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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Attila Kovari Jozsef Katona Ilona Heldal Carsten Helgesen Cristina Costescu Adrian Rosan Andrea Hathazi Serge Thill Robert Demeter CogInfoCom Based LearnAbility Research Team Institute of Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Special Education Department Faculty of Psychology and Educational Sciences Babes-Bolyai University Cluj-Napoca Romania Donders Centre for Cognition Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherland Department of Automation and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

The continuous visual tracking of activity performed in perception and action activities requires continuous eye motion. The visual computer monitoring, recording and evaluation of human motion provides many opportunities in the development of human-computer interfaces. In most activities performed by humans, eye-hand coordination plays a significant role. From the eye-hand coordination aspect, the fixation's direction is an essential factor, since before touching an object, the look is aimed at the object to be touched for a longer period. In this paper, the fixation playing a major role in eye-hand coordination is being analyzed during the Trail Making Test. The test primarily aims to measure visual searching or scanning, visual-spatial sequencing; however, the eye-hand coordination's speed plays a key role in the quick and accurate execution of the test. During the test, eye motion parameters were recorded and determined, where further analyses make certain human cognitive factors indirectly able to be analyzed.

关键词： Tracking Task analysis Conferences Visualization Software Testing Cognition

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Analysis of the Geometrical Features of Photospheric Bright Points in the Quiet-Sun Region

Analysis of the Geometrical Features of Photospheric Bright ...

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第三十二届中国控制会议

作者： Lan Yu Linhua Deng Song Feng Department of Information Engineering Yunnan Land and Resources Vocation College Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of Education National Astronomical Observatories/ Yunnan Astronomical Observatory Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (纸本)9781479900305

The Solar Granulation is an ubiquitous structure in the quiet *** brighter features called "Photospheric Bright Points(PBPs)" are found within inter-granules in G-band images *** are the small-scale feature on the solar *** their characteristics may reveal the significant problems such as coronal hearting,local dynamo theory and the relation of magnetic *** the presented paper,we utilized Laplacian and morphological dilation(LMD) technique to detect and extract PBPs within inter-granular lanes.A data set has been taken with the Dutch Open Telescope(DOT) on July 2, *** was employed to analyze the geometrical feature of *** statistical distributions of PBPs were obtained by the proposed *** diameter distribution of PBPs is consistent with lognormal *** peak diameter occurs at about *** histogram of geometrical features is derived from the PBPs *** distribution of eccentricity indicates that approximately 50%PBPs have a near circular,the average eccentricity occurs at *** distribution accords with an Exponential fit.

关键词： Photospheric Bright Points Laplacian morphological dilation eccentricity Sun

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Synergy Analysis of Industrial, Research and University Training Centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing Center (SMC) at automation Robotics and Intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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Optimizing Neural Network Image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： Image classification

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A quantitative study of using Cisco Packet Tracer simulation software to improve IT students’ creativity and outcomes

A quantitative study of using Cisco Packet Tracer simulation...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Robert Demeter Attila Kovari Jozsef Katona Ilona Heldal Cristina Costescu Adrian Rosan Andrea Hathazi Serge Thill Department of Automation and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania CogInfoCom Based LearnAbility Research Team Institute of Engineering Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Special Education Department Babes-Bolyai University Cluj-Napoca Romania Donders Centre for Cognition Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherland

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

The continued development of ICT technologies has led to new ways of information communication. As is well known, the success of any new training or communication technology incorporates disseminating information, timeliness and efficiency and often incorporates simulation and virtual reality technologies. This paper presents the results of a quantitative study showing that simulation-based training strategies are more effective than traditional methods. The current case is for applying didactic teaching methodologies for learning ICT concepts for complex systems. The paper also compares the results of this study with previous studies and recommends strategies for using virtual reality elements to streamline learning process of engineering students.

关键词： computer networks Software Tools Education Hardware Intelligent sensors

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Robust visual tracking using multi-frame multi-feature joint modeling

arXiv

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arXiv 2018年

作者： Zhang, Peng Yu, Shujian Xu, Jiamiao You, Xinge Jiang, Xiubao Jing, Xiao-Yuan Tao, Dacheng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan Hubei China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China School of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China UBTech Sydney Artificial Intelligence Institute and the School of Information Technologies Faculty of Engineering and Information Technologies at the University of Sydney DarlingtonNSW2008 Australia

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-Task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-The-Art trackers. MATLAB code is available from our project homepage http://***/ project/***. Copyright © 2018, The Authors. All rights reserved.

关键词： MATLAB

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment

Identifying Appropriate Probability Density Function for Win...

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Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Ghulam Abbas Arshad Ali Muhammad Zubair Zohaib Mushtaq Umar Farooq Irfan A. Khan Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan Intelligent Systems Lab & Automation Facility (ISLAF) IEECE University of the Punjab Lahore Pakistan Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston TX USA

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures-Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2 )-are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology.

关键词： Wind energy generation Electric potential Wind energy Wind speed Urban areas Sea measurements Wind power generation Probability density function Probability distribution Weibull distribution

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Efficient visual recognition with deep neural networks: A survey on recent advances and new directions

arXiv

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arXiv 2021年

作者： Wu, Yang Wang, Dingheng Lu, Xiaotong Yang, Fan Li, Guoqi Dong, Weisheng Shi, Jianbo Institute for Research Initiatives Nara Institute of Science and Technology Takayama-cho Ikoma Nara630-0192 Japan School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'An Jiaotong University Shaanxi Xi'an710049 China School of Artificial Intelligence Xidian University China Division of Information Science Nara Institute of Science and Technology Japan Department of Precision Instrumentation Center for Brain Inspired Computing Research Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Department of Computer and Information Science University of Pennsylvania United States

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNNrelated visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation

arXiv

引用

arXiv 2023年

作者： Zhang, Zhiqing Fan, Guojia Liu, Tianyong Li, Nan Liu, Yuyang Liu, Ziyu Dong, Canwei Zhou, Shoujun Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen China College Of Geosciences Northeast Petroleum University Shenzhen China Department of Computer Science City University of Hong Kong Shenzhen China Shenyang Institute of Automation University of Chinese Academy of Sciences Shenzhen China Faculty of Science and Engineering Chinese Academy of Sciences University of Nottingham Ningbo China Ningbo China

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, interclass similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy. The generality and validity of our proposed model are confirmed through extensive experiments. By enabling more precise segmentation of anatomical structures, our framewo

关键词： Anomaly detection

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OTHR multitarget tracking with a GMRF model of ionospheric parameters

arXiv

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arXiv 2020年

作者： Guo, Zhen Wang, Zengfu Lan, Hua Pan, Quan Lu, Kun School of Automation Northwestern Polytechnical University Xi'an Shaanxi710072 China Key Laboratory of Information Fusion Technology Ministry of Education Xi'an Shaanxi710072 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology DelftCD2826 Netherlands Nanjing Research Institute of Electronics Technology Nanjing Jiangsu210039 China

The ionosphere is the propagation medium for radio waves transmitted by an over-The-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Target tracking

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