• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.299-305
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• 2023

Bemisia tabaci is one of polyphagous insect pests that transmits Tomato Yellow Leaf Curl Virus (TYLCV) and Cassava Brown Streak Disease (CBSD). Insecticides are primarily applied to control B. tabaci, but it has limits due to the development of resistance. As a result, a fixed precision sampling plan was developed for its integrated pest management (IPM). The tomato plants were divided into top (more than 130cm from the ground), middle (70 cm to 100 cm above the ground), and bottom (50 cm or less above the ground) strata, before visual sampling of the larvae of B. tabaci. The spatial distribution analysis was conducted using Taylor's power law coefficients with pooled data of top, middle, bottom strata. Fixed precision sampling plan and control decision-making were developed with precision levels and action threshold recommended from published scientific papers. To assess the validation of the developed sampling plans, independent data not used in the analysis were evaluated using the Resampling Validation for Sampling Plan (RVSP) program.

• Journal of Animal Science and Technology
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• v.54 no.4
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• pp.255-265
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• 2012

The objectives of this study were to investigate MC1R genotype, coat color, and muzzle phenotype variationsin the Korean native brindle cattle (KNBC) maintaining family lines and to establish the mating system for increased brindle coat color appearance. KNBC with genotype and phenotype records were selected as experimental animals. The relationship between melanocortin 1 receptor (MC1R) genotypes, verified by PCR-RFLP, and brindle coat color appearance was determined. Fragments of the MC1R gene amplified by PCR were digested with MspI and RFLP was determined. KNBC had $E^+E^+$, $E^+e$, and ee genotypes. The $E^+e$ genotype was most common with 65%, compared to $E^+E^+$ (33.33%), or ee (1.67%). When the sire had $E^+e$ genotype and the dam had $E^+E^+$ genotype, and both of them had the whole body-brindle coat color, all of their offspring (4/4) had whole body-brindle coat color. When the sire had $E^+E^+$ genotype and the dam had $E^+e$ genotype, and both had whole body-brindle coat color, 44.44% (4/9) of the offspring had whole body-brindle coat color. The mating between the sires and dams with these two genotypes with whole body-brindle coat color may have the highest whole body-brindle coat color appearance in their offspring. Muzzle grades 3 or 4 were more common than other muzzle grades. This is the first report indicating the segregation of MC1R genotypes and the inheritance of coat color through family lines in KNBC. The mating system proposed from this study may increase the possibility of brindle coat color appearance in KNBC.

• Journal of Korean Home Economics Education Association
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• v.23 no.1
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• pp.87-111
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• 2011

The purpose of this study was to develop and evaluate practical problem-based Home Economics lesson plans applying to the multiple intelligence teaching learning strategy, focused on the unit 'Nutrition & Meals' of middle school Home Economics subject matter. To achieve this purpose, the lesson plans were developed and evaluated from the 72 middle school students in Chongju after implementing the instruction. The data from the questionnaire were analyzed by SPSS/WIN 12.0 and content analysis. The results were as follows: First, the objectives of practical problem-based 'Nutrition & Meals' Instruction using multiple intelligence teaching strategy were to understand the importance of nutrition and health in an adolescent period and to develop good eating habits. The Practical Problem was 'What should I do for good eating habits?' and the learning contents were healthy life, the kinds and functions of nutriments, food pyramid and a food guide. The learning activities were progressed by various types of teaching and learning methods including 8 types of multiple intelligence teaching strategy. The lesson plans were developed according to the process of practical problem solving model. 6 periods of lesson plans and worksheets were developed. Second, the practical problem-based instruction using multiple intelligence teaching-learning strategy were evaluated to increase students' positive learning attitudes, motivation, and good eating habits.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Radiation Protection and Research
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• v.36 no.1
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• pp.28-34
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• 2011

