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GRU controller-based UPQC compensator design for improving power quality in grid-integrated non-linear load system

Scientific reports 2025年第1期15卷 19677页

作者： B Srikanth Goud Ch Naga Sai Kalyan Gundala Srinivasa Rao Bhabasis Mohapatra Narsimha Reddy Kuppireddy Harish Pulluri Ch Rami Reddy Mohammad Shorfuzzaman Basem Abu Zneid Mukesh Pushkarna Department of Electrical and Electronics Engineering School of Engineering Anurag University Hyderabad 500088 India. Department of Electrical and Electronics Engineering Vasireddy Venkatadri International Technological University (VVITU) Guntur 522 508 India. Department of Electrical and Electronics Engineering CMR College of Engineering & Technology Hyderabad India. Department of Electrical Engineering ITER Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar 751030 Odisha India. bhabasismohapatra@soa.ac.in. Department of Electronics and Communication Engineering Vardhaman College of Engineering Hyderabad Telangana India. Department of Electrical and Electronics Engineering Joginpally B R Engineering College Hyderabad 500075 India. Department of Computer Science College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia. Robotics and Artificial Intelligence Engineering Department Faculty of Engineering Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan. Department of Electrical Engineering GLA University Mathura Mathura Uttar Pradesh India.

Power quality has prominently gained its importance in power systems with the advancement of technology. Voltage sags/swells, harmonics, and other disturbances are the major issues causing most of the technical and financial damages which are reducing the quality of energy supplied. To overcome these challenges design of a unified power quality conditioner (UPQC) plays a vital role in mitigating the PQ issues. In this paper, an advanced neural network base approach is developed to manage UPQC to maintain a constant power supply for the end users. DC link of UPQC is taken from PV, fuel, and battery at a specific range. The compensator DC link is linked in a smart grid with nonlinear load. On the other hand, the switching pulse was performed with the use of the Gated Recurrent Unit (GRU) controller technique. Various fault conditions are created to make a dataset that is utilized to design the GRU that analyses the load voltage and current at each second to generate a pulse for UPQC. The performance is evaluated utilizing an advanced controller under various conditions, including swell, sag, harmonics, and combined three-phase faults. The low harmonic content of voltage is 0.04%, 0.25%, and 0.98%. The suggested controller is accessible with 99.5% specificity, 99% sensitivity, and 98% accuracy. The proposed controller provides low harmonic content while operating in a highly secure, dependable, and efficient manner.

关键词： Battery Fuel GRU Harmonics reduction PV Power quality UPQC

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LoST? Appearance-invariant place recognition for opposite viewpoints using visual semantics

arXiv

引用

arXiv 2018年

作者： Garg, Sourav Suenderhauf, Niko Milford, Michael Australian Centre for Robotic Vision Robotics and Autonomous Systems School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can recognize where they are when travelling in the reverse direction along a route for the first time, without having to turn back and look. The difficulty of this problem exceeds any addressed in past appearance- and viewpoint-invariant visual place recognition (VPR) research, in part because large parts of the scene are not commonly observable from opposite directions. Consequently, as shown in this paper, the precision-recall performance of current state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders of magnitude below what would be usable in a closed-loop system. Current engineered solutions predominantly rely on panoramic camera or LIDAR sensing setups;an eminently suitable engineering solution but one that is clearly very different to how humans navigate, which also has implications for how naturally humans could interact and communicate with the navigation system. In this paper we develop a suite of novel semantic- and appearance-based techniques to enable for the first time high performance place recognition in this challenging scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of images using the convolutional feature maps from a state-of-the-art dense semantic segmentation network. Then, to verify the spatial semantic arrangement of the top matching candidates, we develop a novel approach for mining semanticallysalient keypoint correspondences. On publicly available benchmark datasets that involve both 180 degree viewpoint change and extreme appearance change, we show how meaningful recall at 100% precision can be achieved using our proposed system where existing systems often fail to ever reach 100% precision. We also

