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检索条件"机构=Computer System R&D Laboratory"

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IB-MVS: An Iterative Algorithm for deep Multi-View Stereo based on Binary decisions 32

IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo ba...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Sormann, Christian rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Austria Sony Europe B.V. R&D Center Stuttgart Laboratory 1 Germany

We present a novel deep-learning-based method for Multi-View Stereo. Our method estimates high resolution and highly precise depth maps iteratively, by traversing the continuous space of feasible depth values at each pixel in a binary decision fashion. The decision process leverages a deep-network architecture: this computes a pixelwise binary mask that establishes whether each pixel actual depth is in front or behind its current iteration individual depth hypothesis. Moreover, in order to handle occluded regions, at each iteration the results from different source images are fused using pixelwise weights estimated by a second network. Thanks to the adopted binary decision strategy, which permits an efficient exploration of the depth space, our method can handle high resolution images without trading resolution and precision. This sets it apart from most alternative learning-based Multi-View Stereo methods, where the explicit discretization of the depth space requires the processing of large cost volumes. We compare our method with state-of-the-art Multi-View Stereo methods on the dTU, Tanks and Temples and the challenging ETH3d benchmarks and show competitive results. © 2021. The copyright of this document resides with its authors.

关键词： Pixels

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Photovoltaic Multi-Peak MPPT Method with Improved Particle Swarm Algorithm

Photovoltaic Multi-Peak MPPT Method with Improved Particle S...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Zun Ma Xian Meng Chao Xing Binjiang Hu Yihua Zhu Liang Tu System Analysis and DC Technology Research Institute Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming China Power system simulation and control technology department State Key Laboratory of HVDC EPRI CSG Guangzhou China Power system simulation and control technology department National Energy Power Grid Technology R&D Centre Guangzhou China Power system simulation and control technology department CSG Key Laboratory for Power System Simulation Guangzhou China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

An MPPT control strategy based on the combination of adaptive nonlinear mutation particle swarm optimization algorithm is proposed. The algorithm uses the tanh function to control the inertia weight, balances the ability of particles to optimize globally and locally, uses the ISrU function to control the learning factor, balances the weight relationship between individual learning and social learning, and improves the convergence speed of the algorithm. The nonlinear Cauchy mutation operation is proposed to expand the scope of optimization; Finally, the perturbation and observation method is used to track locally near the maximum power point to reduce power oscillation. The simulation validates that the photovoltaic MPPT control strategy based on the improved particle swarm algorithm can effectively get the maximum power point under shading conditions and improve the output efficiency of the PV array.

关键词： Maximum power point trackers Photovoltaic systems Electrical engineering Perturbation methods Asia Particle swarm optimization Oscillators

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The development of Simulation Mirror system and its Application in HVdC

The Development of Simulation Mirror System and its Applicat...

引用

HVdC (HVdC), International Conference on

作者： Kaijian Ou Wencong Wu Haiqing Cai State Key Laboratory of HVDC Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory for Power System Simulation National Energy Power Grid Technology R&D Centre Electric Power Research Institute China Southern Power Grid Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798350387759

ISBN: (纸本)9798350387766

The testing of HVdC new control function and training of HVdC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&dC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVdC new control function testing and HVdC simulation training cabinet are also introduced in this paper.

关键词： Training Automation HVdC transmission Power system simulation High-voltage techniques dispatching real-time systems Mirrors Personnel Testing

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BYOE: A Flywheel fit for the 21st Century 129

BYOE: A Flywheel fit for the 21st Century

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129th ASEE Annual Conference and Exposition: Excellence Through diversity, ASEE 2022

作者： Ensing, Parker Trudgen, Mark May, dominik College of Engineering R&D Small Satellite Research Laboratory University of Georgia United States School of Electrical and Computer Engineering College of Engineering University of Georgia United States Engineering Education Transformations Institute College of Engineering University of Georgia United States

(BYOE) This paper presents the design, fabrication, and implementation of a flywheel system for an advanced feedback control course. The flywheel setup can be used either in person or in remote lab capacities. Feedback control is a course taken by most electrical, mechanical, and computer engineers. In such a course, students learn to design feedback control systems, i.e. the classical PId controller, often from models developed using first-principle techniques. Since systems theory is mathematical in nature, students are often limited in what implementation and system identification is performed. The flywheel system was created to first allow students to gain a tactile understanding of classical feedback control, but also to be used in an advanced control systems course. This setup allows students to see the limitations of PId control, and to implement higher mathematical models along with gaining experience in performing system identification on an unknown setup. The flywheel setup was chosen as a laboratory testbed since it shares a multidisciplinary interest between several different branches of engineering and the underlying dynamics can be used in a wide variety of applications in aerospace, electrical, computer, and mechanical engineering. In this BYOE presentation, we present the design, fabrication, and implementation of a system along with 1) design and fabrication insights, 2) fundamental equations and learning objectives, and 3) recommendations for remote lab applications. © American Society for Engineering Education, 2022.

