• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2085-2092
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• 2016

One of the most important properties of the IC substrate is that it should be uniform over large areas. Among the various physical approaches of wafer defect characterization, special attention is to be payed to the infrared techniques of inspection. In particular, a high spatial resolution, near infrared absorption method has been adopted to directly observe defects in semi-insulating GaAs. This technique, which relies on the mapping of infrared transmission, is both rapid and non-destructive. This method demonstrates in a direct way that the infrared images of GaAs crystals arise from defect absorption process. A new interpretation is presented for the observed reversal of contrast in the infrared absorption of nonuniformly distributed deep centers, related to EL2, in semi-insulating GaAs. The low temperature photoquenching experiment has demonstrated in a direct way that the contrast inverse images of GaAs wafers arise from both absorption and scattering mechanisms rather than charge re-distribution or local variation of bandgap.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.45-52
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• 2022

In the field of UAM, which is attracting attention as a next-generation transportation system, technology developments for using UAVs have been actively conducted in recent years. Since UAVs adopted with these technologies are mainly operated in urban areas, it is imperative that accidents are prevented. However, it is not easy to predict the abnormal flight state of an UAV causing a crash, because of its strong non-linearity. In this paper, we propose a method for predicting a flight state of an UAV, based on a CNN-LSTM hybrid model. To predict flight state variables at a specific point in the future, the proposed model combines the CNN model extracting temporal and spatial features between flight data, with the LSTM model extracting a short and long-term temporal dependence of the extracted features. Simulation results show that the proposed method has better performance than the prediction methods, which are based on the existing artificial neural network model.

• Journal of Environmental Impact Assessment
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• v.28 no.1
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• pp.23-34
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• 2019

Global warming becomes a serious issue that poses subsidiary issues like a sea level rise or a capricious climate over the world. Because of severe heat-wave of the summer in Korea in 2016, a big attention has been focused on urban heat island since then. Not just about heat-wave itself, many researches have been concentrated on how to adapt in this trendy warming climate and weather in a small scope. A big part of existing studies is mitigating "Urban Heat Island effect" and that is because of huge impervious surface in urban area where highly populated areas do diverse activities. It is a serious problem that this thermal context has a high possibility causing mortality by heat vulnerability. However, there have been many articles of a green infrastructures' cooling impact in summer. This research pays attention to measure cooling effect of a street planting considering urban canyon and type of green infrastructures in neighborhood scale. This quantitative approach was proceeded by ENVI-met simulation with a spatial scope of a commercial block in Seoul, Korea. We found the dense double-row planting is more sensitive to change in temperature than that of the single-row. Among the double-row planting scenarios, shrubs which have narrow space between the plant and the land surface were found to store heat inside during the daytime and prevent emitting heat so as to have a higher temperature at night. The quantifying an amount of vegetated spaces' cooling effect research is expected to contribute to a study of the cost and benefit for the planting scenarios' assessment in the future.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.741-753
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• 2003

This study begins with paying attention to the agglomeration of 'Residential-Commercial Mixed Use Buildings(hereafter, R-C MU Buildings)', which tend to be a high grade and large sized building cluster at various neighboring areas in Seoul. The purpose of this study is to understand the relationship between emerging new housing type and residential area by analyzing the characteristics of residential neighborhood that is mainly created by R-C MU Buildings. For this purpose, the specialization and the regional characteristics(in connection with the hierarchy in urban spatial structure) of R-C MU Building's residential neighborhood were analyzed. In the end, the specific type of housing reveals the tendency to be differentiated in terms of income, education and occupation related with housing and residential area. And the residential characteristics also seem to be different according to the regions based on the central place hierarchical structure. It means that the social and class factors are important in choosing house, but the characteristics of the neighborhood area play an important role in the selection of resident area. Finally, the location of certain type of housing can influence the selecting process of residence and it can also determine the characteristics of the resident area in turn.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.6
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• pp.17-27
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• 2014

Wild boars are one of the major wild life animals of which the number has increased a lot because there has been any predator and/or competitor in the Korean ecosystem. The increase of their number was a cause damaging crops in farm lands during the growing season in South Korea. This study was done to recognize the spatial pattern of farm lands damaged by wild boars. Totally 2,342 farms were known damaged by wild boars in 2012, and used to statistically analyze the perspectives of the farm land damages by wild boars in ArcMap v. 9.3. Damages by wild boars frequently happened in the western part of Gyeongnam Province including Jinju city, Tongyoung city and Namhae county. Most farm lands damaged were located nearby large mountains in this area. It might cause the number of wild boars increased in this area, which could finally stimulate the increase of farm land damaged by the species. Farm land damages by wild boars were also coincident with the preference of wild boars on their food. They preferred crops (e.g., sweet potato and corn) in uplands and rice paddies and orchards. The reason of their preference on rice, upland crops and fruits was related to the efficiency of their getting much more energy in a unit area. Another reason for the species to come into a rice paddy would be that they enjoy mud bath in there for scraping off parasites such as ticks and lice. Wild boars were seemed much overcrowded during the period from July to October when most of crops and fruits get ripen. About three-quarters of total farm land damages happened in this period. This analysis also said that 1,915 fields (81.8% of total targets) appeared within the 100-meter buffer from boundaries of mountain areas. This meant that wild boars were more sensitive to the anthropogenic land uses than we expected. They seemed to conservatively try their feeding activities in farm lands with paying attention to the human activity.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.163-174
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• 2015

Urbanization caused various environmental problems like destruction of natural water cycle and increased urban flood. To solve these problems, LID(Low Impact Development) deserves attention. The main objective of LID is to restore the water circulation to the state before the development. In the previous studies about the LID, the runoff reduction effect is mainly discussed and the effects of each techniques of LID depending on rainfall types have not fully investigated. The objective of this research is to evaluate the effect of LID using the quantitative simulation of rainwater runoff as well as an amount of infiltration according to the rainfall and LID techniques. To evaluate the water circulation of LID on the development area, new land development areas of Hanam in South Korea is decided as the study site. In this research, hydrological model named STORM is used for the simulation of water balance associated with LID. Rainfall types are separated into two categories based on the rainfall intensity. And simulated LID techniques are green roof, permeable pavement and swale. Results of this research indicate that LID is effective on improvement of water balance in case of the low intensity rainfall event rather than the extreme event. The most effective LID technique is permeable pavement in case of the low intensity rainfall event and swale is effective in case of the high intensity rainfall event. The results of this study could be used as a reference when the spatial plan is made considering the water circulation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.405-413
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• 2022

Since the COVID-19 outbreak, the active utilization of new health care service utilizing the ICT technology and data science such as telemedicine, smart hospital, AI dignosis has been increasingly found. In this study we examined the business model of Amazon healthcare which leads disruptive innovation in U.S. health care industry with the introduction of hybrid model of telemedicin, in-person care and customer-centric online drug delivery, home-use diagnostic kit, characterized by the integrated model combining medical care, drug delivery and the use of diagnostic kit. We showed using the multiproduct competition model that the synergy effect between the Amazon's original business areas and the healthcare business area causes the active market penetration and the increase in the customer value from utilization of the Amazon care. Using Hotelling's spatial competition model, we also showed that the competition in the health care market can be greater when consumer's choice of health care providers are available in telemedicine platform. In the long, run the issue of competition being weakened due to the exit of less competent healthcare providers may arise, to which the policymakers in the charge of fair competition in health care industry should pay attention.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.