• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.14-30
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• 2024

This study characterized areas at risk of land creep by focusing on a site that has undergone this phenomenon in Ulju-gun, South Korea. Land creep in the area of interest was catalyzed by road expansion work conducted in 2022. The site was examined on the basis of its geological features, topography, effective soil depth, soil hardness, electrical resistivity, and subsurface profile. It consists of a slope covered with sparse vegetation and a concave top that retains rainwater during rainfall. Compositionally, land creep affected the shale, sandstone, and conglomerate formations on the site, which had little silt and more sand and clay compared with areas that were unaffected by land creep. An electrical resistivity survey enabled us to detect a groundwater zone at the site, which explains the softness of the soil. Finally, the effective soil depth at the land creep-affected area was 30.4 cm on average, indicating deep colluvial deposits. In contrast, unaffected sites had an effective soil depth of 24.7 cm on average. These results should facilitate the creation of systems for monitoring and preemptively responding to land creep, significantly mitigating the socioeconomic losses associated with this phenomenon.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Investigative Magnetic Resonance Imaging
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• v.10 no.1
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• pp.8-15
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• 2006

Purpose : To describe MR imaging features of hypoxic brain damage in relation to time elapse and prognosis of patients. Materials and methods : We reviewed 19 MR studies of 18 patients with hypoxic brain damage. MR imaging studies were performed between 1 to 20 days after the hypoxic insults (mean 8.6 days). MR images were analyzed with regard to the locations of abnormal signal intensities, the presence of brain edema. And imaging findings were correlated with the time elapse after the insults and the prognosis of patients. Results : On 19 cases of MR studies, abnormal high intensities on T2-weighted images were found in the basal ganglia (15, 78.9%), cerebral cortex (13, 68.4%), white matter (9, 47.4%), thalamus (6, 31.6%), cerebellum (4, 21.1%) and brainstem (1, 5.3%), respectively. Cerebral cortical involvement was typically bilateral and diffuse, but sometimes limited to the parieto-occipital area. The brainstem and cerebellar involvement was rare and in all cases, cerebral cortical lesions accompanied. Most of the white matter lesions were accompanied with cortical and deep gray matter lesions and found in subacute period(>6 days). The cortical high signal intensity lesions on T1-weighted image were found mostly in subacute stage, but in some cases involvement was also found in acute stage ($\leq$ 6 days). The cortical edema is found on 11 cases in acute and subacute stages. In cases of recovered consciousness, cortical involvement and edema on MR were rare. Conclusion : MR findings of hypoxic brain damage were various, but diffuse bilateral involvement of cortex and/or deep gray matter was found in most of the cases. White matter involvement was rarely found in acute stage and usually found in subacute stage. In cases of good pronosis, cortical involvement and edema were rare.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.5
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• pp.405-412
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• 1993

This experiment was conducted to investigate the effects of water depths on seedling stand and early growth of califonia rice varieties, S201, M202, A301, Italico livorno and Korean variety, Hwaseongbyeo, and barnyardgrass (Echinochloa crus-galli) The coleoptile length of rice was longer with deep water depth while for the radicle length shorten. As water depth was increased, the percentage of seedling stand were decreased slightly in rice, while sharply increased in barnyardgrass. Plant height of rice with increasing water depth were longer, whereas that of barnyardgrass reduced significantly with weaker. Tiller number of rice and barnyardgrass were significantly reduced as water depth increased. Dry matter weight and healthy score of rice seedling at 35DAS were highest in 7.5cm water depth followed saturated moisture, 15, and 22.5cm water depth, while for barnyardgrass those were especially negatively affected by deep water depth. These results showed that the seedling stand and early growth of barnyardgrass was highly suppressed by deeper water levels compared with rice. Rice cultivars which are showes growth characteristics in deeper water levels at early growth stage were Italico livorno and S201 in Japonica / Indica.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.52-69
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• 2021