The measurement-based verification for intensity modulated radiation therapy (IMRT) is a time-and labor-consuming procedure. Instead, this study aims to develop a MU fluence reconstruction method for IMRT QA. Total actual fluences from treatment planning system (TPS, Eclipse 8.6, Varian) were selected as a reference. Delivered leaf positions according to MU were extracted by the dynalog file generated after IMRT delivery. An in-house software was develop to reconstruct MU fluence from the acquired delivered leaf position data using MATLAB. We investigated five patient's plans delivered by both step-and-shoot IMRT and sliding window technologies. The total actual fluence was compared with the MU fluence reconstructed by using commercial software (Verisoft 3.1, PTW) and gamma analysis method (criteria: 3%/3 mm and 2%/1 mm). Gamma pass rates were $97.8{\pm}1.33$% and the reconstructed fluence was shown good agreement with RTP-based actual fluence. The fluence from step and shoot IMRT was shown slightly higher agreement with the actual fluence than that from sliding window IMRT. If moving from IMRT QA measurements toward independent computer calculations, the developed method can be used for IMRT QA. A point dose calculation method from reconstructed fluences is under development for the routine IMRT QA purpose.

• Land and Housing Review
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• v.14 no.3
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• pp.93-106
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• 2023

This study attempted to simulate changes in the thermal environment according to the type of apartment complex in Korea using CFD techniques and evaluate the thermal environment by type of apartment. First, apartment complex types in the 2000s and 2010s were referred from previous studies and four types of apartment complex were extracted from. Second, the layout of the apartment complex and temperature changes were analyzed by the direction of wind inflow. Third, a standardized model was created from each type using tower type, plate type, and mixed driving. Last, CFD simulations were performed by setting up the inflow of wind from a total of eight directions. The temperature was relatively low in the type consisting of only the tower type and the type of placing the tower type in the center of the complex, regardless of the direction of the wind. It was due to the good inflow of wind from these types to the inside of the complex. It can be interpreted because wind flows easily into the complex in these types. The findings showed that wind flow and resulting temperature distribution patterns differed depending on the building type and complex layout type, confirming the need for careful consideration of the complex layout in the early design stage. The results are expected to be used as basic data for creating a sustainable residential environment in the early design stage of apartment complexes in the future.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.79-95
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• 2016

This study identifies themes and trends in maritime economics and logistics by examining 303 papers published in international journals from 2000 to 2014 using keyword network analysis. Network analysis can be used because the collected data follow Zipf's law and the power law. Utilizing the degree centrality and betweenness centrality, we find the important keywords in each five year period and determine the importance of shared keywords. To further explain keyword centralities, we invented a Delta-C algorithm to show the trends of keywords over time. We found that degree centrality is useful for identifying important research themes in each period because it is mainly concerned with the number of connections. On the other hands, betweenness centrality is useful to determine the unique themes that emerge in each of the specific periods.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1493-1507
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• 2020

The LEOP Cal/Val (Launch and Early Operation Phase Calibration/Validation) was carried out during 6 months after KOMPSAT-3A (KOMPSAT-3A Korea Multi-Purpose Satellite-3A) was launched in March 2015. After LEOP Cal/Val was successfully completed, high resolution KOMPSAT-3A has been successfully distributing to users over the past 8 years. The sub-meter high-resolution satellite image data obtained from KOMPSAT-3A is used as basic data for qualitative and quantitative information extraction in various fields such as mapping, GIS (Geographic Information System), and national land management, etc. The KARI (Korea Aerospace Research Institute) periodically checks and manages the quality of KOMPSAT-3A's product and the characteristics of satellite hardware to ensure the accuracy and reliability of information extracted from satellite data of KOMPSAT-3A. To minimize the deterioration of image quality due to aging of satellite hardware, payload and attitude sensors of KOMPSAT-3A, continuous improvement of image quality has been carried out. In this paper, the Cal/Val work-flow defined in the KOMPSAT-3A development phase was illustrated for the period of before and after the launch. The MTF, SNR, and location accuracy are the key parameters to estimate image quality and the methods of the measurements of each parameter are also described in this work. On the basis of defined quality parameters, the performance was evaluated and measured during the period of after LEOP Cal/Val. The current status and characteristics of MTF, SNR, and location accuracy of KOMPSAT-3A from 2016 to May 2020 were described as well.