• Journal of Korea Port Economic Association
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• v.25 no.1
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• pp.15-28
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• 2009

The purpose of this paper is to show the empirical extraction way of benchmarking ports for overcoming the shortcoming which the traditional DEA method has by using 20 Korean ports in 2003 for 2 inputs (birthing capacity, cargo handling capacity) and 2 outputs(Export and Import Quantity, Number of Ship Calls). Because DEA method has produced the limited set of efficient units which are reference to inefficient units respective of their differences in efficiency scores, it is necessary to adopt the more feasible benchmarking information according to the path analysis(tier or stratification). The core empirical results of this paper are as follows. Benchmarking ports against inefficient ports according to the tier analysis are that Masan Port(Janghang$\rightarrow$Jeju$\rightarrow$Seogoipo$\rightarrow$Yeosu), Jinhae Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando), Pohang&DonghaePort(Janghang$\rightarrow$Samcheonpo$\rightarrow$Pyungtag$\rightarrow$Samcheog), and Sogcho Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando). The policy implication to the Korean seaports and planners is that Korean seaports should introduce the new methods like Tier analysis of this paper for evaluating the port performance and enhancing the efficiency in short term, mid term, and long term according to the tier 3 stage, the tier 2 stage, and the tier 1 stage with original DEA stage.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.121-131
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• 2008

This study was conducted to delineate temporal and spatial patterns of potential risk of cold injury by combining the short-term cold hardiness of Campbell Early grapevine and the IPCC projected climate winter season minimum temperature at a landscape scale. Gridded data sets of daily maximum and minimum temperature with a 270m cell spacing ("High Definition Digital Temperature Map", HD-DTM) were prepared for the current climatological normal year (1971-2000) based on observations at the 56 Korea Meteorological Administration (KMA) stations using a geospatial interpolation scheme for correcting land surface effects (e.g., land use, topography, and elevation). The same procedure was applied to the official temperature projection dataset covering South Korea (under the auspices of the IPCC-SRES A2 and A1B scenarios) for 2071-2100. The dormancy depth model was run with the gridded datasets to estimate the geographical pattern of any changes in the short-term cold hardiness of Campbell Early across South Korea for the current and future normal years (1971-2000 and 2071-2100). We combined this result with the projected mean annual minimum temperature for each period to obtain the potential risk of cold injury. Results showed that both the land areas with the normal cold-hardiness (-150 and below for dormancy depth) and those with the sub-threshold temperature for freezing damage ($-15^{\circ}C$ and below) will decrease in 2071-2100, reducing the freezing risk. Although more land area will encounter less risk in the future, the land area with higher risk (>70%) will expand from 14% at the current normal year to 23 (A1B) ${\sim}5%$ (A2) in the future. Our method can be applied to other deciduous fruit trees for delineating geographical shift of cold-hardiness zone under the projected climate change in the future, thereby providing valuable information for adaptation strategy in fruit industry.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1329-1340
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• 2021

The intertidal zone, which is a transitional zone between the ocean and the land, requires continuous monitoring as various changes occur rapidly due to artificial activity and natural disturbance. Monitoring of coastal topography changes using remote sensing method is evaluated to be effective in overcoming the limitations of intertidal zone accessibility and observing long-term topographic changes in intertidal zone. Most of the existing coastal topographic monitoring studies using remote sensing were conducted through high spatial resolution images such as Landsat and Sentinel. This study extracted the waterline using the NDWI from the GOCI-II (Geostationary Ocean Color Satellite-II) data, identified the changes in the intertidal area in Gyeonggi Bay according to various tidal heights, and examined the utility of DEM generation and topography altitude change observation over a short period of time. GOCI-II (249 scenes), Sentinel-2A/B (39 scenes), Landsat 8 OLI (7 scenes) images were obtained around Gyeonggi Bay from October 8, 2020 to August 16, 2021. If generating intertidal area DEM, Sentinel and Landsat images required at least 3 months to 1 year of data collection, but the GOCI-II satellite was able to generate intertidal area DEM in Gyeonggi Bay using only one day of data according to tidal heights, and the topography altitude was also observed through exposure frequency. When observing coastal topography changes using the GOCI-II satellite, it would be a good idea to detect topography changes early through a short cycle and to accurately interpolate and utilize insufficient spatial resolutions using multi-remote sensing data of high resolution. Based on the above results, it is expected that it will be possible to quickly provide information necessary for the latest topographic map and coastal management of the Korean Peninsula by expanding the research area and developing technologies that can be automatically analyzed and detected.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.3
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• pp.483-494
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• 2019

