• Economic and Environmental Geology
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• v.25 no.2
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• pp.145-151
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• 1992

In order to study the optimum depth for the soil geochemical exploration in the area which is affected by agricultural activities and waste disposal of metal mine, the soil samples were sampled from the B layer of residual soil and vertical 7 layers up to 250 cm in the rice field and 3 layers up to 90 cm in the ordinary field. They were analyzed for Au, As, Cu, Pb and Zn by AAS, AAS-graphite furnace and ICP. To investigate the proper depth for the soil sampling in the contaminated area, the data were treated statistically by applying correlation coefficient, factor analysis and trend analysis. It is conclude that soil geochemical exploration method could be applied in the farm-land and a little contaminated area. The optimum depth of soil sampling is 60 cm in the ordinary field, and 150~200 cm in the rice field. Soil sampling in the area of a huge mine waste disposal is not recommendable. Plotting of geochemical map with factor scores as a input data shows a clear pattern compared with the map of indicater element such as As or Au. The second or third degree trend surface analysis is effective in inferring the continuity of vein in the area where the outcrop is invisible.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.219-226
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• 2013

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.

• Journal of the Korean Society for Library and Information Science
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• v.37 no.2
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• pp.153-168
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• 2003

A hyperlink on academic World Wide Web has started to be recognized as a form of collaborative communication network connecting individual researchers and research groups and expanding their collaboration relations by making possible easy and direct online contact among people or groups anywhere in the world. This paper describes the structure of academic hyperlinks embedded in universities' Web sites hosted at the 10 Asian countries and further, examines the association between the structure of the hyperlink network and collaborative communication pattern among those countries based on their frequency of co-authoring articles. This research found that the number of inter-hyperlinks among universities' Web sites was significantly correlated with the frequency of co-authored articles across the 10 countries.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.729-734
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• 2012

Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.431-446
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• 2017

Rock bridges in rock masses would increase the bearing capacity of Non-persistent discontinuities. In this paper the effect of ratio of rock bridge surface to joint surface, rock bridge shape and normal load on failure behaviour of intermittent rock joint were investigated. A total of 42 various models with dimensions of $15cm{\times}15cm{\times}15cm$ of plaster specimens were fabricated simulating the open joints possessing rock bridge. The introduced rock bridges have various continuities in shear surface. The area of the rock bridge was $45cm^2$ and $90cm^2$ out of the total fixed area of $225cm^2$ respectively. The fabricated specimens were subjected to shear tests under normal loads of 0.5 MPa, 2 MPa and 4 MPa in order to investigate the shear mechanism of rock bridge. The results indicated that the failure pattern and the failure mechanism were affected by two parameters; i.e., the ratio of joint surface to rock bridge surface and normal load. So that increasing in joint area in front of the rock bridge changes the shear failure mode to tensile failure mode. Also the tensile failure change to shear failure by increasing the normal load.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1105-1108
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• 2012

순차 패턴 탐사 기법은 순서를 갖는 패턴들의 집합 중에 빈발하게 발생하는 패턴을 찾아내는 기법이다. USN 환경에서 발생하는 스트림 데이터는 시간 속성을 갖는 이벤트들의 집합으로 표현할 수 있으며 순차 패턴 탐사 기법을 이용하여 유용한 정보를 탐사할 수 있다. 그러나 스트림 데이터 환경에서는 데이터가 무한하고 연속적으로 발생하기 때문에 모든 데이터를 저장하여 패턴을 탐사하는 기법을 적용하는 데는 문제가 있다. 이 논문에서는 향상된 데이터 처리방식을 사용하여 순차패턴을 탐사하는 스트림 데이터 마이닝 기법에 대하여 제안한다. 제안하는 기법은 의미 단위의 가변적 윈도우를 사용하여 스트림 데이터로부터 트랜잭션을 생성하고 이 트랜잭션들의 집합을 해시와 슬라이딩 윈도우를 사용하여 스트림 데이터의 순차 패턴을 탐사한다. 이를 이용한 제안 기법은 실시간 시스템에 적합하게 데이터 저장 공간 사용의 효율성을 높이고 신속하게 유용한 패턴을 탐사할 수 있다.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.517-522
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• 2019

In many businesses, chatbots enhance the user experience by providing the most immediate and direct feedback to user questions. The area of use of chatbots is growing. In this study, the three types of chatbot definition, command method, function, and platform are classified according to their distinct factors. In the process, the functional delimiter element is necessary for the Chatbot UX, which is a key technical element of the functional part of pattern recognition, natural language processing, semantic web, text mining, and context-aware computing. However, the limitations at this stage were also known. Based on this, we analyzed the chatbot's UX elements for Facebook, Skype, Telegram, and Google Assistant for a better user experience. Basic UI elements such as cards, quick response, command, and application of persistent menus are needed as user experience elements.

• BMB Reports
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• v.53 no.12
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• pp.658-663
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• 2020

The N-myc downstream regulated gene (NDRG) family members are dysregulated in several tumors. Functionally, NDRGs play an important role in the malignant progression of cancer cells. However, little is known about the potential implications of NDRG4 in pancreatic ductal adenocarcinoma (PDAC). The aim of the current study was to elucidate the expression pattern of NDRG4 in PDAC and evaluate its potential cellular biological effects. Here, we firstly report that epigenetic-mediated silencing of NDRG4 promotes PDAC by regulating mitochondrial function. Data mining demonstrated that NDRG4 was significantly down-regulated in PDAC tissues and cells. PDAC patients with low NDRG4 expression showed poor prognosis. Epigenetic regulation by DNA methylation was closely associated with NDRG4 down-regulation. NDRG4 overexpression dramatically suppressed PDAC cell growth and metastasis. Further functional analysis demonstrated that up-regulated NDRG4 in SW1990 and Canpan1 cells resulted in attenuated mitochondrial function, including reduced ATP production, decreased mitochondrial membrane potential, and increased fragmented mitochondria. However, opposite results were obtained for HPNE cells with NDRG4 knockdown. These results indicate that hypermethylation-driven silencing of NDRG4 can promote PDAC by regulating mitochondrial function and that NDRG4 could be as a potential biomarker for PDAC patients.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.53-64
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• 2021

Depression is one of the most important psychiatric disorders worldwide. Most depression-related data mining and machine learning studies have been conducted to predict the presence of depression or to derive individual risk factors. However, since depression is caused by a combination of various factors, it is necessary to identify the complex relationship between the factors in order to establish effective anti-depression and management measures. In this study, we propose a methodology for identifying and interpreting patterns of depression expressions using the method of deriving random forest rules, where the random forest rule consists of the condition for the manifestation of the depressive pattern and the prediction result of depression when the condition is met. The analysis was carried out by subdividing into 4 groups in consideration of the different depressive patterns according to gender and age. Depression rules derived by the proposed methodology were validated by comparing them with the results of previous studies. Also, through the AUC comparison test, the depression diagnosis performance of the derived rules was evaluated, and it was not different from the performance of the existing PHQ-9 summing method. The significance of this study can be found in that it enabled the interpretation of the complex relationship between depressive factors beyond the existing studies that focused on prediction and deduction of major factors.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.