• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Environmental Impact Assessment
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• v.4 no.3
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• pp.41-48
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• 1995

In former times the protection of our environment didn't play an important role due to the fact that emissions and effluents were not considered as serious impacts. However, opinions and scientific measurements meanwhile confirmed that the impacts are more serious than expected. Thus measures to protect our earth has to be taken into consideration. A part of these measures in the Environmental Impact Assessment (EIA). One of the most important parts of the EIA is the collection of basic datas and the following evaluation. Experience out of the daily business of Gerling Consulting Group shows that the content of the EIA has to be revised and enlarged in certain fields. The historical development demonstrated that in areas in which the population and the industrial activities reached high concentration there is a high necessity to develop strict environmental laws and regulations. Maximum values of the concentration of hazardous materials were fixed concerning the emission into and water. Companies not following these regulations were punished. The total amount of environmental offences increased rapidly during the last decade, at least in Germany. During this development the public consciousness concerning environmental affairs increased as well in the industrialized countries. But it could clearly be seen that the development in the field of environmental protection went into the wrong direction. The technologies to protect the environment became more and more sophisticated and terms as: "state of the art" guided more and more to lower emissions, Filtertechnologies and wastewater treatment for example reached a high technical level-but all these sophisticated technologies has one and the same characteristic: they were end-of-the pipe solutions. A second effect was that this kind of environmental protection costs a lot of money. High investments are necessary to reduce the dust emission by another ppm! Could this be the correct way? In Germany the discussion started that the environmental laws reduce the attractivity to invest or to enlarge existing investments within the country. Other countries seem to be not so strict with controlling the environmental laws which means it's simply cheaper to produce in Portugal or Greece. Everybody however knows that this is not the correct way and does not solve the environmental problems. Meanwhile the general picture changes a little bit and we think it changes into the correct direction "End-of-the-pipe" solutions are still necessary but this word received a real negative touch and nobody wants to be brought into connection with this word received a real negative touch and nobody wants to be brought into connection with this word especially in connection with environmental management and safety. Modern actual environmental management starts in a different way. Thoughts about emissions start in the very beginning of the production, they start with the design of the product and modification of traditional modes of production. Basis of these ideas are detailed analyses of products and processes. Due to the above mentioned facts that the public environmental consciousness changed dramatically a continous environmental improvement of each single production plant has to be guarantied. This question is already an important question of the EIA. But it was never really checked in a wholistic approach. Environmental risks have to be taken into considerations during the execution of an EIA. This means that the environmental risks have to be reduced down to a capable risk-level. Environmental risks have to be considered within the phase of planning, during the operation of a plant and after shut down. The experience shows that most of the environmental relevant accidents were and caused by human fault. Even in highly protected plants the human risk-factor can not be excluded during evaluation of the risk-potential. Thus the approach of an EIA has to regard technical evaluations as well as organizational thoughts and the human factor. An environmental risk is a threat to the environment. An analysis of the risk concerning the organizational and human aspect however never was properly executed during an EIA. A possible solution could be to use an instrument as the actual EMAS (Environmental Management System) of the EC for more accurate evaluation of the impact to the environment during an EIA. Organizations or investors could demonstrate by an approved EMAS or even by showing their installment of EMAS that not only the technical level of the planned investment meets the requested standards but as well the actual or planned management is able to reduce the environmental impact down to a bearable level.

• Archives of design research
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• v.18 no.4 s.62
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• pp.95-104
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• 2005

The purpose of this study is to estimate the determinants of product design and analyze the impacts of product design on quality competitiveness, product reliability, and consumer satisfaction in an attempt to provide a foundation for the theory of design management. For this empirical analysis, this study has derived the relevant measurement variables from a survey on 400 Korean manufacturing firms during the period of $August{\sim}October$ 2003. The empirical findings are summarized as follows: First, the determinants of product design are very significantly (at p<0.001) estimated to be the R&D capability, the level of R&D expenditure, the level of innovative activities(5S, TQM, 6Sigma, QC, etc.). This empirical result can support Pawar and Driva(1999)'s two principles by which the performance of product design and product development can be simultaneously evaluated in the context of CE(concurrent engineering) of NPD(newly product development) activities. Second, the hypothesis on the causality: product design${\rightarrow}$quality competitiveness${\rightarrow}$customer satisfaction${\rightarrow}$customer loyalty is very significantly (at p<0.001) accepted. This implies that product design positively affects consumer satisfaction, not directly but indirectly, by influencing quality competitiveness. This empirical result of this study can also support the studies of for example Flynn et al.(1994), Ahire et at.(1996), Afire and Dreyfus(2000) which conclude that design management is a significant determinant of product quality. The aforementioned empirical results are important in the following sense: the empirical result that quality competitiveness plays a bridging role between product design and consumer satisfaction can reconcile the traditional debate between QFD(quality function development) approach asserted by product developers and conjoint analysis maintained by marketers. The first empirical result is related to QFD approach whereas the second empirical result is related to conjoint analysis. At the same time, the empirical results of this study can support the rationale of design integration(DI) of Ettlie(1997), i.e., the coordination of the timing and substance of product development activities performed by the various disciplines and organizational functions of a product's life cycle. Finally, the policy implication (at the corporate level) from the empirical results is that successful design management(DM) requires not only the support of top management but also the removal of communication barriers, (i.e. the adoption of cross-functional teams) so that concurrent engineering(CE), the simultaneous development of product and process designs can assure product development speed, design quality, and market success.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• Korean Security Journal
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• no.22
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• pp.197-230
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• 2010

