• Journal of Marine Life Science
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• v.3 no.1
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• pp.22-30
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• 2018

In this study, pilot study on the introduction of stationary fishery was performed to solve the problem of fisheries resource reduction. The Fyke net, which is a test fishing net was selected considering the environment, operation and management costs of Ulleungdo, conditions that can be operated by small fishing vessels and personnel. As a result of 11 times survey using Fyke net from April to May 2017, 2,735 individuals and 983.4 kg caught and the dominant species were red seabream, yellowtail, olive flounder, mitra squid, horse mackerel, filefish, etc. In conclusion, if the production of squid, which is one of the major fisheries resources of Ulleungdo, is continuously decreased, it is considered that introduction of small-scale stationary fishery such as Fyke net would be useful as a means replace income of fishermen.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.386-393
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• 2018

The Korean military supplies industry tends to become limited in terms of its development to specific areas in line with strategic promotion policies of the local private direct industrial site. However, the relation between base and small cluster is getting lower of the local industrial site. In this study, information related to authorized test reports for munitions was collected through the military quality information system and subjected to social network analysis(SNA). SNA was performed through the relationships among defense quality assurance agencies, test institutions, contracts and cooperative firms through UCINET's Two-Mode Network. In the field of weapon systems, the median technology industry, and the test analysis dependent are high in Seoul, so the analysis revealed that strengthening the infrastructure for test analysis is needed. Also, it was deemed necessary for government-driven political support. Besides, the field support system was efficiently utilizing a relatively local test analysis. It was analyzed that they are overcoming the regional boundaries of small clusters by strategically changing their contract and cooperative firms' status. The research found some spatial inconsistencies between base and small clusters in the military supplies industry, and it was judged that a political suggestion was needed.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.115-125
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• 2003

The concrete median barriers are the most popular safety appurtenance that can be installed on narrow medians and are effective in keeping uncontrolled vehicles from crossing into opposing lanes of traffic. It is necessary to install and maintain median barriers because it is very difficult to reserve enough room required for medians in KOREA. Also, concrete median barriers are accepted as the actual alternatives for median barriers, mostly because they require almost no maintenance even after serious collisions. Typical concrete median barriers are 810mm high and have 596mm high glare screens on top of them. However we have experienced a number of "climb" and "roll-over" accidents of heavy vehicles and most of all, there have been some serious accidents caused by the part of broken glare screens. So the improvement study of concrete median barriers started. Prior to this study, a new type of concrete median barrier was suggested which is 1,270mm high and has no glare screens on top of it. So it was required to compare the properties of various types of concrete median barriers including the new type to find the optimal type of concrete median barrier. In this study, we have evaluated the characteristics of four types of concrete median barriers (New Jersey type, F type, constant slope type, and wall type). We have performed many computer simulations for the evaluation of the crashworthiness of them, and through the simulations we have tried to find a proper type of concrete median barrier. Through the computer simulations, we evaluated the structural stability and safety of the four types of concrete median barriers. We confirmed the structural stability and safety of them But in regard to the probability of "roll-over" of heavy vehicles, the higher concrete median barriers showed better performances than the lower. As the result of this study a new type of concrete median barrier was recommended.

• Tunnel and Underground Space
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• v.21 no.2
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• pp.128-137
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• 2011

The shut-in pressure calculated in common hydrofracturing test for vertical borehole equals generally to the minimum horizontal principal stress, so it should be considered as an essential parameter for determining the in-situ stress regime around the rock mass. It shows usually an ambiguous value in pressure-time history curves, however, because of the relationship between the behavior of hydraulic fractures and the condition of remote stress regime. In this study, a series of numerical analyses have been carried out to compare several methods for determining the shut-in pressure during hydrofracturing. The hydraulic-mechanical coupling has been applied to numerical analysis for simulating the fracture propagation by hydraulic pressure, and the different discontinuity geometry has been considered in numerical models to examine the effect of numerical element shape on fracture propagation pattern. From the numerical simulations with the four different discontinuity geometries, it was revealed that the shut-in pressure obtained from graphical methods rather than statistical method was relatively small. Consequently a care should be taken in selecting a method for determining the shut-in pressure when a stress anomaly around borehole and a fracture propagation with complicate mechanism are considered.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.415-421
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• 1999