关键词： Adaptive control systems

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Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring

Improved Vehicle Sub-type Classification for Acoustic Traffi...

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2023 National Conference on Communications, NCC 2023

作者： Ashhad, Mohd Goenka, Umang Jagetia, Aaryan Akhtari, Parwin Ambat, Sooraj K. Samuel, Mary Jamia Hamdard Dept. of Computer Science & Engineering New Delhi India IIIT Lucknow Dept. of Information Technology Lucknow India Nit Rourkeela Dept. of Mechanical Engineering Roorkela India Defence R&d Organization Naval Physical & Oceanographic Laboratory Kochi India IIIT Lucknow Dept. of Mathematics Lucknow India

ISBN: (纸本)9781665456258

The detection and classification of vehicles on the road is a crucial task for traffic monitoring. Usually, computer Vision (CV) algorithms dominate the task of vehicle classification on the road, but CV methodologies might suffer in poor lighting conditions and require greater amounts of computational power. Additionally, there is a privacy concern with installing cameras in sensitive and secure areas. In contrast, acoustic traffic monitoring is cost-effective, and can provide greater accuracy, particularly in low lighting conditions and in places where cameras cannot be installed. In this paper, we consider the task of acoustic vehicle sub-type classification, where we classify acoustic signals into 4 classes: car, truck, bike, and no vehicle. We experimented with Mel spectrograms, MFCC and GFCC as features and performed data pre-processing to train a simple, well optimized CNN that performs well at the task. When used with MFCC as features and careful data pre-processing, our proposed methodology improves upon the established state-of-the-art baseline on the IdMT Traffic dataset with an accuracy of 98.95%. © 2023 IEEE.

关键词： Machine learning

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Clustering and Equivalent Modeling Method for direct-drive Wind Farms Based on Particle Swarm Neural Network

Clustering and Equivalent Modeling Method for Direct-Drive W...

引用

Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Liang Tu Libin Huang Haiping Guo Binjiang Hu Jingyue Zhang Hui Luo China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China National Energy Power Grid Technology R&D Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

In the realm of large-scale integration of wind farms or clusters into power grids, the fault transient characteristics of these grids have experienced significant transformations. Unfortunately, the prevalent single-machine equivalent models are inadequate in accurately depicting the fault transient behavior of wind farms or clusters. Moreover, their simulation computations are exceedingly intensive, resulting in suboptimal simulation efficiency. To address these challenges, this paper introduces a clustering and equivalent modeling approach tailored for direct-drive wind farms, leveraging the capabilities of a particle swarm neural network. Initially, a transient model for direct-drive wind turbines is formulated, and fault characteristic influence factors are established through theoretical analysis. These factors encompass the distance between the wind turbine and the point of common coupling (PCC), the implementation of dC-side current limiting measures, wind speed, and reactive power at the outlet. Following this, the Euclidean distance of the influence factor set is computed, and the initial cluster centers of wind turbines are derived using an enhanced max-min distance method. Ultimately, an equivalent clustering model is formulated by utilizing the influence factors and initial cluster centers as the training set. Simulation outcomes reveal that, in comparison to the single-machine equivalent model, this method enhances simulation efficiency by a range of ${6 2 \%}$ to ${6 7 \%}$ and reduces the steady-state error in fault current simulation by ${0 . 8 \%}$ to ${1 . 9 \%}$.

关键词： Training Computational modeling Wind speed Current measurement Wind farms data models Wind turbines Steady-state Transient analysis Particle swarm optimization

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Social Media Multi-Emotion detection

Social Media Multi-Emotion Detection

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Soha S. Ibrahiem Bahy Ibrahim El-Sayed Mohammed Mohammed Ahmed El Sayed Ahmed Khaled ElBahnasy Computality R&D System Analyst & NLP Researcher Faculty of science Menofia university Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Computer Science and Information Technology Department Jeddah international College Jeddah KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states; however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic regression (Lr), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection.

