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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis

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IEEE Transactions on Neural Networks and Learning systems 2024年第8期35卷 10736-10750页

作者： Lin, Chia-Hsiang Liu, Yangrui Chi, Chong-Yung Hsu, Chih-Chung Ren, Hsuan Quek, Tony Q. S. National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Cheng Kung University Department of Statistics Tainan70101 Taiwan National Central University Center for Space and Remote Sensing Research Taoyuan320317 Taiwan Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor. © 2012 IEEE.

关键词： Convex optimization

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The Asymptotics of Difference systems of Sets for Synchronization and Phase Detection

The Asymptotics of Difference Systems of Sets for Synchroniz...

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IEEE International Symposium on information Theory

作者： Yu Tsunoda Yuichiro Fujiwara Faculty of Engineering Information and Systems University of Tsukuba Division of Mathematics and Informatics Chiba University

We settle the problem of determining the asymptotic behavior of the parameters of optimal difference systems of sets, or DSSes for short, which were originally introduced for computationally efficient frame synchronization under the presence of additive noise. We prove that the lowest achievable redundancy of a DSS asymptotically attains Levenshtein's lower bound for any alphabet size and relative index, answering the question of Levenshtein posed in 1971. Our proof is probabilistic and gives a linear-time randomized algorithm for constructing asymptotically optimal DSSes with high probability for any alphabet size and information rate. This provides efficient self-synchronizing codes with strong noise resilience. We also point out an application of DSSes to phase detection.

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Trajectory Tracking for Wheeled Mobile Robot Using Lyapunov-Based Model Predictive Control

Trajectory Tracking for Wheeled Mobile Robot Using Lyapunov-...

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Chinese Automation Congress (CAC)

作者： Chen Zhang Zehua Jia Huahuan Wang Yuan Lin Weidong Zhang School of Information and Communication Engineering Hainan University Haikou China Ministry of Education Engineering Research Center of Marine Intelligent Systems China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper investigates the trajectory tracking control problem for wheeled mobile robots (WMRs) and proposes a novel Lyapunov-based model predictive control (LMPC) strategy. By incorporating the Lyapunov direct method into the model predictive control (MPC) framework, optimal control signals are obtained to achieve accurate trajectory tracking. The proposed method utilizes the Lyapunov direct method to design the control laws, and then incorporate the control laws into the optimization problem to construct the contractive constraint. Based on the contractive constraint, the stability of the controlled system is ensured. Besides, the recursive feasibility of the LMPC problem is analyzed and guaranteed by ensuring that the auxiliary control laws satisfy the input limitations. Finally, simulations and real-time experiments are conducted to verify the applicability and effectiveness of the proposed method.

关键词： Trajectory tracking Optimal control Prediction algorithms Stability analysis Real-time systems Trajectory Mobile robots Optimization Predictive control Lyapunov methods

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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Flexible Automotive Power Management: Formalization of an Auction-based Approach

Flexible Automotive Power Management: Formalization of an Au...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Tobias Schürmann Max Grobbel Sören Hohmann Embedded Systems and Sensors Engineering FZI Research Center for Information Technology Karlsruhe Germany Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

The electrification of former mechanical components and the expansion of infotainment systems lead to an increasing automotive power network (APN) in terms of components and power consumption. Additionally, customers desire a flexible hardware composition. As a consequence, automotive power management has to ensure power balancing for volatile generation and demand and to support the integration of new hardware during development, vehicle configuration, and even after production. In this paper, we propose an adapted management architecture, formalize an auction framework for automotive power management of APNs with multiple voltage levels, and show respective simulation results.

关键词： HVAC Power demand Power system management Simulation Voltage Propulsion Hardware Temperature control Resource management Automotive engineering

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Development of Cloud Movement Prediction Method for Solar Photovoltaic System

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Journal of Harbin Institute of Technology(New Series) 2022年第1期29卷 64-69页

作者： Fei Lu Siaw Yaw Yoong Sia Mallikarachchi Dilshani Center for Advanced Electrical and Electronic Systems(CAEES) Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityKota DamansaraPetaling Jaya 47810Malaysia

