• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.2
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• pp.1-15
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• 2016

This study aims at the consideration of the usages of traditional waterscape elements, which are difficult to define their concepts and their differences and it has been proceeded mainly with analysis of literature. It elicited various waterscape types by extracting the place names associated with the watersacpe elements from the chapter "Sancheon" of the volumes Gyeongsang-province in "Sinjeung Donggukyeojiseungram", which is a government-compiled geography book in the early period of Joseon Dynasty, and drew the features of each waterscape element by interpreting the dictionary definition and the original text and studying the similar examples. The results of study are drawn as follows. 1. The chapter "Sancheon" includes 22 types of waterscape elements and they are classified by means of locations and water-flow forms: River Landscape, Lake & Pond Landscape, Coast landscape. 2. River landscape maintaining constant natural water-flow constitutes the linear type, related to the class of stream, which includes 'Su(water)', 'Gang(river)', 'Cheon(stream)' and 'Gye(brook)' and the dotty type, created by the nature of trenched meander rivers, which includes 'Tan(beach)', 'Roe(rapids)', 'Pok(waterfall)' and 'Jeo(sandbank)'. 3. Lake & Pond Landscape forming water collected in a certain area constitutes 'Ho(lake)', which is a broad and calm spot created around mid and down stream of river, 'Yeon(pool)', 'Dam(pond)', 'Chu(small pond)', which are naturally created on the water path around mid and down stream of river, 'Ji(pond)', 'Dang(pond)', 'Taek(swamp)', which is collected on a flatland and 'Cheon(spring)', 'Jeong(spring)' which means gushing out naturally. 4. Coast Landscape includes 'Ryang', 'Hang', which are the space between land and an island or islands, 'Got(headland)' which sticks out from the coast into the sea, 'Jeong(sandbank)' which forms sandy beaches and 'Do' which shows high appearance frequency by reflecting the geographical importance of islands. This study comprehended the diversity of traditional waterscape elements and drew the fact that they are the concept reflecting the differentiated locational, scenic and functional features. That way, it understood the aesthetic sense on nature, which ancestors had formed with the interests in natural landscape and the keen observation on it, became the basic idea elucidating the characteristic on Korean traditional gardens, which minimize the artificiality and make nature the subject.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.603-617
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• 2018

This study surveyed the diversity and community characteristics of benthic macroinvertebrates and freshwater fish, which are the main animal classifications in a hydro ecosystem, from May to September 2017 in the western plains of the Demilitarized Zone (DMZ). The results showed a total of 125 species of benthic macroinvertebrates belonging to 66 families, 19 orders, and four phyla in the streams and wetlands. Among benthic macroinvertebrates, Coleoptera (27 spp.: 21.60%) was the largest group in terms of species richness followed by Odonata (26 spp.: 20.80%), non-Insecta (22 spp.: 17.60%), and Hemiptera (11 spp.: 8.80%) occupying in the lentic area. Of the feeding function groups (FFGs), predators (51 spp.: 56.67%) showed a relatively larger presence, indicating the dominance of hygrophilous invertebrates that usually inhabit the freshwater wetlands or ponds. Of the habitat oriented groups (HOGs), climbers (33 spp.: 24.44%) and burrowers (17 spp.: 12.59%) were the dominant groups. This observation is typical in a slow flowing habitat and can lead to the disturbance of the ecosystem due to cannibalism among predators. Cannibalism can be caused by stress induced by various population and environmental factors. For the ecosystem services benchmark (ESB) value, site 13 appeared to be the highest with 82 marks while other sites showed relatively lower rates and indices (III water quality class with ${\alpha}$-mesosaprobic). The analysis result of stability factors showed that almost all sites were evaluated to be the I characteristic group with high resilience and resistance or the III characteristic group that was sensitive to environmental disturbance and formed uneven and unstable communities. Of the freshwater fishes, 46 species (3,405 individuals) belonging to 39 families and 18 orders were identified in all the investigated sites. Among them, Cyprinidae (30 spp.: 65.2%) was the largest group, and Zacco koreanus was identified as the dominant species (728 individuals, 21.4%). The survey of freshwater fish communities found both stable communities (sites 7 and 13) with low dominant index (0.39) and high diversity index (2.29) and unstable communities (sites 2, 3, 8, and 10) in opposite tendency. This survey found five Korean endemic species, 17 species belonging to the export controlled species, two endangered species level II (Lethocerus deyrollei and Cybister chinensis), and rare species (Dytiscus marginalis czerskii) among benthic macroinvertebrates. The survey also found an invasive species, Ampullarius insularus, which was distributed throughout the whole area and thus can continuously disturb the ecosystem in the western plain area in the DMZ. Of freshwater fish, one natural monument (Hemibarbus mylodon) and three endangered species level II (Acheilognathus signifer, Gobiobotia macrocephalus, and G. brevibarba) were observed. The survey also found four introduced species (Pomacea canaliculate, Carassius cuvieri, Lepomis macrochirus, Micropterus salmoides) in the western DMZ area, indicating the need for the protection and conservation measures.

