• Management & Information Systems Review
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• v.38 no.4
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• pp.61-78
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• 2019

When calculating gains from transfers of assets inherited or donated, the value recognized at the market price as of the date of inheritance or acquisition is recognized as the actual transaction value at the time of acquisition. However, Precedents for the appeal for review by the NTS, the request for adjudgment by the Tax Tribunal(TT) and the request of examination by the Board of Audit and Inspection of Korea(BAI) and the cases of the lawsuit have not shown a consistent results on how much such a the actual transaction value will be measured. This study investigates the operating state of the current tax appeal system using the statistical data of the TT, NTS, and BAI and cases of the lawsuit from 2008 to 2017, and suggests the Improvement of Capital Gains Tax Act on the transfer of inherited and donated property. As a result, total number of requested cases has diminished because cases of the pre-assessment review and the reconsideration appeal by the NTS have decreased steadily over the past decade, while the cases of the lawsuit and the administrative trials(the request for adjudgment by the TT, the appeal for review by the NTS, and the request of examination by the BAI) have been steadily increasing. Also This study found that more than 40% of the complainants proceeded with the cases of the lawsuit proceedings in disagreement with the disposition of tax dissatisfaction under the administrative trials. In addition, Even though the retrospective appraisal price is not recognized as the market price due to the strict interpretation of the tax regulations, it can be seen that it is interpreted as a more expanded concept in the application of the market price than the government office or the tax judge. Therefore, according to the precedents of the cases lawsuit, it is necessary to establish a regulation on the recognition of retroactive appraisal value.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.1
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• pp.48-52
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• 2013

Purpose: Bed, living a long time is required in adult patients with brain lesions such as stroke, traumatic brain injury, and Parkinson's disease, causing pneumonia and respiratory diseases may be due to aspiration of food or saliva. In patients with recurrent pneumonia or pulmonary symptoms, there is a need to determine the possibility of pulmonary aspiration due to aspiration of saliva. Materials and Methods: Saliva due to aspiration pneumonia diagnosis in patients with brain lesions request for inspection to the Department of Nuclear Medicine, 10 patients (male 6, female 4) were included in this study. Patients were fasted before the test, $^{99m}Tc_{O4}$ 185 MBq (5 mCi) of less than 1 mL of solution was administered in the oral cavity. Administration and 20 minutes of dynamic imaging acquisition, and immediately after that the static images were acquired. Delayed scan after 2-4 hours if necessary. Results: Positivity rate of all 10 patients was 60%. In 4 patients showed positive reactions after the administration of oral cavity in a 20-minute dynamic imaging were able to confirm whether the aspiration. In the remaining 2 patients, four hours of additional delay tests were able to confirm whether the aspiration. Conclusion: Does not require changes in patient posture compared to the other checks that can be diagnosed with aspiration pneumonia. A simple test and takes less time. Therefore be useful in providing information for the diagnosis and treatment modality.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.