• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.809-820
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• 2017

Efficient management of large-size wastes generated from disasters etc. is always in demand. Large-size wastes are closely connected to the environment, producing adverse effects on the air quality, water quality, living environment and so on. When large-size wastes are generated, we must be able to estimate the generated amount in order to transfer them to a temporary trans-shipment site, or to properly treat them. Currently, we estimate the amount of generated large-size wastes by using satellite images or unit measure for wastes; however, the accuracy of such estimations have been constantly questioned. Therefore, the present study was performed to establish three-dimensional spatial information based on UAS, to measure the amount of waste, and to evaluate the accuracy of the measurement. A measurement was made at a waste site by using UAS, and the X, Y, Z RMSE values of the three-dimensional spatial information were found to be 0.022 m, 0.023 m, and 0.14 m, all of which show relatively high accuracy. The amount of waste measured using these values was computed to be approximately $4,273,400m^3$. In addition, the amount of waste at the same site was measured by using Terrestrial LiDAR, which is used for the precise measurement of geographical features, cultural properties and the like. The resulting value was $4,274,188m^3$, which is not significantly different from the amount of waste computed by using UAS. Thus, the possibility of measuring the amount of waste using UAS was confirmed, and UAS-based measurement is believed to be useful for environmental control with respect to disaster wastes, large-size wastes, and the like.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Clean Technology
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• v.26 no.2
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• pp.151-157
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• 2020

Rapid urbanization has elevated the risk of urban flooding due to the increase in the impervious surface, causing environmental disasters and environmental pollution problems, such as lowering the groundwater level and increasing water pollution. In Korea, low impact development (LID) techniques have been introduced to minimize these environmental impacts and maintain the water cycle soundness. However, most small-scale development projects are in blind spots because there is no legal basis for rainfall runoff management. Small-scale development projects that increase the surface runoff of rainwater are required to mandate the application of LID facilities in accordance with the polluters' responsibility principle. Therefore, it is necessary to implement a preliminary consultation system for water cycle recovery. This study focuses on the cost-benefit analysis on the application of LID techniques for small-scale development projects. The scale of nationwide small-scale development projects used for cost-benefit analysis were defined as buildings with a land area of more than 1,000 ㎡ or a total floor area of 1,500 ㎡. As a result of analyzing the cost-benefits from the installation of LID facilities, they were found to be much lower than the economic standard value of 1. This might be due to the high cost of facilities compared to the scale of the project. However, considering the overall environmental value of improving the water environment and air quality by the installation of LID facilities and the publicity of reducing the operating cost of sewage treatment facilities, the introduction of a prior consultation for small-scale development projects is inevitable. In the future, institutional and financial support from local governments is required to improve the cost-benefits with the introduction of a prior consultation for small-scale development projects.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Information Systems Review
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• v.14 no.1
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• pp.67-84
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• 2012

Recent increases in uncertainty and speed of market change are driving the adoption of new intelligent mobile office systems. Organizational information systems paradigm suggests that a right match between organizational characteristics and the use of technology is critical in producing desired results. Following such perspective this study developed the mobile office system case of Seoul Metropolitan Railway Transit(SMRT) in Korea. SMRT developed the mobile task-supporting environment that help the management of subway lines real-time without the restriction of time and space. They named the system as ST&F(SMRT Talk and Flash). They decided to develop the application systems in-houses they did not want to be overly dependent on external services in future changes and developments of the system. The new practice reduced the time for moving back and forth to 10% of their working time from previous 20%. The time used for paper works and administration chore also reduce to 10% of their working time from previous 30% on the average. The employees could use 80% of their time to concentrate on the completion of assigned task. The effects of this improvement resulted in the heightened efficiency of the use of human resources and the heightened level of railway safety. The case of SMRT shows that the mobile office system can be applied and extended to various business areas such as facility management and maintenance beyond such typical uses as sales and marketing support. Also, The result of case study will be a useful guideline on the construction and using of mobile office system.

• Fire Science and Engineering
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• v.31 no.3
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• pp.97-105
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• 2017

Because asphalt is a solid at normal temperature and is not a hazardous material as stipulated in the Safety Management Act on Hazardous Materials, it is often recognized as having no risk of fire or explosion. On the other hand, it is as dangerous as flammable liquid because it is heated to $170-180^{\circ}C$ and stored in a storage tank. This study analyzed the risk of fire and explosion during the storage and handling of asphalt and the actual conditions of asphalt regulations by fire service organizations. Moreover, this study analyzed the domestic case of explosions in the production process of asphalt concrete (ASCON) and domestic and foreign cases of asphalt storage tank explosions. The analysis suggested that unlike Japan, Korea has no asphalt regulations in fire service organizations. Explosions can occur when ignition is delayed after fuel is sprayed on the dryer drum burner of the aggregates during the production of ASCON. A physical explosion can occur in the storage tank when environmental purification facilities suddenly work strongly to remove air pollutants or bad smells during the heating of asphalt in an asphalt storage tank. In addition, explosions can occur when fires such as welding is performed in the asphalt storage tank.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.

• Journal of Information Management
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• v.41 no.2
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• pp.47-69
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• 2010

Open innovation is a phenomenon that has been widely accepted by both practice and theory over the last few years. On the contrary, partner relationships have attracted little attention while the open innovation could not be emerged without the link to partners. This paper identifies and evaluates a framework for the partner relationships and open innovation processes. Based on the literatures regarding open innovation and partner relationships, we propose the framework of matrix type. We present results based on 352 open innovation cases reported during 2002-2009, and each case is classified into 5 different categories of the framework. JV-C(Joint Venture relationship & Coupled process) archetype has dominated the cases with 178 cases(50.6%) where JV-O(Joint Venture relationship & Outside-In process) follows JV-C with 124 cases(35.2%). No significant change has been found in the number of cases after 2003 when open innovation firstly suggested. However, the number sharply increases in 2009 by boom in JV-C and JV-O. These results show the importance of partner relationships and preference toward Joint Venture relationship in open innovation, while the conventional approaches has just focused on value-chain partnership. We find remarkable collaboration cases contributed by universities and government invested research centers, so the role of non-profit R&D organizations has also been discussed.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.257-271
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• 2008

Wireless broadcast environments has character that a number of mobile client can receive data streaming from central server no matter how they are so many. Because it is asymmetric bandwidth in that uplink and downlink bandwidth are different. This advantage helps wireless broadcast environments is used in many applications. These applications work almost read operation and need control concurrency using transaction unit. Previous concurrency control scheme in single channel is not adapted in multi channel environments because consistency of data are broken when a mobile client tunes in a broadcast cycle in a channel and then move into another channel and listen to different broadcast cycle with already accessed broadcast cycle. In this paper, we propose concurrency control for read-only mobile transactions in multiple wireless broadcast channel. First of all, we adapt index and data dedicated channel and propose LBCPC(Longest Broadcast Cycle Per Channel) as new unit of consistency. In index dedicated channel, it is repeatedly broadcasted data in same BCPC(Broadcast Cycle Per Channel) until LBCPC. And mobile transaction executes validation using control information every LBCPC. As a result, consistency of data is kept and average response time is shorter than one in single channel because waiting time for restart reduces. And as control information is broadcasted more frequently than in single channel, it is guaranteed currency about data accessed by transaction. Finally, according to the simulation result, we verify performance of our scheme in multi channel as comparing average response time with single channel.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.588-595
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• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.