• 한국잡초학회지
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• 제30권2호
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• pp.103-110
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• 2010

일년생 잡초인 물달개비 및 올챙이고랭이의 출아와 초기생장을 예측하기 위한 모델 구축을 위하여 온도조건을 달리한 식물생장상에서 포트실험을 수행하였다. 이들 잡초의 출아 및 초기생장과 유효적산온도와의 관계를 비선형회귀로 분석한 결과 온도조건에 상관없이 각각 Gompertz 모델 및 logistic 모델로 설명이 잘 되었다. 물달개비 및 올챙이고랭이의 최대 출아율의 50%에 필요한 유효적산온도는 각각 69.3 및 $94.8^{\circ}C$이었으며, 4엽기에 이르는데 필요한 유효 적산온도는 각각 247 및 $234^{\circ}C$이었다. 본 연구에서 개발된 모델로 분석한 결과 평균 기온이 $3^{\circ}C$ 상승하게 되면 이들 잡초의 50% 출아는 물달개비의 경우 1일, 올챙이고랭이의 경우는 2일 빨라지고, 4엽기에 다 다르는 날짜는 이들 잡초 모두 3일이 빨라질 것으로 예측되었다. 따라서 온도상승조건에서 물달개비 및 올챙이고랭이를 효과적으로 방제하기 위해서는 현재의 처리시기보다 약 2-3일 빨라져야 할 것으로 예상된다.

• 스마트미디어저널
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• 제5권4호
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• pp.75-82
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• 2016

우리나라의 경제 성장과 도로 환경의 변화를 통해 국내 자동차 시장이 성장하였으나, 이로 인해 교통사고율 또한 증가하였고, 인명 피해가 심각한 수준이다. 이에 따라, 정부에서는 교통사고 데이터를 개방하고 문제를 해결하기 위한 정책을 수립 및 추진 중이다. 본 논문에서는 교통사고 데이터를 이용하여 클래스의 불균형을 해소하고, Hybrid Model 구축을 통한 교통사고 예측을 위해 원본 교통사고 데이터와 Sampling을 수행한 데이터를 학습 데이터로 사용한다. 두 학습데이터에 연관규칙 학습기법인 FP-Growth 알고리즘을 이용하여 교통사고 상해 심각도와 연관된 패턴을 학습한다. 두 학습 데이터의 연관 패턴을 분석을 통해 같은 연관된 패턴을 추출하고 의사결정트리와 다항 로지스틱 회귀분석기법에 연관된 속성에 가중치를 부여하여 융합형 Hybrid Model을 구축하고 교통사고 피해자 상해 심각도를 예측하는 방법에 대해 제안한다.

• Environmental Analysis Health and Toxicology
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• 제16권4호
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• pp.205-209
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• 2001

Lemna gibba was newly cultured and provided for toxicity tests. In this study, the chromate toxicity tests for Lemna gibba were performed according to the OECD Lemna growth inhibition test guideline. The test species was Lemna gibba, and the tests were repeated 5 times. To evaluate the toxicity test results, the average specific growth rate, EC50, 95％ confidential limit, and variances were calculated. The test performance was analyzed by the doubling time and test statistics. The average values of EC50 data determined by logistic and linear interpolation curves were 25.9 ppm and 35.4 ppm respectively (by chromate concentration). The doubling time of all controls were below 2.5 day, so all tests passed the criteria for the test performance. This study introduced a new test method, Lemna growth inhibition test, which is provided for the hazard assessment of aquatic environment.

• 아태비즈니스연구
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• 제3권1호
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• pp.29-38
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• 2012

This study aims at evaluating the impact of network formation variables and found to have positive impact on the economic performance and growth of the enterprises. The calucation of the weighted scores of networking statements brought some affirmative results to influence the performance of the enterprises. Through multiple regression and logistic regression models it is identified that network formation variables like service receiving status, consultation of the family, other business dummy and attendance in fair have some significant positive impact both on the growth and performance of the enterprises. In addition to above variables, from the set of enterprise characteristics natural logarithm of the market value of total assets and from the entrepreneurs' characteristics set of variables, schooling year and squared value of the experience have been found to have significant positive impact. Finally, it is concluded in the study that to enhance the performance and growth of the enterprises, government and policy rlated organizations need to consider important variables that have positive impact in supplying the entrepreneurial resources especially, developing the net-working relationship.