The transcriptions of Buddhist sutra in the Goryeo Dynasty are more elaborate and splendid than those of any other period and occupy a very important position in Korean bibliography. Among them, the transcriptions made on indigo paper show decorative features that represent the dignity and quality that nobles would have preferred. Particularly, during the Goryeo Dynasty, a large number of transcriptions were made on indigo paper, often in hand-scrolled and folded forms. If flexibility was not guaranteed, the hand-scrolled form caused inconvenience and damage when handling the transcription because of the structural limitations of the material that is rolled up and opened. It was possible to overcome these shortcomings by changing from the hand-scrolled to the folded form to obtain convenience and structural stability. The folded form of the transcription utilizes the same principle as the folding screen, so it is a structure that can be folded and unfolded, and it is made by connecting parts at regularly spaced intervals. No matter how small the transcription is, if it is made of thin paper, it is difficult to handle it and to maintain its shape and structure. For this reason, the folded transcription was usually made of thick paper to support the structure, and the cover was made thicker than the inner part to protect the contents. In other words, the forded form was generally manufactured to suit the characteristics of maintaining strength by making the paper thick. Because a large amount of indigo paper was needed to make this type of transcription, it is assumed that there were craftsmen who were in charge only of dark dyeing the papers. Usually, paper dyeing requires much more dye than silk dyeing, and dyeing dozens of times would be required to obtain the deep indigo color of the base of the transcription of Buddhist sutra in the Goryeo Dynasty. Unfortunately, there is no record of the Goryeo Dynasty's indigo blue paper manufacturing technique, and the craftsmen who made indigo paper no longer remain, so no one knows the exact method of making indigo paper. Recently, Hanji artisans, natural dyers, and conservators attempted to restore the Goryeo Dynasty's indigo paper, but the texture and deep colors found in the relics could not be reproduced. This study introduces the process of restoring indigo paper in the Goryeo Dynasty through collaboration between dyeing artisans, Hanji artisans, and conservators for conservation of the transcription of Buddhist sutra in the late Goryeo dynasty, yielding a suggested method of making indigo paper.

• Journal of Popular Narrative
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• v.26 no.3
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• pp.189-219
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• 2020

This study is an analysis of Handol Heung-Gun Lee's Tarae, which is a coinage combining the Korean words for "playing an instrument" and "song", in terms of narrative and aesthetics. The components for analysis are the phenomena and nature of binary oppositions between nature and human beings, between alienation and interest, between division and unification, and between diaspora and people of the national community. Tarae in the period from the late 1970s to the early 1990s described the experience of pain and loss from non-resistance and disobedience in protest against social problems that emerged during the era of miliary dictatorship, such as industrialization, urbanization, reckless development, Westernization, university-oriented education, the gap between rich and poor, human alienation, and the conflicts arising from the division of the nation. After Handol overcame the lack of creative motivation with self-reflection and effort, Tarae took the form of a diaspora epic meta-narratives integrating the "sound of nature and his true nature" and "the awareness of diaspora and the spirit of the Korean people". The epics of the homeland, the national soil and the people, which began with "Teo", became more intense in terms of a sense of diaspora as they shifted their focus from an origin to a path with "Hanmoejulghi" as the turning point. Handol seeks inspiration in the source of narrative rather than in music. His Tarae focuses on "adding rhythm for lyrics". For this reason, the semiotic features of Tarae have a limitation in that its extrinsic phonology is simple even if its intrinsic meaning (i.e., emotion of sadness) is profound and subtle. In order to elicit sympathy from the audience and impress them, it is necessary to strike a balance between the implicit (semantic) part and the explicit (phonological) part. To share the emotion of sadness with more people, it is necessary to strengthen phonological elements. Sympathy for sadness and deep impression on the audience are more often induced by the mood of similar sentiments than by the stories of the same experience. The aesthetics of sadness in Tarae began with the narratives of past experience which were expressed in the contexts of loss, loneliness, and poverty that Handol had experienced since childhood. However, the aesthetics of sadness, deepened over the period of a long hiatus in Handol's career as a composer, formed the narratives of ultimate salvation, embodying even the diaspora experience of others (e.g., displaced people, overseas adoptees, ethnic Koreans in Russia, victims of Japanese military sexual slavery, etc.). This gave Tarae the potential to go beyond the limits of the ethnic group of Korea. Tarae, as a "dispersed sound", can benefit from the appeal of deep sadness at the point of contact with other forms of world music. It may form a global diaspora discourse because Tarae is oriented towards interculturalism rather than anti-multiculturalism. The future challenge and goal of Handol's Tarae would be to continue to find areas of sympathy and broaden the horizon of awareness as diaspora music.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Clinical and Experimental Pediatrics
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• v.45 no.12
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• pp.1528-1533
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• 2002

