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- 검색키워드
- 저자 : Wei-Hsi Li (검색결과 3 건)
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Li-Hsi Yang,Liuya Xu,Wei-Chih Wang,Shih-Hsu Wang 대한토목학회 2021 KSCE Journal of Civil Engineering Vol.25 No.1
Effective management for the maintenance of water dispensers dispersed throughout an academic campus is essential for ensuring the quality of drinking water. Conventionally, water dispenser maintenance is conducted approximately bimonthly or when a passive fault notice is obtained. This maintenance frequency usually results in ineffective allocation of maintenance staff and poor maintenance quality. This study proposes a new model for campus facility maintenance management that enables maintenance staff to maintain water dispensers at the optimal time and select the shortest maintenance path. The proposed model was developed using the maintenance information of the Construction Operations Building Information Exchange obtained from building information models of multiple buildings, water dispenser operation data from a water dispenser monitoring module, and an optimization algorithm developed by integrating Dijkstra’s algorithm, simulated annealing, and a genetic algorithm to identify the shortest maintenance path. The proposed model was tested on a campus in Northern Taiwan. The application results revealed that maintenance strategies could be systematically established to determine the optimal time to dispatch maintenance staff based on the lowest unit cost criterion; this approach was also used to identify the shortest maintenance path through multiple buildings.
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The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen Liang,Yu-Ting Fang,Ting-Chun Lin,Cheng-Ru Yang,Chih-Chang Chang,Hsuan-Kan Chang,Chin-Chu Ko,Tsung-Hsi Tu,Li-Yu Fay,Jau-Ching Wu,Wen-Cheng Huang,Hsiang-Wei Hu,You-Yin Chen,Chao-Hung Kuo 대한척추신경외과학회 2024 Neurospine Vol.21 No.2
Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion: Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.
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