Altman의 연구(1965, 1977)나 Beaver의 연구(1986)와 같은 전통적 예측모형은 분석자의 판단에 따른 예측도가 높은 재무비율을 선정하여 다변량판별분석(MDA： multiple discriminant analysis), 로지스틱회귀분석 등과 같은 통계기법을 주로 이용해 왔으나 1980년 후반부터 인공지능 기법인 귀납적 학습방법, 인공신경망모형, 유전모형 둥이 부실기업예측에 응용되기 시작했다. 최근 연구에서는 인공신경망을 활용한 변수 및 모형개발에 관한 보고가 있다. 그러나 지금까지의 연구가 주로 기업의 재무적 비율지표를 고려한 모형에 치중되었으며 정성적 자료인 비재무지표에 대한 검증과 선정이 자의적으로 이루어져온 경향이었다. 또한 너무 많은 입력변수를 사용할 경우 다중공선성 문제를 유발시킬 위험을 내포하고 있다. 본 연구에서는 부실기업예측모형을 수립하기 위하여 정량적 요인인 재무적 지표변수와 정성적요인인 비재무적 지표변수를 모두 고려하였다. 재무적 지표변수는 상관분석 및 요인분석들을 통하여 유의한 변수들을 도출하였으며 비재무적 지표변수는 조직생태학내에서의 조직군내 조직사멸과 관련된 생태적 과정에 대한 요인들 중 조직군 내적요인으로 조직의 연령, 조직의 규모, 조직의 산업밀도를 도출하여 4개의 실험집단으로 분류하여 비재무적 지표변수를 보완하였다. 인공신경망은 다층퍼셉트론(multi-layer perceptrons)과 역방향 학습(back-propagation )알고리듬으로 입력변수와 출력변수, 그리고 하나의 은닉층을 가지는 3층 퍼셉트론(three layer perceptron)을 사용하였으며 은닉충의 노드(node)수는 3개를 사용하였다. 입력변수로 안정성, 활동성, 수익성, 성장성을 나타내는 재무적 지표변수와 조직규모, 조직연령, 그 조직이 속한 산업의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적 중률을 나타내었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.415-421
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• 1999

Altman의 연구(1965, 1977)나 Beaver의 연구(1986)와 같은 전통적 예측모형은 분석자의 판단에 따른 예측도가 높은 재무비율을 선정하여 다변량판별분석(MDA:multiple discriminant analysis), 로지스틱회귀분석 등과 같은 통계기법을 주로 이용해 왔으나 1980년 후반부터 인공지능 기법인 귀납적 학습방법, 인공신경망모형, 유전모형 등이 부실기업예측에 응용되기 시작했다. 최근 연구에서는 인공신경망을 활용한 변수 및 모형개발에 관한 보고가 있다. 그러나 지금까지의 연구가 주로 기업의 재무적 비율지표를 고려한 모형에 치중되었으며 정성적 자료인 비재무지표에 대한 검증과 선정이 자의적으로 이루어져온 경향이었다. 또한 너무 많은 입력변수를 사용할 경우 다중공선성 문제를 유발시킬 위험을 내포하고 있다. 본 연구에서는 부실기업예측모형을 수립하기 위하여 정량적 요인인 재무적 지표변수와 정성적 요인인 비재무적 지표변수를 모두 고려하였다. 재무적 지표변수는 상관분석 및 요인분석들을 통하여 유의한 변수들을 도출하였으며 비재무적 지표변수는 조직생태학내에서의 조직군내 조직사멸과 관련된 생태적 과정에 대한 요인들 중 조직군 내적요인으로 조직의 연령, 조직의 규모, 조직의 산업밀도를 도출하여 4개의 실험집단으로 분류하여 비재무적 지표변수를 보완하였다. 인공신경망은 다층퍼셉트론(multi-layer perceptrons)과 역방향 학습(back-propagation)알고리듬으로 입력변수와 출력변수, 그리고 하나의 은닉층을 가지는 3층 퍼셉트론(three layer perceptron)을 사용하였으며 은닉층의 노드(node)수는 3개를 사용하였다. 입력변수로 안정성, 활동성, 수익성, 성장성을 나타내는 재무적 지표변수와 조직규모, 조직연령, 그 조직이 속한 산업의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.746-756
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• 2017

An algorithm is developed to generate measurement data of deep space network for Korea Pathfinder Lunar Orbiter (KPLO) mission. The algorithm can provide corrected measurement data for the Orbit Determination (OD) module in deep space. This study describes how to generate the computed data such as range, Doppler, azimuth angle and elevation angle. The geometric data were obtained by General Mission Analysis Tool (GMAT) simulation and the corrected data were calculated with measurement models. Therefore, the result of total delay includes effects of tropospheric delay, ionospheric delay, charged particle delay, antenna offset delay, and tropospheric refraction delay. The computed measurement data were validated by comparison with the results from Orbit Determination ToolBoX (ODTBX).

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.2 s.332
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• pp.89-96
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• 2005

In this paper, we propose a customer premise platform for processing multimedia data service on the ATM network. The proposed platform has a specific AAL2 processor that includes AAL2 protocol and scheduler algorithm so as to off-load large potion of burden from host processor and make it easy to process multimedia data from the ATM network in real time compared with conventional platform in which AAL/ATM tasks are processed by software. The ATS scheduler that is implemented based on 2-level time slot ring provides a simple and efficient method for scheduling data of VBR-rt, UBR and CBR traffics. TMS320C5402 DSP is used to process voice-related tasks such as voice compression and voice packet manupulation and AAL2 processor is implemented on $0.35\;{\mu}m$ process line. We implemented the customer premise equipment for VoDSL service and tested the proposed platform on a test bed network. The experimental results show that the proposed equipment has the call success rate of $97\%$ at least and provides voice service of toll-qualify.

• Journal of the Korean Chemical Society
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• v.45 no.5
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• pp.422-428
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• 2001

Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.