• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.107-123
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• 2018

The global impact of climate change on agriculture is now increasing. The purpose of this study was to investigate the effect of climate change on livestock productivity. The variables that have the greatest influence on climate change factors were examined through previous studies and expert surveys. We also used the actual productivity data of livestock farmers to investigate the relationship with climate change. In order to evaluate the climate for changes in livestock productivity, national representative data (such as bullocks, dairy, pigs, laying hens, and broilers) were surveyed in Korea. Also, to select and classify evaluation indexes, we selected climate change factor variables as prior studies and studied the weighting factor of climate variable factors. In this study, the researchers of industry, academia, and farmers in the livestock sector conducted questionnaires on the indicators of vulnerability to climate change using experts, and then weighed the selected indicators using the hierarchical analysis process (AHP). In order to verify the validity of the evaluation index, was examined using domestic climate data (temperature, precipitation, humidity, etc.). Correlation and regression analysis were performed. The empirical relationship between climate change and livestock productivity was examined through this study. As a result, we used data with high reliability of statistical analysis and found that there are significant variables.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.77-91
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• 2008

In this study, we developed a technique of applying DRASTIC, which is the most widely used tool for estimation of groundwater vulnerability to the aqueous phase contaminant infiltrated from the surface, and a groundwater flow model jointly to assess groundwater contamination potential. The developed technique is then applied to Buyeo-eup area in Buyeo-gun, Chungcheongnam-do, Korea. The input thematic data of a depth to water required in DRASTIC model is known to be the most sensitive to the output while only a few observations at a few time schedules are generally available. To overcome this practical shortcoming, both steady-state and transient groundwater level distributions are simulated using a finite difference numerical model, MODFLOW. In the application for the assessment of groundwater vulnerability, it is found that the vulnerability results from the numerical simulation of a groundwater level is much more practical compared to cokriging methods. Those advantages are, first, the results from the simulation enable a practitioner to see the temporally comprehensive vulnerabilities. The second merit of the technique is that the method considers wide variety of engaging data such as field-observed hydrogeologic parameters as well as geographic relief. The depth to water generated through geostatistical methods in the conventional method is unable to incorporate temporally variable data, that is, the seasonal variation of a recharge rate. As a result, we found that the vulnerability out of both the geostatistical method and the steady-state groundwater flow simulation are in similar patterns. By applying the transient simulation results to DRASTIC model, we also found that the vulnerability shows sharp seasonal variation due to the change of groundwater recharge. The change of the vulnerability is found to be most peculiar during summer with the highest recharge rate and winter with the lowest. Our research indicates that numerical modeling can be a useful tool for temporal as well as spatial interpolation of the depth to water when the number of the observed data is inadequate for the vulnerability assessments through the conventional techniques.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.29-40
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• 2013

The object of this research is to elicit the ecological wetland's creating and maintaining conditions through GIS and AHP(Analytic Hierarchy Process) on common characteristics of wetland, by exploring the function of the long-standing and naturally created wetlands in South Han-river basin. Geomorphological, geologic, hydraulic & hydrological, pedological, environmental & ecological approaches of five categorizations were made based on the documents and precedent studies in order to perform the research object. Elicitation of the common creating and maintaining conditions on the objected wetlands through analysis of satisfactory conditions was conducted by performing an overlay analysis from the gathered information in each category and linking its result to each wetland. Also, elicitation of highly believable qualified analysis result was made by calculating the each factor's weight through application of AHP analysis method on each factor. The research result is expected to be applied effectively in suitability selection for creation of an artificial wetland and restoration and maintenance for naturally created wetlands when introducing the No Net Loss of Wetlands. In addition, with a new understanding on wetland and beyond the value of its conservativeness, this research result can be considered as a solution for Eco-Wetland's maintainable development.

• Korean Journal of Construction Engineering and Management
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• v.15 no.5
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• pp.49-60
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• 2014

Electrical construction cost index has been applied fixed-weighted method. But fixed-weighted method can't faithfully reflect industrial structure changes. Because the weighted value and price index of fixed-weighted method is fixed on the basic period. Electrical construction cost index is composed of the cost of labor and material. So it fluctuates sharply whenever the construction association of korea announces the laborer's wage of electrical construction. And it depends on only the producer price index changes that is related to electrical construction since then. So a study is focused on developing electrical construction cost index applied chain-weighted method. Because chain-weighted method can reflect the realities of the electrical construction and alleviate the sudden changes of labor cost with link index. We verify that chain-weighted method relieves the step states of electrical construction cost index applied fixed-weighted method.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.15-28
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• 2016

The purpose of this study is to prepare countermeasures for aggressive driving and road rage which have recently become a hot issue by analysing mechanism of how the driving behavior determinants(personal anger and aggression) and dangerous driving behavior factors(aggressive driving behavior and over-speeding driving behavior, drunk driving behavior, inattentive driving behavior, and inexperience driving behavior) affect aggressive driving and road rage. From the survey conducted by seven branches of the Road Traffic Authority with 351 people who were traffic offenders and drivers who caused car accidents, this study obtained three results as follows. First, seriousness of aggressive driving and road rage and requirements as types of customized educations, proper length of time for education, and contents of education can be understood. Second, specific relation and mechanism between the driving behavior determinants and dangerous driving behavior factors with respect to aggressive driving and road rage can be clearly identified, which helps to set order of priority and weighting of measures for reducing aggressive driving and road rage. Third, countermeasures can be categorized as corporate measures or customized measures through mechanism analysis model of aggressive driving and road rage.

• Survey Research
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• v.6 no.2
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• pp.51-74
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• 2005

The internet survey methods have been more and more widely used as the coverage of the fixed-line telephone is being reduced due to the diffusion of mobile phone. So, there is a need to know the accuracy of this new survey method. This study aims to estimate the accuracy of the internet survey method and identify the factors affecting the accuracy of this method, For this purpose, we analyzed the election poll data during the 17th general election period. These data include fixed-line telephone survey data, internet survey data, mobile phone survey data and the election voting data. The analysis shows that the prediction errors of the internet survey were a little more than those of the telephone or mobile phone survey. But the differences are not significant. It follows from this result that we can use the internet survey method in social survey context. This study also found that the respondent's willingness to participate in the survey, the probability of being at home during survey and the respondent's educational level were affecting the accuracy of the internet survey. Further studies to develop weighting method with these factors are needed.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.315-325
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• 2016

Thanks to the advantages of low cost and quick results, public opinion polls on election in Korea have been generally conducted by telephone survey, even though it has critical disadvantage of low response rate. In public opinion polls on election in Korea, the general method to handle nonresponses is adjusting the survey weight to estimate parameters. This study first drives mathematical expression of estimator and its bias with variance estimators with/without nonresponses in election polls in Korea. We also investigates the nonresponse rate of telephone survey on 2012 Korea presidential election. The average response rate was barely about 14.4%. In addition, we conducted a survey in April 2014 on the respondents's attitude toward telephone surveys. In the survey, the first reason for which respondents do not answer on public opinion polls on election was "feel bothered". And the aged 20s group, the most low response group, also gave the same answer. We here suggest that survey researchers motivate survey respondents, specially younger group, to participate surveys and find methods boosting response rate such as giving incentive.