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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wen, Yang Chen, Leiting Qiao, Lifeng Deng, Yu Chen, Haisheng Zhang, Tian Zhou, Chuan Key Laboratory of Digital Media Technology of Sichuan Province University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Medicine University of Electronic Science and Technology of China China Institute of Electronic and Information Engineering in Guangdong University of Electronic Science and Technology of China China Dept. Biomedical Engineering King's College London London United Kingdom

ISBN: (纸本)9781665438643

Automatic segmentation of fluorescein leakage in fundus fluorescein angiography images is important in the clinical diagnosis of advanced diabetic retinopathy. Despite the recent success of deep-learning-based models in improving medical image segmentation, segmentation of fluorescein leakage has been ignored owing to (1) a lack of publicly available data with sufficient annotations for training a segmentation network and (2) incapability of supervised models to accurately localize fluorescein leakage at different imaging angles. To address these issues, we studied the automatic segmentation of fluorescein leakage in fundus fluorescein angiography images and devised a method involving (1) a cross-modal learning framework for fluorescein leakage segmentation using both image and text data, (2) a dual attention learning module for identifying important linguistic and visual features, and (3) fluorescein-related-keyword classification for identifying meaningful textual expressions pertaining to the location and type of fluorescein leakage. We demonstrate the effectiveness of the proposed method for an in-house fundus fluorescein angiography image data set. © 2021 IEEE

关键词： Convolutional neural networks

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IoT and Machine Learning Approaches for Automation of Farm Irrigation System

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Procedia computer science 2020年 167卷 1250-1257页

作者： Anneketh Vij Singh Vijendra Abhishek Jain Shivam Bajaj Aashima Bassi Aarushi Sharma Dept. of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

In the current age of high competition and risk in markets, technological advancements are a must for better growth and sustainability. The same applies to the agriculture industry. Every farmer has high stakes on the crops, their yield and quality. Rising water issues and need for proper methodologies for farm maintenance is a hot issue that needs to be tackled at utmost propriety. An automation of irrigation systems in farms is proposed in this research. The proposed solution is based on the Internet of Things (IoT), which would be a cheaper and more precise solution to the farm needs. A Monitoring system whose main purpose is to solve the over irrigation, soil erosion and crop-specific irrigation problem will be developed to ease and efficiently manage Irrigation problems. Since it is a well-known fact that the water is a scarce resource and over wastage of such an essential resource should be minimized. The proposed solution will be developed by establishing a distributed wireless sensor network (WSN), wherein each region of the farm would be covered by various sensor modules which will be transmitting data on a common server. Machine learning (ML) algorithms will support predictions for irrigation patterns based on crops and weather scenarios. So, a sustainable approach to irrigation is provided in this paper.

关键词： IoT WSN Machine Learning Algorithms Technology Agriculture

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Active Disturbance Rejection Control for 3D Crane Systems

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Procedia computer science 2024年 242卷 976-983页

作者： Raul-Cristian Roman Radu-Emil Precup Emil M. Petriu Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 300223 Timişoara Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy – Timisoara Br. Bd. Mihai Viteazu 24 300223 Timisoara Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard Ottawa ON K1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications.

关键词： 3D crane systems ADRC data-driven control optimization

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Tool failure recognition using inconsistent data

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Procedia CIRP 2022年 107卷 1204-1209页

作者： Júlia Bergmann Klaudia Éva Zeleny József Váncza Andrea Kő EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Eötvös Loränd Research Network (ELKH) Budapest Hungary Eötvös Loränd University Doctoral School of Informatics Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Budapest Hungary Corvinus University of Budapest Budapest Hungary Budapest University of Technology and Economics Dept. of Manufacturing Science and Technology Budapest Hungary

Data is everything - at least this is one of the main messages of the ongoing industrial revolution. Manufacturing companies all over the world are expanding their digital infrastructure and knowledge on data analysis in the hope of increasing their KPIs with the help of artificial intelligence (AI). Although several well-designed data-driven solutions are available, the most crucial part, data preparation is still not fully supported. In this paper a framework is presented for processing sensor data of machining processes with variable cycle times in an unstable environment. Traditional and novel AI algorithms are tested on the data of a vulcanization process from the automotive industry, namely from tire manufacturing’s curing phase. The process in question consists of several subprocesses, and the quality of curing is mostly dependent of the status of a specific type of machine tool. Conventional methods (e.g., examining the cured product manually) are currently used for failure recognition, however the examination is only feasible after a long delay due to the extreme level of heat, which leads to unnecessary and unwanted scrap production. Therefore, a more sophisticated and complex approach is required to increase quality score. A combination of mathematical methods is proposed combining t-SNE feature representation, convolutional neural network, and linear programming optimization. The model highly relies on the tool’s continuous degradation characteristics. The threshold for the given binary classification is set by maximizing the accuracy of the detection model. The main contribution of the research is the method of inconsistent sensor data manipulation which supports a unique combination of AI models for early failure recognition.

