• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.463-473
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• 2019

This study compared the characteristics of the optimization technique and the water supply and demand forecast using K-WEAP (Korea-Water Evaluation and Planning System) model and MODSIM (Modified SIMYLD) model considering wtaer supply priority. Currently, The national water resources plan applied same priority for municipal, industrial and agricultural demand. the K-WEAP model performs the ratio allocation to satisfy the maximum satisfaction rate, whereas the MODSIM model should be applied to the water supply priority of demands. As a result of applying the priority, water shortage decreased by an average of $1,035,000m^3$ than same prioritized results. It is due to the increase of the return flow rate as the distribution of Municipal and industrial water increases. Comparing the analysis results of K-WEAP and MODSIM applying the priorities, the relative error was within 5.3% and the coefficient of determination ($R^2$) was 0.9999. In addition, if both models provide reasonable water balance analysis results, K-WEAP is superior to GUI convenience for model construction and data processing. However, MODSIM is more effective in simulation time efficiency. It is expected that it will be able to carry out analysis according to various scenarios using the model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.209-221
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• 2015

Temperature is one of the most important factors affecting crop growth. The diurnal cycle of the scale factor [Tsc] for air temperature and the surface temperature of crop leaf and soil could be estimated by the following equation : $[Tsc]=0.5{\times}sin(X+C)+0.5$. The daily air temperature (E[Ti]) according to the E&E time [X] can be estimated by following equation using average (Tavg), maximum (Tm) and minimum (Tn) temperature : $E[Ti]=Tn+(Tm-Tn){\times}[0.5{\times}sin\;\{X+(9.646Tavg+703.65)\}+0.5]$. The crop leaf temperature in 24th June 2014 was high as the order of red pepper without mulching > red pepper with mulching > soybean under drought > soybean with irrigation > Chinese cabbage. The case in estimating crop leaf surface temperature using air temperature and soil surface temperature was lower in the deviation compared to the case using air temperature for Chinese cabbage and red pepper. These results can be utilized for the crop models as input data with estimation.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.4
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• pp.231-242
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• 2018

This experiment was carried out to study the effects of different environmental conditions and production techniques on forage productivity of Italian ryegrass (IRG) in central and southern regions of Korea from 2016 to 2017. Average dry matter yield of 27 IRG cultivation regions was 6,940kg/ha. Forage productivity of IRG have positive correlation with cultivation techniques(p<0.01) but not correlated with cultivation environments. Forage productivity of IRG have positive correlation with seeding and field management techniques(p<0.01) but not correlated with fertilization techniques. This results suggests that practices of cultivation techniques are more important than cultivation environments for increasing the forage productivity of IRG. Therefore, yield prediction techniques of IRG in Korea have to be considered the practices of cultivation techniques along with soil and climate conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.2
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• pp.10-19
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• 2019

Management plan with repairing is essential for RC structures exposed to chloride attack since durability problems occur with extended service life. Conventionally deterministic method is adopted for evaluation of service life and repair cost, however more reasonable repair cost can be obtained through continuous repair cost from probabilistic maintenance technique. Unlike the previous researches considering only normal distribution of life time, PLTFs (Probabilistic Life Time Function) which can be capable of handling log and normal distributions are attempted for initial and repair service life, and repair cost is evaluated for OPC and GGBFS concrete. PLTF with log distributions in initial service life is more effective to save repair cost since it is more dominant after average than normal distribution. Repair cost in GGBFS concrete decreases to 30% of OPC concrete due to longer initial service life and lower repairing event. The proposed PLTF from the work can handle not only normal distributions but also log distributions for initial and repair service life, so that it can provide more reasonable repair cost evaluation.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.461-471
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• 2018

In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the $CO_2$ potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the $CO_2$ potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of $CO_2$ potential reduction hotspot areas among the deforestation areas was 15, taking up the $CO_2$ potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a $CO_2$ potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many $CO_2$ potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the $CO_2$ potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital). Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.79-90
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• 2021

This single-subject study examined the effects of repetitive speech training at the word and sentence levels using speech cues on the percentage of correct consonants (PCC) and speech intelligibility of children with cerebral palsy (CP). Three children aged between 5-8 years with a history of CP participated in the study. Thirty-minute intervention sessions were provided four times a week for four weeks. The intervention included repeated training of words and sentences containing target phonemes using two instructions of speech cues, "big mouse" and "strong voice". First, the children improved their average PCC and speech intelligibility, but an effect size analysis indicated that the effect was different for each child, and the effect size for speech intelligibility was higher than for PCC. Second, the intervention effect was generalized to untrained words and sentences. Third, the maintenance effects of PCC and speech intelligibility were very high. These findings suggests that repeated speech training using speech cues is an intervention technique that can help improve PCC and speech intelligibility in children with CP.

• Journal of Apiculture
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• v.32 no.3
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• pp.155-162
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• 2017

This research was carried out to evaluate the royal jelly production in Apis mellifera through the selection of superior honeybee lines. For the study, two inbred honeybee lines A and C were evaluated for the production of royal jelly by their workers, royal jelly production per colony (g), and the acceptance percentage of grafted larvae (%). The results showed that, the average royal jelly production per colony was highest ($33.7{\pm}7.41g$) in the inbred line C in comparison to other lines and the percentage of larvae acceptance ($87.8{\pm}7.5%$) was also highest in the inbred line C in comparison to other liens. The royal jelly produced by the three honeybee lines was analyzed for their trans-10-hydroxy-2-decenoic acid (10-HDA) content using a column liquid chromatography technique. Chromatographic results showed that the royal jelly produced by the inbred honeybee line C had the maximum amount of 10-HDA. We also observed age-dependent alterations of the major royal jelly proteins (MRJPs), which were differentially expressed in the two inbred lines and the commercial line, using quantitative real time-PCR (qRT-PCR).

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.57-67
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• 2021

A thorough administration of the arterial road network requires a continuous supply of updated and accurate information about the traffic that travels on the roads. One of the ways to effectively obtain the traffic volume and weight distribution of heavy vehicles is the BWIM technique, which is actively being studied. Unlike previous studies, this study was performed to develop a simplified Bridge Weigh-In-Motion (BWIM) algorithm that can easily estimate the axle spacing and weight of a traveling vehicle by utilizing the structural characteristics of the bridge. A short span RC T-beam bridge with no crossbeam installed was selected for the study, and then the strain response characteristics of bridge deck and girder was checked through preliminary field test. Based on the preliminary field test results, a simplified BWIM algorithm suitable for the bridge to be studied was derived. The validity and accuracy of the BWIM algorithm derived in this study were verified through field test. As a result of the verification test, the proposed BWIM algorithm can estimate the axle spacing and gross weight of the travelling vehicles with the average percent error of less than 3%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.