From the cosmopolitan superiority of the as the first world map completed in 1402 with surprisingly detailed images and contents on the Africa Continent it is reasonable to think that the Koreans in early fifteen century were already with highly up-to-date perspectives on the universe and world history and cultures. However, some 490 year later the first phytohydrographic plankton investigation in the neritic seas of Korea was performed by a Japanese company with sampling points covering from Tokyo Bay through Jeju neritic waters to Shanghai estuary, which was in turn preceded by the first oceanographic investigation other than the simple mapping Koreans seas by using two French sailboats. The first phytohydrographic plankton investigation in Korean seas were behind the world first oceanic plankton exploration, the German Plankton Expedition, by 25 years. Starting from the oceanographic investigation including phytohydrographic samplings in the whole Yellow Sea in 1915 the full-scale phytohydrographic plankton studies were tried in Korean seas which is well represented by the 1921 oceanographic investigation on the whole East Sea with 80 sampling stations. In 1932 two separate oceanographic investigations followed, one in the East Sea where 78 stations from Busan to southern Sakhalin Island were simultaneously visited by 50 research vessels for the physical, chemical, and biological oceanographic studies, and the other one in southern coast and western East Sea of Korea where ocean current observation as well as plankton sampling were made in 120 stations to understand the relationship between the ocean current and plankton distribution in the region. In 1933-1934 more intensified investigations on phytohydrography were carried out particularly in the East Sea as an integral part of the basic marine ecosystem studies for the Myeong-Tae (Alaska Pollock) resources estimation. Scientists' attitude for the marine investigation and research activities seemed to be almost unchanging even to the year 1943, which could be reflected by the fact that publication of the results from the investigations performed in 1945 were finally done in 1967 at Tokyo. Some 70 years later from the mid-twenty century we might be standing on the turning-point of "need to be prepared" for the new era of changing paradigm by reviewing, archiving, and analyzing the prior information big data from the previous ocean observation and biohydrographic investigations. At the same time each professional societies for the above mentioned sciences might trigger a continuous project to reorganize and update the records on related bibliography and its history every 30 years.

• Korean Journal of Heritage: History & Science
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• v.51 no.2
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• pp.138-153
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• 2018

The purpose of this study was to analyze , which was made in the late Joseon Dynasty to specify the vegetation landscape of the Donhwamun Gate area in Changdeokgung Palace. The study results can be summarized as below. First, based on "Jieziyuan Huazhuan(芥子園畵傳)", the introductory book of tree expression delivered from China in the 17th century, allowed the classification criteria of the trees described in the picture to be established and helped identify their types. As a result of the classification, there were 10 species and 50 trees in the Donhwamun Gate area of . Second, it was possible to measure the real size of the trees described in the picture through the elevated drawing scale of . The height of the trees ranged from a minimum of 4.37 m to a maximum of 22.37 m. According to the measurement results, compared to the old trees currently living in Changdeokgung Palace, the trees described in the picture were found to be produced in almost actual size without exaggeration. Thus, the measured height of the trees turned out to be appropriate as baseline data for reproduction of the vegetation landscape. Third, through the Rubber Sheeting Transformation of , it was possible to make a ground plan for the planting of on the current digital topographic map. In particular, as the transformed area of was departmentalized and control points were added, the precision of transformation improved. It was possible to grasp the changed position of planting as well as the change in planting density through a ground plan of planting of . Lastly, it was possible to produce a three-dimensional vegetation landscape model by using the information of the shape of the trees and the ground plan for the planting of . Based on the three-dimensional model, it was easy to examine the characteristics of the three-dimensional view of the current vegetation via the view axis, skyline, and openness to and cover from the adjacent regions at the level of the eyes. This study is differentiated from others in that it verified the realism of and suggested the possibility of ascertaining the original form of the vegetation landscape described in the painting.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.109-130
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• 2022

The Vietnamese government has proposed contract farming through a new type of cooperative as an institutional innovation which aims to restructure the agricultural sector. However, policy changes often impact farmers, who bear the primary effects of the transition process. Understanding households' strategies for land use and livelihood is crucial for policymaking in the agricultural development field. This study was conducted in the rural Binh Dao commune in Central Vietnam. We analyzed household members' labor force changes and their livelihood behaviors after their participation in a contract farming scheme using qualitative analysis methods combined with geographic information system (GIS) support, based on secondary data and in-depth interviews of 190 farmers. Simultaneously, we created a digital map of the cooperative's production area to investigate changes in land use and production activities. The findings show that contract farming shaped the vertical coordination of the value chain from the farmers to the cooperative and agricultural product trading companies. Subsequently, it encouraged land use and labor efficiency due to mechanical support. In addition, it also increased productivity and protected farmers from market risks. However, despite its positive effects on agricultural productivity in this case, the contract farming scheme could not achieve the restructuring of the rural labor force toward non-agricultural sectors. Ironically, farmers in the Binh Dao commune tended to increase cultivable land during the agricultural restructuring program, rather than switching their labor forces to non-agricultural sectors. The lack of stable non-farming job opportunities in rural Vietnam results in challenges to the efficiency of agricultural restructuring programs. Consequently, farmers in the Binh Dao commune are still smallholder farmers, depending on the family labor force.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.