http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Yang-Chang Tu,Kuang-Yu Chen,Chung-Kung Chen,Ming-Chu Cheng,Shu-Hwae Lee,Ivan-Chen Cheng 대한수의학회 2019 Journal of Veterinary Science Vol.20 No.1
Monoclonal antibodies (MAbs) are widely applied in disease diagnoses. Herein, we report a MAb, WF-4, against Influenza A virus nucleoprotein (NP), its broad response with Influenza A virus, and its application in an immunohistochemistry (IHC) assay. WF-4 was screened by immunofluorescence assay (IFA). The results showed that its reactivity with baculovirus-expressed full-length recombinant NP (rNP) in Western blot (WB), indicating its IHC applicability. Fifteen Influenza A virus (reference subtypes H1 to H15) infected chicken embryonated chorioallantoic membranes (CAM), fixed by formalin, were all detectable in the WF-4-based IHC assay. Also, the reactivity of the IHC test with NP from experimentally inoculated H6N1 and from all recent outbreaks of H5 subtype avian Influenza A virus (AIV) field cases in Taiwan showed positive results. Our data indicate that CAM, a by-product of Influenza A virus preparation, is helpful for Influenza A virus-specific MAb characterization, and that the WF-4 MAb recognizes conserved and linear epitopes of Influenza A virus NP. Therefore, WF-4 is capable of detecting NP antigens via IHC and may be suitable for developing various tests for diagnosis of Influenza A virus and, especially, AIV infection.
Yuan Zeng,Yang Yang,Chao Qin,Jiangtao Chang,Jian Zhang,Jingzhe Tu 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.1
The dynamic characteristics of power systems become more and more complex because of the integration of large-scale wind power, which needs appropriate control strategy to guarantee stable operation. With wide area measurement system(WAMS) creating conditions for realizing realtime transient stability analysis, a new coordinated control strategy for power system transient stability control based on phase-plane trajectory was proposed. When the outputs of the wind farms change, the proposed control method is capable of selecting optimal generators to balance the deviation of wind power and prevent transient instability. With small disturbance on the base operating point, the coordinated sensitivity of each synchronous generator is obtained. Then the priority matrix can be formed by sorting the coordinated sensitivity in ascending order. Based on the real-time output change of wind farm, coordinated generators can be selected to accomplish the coordinated control with wind farms. The results in New England 10-genrator 39-bus system validate the effectiveness and superiority of the proposed coordinated control strategy.
Approximate Detection Method for Image Up-Sampling
( Ching-ting Tu ),( Hwei-jen Lin ),( Fu-wen Yang ),( Hsiao-wei Chang ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.2
This paper proposes a new resampling detection method for images that detects whether an image has been resampled and recovers the corresponding resampling rate. The proposed method uses a given set of zeroing masks for various resampling factors to evaluate the convolution values of the input image with the zeroing masks. Improving upon our previous work, the proposed method detects more resampling factors by checking for some periodicity with an approximate detection mechanism. The experimental results demonstrate that the proposed method is effective and efficient.
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.