• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.327-336
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• 2020

This study was undertaken to identify ways that enable a successful comeback to everyday life and improvement in the quality of life, by understanding the experiences of cancer survivors in returning to everyday life. Totally, 19 people diagnosed with complete cure after 5 years of cancer treatment, were recruited for the study. Data was collected through in-depth interviews from January 18 to February 25, 2017. One-time interviews took 90 to 120 minutes, and data analysis was achieved by applying the grounded theory. The central phenomenon of 'reality that cannot be escaped' and 'uncertain reality that cannot know tomorrow' was attributed to the causal conditions 'hard reality', 'physical exhaustion', 'psychological exhaustion' and 'economic exhaustion'. Depending on context conditions such as 'lack of family support', 'shift to a vulnerable working class', 'insufficient support system', 'hope for the societal support system', 'daunted gender', 'prejudice against cancer/cancer patients', 'information on life after rare full healing'. The strategy for a successful return was influenced by intervention conditions such as 'robust family fence' and 'effective cancer insurance', which resulted in 'building a new life' or 'enduring'. We conclude that for a successful return to daily lives, cancer survivors require comprehensive information, health and social-welfare interventions.

• Land and Housing Review
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• v.2 no.4
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• pp.453-461
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• 2011

This study is to derive a project model based on potential demand for Korean-style houses, focusing on new town detached housing sites that LH supplies and to test validity of the derived model and to present the direction and supply methods of the projects. The existing high-class new town Korean-style housing developments that have been considered were found to have little business value due to problems in choice of location and discordance of demand, so 6 types of projects were established through the methods of changes in planned scale, combined use, and subdivision of plot of land based on the results of survey. The type that has the highest business value among the project models was block-type multifamily houses, and this can be interpreted as the increase in total construction area leading to increase inrevenues of allotment sales due to economies of scale. The feasibility of mass housing model in which small-scale Korean-style houses are combined with amenities was found to be high, and if the same project conditions as those of the block-type multifamily houses are applied, the business value of the Korean-style tenement houses was found to be high. Besides, the high-class housing models within block-type detached housing areas are typical projects that the private sector generally promotes, and the construction cost was found to be most expensive with 910 million won per house. In order to enhance the business value of the Korean-style housing development, collectivization such as choice of location, diversification of demand classes, optimization of house sizes, and combination of uses is needed. And in order to adopt Korean-style houses in the detached housing sites, the adjustments and division of the existing planned plots are needed, and the strategies to cope with new demand through supplying Korean-style housing types of sites can be suggested. Also breaking away from the existing uniform residential development methods, the development method through supplying original land that is natural land not yet developed besides basic infrastructures (main roads and water and sewage) can be considered, and as the construction of more than 1~2 stories building is impossible due to the structure of Korean-style house roof and furniture. So it can be suggested that original land in the form of hilly land is considered to be most suitable to large-scale development projects.

• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.1-9
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• 2019

Large-scale industrial boilers operating at high temperature and high pressure, have a large amount of water, and a large amount of energy is released at the time of explosion. Currently, most industrial boilers use gas fuel such as LNG and LPG, etc. and fuel exists in the same space as equipment, so there is a high possibility of secondary damage such as fire or explosion in the event of a boiler accident. Both special care and management are required to operate the very dangerous equipment that causes casualty 2.51 per accident. For boilers of a certain size or more, the Korea Energy Agency conducts inspections in accordance with the Energy Usage Rationalization Act, KS, and public notice of the Ministry of Industry, Trade and Resources. In this research, based on the results of the inspection, the hazard factorss are configured, and a questionnaire is conducted to the inspector, the equipment manager, the maintenance person, and the person in charge of the manufacturer. We analyzed the results by using AHP (Analytic Hierarchy Process). As a result of analysis, generally recognized hazard factorss are not good management, measurement failure, specification failure, water leak, leak analysis, but connection, welding, scale, and corrosion, etc. are relatively less important. It is judged that the adverse factors that are recognized to be highly important among all groups and careers are already well managed, but less important and adverse factors should be well managed to ensure that the safe usage of the boiler.

