• The Journal of the Petrological Society of Korea
• /
• v.11 no.3_4
• /
• pp.138-156
• /
• 2002

Precambrian metamorphic rocks of southwest Sobaeksan massif consist of mainly granitic gneiss, porphyroblastic gneiss and quartzofeldspathic gneiss. The orthopyroxene-bearing rocks(charnockites) are found in the west of Hadong-Sancheong anorthosite complex. The charnockites are 3km wide, 12km long and divided into massive and foliated types based on their texture. The compositions of charnockites are comparable to granodiorite to adamellite and subalkaline. Variations in major and trace elemental abundances show typical magmatic differentiation trends. The geochemical data plotted on tectonic discrimination diagrams reveal that these charnockites were formed in the active tectonic environment. The massive and folidated charnockites are mainly composed of plagioclase, orthopyroxene, microcline, quartz and disseminated garnet. Camels generally show characteristic zonal textures with decreasing $X_{alm}$(0.74~0.83), $X_{Py}$ (0.07~0.12) and $X_{Mg}$ (0.12~0.08) and increasing $X_{grs}$(0.03~0.15) from core to rim. Metamorphic temperature and pressure of the charnockites estimated from orthopyroxene-garnet-plagioclase-quartz assemblages show wide range of variation of $600~900^{\circ}C$ and 2.5~7.5 kbar respectively. The results of P-T estimates indicate an anticlockwise P-T evolution path.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.7
• /
• pp.970-976
• /
• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.1095-1106
• /
• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.276-284
• /
• 2019

Seismic research on bridges, dams and nuclear power plants, which are infrastructure in Korea, has been carried out since early on, but in the case of structures in thermal power plants, research is insufficient. In this study, a total of 192 dynamic analyzes were performed for 16 actual seismic waves and 12 PGAs. As a result, the probability of failure increased as the PGA value increased for each applied seismic wave, but it was different for each seismic wave. As a result, at 0.22G, the ratio of the compressive limit reached to the limit state was 25% and the ratio of the relative displacement reached the limit state was 13%. So, the probability of collapse due to compressive failure Is higher. Therefore, the fragility curve of the chimney which is the subject of this study can be used as a quantitative basis to determine the limit state of the target structure when an earthquake occurs and to be used for the safety design of the thermal power plants.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.2
• /
• pp.1-8
• /
• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.24 no.6
• /
• pp.77-84
• /
• 2020

In this paper, we developed a damage evaluation technique that can determine the damage location of a long-sized structure such as a cable-stayed bridge, and verified the performance of the developed technique through experiments. The damage assessment method aims to extract data that can evaluate the damage of the structure without the undamage data and can determine the damage location only by analyzing the response data of the structure. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To evaluate the performance of the developed technique experimentally, cable damage experiments were conducted on model cable-stayed bridges. As a result, the damage assessment method considering variability automatically outputs the damageless data according to external force, and it is confirmed that the performance of extracting information that can determine the damage location of the cable through the analysis of the outputted damageless data and the measured damage data is shown.

• Journal of Korean Society of Transportation
• /
• v.26 no.3
• /
• pp.53-66
• /
• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.3 no.2
• /
• pp.37-47
• /
• 2000

Since 1995, agropolitan cities have been created, in order to pursue the balanced development between urban cities and its surrounding rural counties. However, the inequality of regional level that was caused by indiscreet development has become the ever-serious problems recently. Therefore, this study aims to analyze regional characteristics and patterns for setting up the sustainable spatial planning. Firstly, the regional characteristics were summarized by five factors; development-oriented factor, agricultural factor, living environmental factor, rice growing, fruit gardening factor. The regional patterns were classified with five patterns using cluster analysis; orchard farming, farming of medium and small size, small stagnation, under urbanization, mixed urban-rural properties, and industry of southeastern seashore. Accuracy of the results by discrimination analysis showed that pattern II, V, and VI were confidence level of 100%, but the others had nearly 90% confidence level.

• Journal of Sensor Science and Technology
• /
• v.12 no.6
• /
• pp.289-297
• /
• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.85-98
• /
• 2021

Domestic delivery app services are taking a leap forward as the non-face-to-face culture spreads due to the COVID 19 situation and the industrial scale is also growing. In the expanding delivery app service market, we try to verify the structural relationship between variables by empirically analyzing the influencing factors of users' trust in rational and emotional factors. Delivery app service users trust and discriminate parameters in the relationship between rational and emotional factors. Satisfaction according to the trust of a valid delivery app service and service expansion model through word of mouth was designed. It was verified through a hypothesis whether it had an effect, and it can be used as a variety of service strategies for delivery app service users.