• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.99-118
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• 2012

The aim of this study is to define a correlation between spatial distribution characteristics of FFG(Functional Feeding Groups) of aquatic insects and related environmental factors in the Geum River based on the theory of RCC(River Continuum Concept). For that objective we had used SMRA(Stepwise Multiple Regression Analysis) method to analyze close relationship between the distribution of aquatic insects and the physical and chemical factors that may affect their inhabiting environment in the study area. And then, a probabilistic method named Frequency Ratio Model(FRM) and spatial analysis function of GIS were applied to produce a predictive distribution map of biota community considering their distribution characteristics according to the environmental factors as related variables. As a result of SMRA, the values of decision coefficient for factors of elevation, stream width, flow velocity, conductivity, temperature and percentage of sand showed higher than 0.5. Therefore these 6 environmental factors were considered as major factors that might affect the distribution characteristics of aquatic insects. Finally, we had calculated RMSE(Root Mean Square Error) between the predicted distribution map and prior survey database from other researches to verify the result of this study. The values of RMSE were calculated from 0.1892 to 0.4242 according to each FFG so we could find out a high reliability of this study. The results of this study might be used to develop a new estimation method for aquatic ecosystem with macro invertebrate community and also be used as preliminary data for conservation and restoration of stream habitats.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.533-540
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• 2013

Anonymity is the one of main reasons for substantial improvement of Internet. It encourages various users to express their opinion freely and helps Internet based distributed systems vitalize. But, anonymity can cause unexpected threats because personal information of an online user is hidden. Especially, distributed systems are threatened by Sybil attack, where one malicious user creates and manages multiple fake online identities. To prevent Sybil attack, the traditional solutions include increasing the complexity of identity generation and mapping online identities to real-world identities. But, even though the high complexity of identity generation increases the generation cost of Sybil identities, eventually they are generated and there is no further way to suppress their activity. Also, the mapping between online identities and real identities may cause high possibility of losing anonymity. Recently, some methods using online social network to prevent Sybil attack are researched. In this paper, a new method is proposed for extracting a user's system-wide Sybil-resistant trust value by using the properties embedded in online social network graphs. The proposed method can be categorized into 3 types based on sampling and decision strategies. By using graphs sampled from Facebook, the performance of the 3 types of the proposed method is evaluated. Moreover, the impact of Sybil attack on nodes with different characteristics is evaluated in order to understand the behavior of Sybil attack.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.572-577
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• 2009

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. To prune the redundant speech segments in a large speech segment DB, we can utilize a decision-tree based triphone clustering algorithm widely used in speech recognition area. But, the conventional methods have problems in representing the acoustic transitional characteristics of the phones and in applying context questions with hierarchic priority. In this paper, we propose a new clustering algorithm to downsize the speech DB. Firstly, three 13th order MFCC vectors from first, medial, and final frame of a phone are combined into a 39 dimensional vector to represent the transitional characteristics of a phone. And then the hierarchically grouped three question sets are used to construct the triphone trees. For the performance test, we used DTW algorithm to calculate the acoustic similarity between the target triphone and the triphone from the tree search result. Experimental results show that the proposed method can reduce the size of speech DB by 23% and select better phones with higher acoustic similarity. Therefore the proposed method can be applied to make a small sized TTS.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.36-51
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• 2016

This study assessed urban flood risk using a Bayesian probability statistical method and GIS incorporating a climate change scenario. Risk is assessed based on a combination of hazard probability and its consequences, the degree of impact. Flood probability was calculated on the basis of a Bayesian model and future flood occurrence likelihoods were estimated using climate change scenario data. The flood impacts include human and property damage. Focusing on Seocho-gu, Seoul, the findings are as follows. Current flood probability is high in areas near rivers, as well as low lying and impervious areas, such as Seocho-dong and Banpo-dong. Flood risk areas are predicted to increase by a multiple of 1.3 from 2030 to 2050. Risk assessment results generally show that human risk is relatively high in high-rise residential zones, whereas property risk is high in commercial zones. The magnitude of property damage risk for 2050 increased by 6.6% compared to 2030. The proposed flood risk assessment method provides detailed spatial results that will contribute to decision making for disaster mitigation.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.918-929
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• 1999

