• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.2
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• pp.167-176
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• 2021

When producing recycled asphalt mix, it is important that the old binder of reclaimed asphalt pavement(RAP) should be well melted during blending in the mixer. The recycled asphalt mix is produced by instant mixing(IM) of all materials(RAP, virgin asphalt and new aggregates) all together in the mixer. However, in the same recycled mix, the binder around RAP aggregate was found to show higher oxidation level than the binder coated around the virgin aggregate because the old binder of RAP was not rejuvenated properly while instant mixing. The partially-rejuvenated RAP binder is assumed to be a high stiffness point in IM recycled mix. In this study, the stage mixing(SM) method was introduced; blending RAP and virgin asphalt for the first stage, and then mixing all together with hot new aggregates for the second stage. To compare the effect of the two mixing methods on moisture resistance of recycled mixes, a statistical t-test was performed between SM and IM using indirect tensile strength(ITS) and tensile strength ratio(TSR). Three conditioning methods were used; a 16-h freezing and then 24-h submerging, 48-h submerging, and 72-h submerging in 60℃ water. It was found that the TSR(=ITS_wet/ITS_dry) values of the mixes prepared by SM was clearly higher than the IM mixes, and coefficients of variation of SM mixes were lower than the IM mixes. It was also observed that the ITS_WET of SM was significantly different from the IM at α=0.05 level by statistical t-test. The ITS_WET of SM mix was reduced less than the IM mix in severer conditioned mixes. Therefore, it was concluded that the stage mixing method was an important blending technique for producing better-quality of recycled asphalt mixes, which would show higher moisture resistance than the recycled mixes produced by conventional instant mixing.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.603-611
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• 2022

In urban areas, the impermeable area continues to increase due to urbanization, which interferes with the surface penetrating and infiltrating of rainwater, causing most rainwater runoff to the surface, deepening the distortion of water circulation. Distortion of water circulation affects not only flood disasters caused by rainfall and runoff, but also various aspects such as dry stream phenomenon, deterioration of water quality, and destruction of ecosystem balance, and the Ministry of Environment strongly recommends the use of Low Impact development (LID) techniques. In order to apply the LID technique, it is necessary to set a rainwater management target to handle the increase in outflow after the development of the target site, and the current standard sets the rainwater management target using the 10-year daily rainfall. In this study, the difference from the current standards was analyzed through statistical analysis and classification of independent rainfall ideas using inter-event time definition (IETD) in setting the target amount of rainwater management to improve water circulation. Using 30-year rainfall data from 1991 to 2020, methods such as autocorrelation coefficient (AC) analysis, variation coefficient (VC) analysis, and annual average number of rainfall event (NRE) analysis were applied, and IETD was selected according to the target rainfall period. The more samples the population had, the more IETD tended to increase. In addition, by analyzing the duration and time distribution of independent rainfall according to the IETD, a plan was proposed to calculate the standard design rainfall according to the rainwater management target amount. Therefore, it is expected that it will be possible to set an improved rainwater management target amount if sufficient samples of independent rainfall ideas are used through the selection of IETD as in this study.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.143-152
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• 2023

This study was conducted on 115 students from 4 classes of 2nd year intensive courses at B University of Education. Elementary preservice teachers were surveyed on what teaching methods they were aware of regarding the subject of elementary science astronomy. Recognition data of 80 people from whom the questionnaire was collected were analyzed. For this study, a 5-day survey was conducted. Elementary preservice teachers complained of difficulties in teaching methods in the astronomy area among various areas of the science department. The purpose of this study was to find out what difficulties elementary preservice teachers have in teaching elementary science astronomy topics, and to find more efficient teaching methods for teaching astronomy topics. The topic of the survey was set by discussing with the preparatory elementary teachers about what kind of survey to use in teaching the subject of elementary science astronomy. There are many topics for elementary science astronomy, but two questionnaires were prepared focusing on the unit on the earth and the moon. 'What does the earth look like?' in Unit 4 (1/10) of the 3rd year, 1st semester In Unit 2 (1/11) of the 1st semester of the 6th grade, it was set as 'What does the moon look like?'. Candidly describe how to teach the subject of astronomy to elementary school students by mobilizing all the background knowledge of preparatory elementary teachers. The results of these surveys were visualized and displayed using Netminer as a language analysis method, and the contents of the responses to the actual surveys by pre-service elementary school teachers were described and interpreted. Based on these results, preparatory elementary teachers tried to suggest a more efficient teaching method for the subject of elementary science astronomy. In addition, basic procedures and methods for lecturing on the subject of elementary science astronomy were presented. A more efficient teaching method for teaching elementary science astronomy subjects to pre-service elementary teachers was suggested.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.110-118
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• 2002

