• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.4
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• pp.65-73
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• 2008

Due to the amendment of the Act for Urban Parks in 2005, local governments have to establish long-range plans for securing and managing urban parks and green areas. This study aims to propose a method of setting priorities for green areas of land to be preserved before the development stage through the introduction of the concept of Green Infrastructure Assessment, and provide basic data to establish the network of urban parks and green areas by applying the GIA method to the city of Daejeon. The concept of GIA and the process of analysis have been drawn as a result of literature research and case studies. The results of this study show that an introduction of the GIA concept to set park and open space planning promotes the connection of the city planning process as well as presents very a reasonable source to facilitate sustainable development. Also, other results present a priority ranking for protection of parks and green areas as well as a means to manage potential focus areas. This study, does have research limitations such as a limited study area, scale, and conflicts between domestic and foreign computing data. Further studies need to set the planning process and examine the index survey to apply this method to various situations and areas.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.90-97
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• 2017

Construction of urban emergency shelters based on disaster-prevention green space is an important part of an urban disaster response plan. The accessibility of disaster-prevention green space is directly related to the disaster prevention service effect of green space. Taking the Jung-gu and Nam-gu districts of Ulsan city as research targets, the accessibility of three green spaces was analyzed by a network analysis method based on information regarding the distribution of population and green space and the urban road network. Two indicators for evaluating the service effect of green spaces were service population rate and service area rate. The results showed that the accessibility of the emergency refuge parks (5min) and central refuge parks was relatively good but the service area rate and service population rate of the emergency refuge parks (3min) and temporary refuge parks was less than 60%. In view of the overall situation, the service effect of disaster-prevention green space is at this point only general in Ulsan and there is great room for improvement.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.1
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• pp.25-38
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• 2022

Urban problems are constantly occurring around the world due to rapid industrialization and population decline. In particular, as the number of vacant houses is gradually increasing as the population decreases, it is necessary to prepare countermeasures. A plan to utilize vacant houses has emerged to restore the natural environment of the urban ecosystem where forest destruction, damage to habitats of wild animals and plants, and disconnection have occurred due to large-scale development. Through connectivity analysis, it is possible to understand the overall ecosystem flow based on the movement of species and predict the effect when vacant houses are converted into green spaces. Therefore, this study analyzed the green area network to confirm the possibility of greening of vacant houses neglected in Jeonju based on circuit theory. Using Circuitscape and Least-cost path, we tried to identify the connectivity of green areas and propose an ecological axis based on the analysis. In order to apply the resistance values required for analysis based on previous studies, the 2020 subdivision land cover data were integrated into the major classification evaluation items. When the eight forests in the target site were analyzed as the standard, the overall connectivity and connectivity between forests in the area were high, so it is judged that the existing green areas can perform various functions, such as species movement and provision of habitats. Based on the results of the connectivity analysis, the importance of vacant houses was calculated and the top 20 vacant houses were identified, and it was confirmed that the higher the ranking, the more positive the degree of landscape connectivity was when converted to green areas. In addition, it was confirmed that the results of analyzing the least-cost path based on the resistance values such as connectivity analysis and the existing conceptual map showed some differences when comparing the ecological axes in the form. As a result of checking the vacant houses corresponding to the relevant axis based on the width standards of the main and sub-green areas, a total of 30 vacant houses were included in the 200m width and 6 vacant houses in the 80m width. It is judged that the conversion of vacant houses to green space can contribute to biodiversity conservation as well as connectivity between habitats of species as it is coupled with improved green space connectivity. In addition, it is expected to help solve the problem of vacant houses in the future by showing the possibility of using vacant houses.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.248-250
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• 2008

This paper proposes a prediction method to prevent traffic accident and reduce to vehicle waiting time using neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dose not consider coordinating green time. Moreover, we present neural network approach for traffic accident prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data. Computer simulation results proved reducing traffic accident waiting time which proposed neural network better than conventional system dosen't consider neural network.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2908-2914
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• 1998

This paper presents an intelligent signal for controling the traffic lights on traffic junction network with dynamic traffic flow, When a junction is connected to adjacent junctions on four sides. Prior researches have been done on the single traffic junction. However, it is dificult to apply single junction controller to real traffic situation. In this paper, we develop a fuzzy taffic network controller which adjusts the extension time of current green phase by using teh fuzzy input variables such as the number of entering cars at the green light, the number of waiting cars during the red light, and the traffic volume. The proposed method was compared to the existing junction signal control methods on controllers in terms of average delay time of cars and the cost function defined in this paper.

• Membrane Journal
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• v.29 no.4
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• pp.231-236
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• 2019

In this study, alginate-based hydrogel membranes composed of hydrogel beads and highly tough hydrogel matrix including moisturizing oil and natural emulsifier were prepared and their elution characteristics were evaluated. As a result, it was confirmed that the elution rate of the moisturizing oil component can be controlled within a desired range by controlling the composition of the hydrogel bead and the tough hydrogel matrix. In particular, it has been confirmed that by combining tough hydrogel having a structure of interpenetrating polymer network (IPN) and hydrogel beads, the physical stability of the membranes can be improved and the elution rate of the moisturizing oil can also be controlled more finely.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.2
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• pp.130-141
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• 2022

Due to continuous climate change, greenhouse gases in the atmosphere are gradually accumulating, and various extreme weather events occurring all over the world are a serious threat to human sustainability. Countries around the world are making efforts to convert energy sources from traditional fossil fuels to renewable energy. Hydrogen energy is a clean energy source that exists infinitely on Earth, and can be used in most areas that require energy, such as power generation, transportation, commerce, and household sectors. A fuel cell, a device that produces electric and thermal energy by using hydrogen energy, is a key field to respond to climate change, and major countries around the world are spurring the development of core fuel cell technology. In this paper, research trends in China, the United States, Germany, Japan, and Korea, which have the highest number of papers related to fuel cells, are analyzed through keyword network analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1302-1309
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• 2021

Recently, the Korean Government announced the Korean New Deal as a national development strategy to overcome the economic recession from the pandemic crisis and lead the global action against structural changes. In the Korean New Deal, the Green New Deal related with the energy aims to achieve net-zero emissions and accelerates the transition towards a low-carbon and green economy. To this end, the government plans to promote an increased use of renewable energy in the society at large. This paper introduces a binary power generation using unused low-grade thermal energy to accelerate the transition towards a low-carbon and green economy and examines a control system based on Neural Network which is capable maintenance at low-cost by an unmanned automated operation in actual power generation environment. It is expected that the realization of binary power generation accelerates introduction of renewable energy along with solar and wind power.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.557-560
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• 2021

In the face of the COVID-19 pandemic, the Korean Government announced the Korean New Deal as a national development strategy to overcome the economic recession from the pandemic crisis and lead the global action aginst sturctural changes. The Green New Deal related with the energy aims to achieve net-zero emissions and accelerates the transition towards a low-carbon and green economy. To this end, the government plans to promete an increased use of renewable energy in the the society at large. This paper introduces a compact-binary power plant using unused thermal energy and a control system based on Neural Network in order to accelerate the transition towards a low-carbon and green economy. It is expected that he compact-binary power plant accelerate introduction of renewable energy along with solar and wind power.