• Korean Journal of Ecology and Environment
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• v.48 no.3
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• pp.153-167
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• 2015

In order to understand the relationship between the distribution of epilithic diatoms and the habitual environments, land-use, water qualities, and epilithic diatoms were studied at 141 sampling sites in the midwestern stream of Korean peninsula (Geum river, Mangyeong river, Dongjin river, and Sapgyo river). The total 183 diatom taxa was appeared in the study, while the dominant species were found to be Nitzschia palea (10.9%) and Achnanthes convergens (8.4%). Based on the abundance of epilithic diatoms, a cluster analysis results indicate that the sampling sites divided the sampling sites into 4 groups (G) at the 25% level. In term of geographic and aquatic environments, G-I and -II accounted for the upper and mid streams of the Geum river, and had large forest areas and good in water quality. G-III accounted for farmland and urban, and high concentration nutrient levels (TN and TP) and electric conductivity. G-IV accounted for mostly farmland, and high levels in turbidity, BOD, nutrient and electric conductivity. CCA results showed that the saproxenous taxa Meridion circulare was the indicator species of G-I, which strongly influenced by altitude and forests. In G-II, the indifferent taxa Navicula cryptocephala was influenced by Chl-a, AFDM, and DO. In G-III and -IV, the indifferent taxa Fragilaria elliptica and saprophilous taxa Aulacoseira ambigua were influenced by electric conductivity, turbidity, and nutrient counts. Meanwhile, random forest results showed that the predicting factor of indicator species appearance in G-I, -II, and -III was found to be electric conductivity whereas in G-IV it was found to be turbidity. Collectively, the distribution of diatoms in the midwestern of Korean peninsula was found to depend more on the land-use and its subsequent water qualities than the inherent characteristics of the aquatic environment.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.87-94
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• 1986

This study was performed in order to contemplate their correlations between physical and mechanical properties of the marine clays which were collected from main harbors in Korea. The results obtained are as follows: 1. Most of the soils in experimental districts consist of CH. CL. and ML. and they are considered to be still proceeding. 2. The equations of the relationship between compression index and liquid limit are as, follows: CH : $C_c=0.0137$ (LL-22.60) CL : $C_c=0.0123$ (LL-14.64) 3. The relationship between compression index and initial void ratio appears that the higher the plasticity, the easier the slope of the regression line. The equations are as follows : CH : $C_c=0.431$ ($e_o-0.504$) CH : $C_c=0.471$ ($e_o-0.235$) ML : $C_c=0.641$ ($e_o-0.393$) 4. The equations of the relationship between compression index and natural water content are as follows: CH : $C_c=0.0133$ ($W_n-28.27$) CL : $C_c=0.0225$ ($W_n-23.56$) ML : $C_c=0.0106$ ($W_n-16.42$) 5. The relationship between initial void ratio and natural water content, and compression index is highly positive correlation and the equations are as follows : CH : $C_c=0.301$ ($e_o+0.017W_n-1.05$) CL : $C_c=0.141$ ($e_o+0.0567W_n-1.054$) ML : $C_c=0.421$ ($e_o+0.0214W_n-1.121$) 6. The equations of the relationship between initial void ratio and liquid limit, and compression index are as follows : CH : $C_c=0.36$ ($e_o+0.08LL-0.819$) CL : $C_c=0.269$ ($e_o+0.026LL-0.929$) 7. The cohesion of marine clays is no concerned with the increment of depth. The equations of relationship between cohesion and unconfined compression strength are as follows. CH : qu=1.896C+0.0107 CL : qu=1.849C+0.04.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.158-169
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• 2019