关键词： Navigation systems

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Improved fourier mellin invariant for robust rotation estimation with omni-cameras

arXiv

引用

arXiv 2018年

作者： Xu, Qingwen Chavez, Arturo Gomez Bülow, Heiko Birk, Andreas Schwertfeger, Sören School of Information Science Technology of ShanghaiTech University Jacobs University Bremen Germany Robotics Group Computer Science & Electrical Engineering Jacobs University Bremen Germany

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved to be faster, more robust and accurate than feature based methods on image registration. However, iFMI is restricted to work only when the camera moves in 2D space and has not been applied on omni-cameras images so far. In this work, we extend the iFMI method and apply a motion model to estimate an omni-camera's pose when it moves in 3D space. In the experiment section, we compare the extended iFMI method against ORB and AKAZE feature based approaches on three datasets, showing different types of environments: office, lawn and urban scenery (MPI-omni dataset). The results show that our method reduces the error of the camera pose estimation two to three times with respect to the feature registration techniques, while offering lower processing times. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

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Using simulation to improve sample-efficiency of Bayesian optimization for bipedal robots

arXiv

引用

arXiv 2018年

作者： Rai, Akshara Antonova, Rika Meier, Franziska Atkeson, Christopher G. Robotics Institute School of Computer Science Carnegie Mellon University PA United States Robotics Perception and Learning CSC KTH Royal Institute of Technology Stockholm Sweden Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States

Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically require tuning of parameters for each task scenario. In this context, Bayesian optimization (BO) has emerged as a promising approach for automatically tuning controllers. However, when performing BO on hardware for high-dimensional policies, sample-efficiency can be an issue. Here, we develop an approach that utilizes simulation to map the original parameter space into a domain-informed space. During BO, similarity between controllers is now calculated in this transformed space. Experiments on the ATRIAS robot hardware and another bipedal robot simulation show that our approach succeeds at sample-efficiently learning controllers for multiple robots. Another question arises: What if the simulation significantly differs from hardware? To answer this, we create increasingly approximate simulators and study the effect of increasing simulation-hardware mismatch on the performance of Bayesian optimization. We also compare our approach to other approaches from literature, and find it to be more reliable, especially in cases of high mismatch. Our experiments show that our approach succeeds across different controller types, bipedal robot models and simulator fidelity levels, making it applicable to a wide range of bipedal locomotion problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Controllers

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Selective Ensemble Learning based Human Action Recognition Using Fusing Visual Features

Selective Ensemble Learning based Human Action Recognition U...

引用

International Conference on Automation and Computing (ICAC)

作者： Chao Tang Rustam Stolkin Chunling Hu Huosheng Hu Xiaofeng Wang Le Zou Department of Computer Science and Technology Hefei University Hefei China Extreme Robotics Lab (ERL) School of Metallurgy and Materials University of Birmingham Birmingham UK School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (纸本)9781538648919

The selection of motion feature directly affects the recognition effect of human action recognition method. Single feature is often affected by human appearance, environment, camera settings and other factors, and its recognition effect is limited. This paper propose a novel action recognition method by using selective ensemble learning, which is a special paradigm of ensemble learning. Moreover, this paper presents a fast and efficient action description feature and a novel recognition algorithm. Robust discriminant mixed features are learnt from behavioral video frames as behavioral descriptors, The recogniton algorithm using selective ensemble learning can achieve fast classification. Experimental results show that the proposed method achieves ideal recognition results on the self-built indoor behavior data set and public data set.

关键词： Feature extraction Joints Classification algorithms Data mining Shape Three-dimensional displays

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Contract-based Trading on Parallel Computing Resources for Cellular Networks with Virtualized Base Stations

Contract-based Trading on Parallel Computing Resources for C...

