• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.145-160
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• 2023

The purpose of this study was to compare and analyze diurnal thermal environments using Unmanned Aerial Vehicles(UAV)-derived physical parameters(NDVI, SVF) and ENVI-met modeling. The research findings revealed significant correlations, with a significance level of 1%, between UAV-derived NDVI, SVF, and thermal environment elements such as S↑, S↓, L↓, L↑, Land Surface Temperature(LST), and Tmrt. In particular, NDVI showed a strong negative correlation with S↑, reaching a minimum of -0.52^** at 12:00, and exhibited a positive correlation of 0.53^** or higher with L↓ at all times. A significant negative correlation of -0.61^** with LST was observed at 13:00, suggesting the high relevance of NDVI to long-wavelength radiation. Regarding SVF, the results showed a strong relationship with long-wave radiative flux, depending on the SVF range. These research findings offer an integrated approach to evaluating thermal comfort and microclimates in urban areas. Furthermore, they can be applied to understand the impact of urban design and landscape characteristics on pedestrian thermal comfort.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.109-120
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• 2024

For successful waterproofing construction, it is very important to secure construction quality as well as material performance of waterproofing materials used in construction. Due to the long-term cost reduction policy following the economic downturn in the construction market, most construction companies are using general low-priced waterproof materials rather than high-quality waterproof materials without clear quality control standards. Without clear education on construction, construction is being carried out with meaning only on construction activities. In addition, the waterproofing method applied in combination is a situation where water leakage occurs due to waterproofing failure due to insufficient construction quality because the construction method is complicated. Therefore, it is necessary to review the quality control measures(design, materials, construction) for successful waterproofing work and improve problems that are derived so that stable waterproofing work can be done. In order to expect the leakage prevention effect of a building, first, it is required to select appropriate materials for each part of the building and environment in the design stage, and the selected materials must satisfy all items of the Korean Industrial Standard(KS). Second, to secure the quality of waterproofing construction, sincere construction by workers is required. In this paper, we tried to describe "review of waterproof design", "constructor education", "site inspection", and "criticism(correction/supplementation)" as quality control measures after material selection.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.83-103
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• 2024

Adhesive bonding is currently widely used in many industrial fields, particularly in the aeronautics sector. Despite its advantages over mechanical joints such as riveting and welding, adhesive bonding is mostly used for secondary structures due to its low peel strength; especially if it is simultaneously exposed to temperature and humidity; and often presence of bonding defects. In fact, during joint preparation, several types of defects can be introduced into the adhesive layer such as air bubbles, cavities, or cracks, which induce stress concentrations potentially leading to premature failure. Indeed, the presence of defects in the adhesive joint has a significant effect on adhesive stresses, which emphasizes the need for a good surface treatment. The research in this field is aimed at minimizing the stresses in the adhesive joint at its free edges by geometric modifications of the ovelapping part and/or by changing the nature of the substrates. In this study, the finite element method is used to describe the mechanical behavior of bonded joints. Thus, a three-dimensional model is made to analyze the effect of defects in the adhesive joint at areas of high stress concentrations. The analysis consists of estimating the different stresses in an adhesive joint between two 2024-T3 aluminum plates. Two types of single lap joints(SLJ) were analyzed: a standard SLJ and another modified by removing 0.2 mm of material from the thickness of one plate along the overlap length, taking into account several factors such as the applied load, shape, size and position of the defect. The obtained results clearly show that the presence of a bonding defect significantly affects stresses in the adhesive joint, which become important if the joint is subjected to a higher applied load. On the other hand, the geometric modification made to the plate considerably reduces the various stresses in the adhesive joint even in the presence of a bonding defect.

• 대한공업교육학회지
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• v.42 no.2
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• pp.67-88
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• 2017

The purpose of this research was educational needs of experts for revitalizing youth entrepreneurship education and creating effective conditions for such education. The subjects of the survey were chosen 100 teachers who had participated in entrepreneurship-related professional training for teachers were selected and surveyed. A total of 100 questionnaires were collected, of which 92 (92.00%) were used for the analysis. Eight were excluded as they were not properly answered. As for the used survey tool, a total of 8 areas and 30 items were derived from the review of the literature, and the validity of the contents was examined through expert meetings. The data were analyzed using the SPSS (ver. 20.0) statistical program. The analysis was conducted in terms of the required competency level, perceived competency level and educational needs. As for the used analytical methods, first, the averages of the required competency level and perceived competency level were calculated and the education needs were calculated using Borich's formula, and then the averages were compared through paired t-test. The results turned out to be statistically significant (p<.000). The details are as follows: As a result of the calculation of the educational needs the educational needs in all areas turned out to be very high with the average being 4.94 points, which indicates that the teachers strongly feel the need for educational strengthening in relation to entrepreneurship. These results show that all the educational conditions such as entrepreneurship-related curriculum, teacher professionalism, educational environment, educational support and the perception among school community members are insufficient in the current school settings. For the improvement of the current status, the education conditions in the following areas should be improved: the cooperation from school community members including principals, teacher support such as an exclusive responsibility teacher system, the development of an entrepreneurship curriculum, the securing of teacher professionalism through the implementation of the curriculum, teacher training support for the enhancement of their professionalism and the provision of educational environment and facilities. For enhancing the perception of parents and society regarding entrepreneurship, it is necessary to establish the precise concept of entrepreneurship and promote it based on such work.