This study examined the institutional improvement directions of industrial security programs, particularly focusing upon policies and practices in the U.S., to enhance the effectiveness of industrial security programs in Korea. This study also aimed to investigate the significance of institutional and/or policy implementations in preventing economic espionage attempt. Data leakage and/or loss of trade secrets in corporations has been a scary proposition and a serious headache to both the CEOs and the CSOs(Chief Security Officers). Security professionals or practitioners have always had to deal with data leakage issues that arise from e-mail, instant messaging(IM), and other Internet communication channels. In addition, with the proliferation of wireless and mobile technology, it's now much easier than ever for loss by data breaches to occur, whether accidentally or maliciously or even by an economic espionage attempt. The researcher in this study used both a case study and a comparative research to analyze the different strategies and approaches between the U.S. and Korea in regard of implementing policies to mitigate damages by economic espionage attempts and prevent them from occurring. The researcher first examined the current policies and practices in the U.S. in terms of federal government's and agencies' approach and strategies on industrial security programs and their partnerships with private-commercial-sectors. The purpose of this paper is to explain and suggest selected findings, and a discussion of actions to be taken on implementing a proactive and tactical approach to enhance the effectiveness of industrial security programs to fight against information loss or data leaks. This study used case reviews, literatures, newspapers, articles, and Internet resources relating to the subject of this study for triangulation of data. The findings during this research are as follows. This research suggests that both the private and the governmental sector should closely cooperate in the filed of industrial security to strengthen its traditional prevention strategies and reduce opportunities of economic espionage as well. This study finally recognizes both the very importance of institutional development led by the Government in preventing economic espionage attempts and its effectiveness when properly united with effective industrial security programs.

• Journal of Gifted/Talented Education
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• v.11 no.3
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• pp.131-153
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• 2001

The importance of identifying gifted children during early childhood is becoming recognized. Nonetheless, most researchers preferred to study the primary and secondary levels where children are already and more clearly demonstrating what talents they have, and where more reliable predictions of gifted may be made. Comparatively lisle work has been done in this area. When we identify giftedness during early childhood, we have to consider the potential of the young children rather than on actual achievement. Giftedness during early childhood is still developing and less stable than that of older children and this prevents us from making firm and accurate predictions based on children's actual achievement. Dynamic assessment, based on Vygotsky's concept of the zone of proximal development(ZPD), suggests a new idea in the way the gifted young children are identified. In light of dynamic assessment, for identifying the potential giftedness of young children. we need to involve measuring both unassisted and assisted performance. Dynamic assessment usually consists of a test-intervene-retest format that focuses attention on the improvement in child performance when an adult provides mediated assistance on how to master the testing task. The advantages of the dynamic assessment are as follows: First, the dynamic assessment approach can provide a useful means for assessing young gifted child who have not demonstrated high ability on traditional identification method. Second, the dynamic assessment approach can assess the learning process of young children. Third, the dynamic assessment can lead an individualized education by the early identification of young gifted children. Fourth, the dynamic assessment can be a more accurate predictor of potential by linking diagnosis and instruction. Thus, it can make us provide an educational treatment effectively for young gifted children.

• Journal of Home Health Care Nursing
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• v.10 no.1
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• pp.58-72
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• 2003

The purpose of this study was to describe the perceived burden of the terminally III patients's caregiver and to analyze relationship between the perceived burden and the various demographics, illness characteristics, family relationships, and economic factor of the family & patients. The sample of 132 caregivers who care for the terminally III patients Kyung-Gi province, Seoul, Korea. The period of this study was from August to September, 2002. The perceived burden of the family caregiver was measured by the burden scale(20 items, 4 point scale) developed by Montgomery et al. (1985). The Data was analyzed using SAS-program by t-test and ANOVA. The results were as follows; 1. The mean of the family caregiver's burden score was 3.02. The score showed that caregivers perceive severe the level of burden. The hight items of the family caregiver's burden were' I feel it is painful to watch patient's diseases'(3.77). 'I feel afraid for what the future holds for my patients'(3.66), 'I feel it reduced to amount of privacy time'(3.64). 2. The caregiver's burden was significantly related to patient's gender(F=3.17, p= 0.0020), patient's job(F=2.49, p=0.0476), caregiver's age(F=4.29, p=0.0030), and caregiver's job(F=2.49, p=0.0476). 3. The caregiver's burden according to illness characteristics showed no significant difference. 4. The caregiver's burden was significantly associated with patient's family relationship (F=4.05, p=0.0041), patient's care mean period in a day(F=47.18,

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Life Science
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• v.25 no.6
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• pp.693-702
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• 2015

Blocking new blood-vessel formation (angiogenesis) is now recognized as a useful approach to the therapeutic treatment of many solid tumors. The best validated approach to date is to target the vascular endothelial growth-factor (VEGF) pathway, a key regulator of angiogenesis. Many natural products and extracts that contain a variety of chemopreventive compounds have been shown to suppress the development of malignancies through their anti-angiogenic properties. Phellodendron amurense, which is widely used in Korean traditional medicine, has been shown to possess antitumor, antimicrobial, and anti-inflammatory properties, among others. The present study investigated the effects of P. amurense hot-water extract (PAHWE) on angiogenesis, a key process in tumor growth, invasion, and metastasis. To investigate PAHWE’s anti-angiogenic properties, this study’s authors performed an analysis of angiogenesis and endothelial-cell proliferation, migration, invasion, and tube formation, as well as zymogram assays and the rat aortic ring-sprouting assay. PAHWE inhibited cell growth, mobility, and vessel formation in response to VEGF in vitro and ex vivo. Furthermore, it reduced VEGF-induced intracellular signaling events, such as the activation of matrix metalloproteinases (MMPs) -2 and -9. These results indicate that PAHWE’s anti-angiogenic properties might lead to the development of potential drugs for treating angiogenesis-associated diseases such as cancer.