引用

IEEE Vehicular technology Conference

作者： Mingjin Gao Rujing Shen Jun Li Yonghui Li Jinglin Shi Dushantha Nalin K. Jayakody Institute of Computing Technology Chinese Academy of Sciences School of Electronic and Optical Engineering Nanjing University of Science and Technology School of Electrical and Information Engineering University of Sydney School of Computer Science and Robotics National Research Tomsk Polytechnic University

ISBN: (纸本)9781538663592;9781538663585

As a promising wireless network virtualization technology, virtualized base station (BS) has been proposed to tackle the problem of low-efficient utilization of BS's computing resources, e.g., baseband processing units (BPU). In this paper, we design a novel scheme to achieve the efficient BPU allocation based on a contract-theoretic approach. To achieve this, we consider the BPUs as a kind of trading resources. We establish a monopoly market, where the infrastructure provider (InP) is the monopolist owning all the BPUs, and multiple mobile network operators (MNOs) intend to rent BPUs from the InP for processing their baseband signals. In such a market, the InP offers a set of quantity-price contract items to the MNOs based on statistical information of their types, and at the same time, the MNOs are stimulated to accept the offers for the purpose of making profit. We propose the optimal contract design to maximize the InP's profit, as well as develop an incentive mechanism to guarantee each MNO choosing a proper contract item. Numerical results validate the effectiveness of our incentive mechanism for BPU resource allocation.

关键词： Virtualized base station Cellular networks Contract theory Resource trading Incentive mechanism Cellular networks contractualism Indium phosphide benzoylphenylureas BASE STATION metal-nitride-oxide-silicon contract Baseband statistical information

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Deep neural networks with weighted averaged overnight airflow features for sleep apnea-hypopnea severity classification

arXiv

引用

arXiv 2018年

作者： Lakhan, Payongkit Ditthapron, Apiwat Banluesombatkul, Nannapas Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep Neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46%, 85.39% and 92.69% in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70%. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

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Using simulation to improve sample-efficiency of Bayesian optimization for bipedal robots

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2019年第1期20卷

作者： Akshara Rai Rika Antonova Franziska Meier Christopher G. Atkeson Robotics Institute School of Computer Science Carnegie Mellon University PA Robotics Perception and Learning CSC KTH Royal Institute of Technology Stockholm Sweden Paul G. Allen School of Computer Science & Engineering University of Washington Seattle WA

Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically require tuning of parameters for each task scenario. In this context, Bayesian optimization (BO) has emerged as a promising approach for automatically tuning controllers. However, sample-efficiency can still be an issue for high-dimensional policies on hardware. Here, we develop an approach that utilizes simulation to learn structured feature transforms that map the original parameter space into a domain-informed space. During BO, similarity between controllers is now calculated in this transformed space. Experiments on the ATRIAS robot hardware and simulation show that our approach succeeds at sample-efficiently learning controllers for multiple robots. Another question arises: What if the simulation significantly differs from hardware? To answer this, we create increasingly approximate simulators and study the effect of increasing simulation-hardware mismatch on the performance of Bayesian optimization. We also compare our approach to other approaches from literature, and find it to be more reliable, especially in cases of high mismatch. Our experiments show that our approach succeeds across different controller types, bipedal robot models and simulator fidelity levels, making it applicable to a wide range of bipedal locomotion problems.

关键词： Bayesian optimization bipedal locomotion transfer learning

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Performance study of strongly coupled magnetic resonance

arXiv

引用

arXiv 2019年

作者： Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Sharma, Abhishek Wang, Hwang-Cheng Muthuchidambaranathan, P. Kaur, Kuljeet School of Postgraduate Studies and Research SRI LANKA Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Dept. of Electronics and Communication Engineering LNM Institute of Information and Technology Jaipur India Department of Electronic Engineering National Ilan University Ilan Taiwan Dept. of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India Ecole de Technologie Superieure Universite du Quebec Montreal Canada

Strongly Coupled Magnetic Resonance (SCMR) uses electromagnetic resonance in order to efficiently transfer power wirelessly over mid-range distances. Since the energy exchange capability of resonant objects higher than non-resonant objects, strongly coupled systems are able to achieve more efficient energy transfer than other wireless power transfer systems. The paper presents detailed experimental and simulated analysis of the performance of the SCMR system. A prototype of the SCMR system was implemented and experiments were conducted to analyze the performance of the system. Finally, the resonant frequency of the system was experimentally verified and the factors influencing the wireless power transfer were also studied. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy transfer

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