• Steel and Composite Structures
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• v.48 no.4
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• pp.385-403
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• 2023

To avoid premature damage to the connection joints of a conventional precast concrete shear wall, a new precast concrete shear wall system (NPSW) based on a plastic damage relocation design concept was proposed. Five specimens, including one monolithic cast-in-place concrete shear wall (MSW) as a reference and four NPSWs with different connection details (TNPSW, INPSW, HNPSW, and TNPSW-N), were designed and tested by lateral low-cyclic loading. To accurately assess the damage relocation effect and quantify the damage and deformation, digital image correlation (DIC) and conventional data acquisition methods were used in the experimental program. The concrete cracking development, crack area ratio, maximum residual crack width, curvature of the wall panel, lateral displacement, and deformed shapes of the specimens were investigated. The results showed that the plastic damage relocation design concept was effective; the initial cracking occurred at the bottom of the precast shear wall panel (middle section) of the proposed NPSWs. The test results indicated that the crack area ratio and the maximum residual crack width of the NPSWs were less than those of the MSW. The NPSWs were deformed continuously; significant distortions did not occur in their connection regions, demonstrating the merits of the proposed NPSWs. The curvatures of the middle sections of the NPSWs were lower than that of the MSW after a drift ratio of 0.5%. Among the NPSWs, HNPSW demonstrated the best performance, as its crack area ratio, concrete damage, and maximum residual crack width were the lowest.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.29-36
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• 2019

When dealing with outdoor images in a variety of computer vision applications, the presence of shadow degrades performance. In order to understand the information occluded by shadow, it is essential to remove the shadow. To solve this problem, in many studies, involves a two-step process of shadow detection and removal. However, the field of shadow detection based on CNN has greatly improved, but the field of shadow removal has been difficult because it needs to be restored after removing the shadow. In this paper, it is assumed that shadow is detected, and shadow-less image is generated by using original image and shadow mask. In previous methods, based on CGAN, the image created by the generator was learned from only the aspect of the image patch in the adversarial learning through the discriminator. In the contrast, we propose a novel method using a discriminator that judges both the whole image and the local patch at the same time. We not only use the residual generator to produce high quality images, but we also use joint loss, which combines reconstruction loss and GAN loss for training stability. To evaluate our approach, we used an ISTD datasets consisting of a single image. The images generated by our approach show sharp and restored detailed information compared to previous methods.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• Progress in Medical Physics
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• v.23 no.3
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• pp.145-153
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• 2012

In this study, we quantify the residual motion artifact in 4D-CT scan using the dynamic lung phantom which could simulate respiratory target motion and suggest a simple one-dimension theoretical model to explain and characterize the source of motion artifacts in 4DCT scanning. We set-up regular 1D sine motion and adjusted three level of amplitude (10, 20, 30 mm) with fixed period (4s). The 4DCT scans are acquired in helical mode and phase information provided by the belt type respiratory monitoring system. The images were sorted into ten phase bins ranging from 0% to 90%. The reconstructed images were subsequently imported into the Treatment Planning System (CorePLAN, SC&J) for target delineation using a fixed contour window and dimensions of the three targets are measured along the direction of motion. Target dimension of each phase image have same changing trend. The error is minimum at 50% phase in all case (10, 20, 30 mm) and we found that ${\Delta}S$ (target dimension change) of 10, 20 and 30 mm amplitude were 0 (0%), 0.1 (5%), 0.1 (5%) cm respectively compare to the static image of target diameter (2 cm). while the error is maximum at 30% and 80% phase ${\Delta}S$ of 10, 20 and 30 mm amplitude were 0.2 (10%), 0.7 (35%), 0.9 (45%) cm respectively. Based on these result, we try to analysis the residual motion artifact in 4D-CT scan using a simple one-dimension theoretical model and also we developed a simulation program. Our results explain the effect of residual motion on each phase target displacement and also shown that residual motion artifact was affected that the target velocity at each phase. In this study, we focus on provides a more intuitive understanding about the residual motion artifact and try to explain the relationship motion parameters of the scanner, treatment couch and tumor. In conclusion, our results could help to decide the appropriate reconstruction phase and CT parameters which reduce the residual motion artifact in 4DCT.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.42-49
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• 1996

In this paper, we propose a satellite multispectral image data compression method using classified vector quantization. This method classifies each pixel vector considering band characteristics of multispectral images. For each class, we perform both intraband and interband vector quantization to romove spatial and spectral redundancy, respectively. And residual vector quantization for error images is performed to reduce error of interband vector quantization. Thus, this method improves compression efficiency because of removing both intraband(spatial) and interband (spectral) redundancy in multispectral images, effectively. Experiments on landsat TM multispectral image show that compression efficiency of proposed method is better than that of conventional method.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.1
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• pp.1-11
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• 2000

The hybrid coding algorithm for multi-spectral image obtained from satellite is discussed. As the spatial and spectral resolution of satellite image are rapidly increasing, there are enormous amounts of data to be processed for computer processing and data transmission. Therefore an efficient coding algorithm is essential for multi-spectral image processing. In this paper, VQ(vector quantization), quadtree decomposition, and DCT(discrete cosine transform) are combined to compress the multi-spectral image. VQ is employed for predictive coding by using the fact that each band of multi-spectral image has the same spatial feature, and DCT is for the compression of residual image. Moreover, the image is decomposed into quadtree structure in order to allocate the data bit according to the information content within the image block to improve the coding efficiency. Computer simulation on Landsat TM image shows the validity of the proposed coding algorithm.

• The Korean Journal of Nuclear Medicine
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• v.36 no.1
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• pp.53-63
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• 2002

FDG PET is a functional imaging modality whose ability to detect lesions is directly based on a change of the glycolytic metabolism of targeted tissues, may be advantageous over other techniques. Combined with excellent image qualify, high spatial resolution, and whole body imaging capability, it has become popular as a new approach in the evaluation of patients with various malignancies. Initial staging of nodal and extranodal lymphoma using FDG PET has been proven to be at least equal or superior to conventional imaging modalities. For the assessment of treatment responsiveness, FDG PET has a major impact on the management of patients in differentiating residual lymphoma from treatment related benign changes. Residual FDG uptake after the completion of chemotherapy is a good predictor of early relapse. However, it seems that the absence of FDG uptake in tumor mass may not exclude minimal residual disease causing later relapse. In the early evaluation of treatment response only after a few cycles of chemotherapy, FDG PET may have a promising role in identifying non-responders who could benefit from a different treatment strategy. At present, FDG PET appears to be the cost-effective, diagnostic modality of choice in the management of lymphoma patients. The role of FDG PET based-systems in terms of affecting long-term prognosis and survival benefit should be further elucidated in future prospective studios.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.297-298
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• 2005

We have fabricated twisted nematic (TN) liquid crystal cells doped by carbon nanotubes (CNTs) with different CNT wt. %. With a minute amount doping, multi-walled CNTs did not perturb the liquid crystal orientations at the off- and on-state. The hysteresis studies of voltage-dependent capacitance (V-C) under the influence of electric field generated by ac and dc voltage show that the residual do, which is tightly related to image sticking problem in liquid crystal displays, is greatly reduced due to ion trapping by CNTs. Also, the V-C hysteresis shows dependency of capacitance on concentration of multi-walled CNTs.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1343-1358
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• 2020

In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.