is proposed to let the network focus on which tokens should be paid more attention by assigning different weights to different tokens, which is the first time to our best knowledge. Furthermore, we also propose a multi-receptive field attention module to enlarge the convolution receptive field from different branches. The experimental results on several public datasets demonstrate the superior performance of the proposed HIPA over previous methods quantitatively and qualitatively. We will share our code and models when the paper is accepted. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

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arXiv 2020年

作者： Le Gentil, Cedric Vayugundla, Mallikarjuna Giubilato, Riccardo Stürzl, Wolfgang Vidal-Calleja, Teresa Triebel, Rudolph Centre for Autonomous Systems Faculty of Engineering and IT University of Technology Sydney Australia Institute of Robotics and Mechatronics Germany Department of Computer Science Germany

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE(2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

A Survey on computer Vision based Human Analysis in the COVID-19 Era

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arXiv 2022年

作者： Eyiokur, Fevziye Irem Kantarcı, Alperen Erakın, Mustafa Ekrem Damer, Naser Ofli, Ferda Imran, Muhammad Križaj, Janez Salah, Albert Ali Waibel, Alexander Štruc, Vitomir Ekenel, Hazım Kemal Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Engineering Istanbul Technical University Istanbul Turkey Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Qatar Computing Research Institute HBKU Doha Qatar Faculty of Electrical Engineering University of Ljubljana Tržaška cesta 25 Ljubljana1000 Slovenia Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Carnegie Mellon University Pittsburgh United States

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for comp

关键词： COVID-19

An efficient plant disease detection using transfer learning approach

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Scientific reports 2025年第1期15卷 19082页

作者： Bosubabu Sambana Hillary Sunday Nnadi Mohd Anas Wajid Nwosu Ogochukwu Fidelia Claudia Camacho-Zuñiga Henry Dozie Ajuzie Edeh Michael Onyema Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupathi Andhra Pradesh India. Department of Computer and Robotics Education University of Nigeria Nsukka Nigeria. Institute for the Future of Education Tecnologico de Monterrey Monterrey Mexico. Department of Computer and Robotics Education University of Nigeria Nsukka Nigeria. fidelia.nwosu@unn.edu.ng. Department of Educational Foundations University of Nigeria Nsukka Nigeria. Department of Mathematics and Computer Science Coal City University Enugu Nigeria. Adjunct Faculty Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India.

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-of-the-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.

关键词： Disease detection Food security Plant disease Transfer learning agriculture YOLOv7 YOLOv8

Solving the Time-Optimal Control Problem for Nonlinear Non-Autonomous Linearizable Systems

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SSRN

SSRN 2023年

作者： Sklyar, Katerina V. Ignatovich, Svetlana Yu. Sklyar, Grigory M. Department of Automatics and Robotics West Pomeranian University of Technology str. 26 Kwietnia 10 Szczecin71-126 Poland Department of Applied Mathematics V.N. Karazin Kharkiv National University Svobody sqr. 4 Kharkiv61022 Ukraine Faculty of Computer Science and Information Technology West Pomeranian University of Technology str. Zolnierska 49 Szczecin71-210 Poland Institute of Mathematics University of Szczecin Wielkopolska 15 Szczecin70-451 Poland

We present conditions under which the time-optimal control problem for a nonlinear non-autonomous linearizable system can be solved by the method of successive approximations, at each step of which a power Markov moment minproblem is solved. The proposed method can be efficiently implemented by use of symbolic and numerical calculations. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

An enhanced randomly initialized convolutional neural network for columnar cactus recognition in unmanned aerial vehicle imagery

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arXiv 2021年

作者： Atitallah, Safa Ben Driss, Maha Boulila, Wadii Koubaa, Anis Atitallah, Nesrine Ghézala, Henda Ben RIADI Laboratory National School of Computer Sciences University of Manouba Tunisia IS Department College of Computer Science and Engineering Taibah University Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Saudi Arabia Faculty of Computer Studies Arab Open University Saudi Arabia CES Laboratory National Engineering School of Sfax University of Sfax Tunisia

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss. © 2021, CC BY.

关键词： Convolution