Variability of power generation due to the prevalence of cloud cover over solar photovoltaics(PV)power plants is a challenge faced by grid operators and independent system operators(ISOs)in the integration of solar energy into the *** forecasts generated through ground⁃based sky imaging systems are useful for short⁃term cloud motion ***,the cost of sky imaging systems currently available in industries is relatively ***,a ground⁃based camera system utilizing a simple webcam is proposed in this *** proposed method can produce predictions with high levels of *** were generated through video analysis using MATLAB for the computation of cloud motion *** image processing involved in the implementation of the proposed system is based on the detection of cloud regions in the form of a cluster of white pixels within individual frames and tracking their motion through comparison of subsequent *** study describes the techniques and processes used in the development of the proposed method,along with the evaluation of performance through analysis of the *** predictions were carried out over multiple time *** time horizons selected include 5,10,15,20,25,and 30 *** overall results computed showed promising accuracy levels above 94.60%,which makes it adequate for generating reliable forecasts.

关键词： forecast solar photovoltaic cloud cover MATLAB

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systems engineering Approach for Compliant Over-the-Air Update Development

Systems Engineering Approach for Compliant Over-the-Air Upda...

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Recent Advances in systems Science and engineering (RASSE), IEEE International Conference on

作者： Jacqueline Henle Stefan Otten Eric Sax Embedded Systems and Sensors Engineering (ESS) FZI Research Center for Information Technology Berlin Germany Embedded Systems and Sensors Engineering (ESS) FZI Research Center for Information Technology Karlsruhe Germany Institute for Information Processing Technologies (ITIV) Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798331528263

ISBN: (纸本)9798331528270

A broad spectrum of technical innovations in-fluences the automotive industry. Vehicles are increasingly governed by software, enabling continuous development over the entire product life cycle. In this context, software updates, especially those conducted Over The Air (OTA), represent a significant technological advancement. Regularly implementing OTA updates enhances the sustainability of vehicles as they lead to the possibility of keeping cars continuously functional, safe, secure, and performant. Requirements for OTA updates, their development, and implementation are formulated in industry standards and regulations. With the entry into force of UN Regulation 156, manufacturers operating in the member countries must establish processes for the development and overall management of OTA updates. At the same time, there are numerous other standards that apply in the industry. Furthermore, development process models and standardized approaches, such as the V-model for systems and software engineering, are widely adopted. Following the aim to develop a framework that focuses on secure and efficient OTA updates while adhering to legal and industry-specific requirements, a unified process model is presented in this paper. Requirements from the ISO 24089 standard and the UN Regulation 156 are extracted for that purpose. Interrelationships and overlaps between them, and further applicable standards for vehicle design, system, and software development are identified. Processes and work products not yet covered by other norms and legal guidelines are extracted. These activities are then linked content-wise and organizationally to the established V-model. The resulting integrated concept provides a basis for compliant and efficient software update management.

关键词： Industries Technological innovation Law Wireless networks Software Regulation Modeling Sustainable development Standards Software engineering

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Complex Emotion Estimation Using Analysis-by-Synthesis of Facial Expression Images

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IEEE Access 2025年 13卷 88731-88746页

作者： Shwe Sin Khine, Win Siritanawan, Prarinya Kotani, Kazunori Japan Advanced Institute of Science and Technology School of Information Science Nomi923-1211 Japan Informatics and Interdisciplinary Systems Division Shinshu University Graduate School of Science and Technology Department of Engineering Nagano380-8553 Japan Kanazawa University Faculty of Transdisciplinary Science Institute of Philosophy in Interdisciplinary Science Ishikawa Kanazawa920-1192 Japan

This research proposes an approach for recognizing facial expressions for complex emotion estimation through the utilization of generated facial images. The proposed approach consists of two main parts: facial image generation and facial expression recognition. In the first part, we introduce Conditioned Emotion Generative Adversarial Networks (cEmoGANs) to synthesize images that convey complex facial expressions. Unlike previous methods, our generative model maintains face identity information and expresses various types of complex emotions. This capability encourages the generator to have control over the image generation process, resulting in enhanced image quality, reduced distortion, and increased diversity of generated images. The second part involves the design of a multiple-label classification based on a convolutional neural network trained on the complex facial expression images generated from the first part, which is employed in the recognition of complex facial expressions for emotion estimation. Our model, using images generated by cEmoGANs, demonstrates a notable performance surpassing the capabilities of the previous models in comparative evaluations. © 2013 IEEE.

关键词： Convolutional neural networks

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