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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Jitkongchuen, Duangjai Pawapootanont, Pimploy Thusaranon, Panita College of Innovative Technology and Engineering Big Data Engineering Program Dhurakij Pundit University Bangkok Thailand College of Innovative Business and Accountancy Dhurakij Pundit University Bangkok Thailand

ISBN: (纸本)9798350396546

This paper suggests using the saving algorithm for vehicle routing problems to optimize the maintenance route of the transmission station, which has classified transmission station types into 4 types and taken into account the conditions of time spent on equipment maintenance within the station to get the route with the lowest overall cost, is suggested in this paper as a way to optimize the maintenance route of the transmission station. The experiment results show that the suggested path can lower costs by up to 82% and reduce the time from 112 to 53 days. © 2023 IEEE.

关键词： Maintenance

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TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus Infected Cells in Histopathological Images 8

TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus...

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作者： Sermpanichakij, Warunee Jitkongchuen, Duangjai Prasertchai, Tanatip Big Data Engineering Program Dhurakij Pundit University Bangkok Thailand Faculty of Medicine Ramathibodi Hospital Mahidol University Department of Pathology Bangkok Thailand

ISBN: (纸本)9798350396546

Advances in histopathological image segmentation have a significant role in the diagnosis and begin treatment immediately including a study of Cytomegalovirus(CMV) for the tissues. Histopathological change with confirmation by immuno-histochemical or in situ hybridization study is a gold standard for diagnosis of CMV tissue infection. This required pathologists to identify the histopathological change which is time-consuming and can be missed in tissue with a subtle change. Automatic analysis of histopathological images with Deep Learning(DL) can help pathologists to diagnose CMV tissue infection more accurately. Typical issues with histopathological images which impede automatic analysis are the extremely enormous size of histopathological images more than 1 gigapixel, the limitations of GPU memory, and a limited number of histopathology images. Additionally, whole slide histopathological images are split huge images into multiple small image patches by cropping using the sliding window technique. In this paper, we propose TransEN U-Net which derives a benefit of a hybrid CNN-Transformer base on the U-shaped architecture for boosting the performance of segmentation of histopathology. The transformer encoder not only is able to the patches but also the relative self-attention mechanism in order to share information between sequences. Experiment results of segmenting images by the two-dimensional indicate that the TransEN U-Net can productively discriminate CMV viral inclusions including achieving higher values in terms of DSC score. © 2023 IEEE.

关键词： Tissue

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Resonance Behavior of a Conducting Wire Object above a Halfspace

Resonance Behavior of a Conducting Wire Object above a Halfs...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Li, Siyuan Hargrave, Chad Lui, Hoi-Shun University of Technology Sydney Global Big Data Technologies Centre School of Electrical and Data Engineering UltimoNSW2007 Australia Commonwealth Scientific and Industrial Research Organisation Sustainable Mining Research Program PullenvaleQLD4069 Australia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

Successful extraction of the resonant modes embedded in the transient target signature is vital to resonance-based radar target recognition. For targets in free space, although these resonant modes are theoretically aspect independent, the corresponding residues are known to be aspect dependent. When the target is located above a halfspace, the interface will electromagnetically interact with the target and alter the residue. Within the context of resonance-based target recognition, the influence from an interface to the residues, to the best of our knowledge, has not been well studied. In this paper, we study this problem through a wire target. Our results show that the residues of the mode follow a similar behavior as the dielectric properties of the halfspace vary. © 2022 IEEE.

关键词： Resonance

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Resonance Behavior of a Conducting Wire Object below a Halfspace

Resonance Behavior of a Conducting Wire Object below a Halfs...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Li, Siyuan Hargrave, Chad Lui, Hoi-Shun University of Technology Sydney Ultimo Global Big Data Technologies Centre School of Electrical and Data Engineering NSW2007 Australia Commonwealth Scientific and Industrial Research Organisation Sustainable Mining Research Program PullenvaleQLD4069 Australia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

The resonance scattering phenomena of an object buried below a dielectric halfspace has been studied with potential applications such as unexploded ordnance detection and breast tumor screening. These studies adopted a theoretical approach where the resonant frequencies are numerically determined via a complex root searching procedure from the impedance matrix obtained from an integral equation formulation. The practically of retrieving these resonant modes from transient responses under different excitation aspects, though is crucial for subsurface target detection application, has not been well studied. In this paper, the resonant frequencies and the corresponding residues are extracted from the transient responses of a metallic wire object below halfspace. We found that the same set of resonant modes is extracted in most cases, but the residues vary significantly as the incident angle. © 2022 IEEE.

关键词： Transient analysis

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An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems 7

An Adaptive Whale Optimization Algorithm with Mahalanobis Di...

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7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, NCON 2022

作者： Jitkongchuen, Duangjai Sirikayon, Chaloemphon Thummano, Arit Dhurakij Pundit University Big Data Engineering Program College of Innovative Technology and Engineering Bangkok Thailand King Mongkut's Institute of Technology Ladkrabang Computational Intelligence Laboratory Faculty of Information Technology Bangkok Thailand

ISBN: (纸本)9781665495103

This paper suggests using Mahalanobis distance to regenerate a new whale position to increase the performance of the whale optimization algorithm. Learning from previous evolutionary searches allows the probability parameters to be self-adapted. The suggested approach was compared to the classical whale optimization algorithm (WOA), particle swarm optimization (PSO), and differential evolution algorithm (DE) on 11 well-known benchmark functions. The results of the experiments showed that the proposed algorithm was effective in solving optimization problems. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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Interference-Based Reliability and Capacity Analysis for IEEE 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Markov processes

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An Iterative Thresholding Method for the Minimum Compliance Problem

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Communications in Computational Physics 2023年第4期33卷 1189-1216页

作者： Luyu Cen Wei Hu Dong Wang Xiaoping Wang Department of Mathematics Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina The Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA School of Science and Engineering The Chinese University of Hong KongShenzhenGuangdong 518172China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big DataGuangdong 518172China

In this paper,we propose a simple energy decaying iterative thresholding algorithm to solve the two-phase minimum compliance *** material domain is implicitly represented by its characteristic function,and the problem is formulated into a minimization problem by the principle of minimum complementary *** prove that the energy is decreasing in each *** effective continuation schemes are proposed to avoid trapping into the local *** results on 2D isotropic linear material demonstrate the effectiveness of the proposed methods.

关键词： Minimum compliance convolution thresholding linear elasticity

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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