• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.2 no.1
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• pp.102-115
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• 1991

This study reviewed the stuttering literature and reported the clinical experiment in stuttering intervention. There is still no single answer as to the cause of stuttering or to the most effective therapy for stutterers despite the vast amount of research. One certain thing is that we have come closer to a better understanding of the stuttering and to more effective therapy. There have been three main statements about the origins of stuttering ; biologic origins ; psychodynamic origins ; environmental-learning origins. There also have been various methods of the treatment of stuttering. Broadly, two major treatment approaches are attentive ; stuttering modification therapy and fluency shaping therapy. In this experiment, the researcher attempted to investigate complex elements that each child might have and to use an integrative approach rather than to keep the specific one. Individual subjects were evaluated by a multidisciplinary team. Initially, the subjects received individual therapy. They then were placed in group therapy. The purpose of the group therapy was to raise their fluencies to the higher communicative situation and to maintain improved fluency over time. All three subjects improved their fluencies in reading and in conversation and showed the better(SSI)scores in total stuttering behaviors. It was also discussed that it is necessary to have sensitive assessment tools to investigate each element of stuttering ; and to develop a therapy program reflecting current advanced stuttering theories.

• Journal of East-West Nursing Research
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• v.10 no.1
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• pp.27-40
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• 2004

Relapse is one of the most series problems in alcoholics treatment. Previous studies have shown that significant proportions of treated alcoholics show an early relapse and go through a chronic phase. It is necessity to find more effective relapse prevention program. The Purpose of this study was to develop a relapse prevention program that emphasis on group activities using various action methods. Previous studies revealed that there was no effective therapeutic strategy to prevent relapse and proposed that action methods were more practical ways to be able ti cope with high-risk situation than verbal methods such as discussions and lectures. The special attempt of this program was the application of various actions methods and the integration of many psychosocial therapeutic strategies as compared with many relapse prevention programs. The theoretical framework of this relapse prevention program was based on mainly the Marlatt's Relapse Prevention model and Prochescha and DiClemente's Transtheoretical model. This Program consists of eight structure sessions. Every session has three phase: Warm-up phase, action phase, and sharing phase as sociodrama structure. Sociodrama is based on many of the principles of adult learning. And sociodrama looks at how groups work through an understanding of systems and role theory. Therefore, in working with a group a therapist might explore with them the roles that people play, roles that are missing at present such a visionary and how people can develop new roles or new ways of playing existing roles. The researchers explained the purpose of this study to all participants after their agreement to participate. Voluntary informed consent was obtained from all participants. Every session allows participants to recognize personal specific high-risk situation and to examine possible coping behaviors creatively. Multiple solutions can be proposed, tested and evaluated dramatically, giving new insights or breakthroughs in thinking. This is vital for the initiation of change, and if appropriate, expanding new role development. The first two sessions aim at understanding of relapse process and recognize of high-risk situations focused on orientation about action methods. The next four sessions deal with high-risk situations. The last two sessions give participants opportunities to venture new life-styles. The methods and approaches used in this program utilized as a tool to explore and practice possible coping strategies. and this program can contribute to prevent relapse episode if tune with the particular high-risk situation by using active practices in safe environment.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.17-27
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• 2010

This study aimed to identify the problems of college students in reading the literature and on the basis of the identified problems, to suggest the approaches to solve the problems. To this end, time required for reading passages, reading patterns, understanding, memory and reading habits and attitudes were analyzed with the freshmen in A university. In accordance with the analysis results, 58% of subjects was good and 42% was not sufficient on the basis of the averages in Scholastic Aptitude Test. Second, 77% of subjects had the good patterns but 23% showed certain problems in reading patterns. Third, 69% and 67% of subjects illustrated good results in the analysis on understanding and memory, respectively. However, 31% and 33% were evaluated as being on the general level or requiring efforts in the analysis on understanding and memory, respectively. Next, according to the analysis on reading habits and attitudes, 77% had no problems but 23% required improvement. For solving the problems identified through the analysis, it is recommended to develop the scientific and standardized evaluation tools for evaluating the reading ability of college students. Second, it is necessary to evaluate the reading ability, habit and attitude during the screening process for admission or after admission. Finally, it is required to operate the Fundamental Academic Ability Learning Center(tentative name) to improve the ability of students who show the insufficient results in evaluation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Asia Marketing Journal
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• v.7 no.3
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• pp.53-76
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• 2005

