• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.2
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• pp.53-62
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• 2012

Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1451-1458
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• 2013

This paper deals with the programs and algorithms that generate the full data set that satisfy the basic combinatorial requirement of combination, permutation, partial permutation or shortly r-permutation, which are used in the application of the total data testing or the simulation input. We search the programs able to meet the rules which is permutations and combinations, r-permutations, select the fastest program by field. With further study, we developed a new program reducing the time required to processing. Our research performs the following pre-study. Firstly, hundreds of algorithms and programs in the internet are collected and corrected to be executable. Secondly, we measure running time for all completed programs and select a few fast ones. Thirdly, the fast programs are analyzed in depth and its pseudo-code programs are provided. We succeeded in developing two programs that run faster. Firstly, the combination program can save the running time by removing recursive function and the r-permutation program become faster by combining the best combination program and the best permutation program. According to our performance test, the former and later program enhance the running speed by 22% to 34% and 62% to 226% respectively compared with the fastest collected program. The programs suggested in this study could apply to a particular cases easily based on Pseudo-code., Predicts the execution time spent on data processing, determine the validity of the processing, and also generates total data with minimum access programming.

• Korean Journal of Food Science and Technology
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• v.34 no.3
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• pp.437-443
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• 2002

Using agar and gelatin as wall materials, onion oil was microencapsulated using the extrusion spraying technology. A sensitive methodology was developed for quantitative determination of the microencapsulation yield through ethyl acetate extraction and gas chromatographic analyses. Optimal conditions for the microencapsulation process consisting of the ratio of [core material, Cm] to [wall material, Wm] ($X_1$), temperature of dispersion fluid ($X_2$), detergent concentration in dispersion fluid ($X_3$), and concentration of emulsifier $(X_4)$ were determined using response surface methodology. The regression model equation for the yield of microencapsulation (Y, %) of onion oil could be predicted as $Y\;=\;97.028571-0.775000\;(X_1)-0.746726\;(X_1){\cdot}(X_1)\;-\;1.100000\;(X_3){\cdot}(X_2)$. The optimal conditions for the microencapsulation of the onion oil were determined as the ratio of [core material] to [wall material] of 4.5 : 5.5 (w/w), the temperature of dispersion fluid of $17.1^{\circ}C$ detergent concentration in dispersion fluid of 0.03%, and the concentration of emulsifier of 0.42%. Results revealed the most stable microcapsule of onion oil could be formed with the highest yield of microencapsulation (more than 95%) under optimal conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.696-701
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• 2016

Multi-stage deep drawing is a widely used industrial manufacturing process, and its applications are gradually expanding to both small products and large metallic products. The USB C-type socket used in smart phones, for example, is manufactured using oval multi-stage deep drawing. The socket is very small and slender and it requires precise manufacturing. The thickness distribution of the final product is guaranteed only if it is uniform throughout the overall process. Therefore, minimizing the height difference between long and short sidewalls after the first operation is important for this goal. An initial blank optimization was performed for an oval-type drawing process based on finite element simulations. The goal was to determine an initial blank geometry that can maintain uniform height and thickness after the first draw operation. The initial blank shape of the sheet metal was optimized, and the results show that it satisfied the conditions of minimal thickness reduction and even thickness distribution. The geometry from the optimized simulation was compared with experimental results, which showed good agreement.

• Journal of The Geomorphological Association of Korea
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• v.20 no.1
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• pp.21-33
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• 2013

Weight of evidence model was applied for potential mapping of mountainous wetland to reduce the range of the field survey and to increase the efficiency of operations because the surveys of mountainous wetland need a lot of time and money owing to inaccessibility and extensiveness. The relationship between mountainous wetland location and related factors is expressed as a probability by Weight of evidence model. For this, the spatial database consist of slope map, curvature map, vegetation index map, wetness index map, soil drainage rating map was constructed in Yeongnam area, Korea, and weights of evidence based on the relationship between mountainous wetland location and each factor rating were calculated. As a result of correlation analysis between mountainous wetland location and each factors rating using likelihood ratio values, the probability of mountainous wetlands were increased at condition of lower slope, lower curvature, lower vegetation index value, lower wetness value, moderate soil drainage rating. Mountainous Wetland Potential Index(MWPI) was calculated by summation of the likelihood ratio and mountainous wetland potential map was constucted from GIS integration. The mountain wetland potential map was verified by comparison with the known mountainous wetland locations. The result showed the 75.48% in prediction accuracy.

