• Journal of the Korean Institute of Educational Facilities
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• v.24 no.6
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• pp.27-36
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• 2017

Educational facilities have an affect to make a decently learning environment. After constructed, it needs a maintenance plan to keep the performance or function which provide the repair time, repair scope and ratio. But the fundamental data are so insufficient that the field worker can't provide the maintenance plan and has no choice use the other data which concerned with apartment or office building. Above all, the service life is indispensible to make a repair plan because the repair time and scope would be provided within the service life. This study aimed at providing the method to make a service life of component in educational facilities and applying the method into the roof proof and floor finishing. Results are shown that first, it is important to set the $1^{st}$ repair time after constructed. when it proposes the three ways with the probability approach, choice probability model and cumulative cost function. Second, the service life of roof proof is provided with about 35 years. In addition, the service life of the floor finishing is about 40 years. These result would be utilized to conduct the repair plan under the service life.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• Korea Trade Review
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• v.46 no.2
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• pp.141-160
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• 2021

The scale of global FDI has been decreasing since 2016 due to the ongoing US-China trade dispute, the strengthening of FDI inflow screening regulations with concern over strategic technology leaks, and the spread of reshoring trends due to the reinforcement of national preferences. Eventually, the competition to attract FDI between countries is expected to become more intense. Therefore, in order to attract high-quality FDI for Korea that will contribute to the development of the national economy, it is pressing to evaluate and improve the domestic FDI attraction environment. This study aims to analyze which areas of Korea's economic and non-economic environments need improvement for gaining advantage amid the fierce competition to attract FDI between countries, by the relative comparison between Korea and the U.S., and based on the ranking indicated in key FDI attractiveness indices. As a result, improvement is needed in the following areas. First, according to IMD's "World Competitiveness Ranking 2020," Korea was inferior to the US in terms of business efficiency, productivity, finance and business legislation in terms of government efficiency. Second, according to INSEAD's "Global Talent Competitiveness Index 2020," Korea was inferior to the US in terms of internal openness, external openness, employability, lifelong learning, access to growth opportunity, and business and labor landscapes. Third, according to WEF's "Global Competitiveness Index 2019", Korea was inferior to the US in terms of product market, labor market, business dynamism and workforce skills.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.1-12
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• 2021

In this paper, we study empirically examined the adventurous investments in corporate venture capital (CVC) firms' investment in the U.S. based corporate venture capital industry. Unlike existing studies focusing CVC firm's characteristics related to parent corporates and regarding CVC firm as a vehicle of corporate venturing, we identified CVC firm as an independent learning agent to adapt to dynamic environment and investigate their exploration and exploitation in investments based on organizational learning theory. Specifically, we investigate the market-environmental factors affecting CVC's adventurous investment in different sector rather than previously done. First, we examined competition intensity in CVC industry might be related to CVC firm's explorative investments. Second, CVC firm's investment experiences might affect as an inertia to invest on unexperienced sector. Finally, we investigated risk preference effect on CVC firm's venturing investments. The empirical data analyzed in the study contained a total of 85 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a GEE regression analysis and a Logit regression analysis, we found the significance and direction of our independent and moderating variables strongly supported all of our four hypotheses in a highly robust manner.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.476-509
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• 2008

This research investigates the determinant factors of technology licensing contracts and the relative importance of each factor. To accomplish this objective we classify various factors affecting technology licenses into two categories, technology and environment, and conduct an Analytic Hierarchy Process (AHP) with a Korean expert survey. From the AHP results, we find that the possibility of commercial success, as well as the scope and levels of exclusive rights which are transferred together with technology to the licensee, are very important among technological factors in technology transfer. Moreover, we conclude that the technology utilization capacity and the learning capabilities of the licensee are also important environmental factors. Finally, we conclude that the factors which the licensor and licensee consider in technology transfer are different from each other. Based on this result, we discuss implications with regard to reducing this factor gap between the licensor and licensee as a means of promoting and improving technology transfer in the Republic of Korea.

• Journal of Ecology and Environment
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• v.38 no.2
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• pp.133-143
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• 2015

Freshwater macrophytes improve the structural heterogeneity of microhabitats in water, often providing an important habitat for zooplankton. Some studies have focused on the overall influence of macrophytes on zooplankton, but the effects of macrophyte in relation to different habitat characteristics of zooplankton (e.g., epiphytic and pelagic) have not been intensively studied. We hypothesized that different habitat structures (i.e., macrophyte habitat) would strongly affect zooplankton distribution. We investigated zooplankton density and diversity, macrophyte characteristics (dry weight and species number), and environmental parameters in 40 shallow wetlands in South Korea. Patterns in the data were analyzed using a self-organizing map (SOM), which extracts information through competitive and adaptive properties. A total of 20 variables (11 environmental parameters and 9 zooplankton groups) were patterned onto the SOM. Based on a U-matrix, 3 clusters were identified from the model. Zooplankton assemblages were positively related to macrophyte characteristics (i.e., dry weight and species number). In particular, epiphytic species (i.e., epiphytic rotifers and cladocerans) exhibited a clear relationship with macrophyte characteristics, while large biomass and greater numbers of macrophyte species supported high zooplankton assemblages. Consequently, habitat heterogeneity in the macrophyte bed was recognized as an important factor to determine zooplankton distribution, particularly in epiphytic species. The results indicate that macrophytes are critical for heterogeneity in lentic freshwater ecosystems, and the inclusion of diverse plant species in wetland construction or restoration schemes is expected to generate ecologically healthy food webs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.