• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.96-106
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• 2021

The PC structure based on the OSC (Off-Site Construction) is mentioned as a representative method of innovation in the construction industry that converts the existing construction environment from site-centered to factory production-transportation-site assembly. However, recent research on PC method has focused on improving the functions of subsidiary materials and improving the production system to increase productivity and institutional / policy R&D that can be universally applied to the life-cycle stage of ordering / design /construction is insufficient. In particular, the absence of standardized cost calculation standards makes it difficult to calculate and verify of objectified appropriate construction cost. So which is an obstacle to the activation of the PC method. In this study, the standards for construction costs of domestic and foreign PC method were surveyed and similar Construction Standard Production Rates were analyzed to confirm the product structure suitable for PC method. Subsequently, the construction procedures and input resources for each PC subsidiary materials were identified through on-site surveys to derive component for subsidiary materials, and the factors of change in the product according to the construction characteristics(height, weight of subsidiary material) were verified. As a result the standard product calculation system suitable for the site installation of the PC method for apartment was presented.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.129-134
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• 2021

Recently, the demand for electricity is gradually increasing due to the rapid industrial development and the improvement of living standards. In the case of Korea, which is highly dependent on fossil fuels due to such a surge in electricity demand, reduction and freezing of greenhouse gas emissions due to international environmental regulations will immediately lead to a contraction in industrial activities. Accordingly, there are many difficulties in competition with advanced countries that want to link the environment with the country's industrial production activities, and the development of alternative energy as a countermeasure is of great interest around the world. Among these new power generation methods, small-scale power generation facilities with relatively small capacity include photovoltaic generation, wind power generation, and fuel cell generation. Among them, the fuel cell attracts the most attention in consideration of continuous operation, high power generation efficiency, and long-term durability, which are important factors for practical use. Therefore, in this paper, the fuel cell power generation system was researched and constructed by designing the power conversion circuit necessary to finally obtain the AC power used in our daily life by using the DC power generated from the fuel cell as an input.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.143-154
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• 2018

This study is intended to look into the effects of SCM(supply chain management) competency and process improvement on operational performance in small and venture companies. To achieve this, a survey was empirically carried out to 179 small and venture manufacturing companies. The findings showed that the SCM competency had a significant effect on the process improvement and operational performance in small and venture companies, adopting all hypotheses. And the process improvement had a significant mediating effect on the relationship between SCM competency and operational performance in small and venture companies, adopting hypothesis 4. As for the findings, strategic alliance, technology development, competency concentration as SCM competencies and starting preparation, detailed planning, implementation management as process improvements were factors that have positive effects on quality performance, cost reduction and profit increase as operational performances in small and venture companies. In other words, the better process and performance by the maximized SCM competencies require selective input strategies for strategic alliance, technology development and competency concentration in small and venture companies. And for its early application and settlement, the starting preparation and detailed planning of business process within small and venture companies need to be jointly put in action under clear company-wide goal management. Consequently, the expected performance can be maximized when strict management and implementation lead to these attributes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.895-906
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• 2018

The improvement project for school zone in Korea is occurring mostly in kindergartens and elementary schools in which children are mainly active, and is being promoted as part of measures to prevent children's traffic accidents. However, since the study on the essential installation facilities and proper level of safety facilities for School zone is lacking, this study relatively evaluated the efficiency of safety facility installation using DEA (Data Envelope Analysis) and suggested improvement plan. We built the facilities and incident data for 28 school zones in Hwaseong City. Six major facilities (Raised intersection, Raised crosswalk, Protective fence, Skid Proof, Speed hump, Speed cameras) that have proven to be effective in the preceding research were analyzed as input factors. As a result of the analysis, only 12 out of 28 showed efficiency and 16 out of efficiency. Effective groups of school zones were found to have fewer installed Protective fences, Skid Proof, and Speed cameras installations than school zones classified as ineffective groups. Protective fences were found to be efficient even if only 47% of the total extension of the school zone was installed, and the number of slip prevention facilities was 8.7 per square km. The number of subjects to be compared is 28, so this study is limited to use as a relative efficiency evaluation index, and it can be grouped into nationwide units and efficiency evaluation can be done for each group.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.257-264
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• 2019

Objective: Dairy cattle nutrient requirement systems acknowledge amino acid (AAs) requirements in aggregate as metabolizable protein (MP) and assume fixed efficiencies of MP used for milk protein. Regulation of mammary protein synthesis may be associated with AA input and milk protein output. The aim of this study was to evaluate the effect of nanoemulsified methionine and cysteine on the in-vitro expression of milk protein (casein) in bovine mammary epithelial cells (MAC-T cells). Methods: Methionine and cysteine were nonionized using Lipoid S 75 by high-speed homogenizer. The nanoemulsified AA particle size and polydispersity index were determined by dynamic light scattering correlation spectroscopy using a high-performance particle sizer instrument. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was performed to determine the cytotoxicity effect of AAs with and without nanoionization at various concentrations (100 to $500{\mu}g/mL$) in mammary epithelial cells. MAC-T cells were subjected to 100% of free AA and nanoemulsified AA concentration in Dulbecco's modified Eagle medium/nutrient mixture F-12 (DMEM/F12) for the analysis of milk protein (casein) expression by the quantitative reverse transcription polymerase chain reaction method. Results: The AA-treated cells showed that cell viability tended to decrease (80%) in proportion to the concentration before nanogenesis, but cell viability increased as much as 90% after nanogenesis. The analysis of the expression of genetic markers related to milk protein indicated that; ${\alpha}_{s2}$-casein increased 2-fold, ${\kappa}$-casein increased 5-fold, and the amount of unchanged ${\beta}$-casein expression was nearly doubled in the nanoemulsified methionine-treated group when compared with the free-nanoemulsified methionine-supplemented group. On the contrary, the non-emulsified cysteine-administered group showed higher expression of genetic markers related to milk protein ${\alpha}_{s2}$-casein, ${\kappa}$-casein, and ${\beta}$-casein, but all the genetic markers related to milk protein decreased significantly after nanoemulsification. Conclusion: Detailed knowledge of factors, such nanogenesis of methionine, associated with increasing cysteine and decreasing production of genetic markers related to milk protein (casein) will help guide future recommendations to producers for maximizing milk yield with a high level of milk protein casein.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.193-203
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• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.