동기식 스트림 암호통신 방식을 사용하는 암호통신에서는 암/복호화 과정 수행시 암호통신 과정에서 발생하는 사이클슬립으로 인해 키수열의 동기이탈 현상이 발생되고 이로 인해 오복호된 데이타를 얻게된다. 이러한 위험성을 감소하기 위한 방안으로 현재까지 암호문에 동기신호와 세션키를 주기적으로 삽입하여 동기를 이루는 주기적인 동기암호 통신방식을 사용하여 왔다. 본 논문에서는 CDMA(Cellular Division Multiple Access) 이동망에서 데이타서비스를 제공할 때 사용되는 점대점 프로토콜의 주소영역의 특성을 이용하여 단위 측정시간 동안 측정된 주소비트 정보와 플래그 패턴의 수신률을 이용하여 문턱 값보다 작은경우 동기신호와 세션키를 전송하는 비주기적인 동기방식을 사용하므로써 종래의 주기적인 동기방식으로 인한 전송효율성 저하와 주기적인 상이한 세션키 발생 및 다음 주기까지의 동기이탈 상태의 지속으로 인한 오류확산 등의 단점을 해결하였다. 제안된 알고리즘을 링크계층의 점대점 프로토콜(Point to Point Protocol)을 사용하는 CDMA 이동망에서 동기식 스트림 암호 통신방식에 적용시 동기이탈율 10-7의 환경에서 주기가 1sec인 주기적인 동기방식에서 요구되는 6.45x107비트에 비해 3.84x105비트가 소요됨으로써 전송율측면에서의 성능향상과 오복호율과 오복호 데이타 비트측면에서 성능향상을 얻었다. Abstract In the cipher system using the synchronous stream cipher system, encryption / decryption cause the synchronization loss (of key arrangement) by cycle slip, then it makes incorrect decrypted data. To lessen the risk, we have used a periodic synchronous cipher system which achieve synchronization at fixed timesteps by inserting synchronization signal and session key. In this paper, we solved the problem(fault) like the transfer efficiency drops by a periodic synchronous method, the periodic generations of different session key, and the incorrectness increases by continuing synchronization loss in next time step. They are achieved by the transfer of a non-periodic synchronous signal which carries synchronous signal and session key when it is less than the threshold value, analyzing the address field of point-to-point protocol, using the receiving rate of address bits information and flag patterns in the decision duration, in providing data services by CDMA mobile network. When the proposed algorithm is applied to the synchronous stream cipher system using point-to-point protocol, which is used data link level in CDMA mobile network, it has advanced the result in Rerror and Derror and in transmission rate, by the use of 3.84$\times$105bits, not 6.45$\times$107bits required in periodic synchronous method, having lsec time step, in slip rate 10-7.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.765-774
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• 2011

The purpose of this study is to provide informative statistics which can be used for effective Diabetes Management Programs. We collected and analyzed the data of 666 diabetic people who had participated in Korean National Health and Nutrition Examination Survey in 2007 and 2008. Group classification on management behavior of Diabetic Mellitus is based on the K-means clustering method. The Decision Tree method and Multiple Regression Analysis were used to study factors of the management behavior of Diabetic Mellitus. Diabetic people were largely classified into three categories: Health Behavior Program Group, Focused Management Program Group, and Complication Test Program Group. First, Health Behavior Program Group means that even though drug therapy and complication test are being well performed, people should still need to improve their health behavior such as exercising regularly and avoid drinking and smoking. Second, Focused Management Program Group means that they show an uncooperative attitude about treatment and complication test and also take a passive action to improve their health behavior. Third, Complication Test Program Group means that they take a positive attitude about treatment and improving their health behavior but they pay no attention to complication test to detect acute and chronic disease early. The main factor for group classification was to prove whether they have hyperlipidemia or not. This varied widely with an individual's gender, income, age, occupation, and self rated health. To improve the rate of diabetic management, specialized diabetic management programs should be applied depending on each group's character.

• The Journal of Bigdata
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• v.9 no.1
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• pp.101-113
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• 2024

Federated learning has garnered attention as an efficient method for training machine learning models in a distributed environment while maintaining data privacy and security. This study proposes a novel FedRFBagging algorithm to optimize the performance of random forest models in such federated learning environments. By dynamically adjusting the trees of local random forest models based on client-specific data characteristics, the proposed approach reduces communication costs and achieves high prediction accuracy even in environments with numerous clients. This method adapts to various data conditions, significantly enhancing model stability and training speed. While random forest models consist of multiple decision trees, transmitting all trees to the server in a federated learning environment results in exponentially increasing communication overhead, making their use impractical. Additionally, differences in data distribution among clients can lead to quality imbalances in the trees. To address this, the FedRFBagging algorithm selects only the highest-performing trees from each client for transmission to the server, which then reselects trees based on impurity values to construct the optimal global model. This reduces communication overhead and maintains high prediction performance across diverse data distributions. Although the global model reflects data from various clients, the data characteristics of each client may differ. To compensate for this, clients further train additional trees on the global model to perform local optimizations tailored to their data. This improves the overall model's prediction accuracy and adapts to changing data distributions. Our study demonstrates that the FedRFBagging algorithm effectively addresses the communication cost and performance issues associated with random forest models in federated learning environments, suggesting its applicability in such settings.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of the Korean Geographical Society
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• v.38
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• pp.15-36
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• 1988