关键词： Type your keywords here separated by semicolons artificial intelligence failure detection data preparation t-SNE deep insight linear programming

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Arduino based Prognostic Tool for Cerebral Palsy

Arduino based Prognostic Tool for Cerebral Palsy

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： M Gokul P Sivakumar Sheik S Buhari K Vignesh M Harshavardhan Aruna V Janani Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil India Dept. of Computer Science Dr. N.G.P Arts and Science College Coimbatore India Department of Chemical Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9781665489638

Cerebral Palsy (CP) is a group of disorder that affects a person's ability to move and maintain balance and posture. Since Cerebral Palsy is non progressive disease, the condition does not get better or worse as times goes on. But several symptoms can be improved and managed with adequate treatment. Through Electromyogram (EMG) signal it is possible to study the response of cerebral palsy patient to the therapeutic exercises commonly prescribed by physiotherapists. The use of this prognostic tool is to predict whether the condition of the patient is improved in future or not. MATLAB is used to calculate parameters such as Root Mean Square (RMS) and Average Rectified Value (ARV) from muscle potential of both healthy and Cerebral Palsy participants. The findings imply that abnormal subjects have higher muscular potential than normal subjects. This rationale will be used as a criterion to track the progress of cerebral palsy patients.

关键词： Pediatrics Electric potential Medical treatment Muscles Electromyography Software Root mean square

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GWO-based Modeling of an Unstable Transport System 9th

GWO-based Modeling of an Unstable Transport System

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Gale-Cazan, Cristiana-Bogdana Bojan-Dragos, Claudia-Adina Precup, Radu-Emil Roman, Raul-Cristian Petriu, Emil M Szedlak-Stinean, Alexandra-Iulia Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Technical Research Romanian Academy Timisoara Branch Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

The goal of this paper is to obtain optimal models of an unstable transport system, which is a nonlinear process represented by the two-wheeled unstable transport system. An optimization problem is defined in order to ideally minimize and practically reduce the differences of the outputs of the real-time laboratory equipment with respect to the outputs of the nonlinear model. The parameters of the nonlinear models are optimally tuned using a recent metaheuristic optimization algorithm, namely the Grey Wolf Optimizer, which solves three optimization problems. A comparison of the responses of the real-time laboratory equipment and the derived optimal nonlinear models is carried out in various simulation scenarios, which are discussed and analyzed. © 2022 The Authors. Published by Elsevier B.V.

关键词： Optimization

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Queuing Theory Model of Expected Waiting Time for Fast Diagnosis nCovid-19: A Case Study 3

Queuing Theory Model of Expected Waiting Time for Fast Diagn...

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3rd International Conference on Advanced science and Engineering, ICOASE 2020

作者： Majedkan, Naji A. Idrees, Barzan A. Ahmed, Omar M. Haji, Lailan M. Dino, Hivi Ismat Technical College of Informatics - Akre Duhok Polytechnic University Dept. of Network and Information Security Duhok Iraq College of Physical Education and Sport Sciences University of Duhok Duhok Iraq Zakho Technical Iust Duhok Polytechnic University Zakho Iraq University of Zakho Computer Science Dept. Zakho Iraq University of Zakho Dept. of Computer Science Duhok Iraq

ISBN: (纸本)9781665415798

This Queuing theory analysis has been used in hospitals and other healthcare settings;its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose from epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which they are the most critical time setting. The main interest in this study was calculating the average waiting time spent after the process to improve arrivals satisfaction using the queuing theory model. The results of this analysis indicate for each citizen checked by the healthcare team may be waiting 32.44 minutes in a queue. Also, was estimate to provide the results of system capabilities with characterization related to the expected waiting time. Finally, Medical staff at the city outlets can estimate;how many arrivals will be waiting in the line and the number of clients that will walk away each day. © 2020 IEEE.

关键词： Diagnosis

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Improving value-based e-government towards the achievement of smart government 5

Improving value-based e-government towards the achievement o...

