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检索条件"机构=Computer Engineering and Systems"

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Fault-Ride-Through (FRT) Control of a grid-connected Fixed-Speed Wind Energy Conversion System using STATCOM 50

Fault-Ride-Through (FRT) Control of a grid-connected Fixed-S...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Olajube, Ayobami Vedula, Satish Omiloli, Koto Anubi, Olugbenga Moses Department of Electrical and Computer Engineering FAMU-FSU College of Engineering Center for Advanced Power Systems Florida State University United States

ISBN: (纸本)9781665464543

Wind energy conversion system (WECS) is stochastic in nature and has low inertia leading to grid voltage instability, poor reactive power compensation and most importantly fault susceptibility. Variable speed WECS such as the doubly-fed induction generators (DFIG) are well known to reach steady state quickly after fault occurrence without the need for an external reactive power source because of the presence of a back-to-back converter that provides independent control of the active and reactive power unlike in the fixed-speed squirrel cage induction generator (SCIG) counterpart that can’t be stabilized unless an external source of reactive power support is present. However, controlling DFIG is complicated and costly due to complete tripping unlike the fixed-speed generators which doesn’t trip completely when fault occurs. Hence, in this work, a 48-pulse, 3-phase static synchronous compensator (STATCOM) is used to ensure reactive power compensation and fault-ride through (FRT) control of the SCIG against over-voltage emanating from fault occurrence in a grid-connected power system. The goal here is to guarantee voltage stability and fault-ride-through (FRT) control against injected faults within certain time ranges at the point of common coupling (PCC) between the AC source, the load, and the fixed-speed WECS. The numerical simulation shows the fault-ride-through capability of the STATCOM-controlled fixed-speed WECS, good voltage regulation and effective reactive power compensation. © 2024 IEEE.

关键词： Wind power

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Extracting Motion Patterns of Greeting Gestures Using GNG with Spiking Neuron Model 13

Extracting Motion Patterns of Greeting Gestures Using GNG wi...

引用

Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Obo, Takenori Wibowo, Angga Wahyu Sato-Shimokawara, Eri Shibata, Hiroki Ho, Yihsin Kobayashi, Ichiro Tokyo Metropolitan University Department of Mechanical Systems Engineering Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan Takushoku University Department of Electronics and Computer Systems Tokyo Japan Takushoku University Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350373332

This study focuses on behavior patterns, which are characteristic actions common to groups or communities, to promote intercultural understanding. Understanding greeting gestures is crucial for comprehending social relationships, but these vary across cultures and regions. Recent advancements in sensing technology and artificial intelligence, such as RGB-D cameras and markerless motion capture systems, have made it easier to capture motion data in diverse formats. Therefore, it is important to extract relevant motion patterns and apply appropriate structuring for effective long-term archiving. We propose a method for extracting motion patterns using a Growing Neural Gas (GNG) algorithm with a Spiking Neuron Model. Additionally, this paper presents an experimental example of extracting a gesture pattern in Indonesian style. © 2024 IEEE.

关键词： Economic and social effects

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Challenges Towards VR Technology: VR Architecture Optimization 13

Challenges Towards VR Technology: VR Architecture Optimizati...

引用

13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Kachur, Artem Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine

ISBN: (纸本)9798350396119

This paper provides an exhaustive exploration of the challenges faced in the development and application of Virtual Reality (VR) technology. Despite VR's revolutionary potential across various sectors, its full-fledged realization is impeded by technical, financial, and ethical hurdles. Technical issues involve the development of high-quality hardware and software, seamless integration with existing infrastructures, and the demand for extensive computational resources. Financial constraints arise from high production and maintenance costs, which limit accessibility. Ethically, VR's immersive nature presents the potential for addiction, antisocial behavior, and data privacy concerns. The paper further discusses the trade-off decisions, complexity, technological limitations, hardware-software co-optimization, future-proofing, testing, and validation associated with VR development. Furthermore, a survey of resolutions to these challenges, as proposed in recent scientific literature, is presented. The article underscores the need for continued exploration, innovation, and refinement to overcome these hurdles and unlock the transformative power of VR technology. © 2023 IEEE.

关键词： Virtual reality

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A Novel Ensemble Learning Model Using CNNs and Vision Transformers for Waste Classification 8

A Novel Ensemble Learning Model Using CNNs and Vision Transf...

