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RISS 인기검색어
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- 저자 : Chia-Hsiang Hsu (검색결과 3 건)
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Chia-Ming Chang,Shun-Hsiang Hsu,Ting-Wei Chang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.
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Yen-Hsiang Huang,Kuo-Hsuan Hsu,Jeng-Sen Tseng,Kun-Chieh Chen,Chia-Hung Hsu,Kang-Yi Su,Jeremy J. W. Chen,Huei-Wen Chen,Sung-Liang Yu,Tsung-Ying Yang,Gee-Chen Chang 대한암학회 2018 Cancer Research and Treatment Vol.50 No.4
Purpose The main objective of this study was to investigate the relationship among the clinical characteristics and the frequency of T790M mutation in advanced epidermal growth factor receptor (EGFR)mutant lung adenocarcinoma patients with acquired resistance after firstline EGFRtyrosine kinase inhibitor (TKI) treatment. Materials and Methods We enrolled EGFR-mutant stage IIIB-IV lung adenocarcinoma patients, who had progressed to prior EGFR-TKI therapy, and evaluated their rebiopsy EGFRmutation status. Results A total of 205 patients were enrolled for analysis. The overall T790M mutation rate of rebiopsy was 46.3%. The T790M mutation rates among patients with exon 19 deletion mutation, exon 21 L858R point mutation, and other mutations were 55.0%, 37.3%, and 27.3%, respectively. Baseline exon 19 deletion was associated with a significantly higher frequency of T790M mutation (adjusted odds ratio, 2.14; 95% confidence interval [CI], 1.20 to 3.83; p=0.010). In the exon 19 deletion subgroup, there was a greater prevalence of T790M mutation than other exon 19 deletion subtypes in patients with the Del E746-A750 mutation (61.6% vs. 40.6%; odds ratio, 2.35; 95% CI, 1.01 to 5.49; p=0.049). The progression- free survival (PFS) of first-line TKI treatment > 11 months was also associated with a higher T790M mutation rate (54.1% vs. 39.3%; adjusted odds ratio, 1.82; 95% CI, 1.02 to 3.25; p=0.044). Patients who underwent rebiopsy at metastatic sites had more chance to harbor T790M mutation (52.6% vs. 33.8%; adjusted odds ratio, 1.97; 95% CI, 1.06 to 3.67; p=0.032). Conclusion PFS of first-line EGFR-TKI, rebiopsy site, EGFR exon 19 deletion and its subtype Del E746- A750 mutation are associated with the frequency of T790M mutation.
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Fabrication of Resistive Random Access Memory by Atomic Force Microscope Local Anodic Oxidation
Jeff T.H. Tsai,Chia-Yun Hsu,Chia-Hsiang Hsu 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2015 NANO Vol.10 No.2
The fabrication of gallium, zinc and nickel oxide nanodots for application of resistive random access memory (RRAM) was demonstrated using the atomic force microscopy (AFM) local anodic oxidation technique. Thin metal ¯lms were deposited on indium tin oxide conductive glass substrates. In the atmospheric environment, using AFM equipped with an Ag-coated probe can generate metal oxide nanodots locally on the metal films. These nanodots act as an insulator layer in a single unit cell of the RRAM. The voltage-biased method allows devices to reset from a lowresistance state (LRS) to a high-resistance state (HRS) at 0.9 V. These results show the ability of the AFM local anodic oxidation to produce 50 nm NiO nanodots on glass substrates for potentially high-density RRAMs. As we developed the characteristics of the structure, we found that a lateral NiO nanobelt RRAM performs very low power operation from such experimental manufacturing process. Using a current-biased method, the lateral device switches from a HRS to a LRS with a low writing voltage of 0.64 V.
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