A probabilistic model that can predict the residual useful lifetime of structure is formulated by using the gamma process which is one of the stochastic processes. The formulated stochastic model can take into account both the sampling uncertainty associated with damages measured up to now and the temporal uncertainty of cumulative damage over time. A method estimating several parameters of stochastic model is additionally proposed by introducing of the least square method and the method of moments, so that the age of a structure, the operational environment, and the evolution of damage with time can be considered. Some features related to the residual useful lifetime are firstly investigated into through the sensitivity analysis on parameters under a simple setting of single damage data measured at the current age. The stochastic model are then applied to the rubble-mound breakwater straightforwardly. The parameters of gamma process can be estimated for several experimental data on the damage processes of armor rocks of rubble-mound breakwater. The expected damage levels over time, which are numerically simulated with the estimated parameters, are in very good agreement with those from the flume testing. It has been found from various numerical calculations that the probabilities exceeding the failure limit are converged to the constraint that the model must be satisfied after lasting for a long time from now. Meanwhile, the expected residual useful lifetimes evaluated from the failure probabilities are seen to be different with respect to the behavior of damage history. As the coefficient of variation of cumulative damage is becoming large, in particular, it has been shown that the expected residual useful lifetimes have significant discrepancies from those of the deterministic regression model. This is mainly due to the effect of sampling and temporal uncertainties associated with damage, by which the first time to failure tends to be widely distributed. Therefore, the stochastic model presented in this paper for predicting the residual useful lifetime of structure can properly implement the probabilistic assessment on current damage state of structure as well as take account of the temporal uncertainty of future cumulative damage.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Asia Marketing Journal
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• v.10 no.2
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• pp.1-29
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• 2008

The objective of our study is to provide an exploratory model for forecasting sales take-off timing of a product in the context of multi-national markets. We evaluated the usefulness of key predictors such as multiple market information, product attributes, price, and sales for the forecasting of sales take-off timing by applying the suggested model to monthly sales data for PDP and LCD TV provided by a Korean electronics manufacturer. We have found some important results for global companies from the empirical analysis. Firstly, innovation coefficients obtained from sales data of a particular product in other markets can provide the most useful information on sales take-off timing of the product in a target market. However, imitation coefficients obtained from the sales data of a particular product in the target market and other markets are not useful for sales take-off timing of the product in the target market. Secondly, price and product attributes significantly influence on take-off timing. It is noteworthy that the ratio of the price of the target product to the average price of the market is more important than the price ofthe target product itself. Lastly, the cumulative sales of the product are still useful for the prediction of sales take-off timing. Our model outperformed the average model in terms of hit-rate.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Korean Society of Disaster and Security
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• v.6 no.1
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• pp.65-72
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• 2013

In this study, the early suicide prevention program was applied to the elementary school students and compared the prior & post effect of the program, and verified the status of psychology change like emotional status, or temptation to take a suicide, and presented the possibility as a suicide prevention program. The period of adolescence is the very unstable period in the process of growth being cognitively immature, emotionally impulsive period. It is the period emotionally unstable and unpredictable possible to select the method of suicide as an extreme method to escape the reality, or impulsive problem solving against small conflict or dispute situation. Many stress of the student such as recent nuclear family, expectation of parents to their children, education problem, socio-environmental elements, individual psychological factor lead students to the extreme activity of suicide in recent days. In this study, the scope of stress experienced in the elementary school as well as idea and degree of temptation regarding suicide by the suicide prevention program were identified, and through prevention program such as meditation training, breath training and through experience of anger control, emotion-expression, self overcome and establish positive self-identity and make understanding Self-control, Self-esteem & preciousness of life based on which the effect to suicide prevention was analyzed. The study was made targeting 51 students of 2 classes of 6th grade of elementary school of Goyang-si and processed 30 minutes every morning focused on through experience & activity of the principle & method of brain science. The data was collected for 20 times before starting morning class by using Suicide Probability Scale(herein SPS-A) designed to predict effectively suicide Probability, suicide risk prediction scale, surveyed by 7 areas such as Positive outlook, Within the family closeness, Impulsivity, Interpersonal hostility, Hopelessness, Hopelessness syndrome, suicide accident. Analytical methods and validation was used the Wilcoxon's signed rank test using SPSS Program. Though the process of program in short period, but there was a effective and positive results in the 7 areas in the average comparison. But in the t-test result, there was a different outcome. It indicated changes in the 3 questionnaires (No.7, No.14, No.19) out of 31 SPS-A questionnaires, and there was a no change to the rest item. It also indicated more changes of the students in the class A than class B. And in case of the class A students, psychological changes were verified in the areas of Hopelessness syndrome, suicide accident among 7 areas after the program was processed. Through this study, it could be verified that different results could be derived depending on the Student tendency, program professional(teacher in charge, processing lecturer). The suicide prevention program presented in this article can be a help in learning and suicide prevention with consistent systematization, activation through emotion and impulse control based on emotional stress relief and positive self-identity recovery, stabilization of brain waves, and let the short period program not to be died out but to be continued connecting from childhood to adolescence capable to make surrounding environment for spiritual, physical healthy growth for which this could be an effective program for suicide prevention of the social problem.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.