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5th International Conference on informatics and Computing, ICIC 2020

作者： Hermanto, Agus Binti Ibrahim, Rosziati Kusnanto, Geri University of 17 Agustus 1945 Universiti Tun Hussein Onn Malaysia Dept. of Informatics Engineering Surabaya Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia University of 17 Agustus 1945 Dept. of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9781728180472

The development of information technology in government so far is still experiencing problems with the lack of achievement of public value from e-government development. Therefore, this study aims to develop new perspectives at the macro level rather than the micro-level which has been widely used. The research method in this paper uses a value chain approach and digital transformation stages by strengthening technology and services that focus on the level of efficiency of government operational services which is increased from the micro level to the macro level and develops a reference model for increasing the value of services to achieve sustainable development goals. National. The result is the availability of models and frameworks that can be applied in e-government to build value-based e-government. © 2020 IEEE.

关键词： Smart city

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BiaPy: a ready-to-use library for Bioimage Analysis Pipelines

BiaPy: a ready-to-use library for Bioimage Analysis Pipeline...

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IEEE International Symposium on biomedical Imaging

作者： Daniel Franco-Barranco Jesús A. Andrés-San Román Pedro Gómez-Gálvez Luis M. Escudero Arrate Muñoz-Barrutia Ignacio Arganda-Carreras Dept. of Computer Science and Artificial Intelligence University of the Basque Country (UPV/EHU) Donostia International Physics Center (DIPC) Instituto de Biomedicina de Sevilla (IBiS) Hospital Universitario Virgen del Rocío/CSIC/ Universidad de Sevilla and Dept. de Biología Celular Facultad de Biología Universidad de Sevilla Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED) MRC Laboratory of Molecular Biology Department of Physiology Development and Neuroscience University of Cambridge Dept. de Bioingenieria Universidad Carlos III de Madrid Ikerbasque Basque Foundation for Science

In recent years, technological advances in microscopy have made available large amounts of data to biomedical researchers in the form of images. By learning from such large datasets, deep learning-based methods have successfully addressed previously inaccessible bioimage analysis tasks. However, most available solutions target a particular subset of problems, forcing users to be familiarized with different applications to complete their data analysis. On top of that, other issues, such as reproducibility, lack of documentation, or access to the code, arise. For these reasons, we introduce BiaPy, an open-source ready-to-use all-in-one library that provides deep-learning workflows for a large variety of bioimage analysis tasks, including 2D and 3D semantic and instance segmentation, object detection, super-resolution, denoising, self-supervised learning, and classification. All code and documentation are publicly available at https://***/danifranco/BiaPy.

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Correlation Network Analysis of ASEAN Foreign Direct Investment

Correlation Network Analysis of ASEAN Foreign Direct Investm...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Ivan Yeo Mohd Aftar Abu Bakar Sukhminder Kaur Fatimah Abdul Razak School of Mathematical Sciences College of Computing Informatics & Mathematics Universiti Teknologi MARA Shah Alam Malaysia Dept. of Mathematical & Acturial Sciences Lee Kong Chian Faculty of Eng. & Science Universiti Tunku Abdul Rahman Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Innovation & Technology School of Computer Science Taylor's University Subang Jaya Malaysia

Although ASEAN aims to promote regional economic and security cooperation, among others, the empirical evidence in this study evinces that the impact of ASEAN on the economic integration between ASEAN countries remains limited. The economic integration is investigated based on the association of foreign direct investments (FDI) between Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. The longest common period of the FDI series is 29 years beginning in 1993. We examine the yearly FDI performances using a time series plot and descriptive statistics including the coefficient of variance for performance consistency. The association, strength, and effect of ASEAN economic integration are measured and illustrated by using Pearson's correlation matrix-circle plot. There are 45 unique associations representing the economic integration between ASEAN countries that unfortunately exist only in moderate strength, at most. The highest association of 0.62 is between Cambodia and Laos, and there is negligible association between Philippines-Vietnam and Indonesia-Philippines. It is also noted that almost 60% of the associations are positively correlated to one another. For the second objective, the correlations are clustered and visualized using network plots. The correlation- based network plot reveals two subgroups linking Indonesia- Cambodia-Singapore-Laos and Malaysian-Vietnam-Brunei closer together, while Thailand, Myanmar, and the Philippines are isolated from the subgroups. The most long-lasting interconnections between economies are found in the subgroup centered on Cambodia. In addition, there are only 14 weak- medium integrated links when clustering the correlation values above magnitude 0.3. The results suggest that the vast economic cooperation opportunities have not been fully explored among the ASEAN countries. Therefore, strategies may be necessary, such as adding packages and quotas to attract FDI from individual i

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