• Journal of Agricultural Extension & Community Development
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• v.3 no.2
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• pp.285-297
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• 1996

One of the characteristics of the last four decades after 'World War II', was the 'discovery of famine' in the underdeveloped country, like Korea. A flurry of activities followed this sad discovery. Countless organizations and programs were set up to fight poverty and to combat famine in rural sector. In these days, the dominant development theory was 'modernization theory' which have gratuitously assumed that third world countries are like western countries are, and respond to the same stimuli as western countries do, although third world countries have completely different cultures, traditions, and mentality from western countries. Among the many problems caused by 'modernization theory', this research focused on the noel environmental problems. In the West the discovery of nature and its progressive control by means of science and technology are phenomena. Modem progress born in the West and carried to the rest of the world is not integrally positive and therefore can't be identified with the internal development of man and nature. As a result, the so-called modernization of Korea and other countries is contributing to the degradation of the nature and environment. It is important to give up the illusion that the fight against famine is a simple matter that could be solved through the imitation of western countries. It is also necessary to abandon the belief that the earth as a reservoir of unlimited resources, there to be exploited ad hoc for mankind's survival. Man-environment relationship must, essentially, be one of mutualism and not a case of survival of the fittest: In other words, man's survival is directly related to the survival of the earth and its resources.

• Journal of the Korean Society for information Management
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• v.19 no.2
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• pp.159-179
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• 2002

The Dublin Core, MARC. IFLA-FRBR user communities are developing international standards for describing textual, physical and audiovisual resources to enable their resources discovery over the Internet. Therefore the metadata interoperability Problem has been exacerbated by the need for more complex metadata descriptions. In this paper we propose a new mechanism for metadata interoperability based on the new semantic web applications : IFLA-FRBR framework, INDECS metadata and an event-aware ABC models. This study introduces a new approach method which is essential to the generation of interoperable metadata descriptions, particularly in the context of multimedia contents.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.759-770
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• 2009

A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.

• Proceedings of the KSRS Conference
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• v.2
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Genomics & Informatics
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• v.1 no.2
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• pp.65-74
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• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.293-300
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• 2004

Ad hoc networks are groups of mobile hosts without any fixed infrastructure. Frequent changes in network topology owing to node mobility make these networks very difficult to manage. Therefore, enhancing the reliability of routing paths in ad hoc networks gets more important. In this paper, we propose a ZRP(Zone Routing Protocol)-based route discovery scheme that can not only reduce the total hops of routing path, but Improve security through authentications between two nodes. And to solve the problem in maintenance of routing paths owing to frequent changes of the network topology, we adopt a query control mechanism. The effectiveness of our scheme is shown by simulation methods.

• Communications of Mathematical Education
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• v.26 no.1
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• pp.1-13
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• 2012

Ki-Won Chang(1903-1966) is considered as the first mathematician who made a contribution to the study of the history of Korean mathematics. In this paper, we introduce contributions of Ki-Won Chang, his discovery of old Korean literatures on mathematics, and his academic contribution on the history of Korean mathematics. Then we analyze and compare his conclusions on old Korean mathematics with recent works of others. This work shows some interesting discovery.