• Geomechanics and Engineering
• /
• v.33 no.3
• /
• pp.231-242
• /
• 2023

This paper represents a comparative study in which Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time-dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, Data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization; therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.5
• /
• pp.611-619
• /
• 2014

Combining similar data provides larger data sets through conducting test for homogeneity of several samples under various production processes or samples from different LOTs. The test for homogeneity has been applied to either variable or attribute data, and for variable data set physical homogeneity has been tested without consideration of the specification to the set. This paper proposes a method for test of homogeneity based on quality level through using both variable data and the specification. Quality-based test for homogeneity as a way of combining data is implemented by test for coefficient of variation in the proposed method. The method was verified through the application to the data set in open literature. And possibility to combine performance data for various types of thermal battery was discussed in order to estimate operation reliability.

• Journal of Broadcast Engineering
• /
• v.13 no.1
• /
• pp.86-91
• /
• 2008

In the field of network data analysis, the research of how much the estimation data reflects the population data is inevitable. This paper compares and analyzes the well known MIT Lincoln Lab network data, which is composed of collectable standard information from the network with the KDD CUP 99 dataset which was composed from the MIT/LL data. For comparison and analysis, the protocol information of both the data was used. Correspondence analysis was used for analysis, SVD was used for 2 dimensional visualization and weigthed euclidean distance was used for network data quantification.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1323-1326
• /
• 2003

In this paper, we propose set-up model of silicon steel cold rolling mill. Until now, the working of Silicon Steel is operated using the look-up table value of roll force which a field operator finds by making good use of his experience. Therefore, the standardization of data and an improvement of the quality on product are very difficult. So we establish neural model using field data of various kinds of coil at each pass.

• Genomics & Informatics
• /
• v.18 no.1
• /
• pp.2.1-2.11
• /
• 2020

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

• 정보화사회
• /
• s.129
• /
• pp.42-49
• /
• 1999

디지털 비디오의 혁명이 가시화 되면서 가정의 각 Consumer기기들 간의 고속 Digital Interface가 요구되어져 왔다. 이러한 요구의 예는 MPEG-2 Transport Stream을 이용한 Set-Top Bx, Digital Television, DVCR 또는 Camcorder간의 Interface와 Interactive Games, Computers 및 주변기기간의 Control/Data Interface를 포함하고 있다. 엄밀히 말해 Data의 일반적인 전송을 지원하는 Interface가 요구되어지며, IEEE1394 Standard는 이에 대한 최적의 Solution을 제공해 준다. 본 기술은 IEEE1394를 기반으로 MPEG-2 Transport Stream을 주고받을 수 있는 방법을 제공하기 위한 Hardware를 개발하는 기술인 MPEG2용 IEEE1394 Link Chip Set 개발 기술에 대하여 설명한다.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1545-1546
• /
• 2008

In this study, an image processing method with FRANSAC(Fuzzy RANSAC) is presented and discussed for the development of a microscopic gap measuring algorithm. Many problems in edge detection processing are mainly occurred by the illumination system. A serious problem is that the edge set of gap could include the error elements that have relatively larger error than normal. This problem leads to a incorrect measurement of gap. We present a gap measuring algorithm using FRANSAC[1] that is a representative robust estimation algorithm. FRANSAC is peformed by first categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification and then sampling in only good sample set. Experimental results show that the presented gap measuring algorithm gives a higher accurate value of gap especially for the more noisy image data.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.4
• /
• pp.269-274
• /
• 2009

Measuring uncertainty for fuzzy sets has been carried out by calculating fuzzy entropy. Fuzzy entropy of fuzzy set is derived with the help of distance measure. The distance proportional value between the fuzzy set and the corresponding crisp set is designed as the fuzzy entropy. The usefulness is verified by proving the proposed entropy. Generally, fuzzy entropy contains the complementary characteristics that the fuzzy entropies of fuzzy set and complementary fuzzy set have the same entropies. Discrepancy that low fuzzy entropy did not guarantee the data certainty was overcome by modifying fuzzy entropy formulation. Obtained fuzzy entropy is analyzed and discussed through simple example.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.816-821
• /
• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• Journal of KIISE:Software and Applications
• /
• v.33 no.2
• /
• pp.252-266
• /
• 2006

Many problems in computer vision are mainly based on mathematical models. Their optimal solutions can be found by estimating the parameters of each model. However, provided an input data set is involved outliers which are relative]V larger than normal noises, they lead to incorrect results. RANSAC is a representative robust algorithm which is used to resolve the problem. One major problem with RANSAC is that it needs priori knowledge(i.e. a percentage of outliers) of the distribution of data. To solve this problem, we propose a FRANSAC algorithm which improves the rejection rate of outliers and the accuracy of solutions. This is peformed by categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification at each iteration and sampling in only good sample set. In the experimental results, we show that the performance of the proposed algorithm when it is applied to the linear regression and the calculation of a homography.