• Korean Journal of Optics and Photonics
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• v.16 no.5
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• pp.446-449
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• 2005

In a directional coupler, the design process requires repeated calculation of the characteristics of every changed structure, because it is generally difficult to expect the extinction ratio to be optimized over the entire variation of design parameters. In this paper, we systematically simulated the extinction ratio as a function of the design parameters, and analyzed the general tendency of that characteristic. In other words, we could find the generalized extinction ratio curve if the separation distance is normalized by the waveguide width. Here, the extinction ratio is shown to be increased as the normalized frequency (v) and the ratio (d) of the separation distance over the waveguide width were increased. For various structures with same ratio d, all corresponding extinction ratio curves as a function of v coincide with each other. We showed the usefulness of the generalized extinction ratio curve by applying it to the design and the fabrication of 1310/l550 nm demultiplexer, as it was convenient to design a shorter directional coupler with targeted extinction ratio from this curve.

• Korea journal of population studies
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• v.35 no.2
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• pp.55-72
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• 2012

Few studies have done on the intergenerational relations of married women and their own parents in Japan. This study approaches the topic by examining coresidence and contact frequency between generations. The study expects the likelihood of living together (including living next door) and the extent of contact would differ by the characteristics of woman, her husband, children, her brothers and sisters, her own parents and parents-in-law. From the 2003 Survey for National Family Research in Japan, selected are 853 currently married women in their 30s and 40s whose parent and parents-in-law are alive. The analysis shows that the likelihood of living together with parents decreases as the number of brothers and sisters increases. In particular, the presence of brother substantially decreases the likelihood. Having father only alive (vs. having both parents alive) also increases the likelihood. The frequency of contact with parents is conditioned by the coresidence with parents-in-law. It also differs by the level of education and its gap between spouses. Subjective evaluation of husband's attitude toward her parents is important. As in the case of living together, the number of brothers and sisters and the survival status of parents are significant in explaining the frequency of contact with her parents. The results indicate that number of brothers and sisters as well as widowhood of parents serves as its demographic condition. The findings that the frequency of contact with parent are affected by coresidene with parents-in-law, education gap between spouses and husband's attitude toward her parents suggest that the relationship of married women with her own parents are conditioned by her husband and his parents.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.45-53
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• 2019

We face problems from excessive information served with websites in this rapidly changing information era. We find little information useful and much useless and spend a lot of time to select information needed. Many websites including search engines use web crawling in order to make data updated. Web crawling is usually used to generate copies of all the pages of visited sites. Search engines index the pages for faster searching. With regard to data collection for wholesale and order information changing in realtime, the keyword-oriented web data collection is not adequate. The alternative for selective collection of web information in realtime has not been suggested. In this paper, we propose a method of collecting information of restricted web sites by using Web crawling distributed monitoring system (R-WCMS) and estimating collection time through detailed analysis of data and storing them in parallel system. Experimental results show that web site information retrieval is applied to the proposed model, reducing the time of 15-17%.

• Management & Information Systems Review
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• v.38 no.2
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• pp.209-223
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• 2019

The purpose of this study was to stimulate the online word-of-mouth advertising by developing the concept of word-of-mouth constraint factors and measurement tools in connection with the SNS advertising on social networks. To achieve the objective of this study, this study was conducted in 3 phases. First, the exploratory investigation(target group interview, in-depth interview, and expert interview) was performed to determine the concept and scope of the word-of-mouth constraint based on literature study and qualitative investigation method. Second, the reliability and validity of the measurement questions were verified through the survey in order to refine the developed measurement items. Third, the predictive validity of measurement items was verified by examining the relationship with other major construct concept for which the developed measurement items were different. Based on the results of study, 6 components and a total of 23 measurement questions for those components were derived. Each was called intrapersonal and interpersonal constraint(psychological sensitivity, compensatory sensitivity, and other person assessment), structural constraint(reliability, informativity, and entertainment). We developed the measurement questions related to word-of-mouth constraint based on qualitative study and quantitative study and holistically examined the social and psychological, environmental interruption factors acting as the word-of-mouth constraint factors for SNS advertising in terms of SNS achievements and evaluation from the perspective of word-of-mouth constraint. The results will lead to creation of basic framework for systematic and empirical research on the online word-of-mouth constraint and to achievement of effective SNS word-of-mouth advertising.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.