• Cartoon and Animation Studies
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• s.40
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• pp.55-81
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• 2015

Animation as a public visuals media have been expanding increasingly its social and cultural influences beyond the ages and nations on the basis of global consumption. However, animation increases the negative impact in modern popular culture, and in regard to this, 'the recovery of ethics' should be considered in a reflexive and educational perspectives for the social role of animation. Thus, the research addresses the animation films of Brigham Young University students which contain a ethical values and receive attention by New York Times, etc. as a successful educational model. To do this, firstly, literature has reviewed by focusing on the negative impact of animation, 1) violence, 2) excessive sensationalism, 3) confusion of cultural identity, 4) gender discrimination, and 5) distorted view of history. Secondly, the education system of animation course at Brigham Young University will be analysed. Thirdly, based on this, the case study will be conducted by focusing on the 13 animation films of students to reveal the characteristics of the way of film direction. Through this research, firstly, most of animation films are comic genre, consisting of children and animal characters, family-friendly and lyrical story style and deployment of coincidental and allegoric incident. Thirdly, the religious spirit and multidisciplinary methods of education in Brigham Young University has influenced to the ethical expression and technical perfection in animation filmmaking. In the light of this, the research and suggests the new paradigm is for the practical disciplines of animation in the restoration of the ethical perspective and explores how the animation production adopts the moral significance.

• Archives of design research
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• v.18 no.1 s.59
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• pp.91-104
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• 2005

In the past, the design education focused on the technique and skill to satisfy the production and consumption activities of a corporation based on beauty and practicality in the industrial age. Now, it's time to switchover to a life-quality enhancing education by harmonizing and meeting the public's physical and mental needs because design education is a character education which enhances the quality of life by uplifting people's aesthetic sense. This paper has emphasized on the importance of public education of design through the theoretical investigation on social environmental changes caused by the emergence of information society,,education problems, public education, and the necessity of design education. The reason why this study should be done has been suggested by investigating the necessity of this research and bringing up the issue. Furthermore, the current status and problems of public education on design have been analyzed. Then, based on the result, the development direction of design education has been suggested. This study can be concluded as follows: First, the design education should change from its vertical structure to a horizontal one. It should be widely spread to the public, getting off from its privatization for a certain group. Second, designers and the public should correct their way of thinking about design. The ordinary people as well as the designers should cultivate their capability to find and take care of design related issues in their everyday lives. Third, all people should be the subject of design education for the public. As a part of cultural education on the public's aesthetic sense, design education should be reborn as a field of study in which a sound public culture can be developed by the integration of human life and culture, exceeding the limit of school curriculum.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.3
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• pp.789-806
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• 2010

As calls for more attention toward social minority group increases in our society recently, in the field of mathematics education more attention toward an issue about mathematics underachievers is being amplified. Thus, the present study is to examine the effects of teaching method considering students' cognitive characteristics on mathematical underachievers' problem solving and mathematical disposition. For this study, 10 fifth graders identified as mathematical underachievers based on the results of the national level diagnosis assessment and school based assessment were voluntarily selected from an elementary school in Seoul. The results of this study found out the fact that students participating in this program improved in terms of an ability both to solve problems in various ways and to explain an process of problem solving using spoken or written language and drawings. In addition, learning environment respecting students' own mathematical ideas seems to positively influence students' attitudes toward mathematics learning and mathematical dispositions. Furthermore, this study pointed out that mathematical underachievers tend to have difficulty in expressing their own mathematical thinking by reason of linguistic limitation. Finally, the findings of this study imply that for effective teaching of mathematics underachievers, these students' own informal experience and knowledge about mathematics as well as their characteristics regarding learning difficulties should be strongly considered.

• Journal of the Korean Geographical Society
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• v.41 no.3 s.114
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• pp.245-266
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• 2006

This study aims to explain the emergence of Gangnam Region of Seoul as a new cluster of the Korean film industry in the context of its history of growth, recent development characteristics, and its production system. Statistical analyses of Korean industries and in-depth interview surveys to film producers were conducted. The results of the analyses show that there has been obvious tendency of film makers' move from Chungmuro, a traditional cluster of the Korean film industry, to Gangnam and many births of new film makers in Gangnam. This new cluster, however, is operating on informal networking between film makers and related personnel, not on formalized production system composed of specialized functions and labor market. Therefore, it can be called as loosely-articulated cluster. This form of cluster has much to do with production milieu of Gangnam, such as advantages of obtaining information and consumer trend, diverse atmosphere which makes it easier to meet diverse people, favorable environment to embed creativity and fresh idea, etc. This trend of the film industry to orient a specific area can be explained in conjunction with the uniqueness of the film industry, such as uncertainty, project-based work, generation gap between film makers, participation of large companies or increasing size of investment.