• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of Sericultural and Entomological Science
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• v.37 no.1
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• pp.74-85
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• 1995

A study was conducted to provide some basic data for controlling the Mulberry scale insect(Pseudaulacaspis pentagona T.). Experiments were carried out with checking their field-life, and a few selected insecticides were tested when mulberry leaves were not in use. About 57% of female were survived from the overwintering attached to the bark. Over 80% of the scale were distributed within the range of 30cm from the base of each stem. One hundred percent and 96.7% of eggs were hatched under 25$^{\circ}C$ and 27$^{\circ}C$ respectively. Eighty percent of pupae emerged both under 25 and 27$^{\circ}C$. The Mulberry scale require about 33 days under 25$^{\circ}C$ and 30 days under 27$^{\circ}C$. This species had 2 generations each year in mulberry field in Suwon. Overwintered as a mated female only. Oviposited about 170 eggs under their scale starting from the late April (in 1994) through the mid May (in 1993). Phenthoate EC sprayed in the mid June against the larvae was very effect with over 96% of control value.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.15-22
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• 2002

Transportation project appraisal should be precise in order to increase the social welfare and efficiency, and it has been evaluated by only a single criterion analysis such as benefit/cost analysis. However, this method cannot assess some qualitative items, and cannot get a proper solution for the clash of interests among various groups. Therefore, the multi-criteria analysis, which can control these problems, is needed, and then Saaty has developed one of these methods, AHP(Analytic Hierarchy Process) method. In AHP, the project is evaluated through weighted score of the criteria and the alternatives, which is surveyed by a questionnaire of specialists. It is based on some strict suppositions such as reciprocal comparison, homogeneity, expectation, independence relationship between multi-criteria, but supposing that each criterion has independence relation with others is too difficult in two reasons. First, in real situation, there cannot be perfect independence relationship between standards. Second, individuals, even though they are specialists of that area, do not feel the degree of independence relation as same as others. This paper develops a modified AHP method for solving this dependence relationship between multi-criteria. First of all. in this method, the degree of dependence relationship between multi-criteria that the specialist feels is surveyed and included to the weighted score of multi-criteria This study supposes three methods to implement this idea. The first model products the degree of dependence relationship in the first step for calculating the weighted score, and the others adjust the result of weighted score from the basic AHP method to the dependence relationship. One of the second methods distributes the cross weighted score to each standard by constant ratio, and the other splits them using Fuzzy measure such as Bel and Pl. Finally, in order to validate these methods, this paper applies them to evaluate the alternatives which can control public resentments against Korean rail path in a city area.