This study was conducted to investigate the physiology of optimal reproductive age in Hanwoo for protection to decrease of reproductivity and improvement of production of offspring. Thirty two cows were devided into 4 groups of treatments : T1(12 months of age and 0.5kg daily gain), T2(12 months of age and 0.8kg daily gain), T3(15 months of age and 0.5kg daily gain) and T4(18 months of age and 0.5kg daily gain). The days of the first heat of treated cows were 263.3±6.4 days and average weight was 181.1±11.3kg. The conception rates of first insemination were 25%(T1) and 75%(T4), and the number of insemination of T3 and T4(both 1.5) was lower than those of T1 and T2(2.3 and 2.4), respectively. With regard to estrus return after the first parturition, T1, T2, T3 and T4 showed 66.2,76.7, 62.4 and 68.5 days, respectively, indicating the average days of estrus return was 65.7. Plasma progesterone(P4) concentration was nearly the same during the observation periods of treated cows and P4 began to be detected after 12months. Only 5(15.6%) out of 32cows showed normal estrus cycle and ovulation before 12 months. During the peri-parturition period, P4 concentration was rapidly decreased and there was no detection of P4 from parturition to 40 days after milking. P4 would be released again on 45 day after parturition. The results imply that the optimal reproductive age of Hanwoo heifers would be around at the 14 months of age, 110cm height and 265kg weight.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.8
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• pp.1-9
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• 2018

Emotional Intelligence is the ability to recognize, facilitate, understand and control and utilize one's and other's emotions and has been researched extensively in last 20 years. Of the four domains of emotional intelligence, Emotional Regulation, the ability for one to manage and modify one's emotional reactions in order to achieve goal-directed outcomes, with its influence on authentic leadership and change oriented organizational citizenship behavior was researched by surveying 300 nurses at large metropolitan hospitals in B city in South Korea. Previous research demonstrated in relationship based and long term oriented cultures, such as Korea, Japan and Chinese cultures, ability to regulate emotions is critical component in successful social dynamics yet research the topic is minimal in Korea. Authentic leadership is a leader displaying sincerity and authentic behavior and through such, trust is gained in followers and collaboration is formed. Change oriented organizational citizenship behavior is a proactive behavior where the individual performs behaviors not included in his job functions voluntarily. The results indicate the three out of four sub domains of authentic leadership influenced positively to change oriented organizational citizenship behavior with the exception of balanced information processing. Moreover, Emotional Regulation partially mediated between authentic leadership and change oriented organizational citizenship behavior. Such results validated previous studies that indicated authentic leadership as possible antecedents of individual proactive behaviors and by examining authentic leadership and change oriented organizational citizenship behavior with emotional regulation as a mediator proved possibility as another potential antecedent of change oriented organizational citizenship behavior in hospital setting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.25-32
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• 2009

Currently, various types of TDM (Transportation Demand Management) policies are being studied and implemented in an attempt to overcome the limitations of supply oriented policies. In this context, this paper addressed issues of effectiveness and possible domestic implementation of the HOT lane. The possible site of implementation selected for this simulation study is part of the Kyung-bu freeway, where a dedicated bus lane is currently being operated. Minimum length of distance required in between interchanges and access points of the HOT lane for vehicles to safely enter and exit the lane, and traffic management policies for effectively managing the weaving traffic trying to enter and exit the HOT lane were presented. A 5.2km section of freeway from Ki-heuing IC to Suwon IC and a 8.3km section from Hak-uei JC to Pan-gyo JC have been selected as possible sites of implementation for the HOT lane, in which congestion occurs regularly due to the high level of travel demand. VISSIM simulation program has been used to analyze the effects of the HOT lane under the assumption that one-lane HOT lane has been put into operation in these sections and that the lane change rate were in between 5% to 30%. The results of each possible scenario have proven that overall travel speed on the general lanes have increased as well by 1.57~2.62km/h after the implementation of the HOT lane. It is meaningful that this study could serve as a basic reference data for possible follow-up studies on the HOT lane as one effective method of TDM policies. Considering that the bus travel rate would continue increase and assuming the improvement in travel speed on general lanes, similar case study can be implemented where gaps between buses on bus lane are available, as a possible alternative of efficient bus lane management policies.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.