• Journal of The Korean Astronomical Society
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• v.52 no.1
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• pp.11-21
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• 2019

Intensive Monitoring Survey of Nearby Galaxies (IMSNG) is a high cadence observation program monitoring nearby galaxies with high probabilities of hosting supernovae (SNe). IMSNG aims to constrain the SN explosion mechanism by inferring sizes of SN progenitor systems through the detection of the shock-heated emission that lasts less than a few days after the SN explosion. To catch the signal, IMSNG utilizes a network of 0.5-m to 1-m class telescopes around the world and monitors the images of 60 nearby galaxies at distances D < 50 Mpc to a cadence as short as a few hours. The target galaxies are bright in near-ultraviolet (NUV) with $M_{NUV}$ < -18.4 AB mag and have high probabilities of hosting SNe ($0.06SN\;yr^{-1}$ per galaxy). With this strategy, we expect to detect the early light curves of 3.4 SNe per year to a depth of R ~ 19.5 mag, enabling us to detect the shock-heated emission from a progenitor star with a radius as small as $0.1R_{\odot}$. The accumulated data will be also useful for studying faint features around the target galaxies and other science projects. So far, 18 SNe have occurred in our target fields (16 in IMSNG galaxies) over 5 years, confirming our SN rate estimate of $0.06SN\;yr^{-1}$ per galaxy.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.39-56
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• 2018

The purpose of this study was to develop Home Economics(HE) teaching and learning process plans applied Problem Based Learning(PBL) focusing on 'food and nutrition' unit for students with intellectual disability and to evaluate the effects of the HE instruction on their food choice·management knowledge and problem-solving skills after implementing the instruction for students with intellectual disability. To develop HE teaching and learning process plans applied PBL focusing on 'food and nutrition' unit for students with intellectual disability, problems that arise in daily life to trigger interest of students were firstly developed. The selected problems and teaching and learning process plans were reviewed for validity by one home economics education professor and three teachers who are experts in special education. This study used the one group pretest and posttest design, sampling 6 students who are in special-education middle school with the intellectual disability. After HE instruction of 6 sessions applied PBL method, this study tested the effects of the instruction. The first three sessions taught how to choose and keep food. The fourth session taught purchasing food ingredients and keeping them for sandwiches. The fifth and sixth sessions let the students make sandwiches and give them to others. The instruments of the study comprised of tools for food choice and management knowledge, tools for problem-solving skills evaluation, self-evaluation sheets, evaluation form of course satisfaction for students, evaluation form of behavior in class for teachers, and daily observation journal and all tools. These instruments were proved to have reliability and validity. The results of this study are as follows. First, all six students who took HE instruction applied PBL method focusing on 'food and nutrition' unit scored 30 points higher out of 100 points after taking the instruction in food choice and management knowledge and scored 5 points higher out of 14 points in problem-solving skills on average. Therefore, it was interpreted that HE instruction applied PBL affected the food choice·management knowledge and the problem solving skills of students with intellectual disability. Secondly, the students with intellectual disability participated actively in HE instruction applied PBL focusing on 'food and nutrition' unit and expressed satisfaction. Three special education experts evaluated HE teaching·learning process plans applied PBL focusing on 'food and nutrition' unit to be well-developed. This study showed that HE instruction applied PBL focusing on 'food and nutrition' unit allowed the students with intellectual disability to acquire comprehensive skills in choosing, keeping, and making safe food and helped them solve problems of their life by themselves. Therefore I suggest that Home Economics should be adopted as a formal subject matter in special school curriculum for students with intellectual disability.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.10 no.2
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• pp.145-157
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• 1999

This study was performed by the children and adolescents who were abused or neglected physically, emotionally that were selected in child & adolescents psychiatric ward. We investigated the number of these case in admitted children & adolescents, and also observed characteristics of symptoms, developmental history, characteristics of abuse style, characteristics of abusers, family dynamics and psychopathology. We hypothesized that all kinds of abuse will influnced to emotional, behavioral problems, developmental courses on victims, interactive effects on family dynamics and psychopathology. That subjects were 22 persons of victims who be determined by clinical observation and clinical note. The results of the study were as follows：1) Demographic characteristics of victims：ratio of sex was 1：6.3(male：female), mean age was $11.1{\pm}2.5$. According to birth order, lst was 12(54.5%), 2nd was 5(23%), 3rd was 2(9%) and only child was 3(13.5%). 2) Characteristics of family：According to socioeconomic status, middle to high class was 3(13.5%), middle one was 9(41.% ), middle to low one was 9(41%), low one was 1(0.5%). according to number of family, under the 3 person was 3(13.5%), 4-5 was 17(77.5%), 6-7 was 2(9%). according to marital status of parents, divorce or seperation were 5(23%), remarriage 2(9%), severe marital discord was 19(86.5%). In father, antisocial behavior was 7(32%), alcohol dependence was 10(45.5%). In mother, alcohol abuse was 5(23%), depression was 17(77.3%), history of psychiatric management was 6(27%). 3) Characteristics of abuse：Physical abuse was 18(81.8%), physical and emotional abuse and neglect were 4(18.2%). according to onset of abuse, before 3 years was 15(54.5%), 3-6 years was 5(27.5%), schooler was 1(15%). Only father offender was 2(19%), only mother offender was 8(35.4%), both offender was 8(35.4%), accompaning with spouse abuse was 7(27%), and accompaning with other sibling abuse was 4(18.2%). 4) General characteristics and developmental history of victims：Unwanted baby was 12(54.5%), developmental delay before abuse was9(41%), comorbid developmental disorder was 15(68%). there were 6(27.5%) who didn‘t show definite sign of developmental delay before abuse. 5) Main diagnosis and comorbid diagnosis：According to main diagnosis, conduct disorder 6(27.3%), borderline child 5(23%), depression4(18%), attention deficit hyperactivity disorder(ADHD) 4(18%), pervasive developmental disorder not otherwise specified 2(9%), selective mutism 1(5%). According to comorbid diagnosis, ADHD, borderline intelligence, mental retardation, learning disorder, developmental language disorder, oppositional defiant disorder, chronic tic disorder, functional enuresis and encoporesis, anxiety disorder, dissociative disorder, personality disorder due to medical condition. 5) Course of treatment：A mean duration of admission was $2.4{\pm}1.5$ months. 11(15%) showed improvement of symtoms, however 11(50%) was not changed of symtoms.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.