In this paper, I consider the development of methods in contemporary human geography in terms of a dialectical relation of action and structure, and try to draw a new horizon of method toward which geographical research and spatial theory would develop. The positivist geography which was dominent during 1960s has been faced both with serious internal reflections and strong external criticisms in the 1970s. The internal reflections that pointed out its ignorance of spatial behavior of decision-makers and its simplication of complex spatial relations have developed behavioural geography and systems-theoretical approach. Yet this kinds of alternatives have still standed on the positivist, geography, even though they have seemed to be more real and complicate than the previous one, The external criticisms that have argued against the positivist method as phenomenalism and instrumentalism suggest some alternatives: humanistic geography which emphasizes intention and action of human subject and meaning-understanding, and structuralist geography which stresses on social structure as a totality which would produce spatial phenomena, and a theoretical formulation. Human geography today can be characterized by a strain and conflict between these methods, and hence rezuires a synthetic integration between them. Philosophy and social theory in general are in the same in which theories of action and structural analysis have been complementary or conflict with each other. Human geography has fallen into a further problematic with the introduction of a method based on so-called political ecnomy. This method has been suggested not merely as analternative to the positivist geography, but also as a theoretical foundation for critical analysis of space. The political economy of space with has analyzed the capitalist space and tried to theorize its transformation may be seen either as following humanistic(or Hegelian) Marxism, such as represented in Lefebvre's work, or as following structuralist Marxism, such as developed in Castelles's or Harvey's work. The spatial theory following humanistic Marxism has argued for a dialectic relation between 'the spatial' and 'the social', and given more attention to practicing human agents than to explaining social structures. on the contray, that based on structuralist Marxism has argued for social structures producing spatial phenomena, and focused on theorising the totality of structures, Even though these two perspectives tend more recently to be convergent in a way that structuralist-Marxist. geographers relate the domain of economic and political structures with that of action in their studies of urban culture and experience under capitalism, the political ecnomy of space needs an integrated method with which one can overcome difficulties of orthhodox Marxism. Some novel works in philosophy and social theory have been developed since the end of 1970s which have oriented towards an integrated method relating a series of concepts of action and structure, and reconstructing historical materialism. They include Giddens's theory of structuration, foucault's geneological analysis of power-knowledge, and Habermas's theory of communicative action. Ther are, of course, some fundamental differences between these works. Giddens develops a theory which relates explicitly the domain of action and that of structure in terms of what he calls the 'duality of structure', and wants to bring time-space relations into the core of social theory. Foucault writes a history in which strategically intentional but nonsubjective power relations have emerged and operated by virtue of multiple forms of constrainst wihthin specific spaces, while refusing to elaborate any theory which would underlie a political rationalization. Habermas analyzes how the Western rationalization of ecnomic and political systems has colonized the lifeworld in which we communicate each other, and wants to formulate a new normative foundation for critical theory of society which highlights communicatie reason (without any consideration of spatial concepts). On the basis of the above consideration, this paper draws a new norizon of method in human geography and spatial theory, some essential ideas of which can be summarized as follows: (1) the concept of space especially in terms of its relation to sociery. Space is not an ontological entity whch is independent of society and has its own laws of constitution and transformation, but it can be produced and reproduced only by virtue of its relation to society. Yet space is not merlely a material product of society, but also a place and medium in and through which socety can be maintained or transformed.(2) the constitution of space in terms of the relation between action and structure. Spatial actors who are always knowledgeable under conditions of socio-spatial structure produce and reproduce their context of action, that is, structure; and spatial structures as results of human action enable as well as constrain it. Spatial actions can be distinguished between instrumental-strategicaction oriented to success and communicative action oriented to understanding, which (re)produce respectively two different spheres of spatial structure in different ways: the material structure of economic and political systems-space in an unknowledged and unitended way, and the symbolic structure of social and cultural life-space in an acknowledged and intended way. (3) the capitalist space in terms of its rationalization. The ideal development of space would balance the rationalizations of system space and life-space in a way that system space providers material conditions for the maintainance of the life-space, and the life-space for its further development. But the development of capitalist space in reality is paradoxical and hence crisis-ridden. The economic and poltical system-space, propelled with the steering media like money, and power, has outstriped the significance of communicative action, and colonized the life-space. That is, we no longer live in a space mediated communicative action, but one created for and by money and power. But no matter how seriously our everyday life-space has been monetalrized and bureaucratised, here lies nevertheless the practical potential which would rehabilitate the meaning of space, the meaning of our life on the Earth.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.