• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• Journal of IKEEE
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• v.20 no.3
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• pp.251-259
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• 2016

This paper describes that how to enhance the robustness of semiconductor TRM(Transmitter and Receiver Module) through the bias sequencing and tuning the switching time. Previous circuit designs focused on improving the MDS(Minimum Detection Signal) performance. Because TRM has critical problem which transmission output signal leak into receiver by it's compact design. Under this condition, TRM was frequently broken down within the MTBF(Mean Time Between Failure). This study proposes the bias sequencing and tuning the switching time to improve above problem. At first, we collected major failure symptom and infer it's cause. Second, we demonstrated it's effect by derive the improvement method and apply it to our system. And finally we can convinced that the proposed method clear the frequent failure problem with its lack of isolation.

• Education of Primary School Mathematics
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• v.21 no.2
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• pp.237-260
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• 2018

This study investigated the models of four countries' elementary mathematics textbooks in Ratio and Rate and identified how multiplicative perspectives on proportional relationships and the structure of proportion situations are reflected in the textbooks. For this, textbooks of 5th and 6th grade textbooks in Korea Japan, Singapore and U.S. are compared and analyzed. As a result, we can find multiplicative perspectives on proportional relationships and the structure of proportion situations on pictorial models, ratio tables, double number lines and double tape diagrams. Also, the development of Japanese textbooks from multiple batches perspectives to variable parts perspectives and the examples of the use with two models together implied the connection and union of two multiplicative perspectives. Based on these results, careful verification and discussion for the next textbook is needed to develop students' proportional reasoning and teach some effective reasoning strategies. And this study will provide the implication for what kinds of and how visual models are presented in the next textbook.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.945-956
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• 2020

The publication of data is essential in order to receive high quality services from many applications. However, if the original data is published as it is, there is a risk that sensitive information (political tendency, disease, ets.) may reveal. Therefore, many research have been proposed, not the original data but the synthetic data generating and publishing to privacy preserve. but, there is a risk of privacy leakage still even if simply generate and publish the synthetic data by various attacks (linkage attack, inference attack, etc.). In this paper, we propose a synthetic data generation algorithm in which privacy preserved by applying differential privacy the latest privacy protection technique to GAN, which is drawing attention as a synthetic data generative model in order to prevent the leakage of such sensitive information. The generative model used CGAN for efficient learning of labeled data, and applied Rényi differential privacy, which is relaxation of differential privacy, considering the utility aspects of the data. And validation of the utility of the generated data is conducted and compared through various classifiers.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.231-238
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• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.971-978
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• 2008

This paper designs a service-oriented wireless sensor network ontology model which can be used as a knowledge base in future ubiquitous computing. In contrast to legacy approaches, this paper defines the new service classes (ServiceProperty, LocationProperty, and PhysicalProperty), as well as their properties and constraints that enable the service-oriented service based on service items. The service item merging between the proposed model and the legacy ontology was processed using the "equivalentClass" object property of OWL. The Protege 3.3.1 and RACER 1.9.0 inference tools were used for the validation and consistency check of the proposed ontology model, respectively, and the results of service query was applied to the newly defined property in SPARQL language without reference to the properties of legacy ontology.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.5-12
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• 2021

The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

• The Journal of Korean Philosophical History
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• no.36
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• pp.7-42
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• 2013

This article is on the introduction and origin of Korean Neo-Confucianism. In this article, it is verified and clarified that a scholar named Jeong, Sin-bo (鄭臣保) from Southern Song settled on today's Seosan Ganwoldo (看月島) in the year of 1237 (24th year of the king Gojong in Korean Dynasty) and he introduced the Neo-Confucianism both by Jeong, Myung-do (程明道) and Jeong, Yi-cheon (程伊川) who are also called Double Jeong to Korean scholars. Based on these facts, it overturns the history that Anhyang (安珦) first introduced Neo-Confucianism to Korean Dynasty in the year of 1290 even with 35 years ahead. When this gains official approval by the academia, the history of Neo-Confucianism seems to be rewritten. This article first examines changes in history of Korean Neo-Confucianism with three stages and then concentrates on the life of Jeong, Sin-bo. It presents that Jeong, Sin-bo was a descendant of a Southern Song's noble family named Pogang Jeong (浦江鄭氏) and he committed to Chunqiu thoughts (春秋思想) and spirit of loyalty (義理精神) naturally as the posterity of Pogang Jeong. Lastly, it also infers the transmission of Jeong, Sin-bo's scholastic mantle and his influence on the posterity.

• Journal of IKEEE
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• v.25 no.4
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• pp.664-671
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• 2021

This paper proposes a GPGPU structure that can reduce the number of operations and memory access by effectively applying a data reuse method to a convolutional neural network(CNN). Convolution is a two-dimensional operation using kernel and input data, and the operation is performed by sliding the kernel. In this case, a reuse method using an internal register is proposed instead of loading kernel from a cache memory until the convolution operation is completed. The serial operation method was applied to the convolution to increase the effect of data reuse by using the principle of GPGPU in which instructions are executed by the SIMT method. In this paper, for register-based data reuse, the kernel was fixed at 4×4 and GPGPU was designed considering the warp size and register bank to effectively support it. To verify the performance of the designed GPGPU on the CNN, we implemented it as an FPGA and then ran LeNet and measured the performance on AlexNet by comparison using TensorFlow. As a result of the measurement, 1-iteration learning speed based on AlexNet is 0.468sec and the inference speed is 0.135sec.