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International Conference on control, automation and Systems ( ICCAS)

作者： B. Chatchapol W. Autanan W. Anan Department of Electrical Engineering Faculty of Electric Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand Robotics Engineering and AI Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Electronic Engineering and Automation Control System Faculty of Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. Image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Liquids Image processing Velocity control Production Quality control Inspection Belts Filling Beverage industry Monitoring

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R2F-UGCGAN: a regional fusion factor-based union gradient and contrast generative adversarial network for infrared and visible image fusion

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Optica Acta: International Journal of Optics 2023年第1期70卷 52-68页

作者： Yuning Xie Gang Liu Rui Xu Durga Prasad Bavirisetti Haojie Tang Mengliang Xing a School of Automation Engineering Shanghai University of Electric Power Shanghai People's Republic of China b Faculty of Information Technology and Electrical Engineering Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ABSTRACTABSTRACTTo efficiently preserve texture and target information in source images, an image fusion algorithm of Regional Fusion Factor-Based Union Gradient and Contrast Generative Adversarial Network (R2F-UGCGAN) is proposed. Firstly, an adaptive gradient diffusion (AGD) decomposition algorithm is designed to extract representative features. A pair of infrared (IR) and visible (VIS) images are decomposed by AGD to obtain low-frequency components with salient targets and high-frequency components with rich edge gradient information. Secondly, In the high-frequency components, principal component analysis (PCA) is used for fusion to obtain more detailed images with texture gradients. R2F-UGCGAN is used to fuse the low-frequency components, which can effectively ensure good consistency between the target region and the background region. Therefore, a fused image is produced, which inherits more thermal radiation information and important texture details. Finally, subjective and objective comparison experiments are performed on TNO and RoadScene datasets with state-of-the-art image fusion methods. The experimental results of R2F-UGCGAN are prominent and consistent compared to these fusion algorithms in terms of both subjective and objective evaluation.

关键词： Image fusion infrared visible generative adversarial network PCA adaptive gradient diffusion

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Cardiovascular Model Identification Using Neural ODE

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IFAC-PapersOnLine 2024年第24期58卷 374-379页

作者： Bálint Szabó Ákos Antal Ákos Szlávecz Béla Paláncz Katalin Kovács Liam Murphy James Cushway Nicolas Davey Cong Zhou J. Geoffrey Chase Balázs Benyó Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Oral Diagnostics Faculty of Dentistry Semmelweis University Budapest Hungary Budapest University of Technology and Economics Faculty of Civil Engineering Department of Geodesy and Surveying Budapest Hungary University of Canterbury Christchurch NZ Széchenyi István University Faculty of Mechanical Engineering Informatics and Electrical Engineering Department of Informatics Győr Hungary University of Liege (ULg) GIGA-in silico medicine Liege Belgium

Acute circulatory failure (ACF) is a clinical syndrome when the heart and circulatory circulation cannot provide adequate blood supply to meet metabolic needs of the organs. ACF affects 30%- 50% of intensive care unit (ICU) patients. Fluid resuscitation is the primary treatment of ACF. However, it fails in a significant proportion (about 50%) of cases due to lack of clinically feasible non-invasive perfusion markers to assess the efficacy of the fluid therapy. Unfortunately, unsuccessful fluid therapy negatively affects patient outcome, increasing ICU length of stay and costs. Recent studies show identifying Stressed Blood Volume (SBV) of the cardiovascular system can be used to assess the potential efficacy of fluid therapy. The development of the diagnostic method requires the identification of the central arterial pressure curve based on the femoral arterial pressure, which is clinically available. This central arterial pressure curve can be used to identify the cardiovascular system parameters. In this study, the main goal was to develop a parameter-identification method for the Tube-load model-based transfer function connecting the femoral and central arterial pressure curve by using the so-called Physics-informed Neural Network methodology, namely the Neural ODE method. The study presents the adaptation of the Neural ODE method to the given parameter identification problem and the validation of the developed identification method. The robustness of the developed identification method was tested and used on a series of measurement data recorded in animal experiments.

关键词： personalized model-based medical therapy methods cardiovascular system physiological system modelling parameter identification transfer function Neural ODE intensive care

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Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients

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IFAC-PapersOnLine 2023年第2期56卷 2091-2095页

作者： Bálint Szabó Ákos Szlávecz Béla Paláncz Katalin Kovács J. Geoffrey Chase Balázs István Benyó Budapest University of Technology and Economics Faculty of Electrical Engineering and Information Technology Department of Control Engineering and Information Technology Budapest Hungary Department of Informatics Széchenyi István University Győr Hungary Department of Mechanical Engineering University of Canterbury Christchurch New Zealand

Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied.

关键词： Insulin Sensitivity Tight Glycaemic control Prediction Method Artificial Intelligence Artificial Neural Network

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Online Residual Learning from Offline Experts for Pedestrian Tracking

arXiv

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arXiv 2024年

作者： Vlachos, Anastasios Tsiamis, Anastasios Karapetyan, Aren Balta, Efe C. Lygeros, John The Automatic Control Laboratory Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland THe Control and Automation Group Inspire AG Zürich8005 Switzerland The Automatic Control Laboratory Switzerland

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance. © 2024, CC BY.