关键词： Support vector machines Technological innovation Accuracy Social networking (online) Smart cities Profitability Semantics Lexicon Feature extraction Vectors

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Simulation Study on the Influence of rocket Tail Flame of Ejection Seat on the Thermal Flow Field in Engine room 5th

Simulation Study on the Influence of Rocket Tail Flame of Ej...

引用

5th China Aviation Science and Technology Conference, 2021

作者： Jin, Lou Wu, Yibin Wu, Liang Cui, dongyue Yang, Pin Overall Technology R&D Department AVIC Aerospace Life-Support Industries Ltd Xianyang441000 China Aviation Key Laboratory of Science and Technology On Life-Support Technology Xianyang441000 China Institute of Mechanical and Electrical System AVIC The First Aircraft Institute Xi’an710000 China

ISBN: (纸本)9789811674228

This paper proposes a simulation method and technology for the effect of the ejection seat rocket pack tail flame on the heat flow field of the engine room under the multi-seat ejection condition, combines the six-degree-of-freedom motion model of the ejection seat with the CFd numerical simulation method to realize the simulation process of the thermal flow field in the engine room according to the working mechanism of the seat. The motion trajectory and attitude change law of the first ejected seat under different ejection conditions is calculated by a program written in C#, the movement of the human-seat system and the rocket at the boundary in FLUENT is specified with UdF, then the temperature and pressure change law of the second ejected seat, occupant and the surface of the cockpit during a side ejection at different ejection speeds are simulated. The simulation results show that the temperature of different characteristic points in the engine room rises first and then decreases after the rocket package is working, reaching a peak at about 60ms. The temperature at the back of the seat is the highest, and the temperature in the engine room drops faster while the pressure peak is lower when the seat is ejected at a certain speed. The analysis results can provide a theoretical basis for the design and verification of occupant thermal protection equipment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Generic and scalable multi-layered architecture for digital Twin implementation in industrial processes: Mineral Processing case study 23

Generic and scalable multi-layered architecture for Digital ...

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Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence

作者： Oussama Hasidi El Hassan Abdelwahed Aimad Qazdar Moulay Abdellah El Alaoui-Chrifi Intissar Benzakour rachida Chahid Ahmed Bendaouia François Bourzeix Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco and Analytics Lab Mohammed VI Polytechnic University Morocco Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco ESTIDMA Laboratory Ibn Zohr University Morocco R&D and Engineering center Reminex Managem Group Morocco Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco and SEIA Departement MAScIR Morocco Analytics Lab Mohammed VI Polytechnic University Morocco

ISBN: (纸本)9798400708985

digitalization is crucial for achieving product quality, cost reduction, and timely delivery in manufacturing. With the rise of Industry 4.0, digital Twin technology has gained popularity, enabling real-time monitoring, predictive maintenance, process optimization, and assumption testing. Manufacturing digital Twins utilize digital models to represent physical processes, facilitating continuous simulation, interaction, correction, and optimization for autonomous control. However, implementing digital Twins can be challenging due to diverse data requirements and multiple services. This paper proposes a scalable, service-oriented multi-layered architecture for developing digital Twins in mineral processing, which can be adapted to other manufacturing systems. The paper provides an overview of digital Twins, their data-centric workflow, and industrial applications. Additionally, a case study on implementing a digital Twin for the froth flotation process in the mining industry is presented based on the proposed architecture.

关键词： digital Twin Implementation Industry 4.0 Multi-layered Architecture Optimisation Predictive model real time control and monitoring

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Adjacent channel co-existence study between 5G Nr and Wi-Fi in the 6 GHz band for indoor scenario

Adjacent channel co-existence study between 5G NR and Wi-Fi ...

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IEEE International Symposium on Electromagnetic Compatibility - EMC Europe

作者： Alexander Pastukh Valery Tikhvinskiy Evgeny devyatkin Eugene Sinkevich Institute of Radio and Information System (IRIS) Vienna Austria International Information Technology University (IITU) Almaty Kazakhstan EMC R&D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

This study presents indoor compatibility between Wi-Fi operating in the 5925-6425 MHz band and 5G Nr operating in the 6425-7125 MHz band. The study estimates interference levels between 5G Nr and Wi-Fi equipment and presents the results as a performance degradation of the 5G Nr. The analysis considers two scenarios, the first is a rician channel between 5G Nr and Wi-Fi equipment, and the second is when there is a rayleigh channel.

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