• 한국수산경영학회:학술대회논문집
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• 한국수산경영학회 2007년도 추계학술발표회 및 심포지엄
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• pp.167-184
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• 2007

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut-down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t-test is used to identify the differences in financial variables between the distressed group and the non-distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990$\sim$1993), period 2(1994$\sim$1997), period 3(1998$\sim$2002). The final model built from whole sample appled each three sub-samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub-sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• 수산경영론집
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• 제39권2호
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• pp.61-79
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• 2008

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut - down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t - test is used to identify the differences in financial variables between the distressed group and the non - distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990 - 1993), period 2(1994 - 1997), period 3(1998 - 2002). The final model built from whole sample appled each three sub - samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub - sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• 수산경영론집
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• 제46권3호
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• pp.103-118
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• 2015

The purpose of this paper is to discuss possibilities of applying growth curve models, such as Logistic, Log-Logistic, Log-Normal, Gompertz and Weibull, to three specific technology areas of Fishery Science and Technology in the process of measuring their technology level between Korea and countries with the state-of-the art level. Technology areas of hazard control of organism, environment restoration, and fish cluster detect were selected for this study. Expert panel survey was conducted to construct relevant panel data for years of 2013, 2016, and a future time of approaching the theoretical maximum technology level. The size of data was 70, 70 and 40 respectively. First finding is that estimation of shape and location parameters of each model was statistically significant, and lack-of-fit test using estimated parameters was statistically rejected for each model, meaning all models were good enough to apply for measuring technology levels. Second, three models other than Pearl and Gompertz seemed very appropriate to apply despite the fact that previous case studies have used only Gompertz and Pearl. This study suggests that Weibull model would be a very valid candidate for the purpose. Third, fish cluster detect technology level is relatively higher for both Korea and a country with the state-of-the-art among three areas as of 2013. However, all three areas seem to be approaching their limits(highest technology level point) until 2020 for countries with the state-of-the-art. This implies that Korea might have to speed up her research activities in order to catch up them prior to 2020. Final suggestion is that future study may better apply various and more appropriate models respectively considering each technology characteristics and other factors.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.207-215
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• 2021

The number of tourists visiting Nepal has shown rapid growth in recent years, and Nepal is expecting more tourist arrivals in the future. This paper, thus, attempts to analyze the tourist arrivals in Nepal and predict the number of visitors until 2025. This paper has examined the international tourist arrival trend in Nepal using the Gompertz and Logistic growth model. The international tourist arrival data from 1991 to 2018 is used to investigate international tourist arrival trends. The result of the analysis found that the Gompertz model performs a better fit than the Logistic model. The study further forecast the expected tourist arrival below one million (844,319) by 2025. Nevertheless, the government of Nepal has the goal of two million tourists in a year. The present study also discusses system dynamics scenarios for the two million potential visitors within a year. Scenario analysis shows that proper advertisement and positive word-of-mouth will be key factors in achieving a higher number of tourists. The current study could fill the gap of theoretical and empirical forecasting of tourist arrivals in the Nepalese tourism industry. Also, the study findings would be beneficial for government officers, planners and investors, and policy-makers in the Nepalese tourism industry.

• 정보처리학회논문지D
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• 제11D권7호
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• pp.1451-1458
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• 2004

Gompertz 성장곡선에 기반한 기존의 소프트웨어 신뢰성 성장모델들은 모두 대수형이다. 대수형 Gompertz 성장 곡선에 기반한 소프트웨어 신뢰성 성장 모델들은 모수 추정에 어려움을 갖고 있다. 그러므로 본 논문은 로지스틱형 Gompertz 성장곡선에 기반한 신뢰성 성장 모델을 제안한다. 13개의 다른 소프트웨어 프로젝트로부터 얻은 고장 데이터를 분석하여 그 유용성을 검토하였다. 모델의 모수들은 변수변환을 통한 선형희귀분석과 Virence의 방법으로 추정되었다. 제안된 모델은 평균 상대 예측 오차에 기반하여 성능을 비교하였다. 실험 결과 제안된 모델은 대수형 Gompertz 성장 곡선에 기반한 모델보다 좋은 성능을 보였다.

• Management Science and Financial Engineering
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• 제14권2호
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.