关键词： Prediction models

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Xuebin Sun Jinxi Wang Xiaofei Yang School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Aerospace Laser Technology and System Shanghai Institute of Optics and Fine Mechanics Shanghai China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Quaternions Fitting Robot sensing systems Surface fitting Encoding Surface treatment

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A Generative Adversarial Network for infrared and visible image fusion using adaptive dense generator and Markovian discriminator

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Optik 2023年第1期288卷

作者： Liu, Gang Liu, Yang Tang, Lili Bavirisetti, Durga Prasad Wang, Xiao School of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Existing infrared and visible image fusion algorithms have problems with reduced saliency for infrared targets and a lack of background texture information. To address this problem, a generative adversarial network for image fusion based on an attention mechanism is proposed in this study. This network consists of a generator, a discriminator, and an adaptive decision block. The generator employs a DenseNet-based cascade approach, which substantially lowers the information loss during the convolution process. A Markovian discriminator, unlike global discriminators, is embedded in the network. It divides images into patches, causing the network to focus on local regions and distinguishing the generated images as a whole. In addition, an adaptive decision block is included in the model to build an intensity dynamic mapping mechanism. To guide the computation for a certain content loss, a weighted map is created based on the intensity information of infrared images. Unlike the other loss functions, the weighted parameters of each component of the loss function are adaptively evaluated. To demonstrate the effectiveness of the proposed method, qualitative and quantitative experiments on TNO and RoadScene datasets are carried out. The experimental findings suggest that the proposed technique retains more significant characteristics in source images and produces fused images with rich texture details. Furthermore, the system noise resistance of each approach is examined. Under the condition of different noise, the fusion results of our approach still exhibit less distortion and a higher peak signal-to-noise ratio when compared to other state-of-the-art fusion methods. © 2023 Elsevier GmbH

关键词： Generative adversarial networks

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Speed Profile Optimization for Autonomous Electric Bus in Urban Area: Based on Service Route Parameter in Thailand

Speed Profile Optimization for Autonomous Electric Bus in Ur...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Guntinan Sakulphaisan Sirirat Son-In Nitikorn Junhuathon Theerapol Muankhaw Industrial technology and innovation management Faculty of science and technology Pathumwan institute of technology Bangkok Thailand Electrical Engineering and Automation Control Systems Faculty of engineering and industrial technology Bansomdejchaopraya Rajabhat University Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathumthani Thailand

This study conducts a comprehensive analysis of optimizing the speed profile of an AE-bus, a critical step in enhancing its operational efficiency. It encompasses three distinct case studies, each targeting specific objectives. The first case emphasizes energy consumption reduction, contributing to overall resource efficiency. The second focuses on eco-conscious speed profiles within predefined travel durations, balancing environmental sustainability and timeliness. The third case prioritizes travel time minimization for streamlined efficiency. Detailed insights into speed adjustments, bus positions, and energy consumption patterns are provided for each case. The findings reveal tailored approaches for energy conservation, speed, and travel time optimization. Additionally, emissions analysis demonstrates that electric buses significantly reduce CO 2 emissions compared to conventional NGV buses, showcasing their potential for environmental benefits.

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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Computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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A Two-Stage Interleaved Bridgeless SEPIC based PFC Converter for Electric Vehicle Charging Application 9

A Two-Stage Interleaved Bridgeless SEPIC based PFC Converter...

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9th IEEE International Conference on Communications and Electronics, ICCE 2022

作者： Luckose, Vinukumar Kannan, Ramani Hasan, Khairul Nisak Md Ibrahim, Taib B SEGi University Center for Advanced Electrical and Electronic System Faculty of Engineering Built Environment and Information Technology Petaling Jaya Malaysia Universiti Teknologi Petronas Power Control and Instrumetnation Cluster Department of Electrical and Electronics Engineering Perak Seri Iskandar Malaysia

ISBN: (数字)9781665497459

ISBN: (纸本)9781665497442

Electric vehicles are become more popular nowadays due to negligible gas emissions and less dependent on fossil fuel. However, the battery charging process is the main challenge that influences the electric vehicle (EV) transportation system. The power factor correction (PFC) and dc-dc converter stages are the main phases in the EV charging system. Various electric vehicle charging technologies are developed using different power electronics converter topologies, such as cuck, Zeta, SEPIC, and buck-boost converter. SEPIC (Single Ended Primary Inductor Converter) converter topology is the most preferred topology for battery charging applications due to providing non-inverting output with buck boost operation and reduced input and output ripple current. Although the SEPIC convertor topology has many advantages still possess a lot of challenges. The main challenges are reducing the converter size, conduction loss, voltage stress and increasing the efficiency when compared with the existing recent developed Bridgeless SEPIC topologies with interleaved concept. In this proposed research, a modified interleaved bridgeless, SEPIC based converter is proposed to improve the Power Quality (PQ) for vehicle charging station. The proposed topology is able to reduce the voltage stress, power loss, increase the converter efficiency and improve the power factor due to the bridgeless with interleaved configuration compared to other topologies. The preliminary design of the of the bridgeless interleaved topology is developed with the help of MATLAB Simulink software. The various preliminary results were obtained at the input supply voltage of 70-230 V ac supply. © 2022 IEEE.

关键词： Topology

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