• Journal of The Korean Association For Science Education
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• v.18 no.4
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• pp.493-502
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• 1998

Science curricula of public and government institution schools from 1895 to 1910 in Korea were studied And through tracing people in charge of science education actual status was researched. The result of the study showed that 'physics, chemistry, nature' in the regular course for normal schools and 'department of science' in the short course were used for in the curriculum. Subject of nature were educated by SaitoKinji. 'Science' was educated by MatsumotoSoji in Department of Japanese Language at Foreign Language School and 'science of nature' by Hase in Department of German Language. 'Nature' and 'physics and chemistry' were taught by ShideharaTahira at Hansung Middle School which was established in 1899. MoriTamejo was in charge of subject of nature at Hansung High School which was a new name since 1906. It was also revealed that'physics and chemistry'were taught at Industrial Professional Institute. In short during the era of Taihan (Korea) Empire science education at public and government institution schools were entirely performed by Japanese. Furthermore the first time when professionals majored in natural science began to assume responsibility for science education was during late part of Taihan Empire and before that time tradition of science education was maintained by'non-professionals'like ShideharaTahira.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.378-387
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• 2023

Petrochemical is an energy consuming industry that consumes about 30% of total industrial energy consumption and is a representative carbon dioxide (CO₂) emission source. Among them, the Naphtha Cracking Center (NCC), which produces ethylene, propylene, propane and mixed C4, consumes large amounts of energy and emits significant amounts of CO₂. For this reason, an integrated techno economic- environmental impact assessment aimed at reducing energy consumption and environmental impact factors is necessary to ensure efficiency in terms of economics and environment. This study aims to analyze the efficiency of the heat exchanger network used in the existing NCC base on the pinch analysis and select an improvement plan that can reduced energy consumption. In order to reduces the utility consumption in the process, an optimal heat exchanger network considering the high-temperature and low-temperature stream was derived, and the economic evaluation was conducted by considering the trade-off between the reduction in utility consumption and the increase in heat exchanger installation cost. In addition, an environmental impact assessment was conducted on the reduced CO₂ emission in consideration of the environmental aspect, and the economic environmental impact assessment used the payback period to recover the invested funds to come up with an energy saving plan that can be applied based on the actual process. As a result of considering the economic-environmental impact assessment, when the environmental impact assessment was not considered, it was 4.29 months, 3.21 months, and 3.39 months for each case, and when considering the environmental impact assessment, it was 4.24 months, 3.17 months, and 3.35 months for each case. These results appeared equally both when the environmental impact assessment was not include and when it was include. In addition, a sensitivity analysis was conducted for each case to determine how important factors affect the payback period. As a result of the sensitivity analysis, the cost of the heat exchanger was identified as a major factor influencing the overall cost.

• Journal of the Korean earth science society
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• v.31 no.6
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• pp.600-607
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• 2010

In 2008, multiple episodes of large-scale transport of natural airborne particles and anthropogenically affected particles from different sources in the East Asian continent were identified in the National Oceanic and Atmospheric Administration (NOAA) satellite RGB-composite images and the mass concentrations of ground level particulate matters. To analyze the aerosol size distribution during the large-scale transport of atmospheric aerosols, both aerosol optical depth (AOD; proportional to the aerosol total loading in the vertical column) and fine aerosol weighting (FW; fractional contribution of fine aerosol to the total AOD) of Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products were used over the East Asian region. The six episodes of massive natural airborne particles were observed at Cheongwon, originating from sandstorms in northern China, Mongolia and the loess plateau of China. The $PM_{10}$ and $PM_{2.5}$ stood at 70% and 16% of the total mass concentration of TSP, respectively. However, the mass concentration of $PM_{2.5}$ among TSP increased as high as 23% in the episode in which they were flowing in by way f the industrial area in east China. In the other five episodes of anthropogenically affected particles that flowed into the Korean Peninsula from east China, the mass concentrations of $PM_{10}$ and $PM_{2.5}$ among TSP reached 82% and 65%, respectively. The average AOD for the large-scale transport of anthropogenically affected particle episodes in the East Asian region was measured at $0.42{\pm}0.17$ compared with AOD ($0.36{\pm}0.13$) for the natural airborne particle episodes. Particularly, the regions covering east China, the Yellow Sea, the Korean Peninsula, and the east Korean sea were characterized by high levels of AOD. The average FW values observed during the event of anthropogenically affected aerosols ($0.63{\pm}0.16$) were moderately higher than those of natural airborne particles ($0.52{\pm}0.13$). This observation suggests that anthropogenically affected particles contribute greatly to the atmospheric aerosols in East Asia.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.