Purpose : Isolated noncompaction of the ventricular myocardium(INVM) is one of the unclassified cardiomyopathies that is characterized by numerous, excessively prominent trabeculations, and deep intertrabecular recesses. We performed this study to evaluate the clinical features of INVM in children. Methods : The medical records of 10 patients with INVM were reviewed. We analyzed the clinical manifestations, hemodynamics, pattern of inheritance, and long-term prognosis of INVM in children. Results : Age at diagnosis was $45{\pm}53months$(1 day-14 years) with follow-up lasting as long as 78 months. Most INVM was asymptomatic on diagnosis. Associated cardiac anomalies were noted in six patients(ventricualr or atrial septal defect, patent ductus arteriosus with mitral valve prolapse, or mitral valve cleft). Depressed or flat changes of T wave in lead II, III and aVF were observed on electrocardiography. Various arrhythmia including WPW syndrome with paroxysmal supraventricular tachycardia, third-degree atrioventricular block, and familial sick sinus node dysfuction were observed. The degree of trabeculation in INVM was significantly prominent from level of mitral valve to apex compared to age-matched control. Familial recurrences were noted in two patients. The systolic function of the left ventricle was decreased in 20% of patients during the follow-up period, but systemic embolism or ventricular tachycardia was not observed. Conclusion : INVM is not a rare disorder. The cardiac function may be deteriorated in children as well as adults during long-term follow up. Thus early diagnosis and long-term follow-up must be done. So, the nation-wide multicenter clinical study would be mandatory to evaluate the incidence, long-term prognosis, and establishment of objective diagnostic criteria of INVM.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.106-116
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• 2001

Background : Pulmonary thromboembolism is relatively frequent and potentially fatal. However, it is commonly misdiagnosed. The incidence of pulmonary thromboembolism is not decreasing despite advances in diagnosis and effective prophylatic measures. Its potential for significant sequela necessitates a prompt diagnosis and treatment. Unfortunately, there are many difficulties and problems regarding accurate diagnosis. There is a low prevalence of deep vein thrombosis and pulmonary thromboembolism in Korea and only few reports on this subject are available. Method : The clinical features of 36 patients, who were diagnosed with pulmonary thromboembolism at the Korea University medical center, were reviewed. Results : 1) There was no significant difference in prevalence between men an women, and the mean age was 50.9 years in men 59.2 years in women. 2) The frequent causes of pulmonary thromboembolism were malignancies (22.2%), surgery (22.2%), and heart disease(8.2%). Specific causes were not identified in 33.3%. 3) The most common symptom was dyspnea(72.2%), and the most common sign was tachypnea(61.1%). 4) The EKG findings were normal in 28.6%, an S1Q3T3 pulmonale pattern in 25.7%, ST or QRS changes in others. 5) The chest X-ray findings indicated pulmonary infiltration in 37.5%, cardiomegaly in 15.6%, pleural effusion in 12.5%, and normal in 27.8%. The perfusion lung scan showed a high probability in 66.7%, and intermediate or low probability in 33.3%. 6) The pulmonary arterial pressure(PAP) in the high probability groups was 57.9mmHg with a higher mortality rate(35%). Conclusion : Pulmonary thromboembolism is not uncommon in Korea and its clinical features do not differ greatly from thase reported in the literature. When pulmonary thromboemblism of unknown causes are diagnosed, a search for an occult malignancy is recommended. Rapid diagnosis and treatment are achieved when thromboemblism is suspected.