With rapid technological and market change, new product development (NPD) complexity is a significant issue that organizations continually face in their development projects. There are numerous factors, which cause development projects to become increasingly costly & complex. A product is more likely to be successfully developed and marketed when the complexity inherent in NPD projects is clearly understood and carefully managed. Based upon the previous studies, this study examines the nature and importance of complexity in developing new products and then identifies several issues in managing complexity. Issues considered include: definition of complexity : consequences of complexity; and methods for managing complexity in NPD projects. To achieve high performance in managing complexity in development projects, these issues need to be addressed, for example: A. Complexity inherent in NPD projects is multi-faceted and multidimensional. What factors need to be considered in defining and/or measuring complexity in a development project? For example, is it sufficient if complexity is defined only from a technological perspective, or is it more desirable to consider the entire array of complexity sources which NPD teams with different functions (e.g., marketing, R&D, manufacturing, etc.) face in the development process? Moreover, is it sufficient if complexity is measured only once during a development project, or is it more effective and useful to trace complexity changes over the entire development life cycle? B. Complexity inherent in a project can have negative as well as positive influences on NPD performance. Thus, which complexity impacts are usually considered negative and which are positive? Project complexity also can affect the entire organization. Any complexity could be better assessed in broader and longer perspective. What are some ways in which the long-term impact of complexity on an organization can be assessed and managed? C. Based upon previous studies, several approaches for managing complexity are derived. What are the weaknesses & strengths of each approach? Is there a desirable hierarchy or order among these approaches when more than one approach is used? Are there differences in the outcomes according to industry and product types (incremental or radical)? Answers to these and other questions can help organizations effectively manage the complexity inherent in most development projects. Complexity is worthy of additional attention from researchers and practitioners alike. Large-scale empirical investigations, jointly conducted by researchers and practitioners, will help gain useful insights into understanding and managing complexity. Those organizations that can accurately identify, assess, and manage the complexity inherent in projects are likely to gain important competitive advantages.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.895-910
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• 2016

The purpose of this study is to explore secondary students' progression in group norms and argumentation competency through collaborative reflection about small group argumentation. The progression is identified as the development of group norms and an epistemic understanding of argumentation with the enhancement of group argumentation competency during collaborative reflection and argumentation lessons. Participants were four first grade middle school students who have different academic achievements and learning approaches. They participated in ten argumentation lessons related to photosynthesis and in seven collaborative reflections. As a result, the students' group norms related to participation were developed, and the students' epistemic understanding of argumentation was enhanced. Furthermore, the students' group argumentation competencies, identified as argumentation product and argumentation process, were advanced. As the collaborative reflection and argumentation lessons progressed, statements related to rebuttal increased and different students suggested a range of evidence with which to justify their claims or to rebut others' arguments. These findings will give a better idea of how to present an apt application of argumentation to science teachers and science education researchers.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.454-462
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• 2010

The current curriculum in the 7th informatics, which simply highlights the use of applied programs rather than enhancing creativity through learning several principles in information science and thus making it hard to have optimism on the future as a powerhouse in IT. By diagnosing these problems, Education Ministry revised the existing curriculum to have a more scientific access to the informatics. However, the curricular revision fails to meet the needs of learners sufficiently because it tends to put the considerations of theoretical specialists before the learners them selves. This paper compares and analyzes the overall understanding of learners on informatics, current-curriculum and 7th revised curriculum and that of college-level, thus offering the student-centered one. Also, the research tries to bridge the opinion gap between developer and learner by looking into the understanding level of university majors about the current curriculum. In doing so, thesis may provide the basis on exploring new approaches and contribute to establishing 7th revised curriculum in school.