• Journal of the Korean Regional Science Association
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• v.38 no.3
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• pp.19-39
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• 2022

The aim of this study is to compare and analyze structural characteristics of smart city industry focused on Korea and Spain. Structural characteristics of industries were compared focusing on share, penetration, impact path and network clustering of smart industries. Research data used input-output tables established by Korea and Spain in 1995 and 2015, and industries were reclassified into 8 and 25 industries. The analysis model is the Smart SPIN Model. The key finding as follows: It was analyzed that there are differences in the structure and characteristics of the smart city industry between Korea and Spain. Firstly, It is analyzed that Korea has a larger share and penetration rate of IT manufacturing than Spain. On the other hands, Spain has a higher share and penetration rate in the IT service and knowledge service sectors than Korea. Secondly, Korea had many production paths for the IT service and the knowledge service. On the other hands, Spain included more production paths in the IT manufacturing sector. Thirdly, as a result of network analysis, Korea's smart industry has a characteristic that it is difficult to develop independently because it is dependent on traditional industries. In Spain, most of the smart industries were included in one industrial cluster, and it was analyzed to have an independent form. In conclusion, It was found that Korea has the industrial characteristics of a smart city based on IT manufacturing. Spain has the characteristics of smart city industry based on IT service and knowledge service. The results of this study are expected to provide basic data on the direction of smart city promotion and the establishment of smart city policies in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.375-383
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• 2018

This study was carried out to grasp the economic effects of the social welfare foundation by establishing and operating it. For this purpose, the effects of the social welfare law of Daegu Metropolitan City on the regional economy were analyzed using the input-output analysis model. As a result, the effects of GDP was 43,445 billion won, the total value-added effect was 1,940 billion won, and the total employment inducement effect was 37,411. Based on these results, the future direction of the social welfare corporation is suggested as follows. First, it is necessary to shift the perception of consumer-oriented welfare toward welfare that contributes to the activation of the local economy. Second, efforts should be made to continuously expand employment linked to social welfare services, to create an environment where jobs can be created from a long-term perspective, and to establish a separate support system. Third, the value-added created by the social welfare foundation should be newly recognized and sought to be expanded in various fields. Fourth, efforts should be made to secure the legitimacy of social service provision and ensure accountability by appropriately promoting the economic ripple effects of social welfare foundation to the local community.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4177-4183
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• 2014

This study estimated and decomposed the productivity changes into technological change and efficiency change components for the photonics industry and examined ways of imporving the productive efficiency. Unlike most previous studies, this study employed a non-oriented Malmquist productivity index, which is a non-radial method and deals directly with the input surpluses and output shortfalls. The empirical results show that the productivity of the photonics industry has increased. The main reason for this is the increase in technical efficiency. In addition, there was a statistically significant difference in the productivity changes according to the firm's geographic location and participation in government support programs. These findings suggest that the government's regional strategic industry promotion policy has contributed to improvements in the productive efficiency of photonics industry. The approach presented in this study could be used as a practical reference guide to help enhance the competitiveness of the photonics industry.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.685-693
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• 2021

This study examined the fire risk of the region in Goyang City using the spatial information data of buildings. The economic damage by industry was assessed according to the probability of fire risk. The study area was confined to Goyang-si, Gyeonggi-do, and the same fire risk reduction rate was applied to each region for the convenience of analysis. The possibility of fire was derived based on the buildings' density and usage in the area by National GIS building-integrated information standard data. The calculation of economic damage by industry in Goyang City due to the fire risk was calculated by combining the Goyang-si industry-related model produced by matching with 30 industrial categories in Input-Output Statistics of Korea Bank and 20 industrial categories in the Goyang-si business survey and the possibility of fire. The basic scenario of production impossibility during six months and business loss due to fire was established and analyzed based on the supply model. The analysis showed that Ilsan-dong-gu, Ilsan-seo-gu, and Deokyang-gu suffered the most economic damage. The "electricity, gas, steam, and water business" showed the greatest loss by industry.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.506-514
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• 2013

This study examined the feasibility on the construction of a wood industrialization service center for a wood industry cluster establishment in Jeollanam-do. Construction of the wood industrialization service center is based on a discount rate of 3.5%, an investment period of 4 years, a business operations period of 16 years and an investment cost of 24600 million won; the total amount of the net present value, the cost-benefit ratio and the internal rate of return were assumed to be 2.579 million won, 2.51%, and 10.1%, respectively. In addition, the production inducement coefficient, the induced production effect, the income-induced coefficient, the income inducement effect, the employment inducement coefficient, and the employment inducement effect were estimated 1.4345, 35287 million won, 0.1655, 4000.7 million won, and 0.4665, 1,145 people, in the effects of the wood related industries using the multi-regional input-output model, respectively. Financial independence of operating income to cover its own costs incurred in accordance with the operating project might be practicable.

• Journal of Digital Policy
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• v.1 no.2
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• pp.9-15
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• 2022

The World Economic Forum has included Water-Energy-Food among the three major risk groups in the world, and Water-Energy-Food is related to the development strategies of countries and the lives of their citizens. This study calculates the combined Water-Energy-Food use efficiency in China for 2011-2020 based on the SBM-Malmquist index. The results show that the overall combined Water-Energy-Food efficiency in China is low, but shows an upward trend. There is a clear variability in the combined Water-Energy-Food utilization efficiency in China, with an overall geographic distribution pattern of East > Middle > West. Only Beijing and Shanghai have reached the real above effective nationwide, and all other provinces have inefficiency between input and output. The Malmquist index of integrated Water-Energy-Food utilization efficiency is 1.136, with an up ward trend, and technical efficiency and technological progress lead the improvement of integrated Water-Energy-Food utilization efficiency in China at the sametime. The Water-Energy-Food issue should be raised to a strategic level as soon as possible, and policy support should be provided for its development. Each region should establish a cross-regional coordinating body to formulate targeted measures according to the province's food production and water distribution, so as to promote economic transformation from sloppy development to green development as soon as possible.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.170-186
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• 2024

This study was conducted to identify the usage characteristics of the DMZ Punchbowl Trail (DPT) as a national forest trail (NFT) and to estimate its ripple effects on the local economy. The objective of this study is to provide policy implications for sustainable operational management. Out of the 500 questionnaires distributed, 215 respondents provided their complete travel itineraries and expenditures. The respondents, mainly aged 50 and above and residing in the Seoul Metropolitan Area, spend 3.5 hours of travel time to the DPT. Together with their families, the respondents typically spend approximately 4 hours for leisurely activities, primarily appreciation of scenic views and relaxation by visiting the "O-yubatgil." Furthermore, they extend their travels to other parts of Gangwon Province, where the DPT is situated. Within Gangwon Province, Yanggu County is the most visited destination. The respondents reported a notably higher average expenditure per visitor compared with the typical local walking tourists. Estimates show that the DPT generates an annual average of KRW 2.1 billion in direct expenditure (based on an average of 10,000 visitors for over five years), KRW 2.8 billion in production, and KRW 1.3 billion in added value, and it has created 40 jobs in Gangwon Province. The results of this study lies in empirically determining the specific economic scale and ripple effects of DPT as an NFT in the major sector, which occupies a significant portion of the Gangwon Province's local economy. The results will be instrumental in validating NFT policies and informing policy making for sustainable forest utilization.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.