引用

8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Cisse, Mohamed Lamine Aderinwale, David Adeyinka Sah, Melike Cyprus International University Department of Information Systems Engineering Nicosia Cyprus Cyprus International University Department of Computer Engineering Nicosia Cyprus

ISBN: (纸本)9798331540104

Automatic waste classification is challenging due to the variety and complexity of waste types. Automatic waste classification with deep learning can enable faster and more accurate sorting for waste management. In this work, we focus on this problem and propose a novel ensemble approach by applying transfer learning on various pre-trained model combinations of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) for precise classification of waste images. We used the six-class Trashnet dataset for evaluations. Among the tested pre-trained CNN models, DenseNet121 and InceptionV3 achieved the best accuracies of 95.25 % and 93. 46 % respectively on the Trashnet dataset. For the vision transformer, ViT_b16, and ViT_b32 achieved accuracies of 95. 45 %, and 93. 47 % respectively. We call these models base models. Results analysis on base models showed that specific models learned certain features of the dataset better for different waste class types. Then, we applied two types of ensemble approaches to combine the predictions of base learners: Weighted averaging and model averaging. Weighted averaging assigns different importance to each model's predictions based on their accuracy, while model averaging combines all models' predictions equally. Furthermore, after checking the overall performance of those models on the test set, it was noticed that the model averaging ensemble approach had 97,92 % accuracy, and the weighted averaging ensemble approach had 98, 22 % accuracy. Base models performed worse than the weighted average ensemble approach and the model averaging ensemble approach showing that the ensemble approach improved the accuracy by up to 4%. Our contribution is developing a novel ensemble method based on both ViTs and CNNs for the first time on the TrashNet that demonstrates state-of-the-art performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing Multi-Agent Wide Area Damping Controller to Mitigate False Data Injection Attacks 24

Developing Multi-Agent Wide Area Damping Controller to Mitig...

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24th EEEIC International Conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Vavdareh, Masoud Babaei Ghafouri, Mohsen Ameli, Amir Concordia University Department of Information and Systems Engineering Montreal Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350355185

The effectiveness of wide-area damping controllers (WADCs) is significantly influenced by the integrity of the measurement data collected from phasor measurement units (PMUs). These damping controllers utilize PMU data to make decisions and transmit control commands to system actuators, such as synchronous generators. However, the incorporation of communication links for transmitting these signals exposes the power system to various cyber threats, such as false data injection attacks (FDIAs). On this basis, this study proposes two methods for defending against FDIAs: (i) the utilization of a neural network-based autoencoder as an effective attack detector for FDIAs, and (ii) the development of a multi-agent control strategy to mitigate these attacks. The performance of these proposed methods is analyzied using a two-area Kundur test system, compared against conventional data-driven attack detection methods and a global WADC technique. The findings indicate that the implementation of these methods significantly enhances defense capabilities against FDIA targeting wide-area measurement signals. © 2024 IEEE.

关键词： Phasor measurement units

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Deep Learning for Fault Detection of Digital VLSI Circuits 21

Deep Learning for Fault Detection of Digital VLSI Circuits

引用

21st International Learning and Technology Conference, L and T 2024

作者： Gaber, Lamya Hussein, Aziza I. Minia University Computers and Systems Engineering Department Minia Egypt Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

With the increasing complexity and scale of digital VLSI designs, ensuring reliability in IC design necessitates effective fault detection processes during the pre-silicon stage. Many fault detection algorithms lead to significant computational time due to the problem of search space explosion. To handle the ever-growing volume of data, deep learning algorithms, a subset of machine learning techniques, can be employed. In this paper, we propose two novel approaches for fault detection (FD) of digital VLSI circuits, specifically targeting stuck-at faults. The first proposed model is semi-supervised FD model that aims to mitigate the search space explosion issue by leveraging both unsupervised and supervised learning processes. The second presented model is based on an optimizer for finding the appropriate configurations for detecting stuck-at faults in digital circuits. The initial proposed model achieves maximum validation accuracy of approximately 98% applied to circuits from ISCAS'85. This model yields a higher accuracy compared to the second approach that achieves maximum accuracy of around 95% when applied to the same circuits from the ISCAS'85 benchmark. © 2024 IEEE.

