• 한국지구물리탐사학회:학술대회논문집
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• 2002.09a
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• pp.101-125
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• 2002

A lake seismic survey was carried out to investigate possible geohazards for construction of the underground LPG storage at Namyang Lake. The proposed survey site has a land-lake combined geography and furthermore water depth of the lake is shallow. Therefore, various seismic methods such as marine single channel high resolution seismic reflection survey, sonobuoy refraction survey, land refraction survey and land-lake combined refraction survey were applied. Total survey amounts are 34 line-km of high resolution lake seismic survey, 14 lines of sonobuoy refraction survey, 890 m of land refraction survey and 8 lines of land-lake combined refraction survey. During the reflection survey, there were severe water reverberations from the lake bottom obscured subsurface profiling. These strong multiple events appeared in most of the survey area except the northern and southern area near the embankment where seems to be accumulated mainly mud dominated depositions. The sonobuoy refraction profiles also showed the same Phenomena as those of reflection survey. Meanwhile the results of the land-lake combined refraction survey showed relatively better qualities. However, the land refraction survey did not so due to low velocity soil layer and electrical noise. Summarized results from the lake seismic survey are that acoustic basement with relatively flat pattern appeared 30m below water level and showed three types of bedrock such as fresh, moderately weathered and weathered type. According to the results of the combined refraction survey, a velocity distribution pattern of the lake bottom shows three types of seismic velocity zone such as >4.5 km/s, 4.5-4.0km/s and <4.0km/s. The major fault lineament in the area showed NW-SE trend which was different from the Landsat image interpretation. A drilling was confirmed estimated faults by seismic survey.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.4
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• pp.286-294
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• 2014

The purpose of this study is to investigate the effects of applying density current generator (hereafter referred to as DCG to large lakes on the operating conditions of DCG, de-stratification, water quality improvement and inhibition of algae occurrence. As a result of a survey conducted to derive the optimum operating parameters of DCG in a condition to minimize eco-toxicity, the following conclusions were obtained. During the survey period, a marked stratification appeared in September to October 2011 and May 2012. At this time, the average depth of water to form thermocline was found to be $5{\pm}2$ m, so the location of discharge port for the operation of DCG was determined to be about 5 m below from the surface. To minimize the adverse effects of benthos and obtain the effect of water mixture at the time of water circulation, the mixing ratio of surface water and deep water was designed to be 3:1 by means of ecotoxicological assessment on the DCG operating characteristics. To select the appropriate operating hours for DCG, DCG was operated by 12 hr, 24 hr, 36 hr and 48 hr. As its result, the formation of thermocline did not occur during the operation of 36 hr. Also, It was effected that start reoperating from 3rd day after stop 2days under the condition of operated during 36 hr with calculated power consumption. Under the above conditions, the results of DO and water temperature analysis during the operation of DCG showed that the stratification, which was distinct previously, appeared to be weak, and relatively lower levels than those before operation were found as a result of water quality analysis on COD and chlorophyll-a, which leads to the conclusion that the water body is maintained at a stable condition due to the circulation of water by the occurrence of density current resulting from the operation of DCG.

• Journal of the Korea Concrete Institute
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• v.21 no.4
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• pp.513-521
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• 2009

Recently, the externally prestressed unbonded concrete structures are increasingly being built. The mechanical behavior of prestressed concrete beams with external unbonded tendon is different from that of normal bonded PSC beams in that the slip of tendons at deviators and the change of tendon eccentricity occurs as external loads are applied in external unbonded PSC beams. The purpose of the present paper is therefore to evaluate the flexural behavior by performing static flexural test according to tendon area and tendon force. From experimental results, before flexural cracking, there was no difference between external members and bonded members. However, after cracking, yielding load of reinforcement, ultimate load, and the tendon stress of external members was lower than that of bonded members. For the relationship of load-tendon stress, the increasing of tendon strain was inversely proportional to the initial tendon force. However, even if the initial tendon force was large, the tendon strain with small effective stress was smaller than that with large effective stress. The concrete compressive strain was proportional to the effective stress of external tendon. From the comparison between test results and codes, the ACI-318 could not consider the effect of tendon force or effective stress, and especially the results of ACI-318 were very small, so it was very conservative. And the AASHTO 1994 could be influenced on the tendon area, initial force and effective stress, but as it was made on the basis of internal unbonded tendon, its results were much larger than the test results. For this reason, the new correct predict equation of external tendon stress will be needed.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.167-176
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• 2022

Adaptive optics (AO) systems compensate for atmospheric disturbance, especially phase distortion, by introducing counter-wavefront deformation calculated from real-time wavefront sensing or prediction. Because AO system implementations are time-consuming and costly, it is highly desirable to estimate the system's performance during the development of the AO system or its parts. Among several techniques, we mostly apply statistical analysis, computational simulation, and optical-bench tests. Statistical analysis estimates performance based on the sum of performance variances due to all design parameters, but ignores any correlation between them. Computational simulation models every part of an adaptive optics system, including atmospheric disturbance and a closed loop between wavefront sensor and deformable mirror, as close as possible to reality, but there are still some differences between simulation models and reality. The optical-bench test implements an almost identical AO system on an optical bench, to confirm the predictions of the previous methods. We are currently developing an AO system for a 1.6-m ground telescope using a deformable mirror that was recently developed in South Korea. This paper reports the results of the statistical analysis and computer simulation for the system's design and confirmation. For the analysis, we apply the Strehl ratio as the performance criterion, and the median seeing conditions at the Bohyun observatory in Korea. The statistical analysis predicts a Strehl ratio of 0.31. The simulation method similarly reports a slightly larger value of 0.32. During the study, the simulation method exhibits run-to-run variation due to the random nature of atmospheric disturbance, which converges when the simulation time is longer than 0.9 seconds, i.e., approximately 240 times the critical time constant of the applied atmospheric disturbance.