关键词： Fault detection

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Diabetic Retinopathy Detection Using PCA-Firefly-DNN Based XAI Framework 12th

Diabetic Retinopathy Detection Using PCA-Firefly-DNN Based X...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhattacharya, Sweta Uppamma, Posham School of Computer Science Engineering and Information Systems VIT Vellore Tamil Nadu Vellore63201 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9789819789450

Millions of individuals worldwide are impacted by diabetic retinopathy, which is a leading cause of blindness and vision loss. The early detection of DR is extremely important and machine learning approaches have shown promising results in this regard. The present study thus uses a DR dataset acquired from the publicly available UCI machine learning repository. The dataset is normalized using the StandardScalar technique and then feature extraction is performed using Principal Component Analysis to extract the most significant features. Further, firefly algorithm is implemented for dimensionality reduction. This dataset is fed into a DNN framework which generates superior results in comparison to the contemporary ML models. To make the findings of the model usable for clinical decision-making explainable AI approach is used to ensure transparency and interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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STABILITY AND INTERACTION ANALYSIS OF GRID-FORMING TYPE IV WIND TURBINES 23

STABILITY AND INTERACTION ANALYSIS OF GRID-FORMING TYPE IV W...

引用

23rd Wind and Solar Integration Workshop, WIW 2024

作者： Udawatte, Harith E. Ravanji, Mohammad H. Bahrani, Behrooz Department of Electrical & Computer Systems Engineering Monash University Melbourne Australia Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781837242689

The global shift to renewable energy, especially wind power, is critical for achieving carbon-neutral power systems but poses challenges to grid stability due to reduced system inertia from inverter-based resources. Grid-forming (GFM) control modes are emerging as key solutions to these stability issues. Given the vast potential of wind energy, ensuring that wind turbine generators (WTGs) comply with GFM control standards is essential. This paper explores GFM capabilities in Type IV WTGs, focusing on the interactions between grid-side and machine-side control dynamics. A simplified small-signal model is developed to efficiently analyse the direct coupling between active power and DC-link control, highlighting how GFM parameters like droop and inertia, along with the DC controller bandwidth, affect system stability. Time-domain simulations validate the proposed tuning guidelines and reveal distinct WTG response characteristics at different wind speeds during grid contingencies. These insights lay the groundwork for robust GFM control designs, essential for a stable integration of wind turbines into high-renewable power systems. © Energynautics GmbH.

关键词： Windmill

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Building a Full-text Search Index Using "Transformer" Neural Network 17

Building a Full-text Search Index Using "Transformer" Neural...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Dobrynin, Vyacheslav Abramovich, Roman Platonov, Alexey Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9798350303568

The use of deep neural networks in information retrieval significantly improves its effectiveness, but negatively affects the performance of the process. To deal with this, we propose a new ranking model that uses the deep neural network of the "Transformer"architecture (in particular, BERT) for efficient information retrieval. In accordance with the proposed approach, contextualized vector representations are extracted from documents during indexing, after which these representations are clustered for each independent token. The resulting clusters reflect different meanings of the words and are indirectly used as inverted index keys. The values represent the documents in which these contextualized word meanings occur, along with the distances from each document to the contextualized embedding. Thus, after the indexing process, we obtain an index containing pre-calculated distances between the contextualized meanings of dictionary elements and documents. This approach helps us avoid the performance overhead of calculating distances online. At the search stage, the query is transformed into a set of contextualized vectors representing each query token, which allows us to use these vectors to retrieve most semantically close neighbor-tokens and use them to extract relevant documents from the index. This way of searching for contextualized embeddings consumes less memory and is more performant due to the use of an inverted index. © 2023 IEEE.

关键词： Search engines

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Towards a Software Factory for Developing Recommender systems and Decision Support systems for Smart Tourism

Towards a Software Factory for Developing Recommender System...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Souha, Adnane Naimi, Lahbib Ouaddi, Charaf Benaddi, Lamya Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Techniques Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of smart systems is fundamental to improve the competitiveness of tourist destinations, by exploiting technologies such as Recommender systems (RS) and Decision Support systems (DSS). In this article, we propose an approach based on Model-Driven engineering (MDE) for the creation of a software factory dedicated to the design of RS and DSS for tourism destination managers. This software factory is based on two main assets: a Domain-Specific Language (DSL) for recommendation systems and another DSL for decision support systems. The main aim of this approach is to reduce the complexity and development time of this type of application. © 2024 IEEE.

关键词： Digital subscriber lines

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