• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.293-310
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• 2020

The purpose of this study is to present the most effective smoke exhaust mode by comparing the quantitatively evaluated risks according to the smoke exhaust mode when a train fire occurs in a subway platform. Therefore, applying the typical subway platform as a model, train fire scenarios are developed with the evacuation start time and location of the fire train for each exhaust mode. The fire accident rates (F) are calculated and the number of fatalities (N) was quantitatively estimated by fire analysis and evacuation analysis for each scenario. In addition, the F/N curve compared with the social risk assessment criteria and the following conclusions were obtained. In the event of a train fire at the subway station platform, the evacuation must start up within 600 s in maximum to ensure the evacuees' safety. To secure evacuation safety, it is advantageous to operate the HVAC system of the platform in the air-supply mode at station without TVF. Comparing the F/N curve for each exhaust mode with the social risk criteria, it turned out that the risk significantly exceeds the social risk criteria in case of no mechanical ventilation. As a result, this paper shows that the ventilation mode in which TVF are exhausted and HVAC system is operated in the pressurized mode are the most effective smoke exhaust mode for ensuring evacuation safety.

• Journal of Korean Medical Science
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• v.33 no.51
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• pp.325.1-325.10
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• 2018

Background: To evaluate survival outcomes and prognostic factors for overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC) who received sunitinib (SU) and pazopanib (PZ) as first-line therapy in real-world Korean clinical practice. Methods: Data of 554 patients with mRCC who received SU or PZ at eight institutions between 2012 and 2016 were retrospectively reviewed. Based on the targeted therapy, the patients were divided into SU (n = 293) or PZ (n = 261) groups, and the clinicopathological variables and survival rates of the two groups were compared. A multivariable Cox proportional hazard model was used to determine the prognostic factors for OS. Results: The median follow-up was 16.4 months (interquartile range, 8.3-31.3). Patients in the PZ group were older, and no significant difference was observed in the performance status (PS) between the two groups. In the SU group, the dose reduction rate was higher and the incidence of grade 3 toxicity was more frequent. The objective response rates were comparable between the two groups (SU, 32.1% vs. PZ, 36.4%). OS did not differ significantly between the two groups (SU, 36.5 months vs. PZ, 40.2 months; log-rank, P = 0.955). Body mass index, Eastern Cooperative Oncology Group PS > 2, synchronous metastasis, poor Heng risk criteria, and liver and bone metastases were associated with a shorter OS. Conclusion: Our real-world data of Korean patients with mRCC suggested that SU and PZ had similar efficacies as first-line therapy for mRCC. However, PZ was better tolerated than SU in Korean patients.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.

• Review of Culture and Economy
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• v.21 no.3
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• pp.59-88
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• 2018

This study examines demand of the Korean TV VOD using monthly aggregate data and time series analysis models. In particular, the impact of box office attendance, number of IPTV subscribers, income and price of substitutes on TV VOD market is analyzed. Data on TV VOD download during the period 2013 January to 2018 June are used for the empirical analysis. TV VOD demand shows lower level of seasonality than box office attendance and the share of monthly top1 movie in TV VOD platform is also lower than that of box office attendance. The relationship between a movie's holdback and box office performance does not seem consistent. The empirical result of ARDL model reveals that in the short-run box office attendance, number of IPTV subscribers and price of substitutes have significant impact on TV VOD demand. The result on the long-term relation shows that income is the only determinant of TV VOD demand. The impact of box office attendance on TV VOD is not shown to be robust both for the short-term and long-term.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.115-120
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• 2019

In this paper, we present the results of the performance statistical analysis of the multi-robot formation control based on receding horizon particle swarm optimization (RHPSO). The formation control problem of multi-robot system can be defined as a constrained nonlinear optimization problem when considering collision avoidance between robots. In general, the constrained nonlinear optimization problem has a problem that it takes a long time to find the optimal solution. The RHPSO algorithm was proposed to quickly find a suboptimal solution to the optimization problem of multi-robot formation control. The computational complexity of the RHPSO increases as the number of candidate solutions and generations increases. Therefore, it is important to find a suboptimal solution that can be used for real-time control with minimal candidate solutions and generations. In this paper, we compared the formation error according to the number of candidate solutions and the number of generations. Through numerical simulations under various conditions, the results are analyzed statistically and the minimum number of candidate solutions and the minimum number of generations of the RHPSO algorithm are derived within the allowable control error.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.100-106
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• 2019

Ground and high altitude simulated combustion experiments were conducted using a liquid rocket engine with hydrogen peroxide and kerosene as the propellant. A ground and high altitude simulated combustion test facility was constructed by installing a high altitude model diffuser and TMS (Thrust Measuring System) on a vertical combustion test bench. The thrust characteristics according to altitude were investigated using the combustion test equipment. The diffuser was designed on a 1:4.8 scale to verify the characteristics of the high diffusing diffuser and starting pressure. The cold flow tests were conducted using nitrogen gas, and the performance characteristics and starting characteristics of the scale down diffuser were verified. A diffuser and TMS were installed on the vertical combustion test bench, and the thrust correction equations for the system resistance were derived. The thrust correction equations were derived from the step test and vacuum step test before the actual hot firing test. Nozzles with an operating altitude of 10km were designed. Hot firing tests were conducted to analyze the thrust characteristics according to the operating altitude changes. The actual thrust was calculated using each correction equation with the thrust value measured by the TMS.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.107-116
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• 2019

Korean construction engineering firms want to pave the way for expansion of overseas markets through the World Bank's Official Development Assistance (ODA) projects as a way to improve their overseas project performance. However, since the World Bank project competes with global companies for limited projects, building partnerships with suitable business partners is essential to gain an upper hand in bidding competition and meet the institutional conditions of the recipient country. In this regard, many network studies have been conducted in the past through Social Network Analysis (SNA), but few have been analyzed based on the process of changes in the network. So, This study collected winning data from the three Southeast Asian countries that ended after the World Bank's ODA project performed smoothly, and established a learning-based link prediction model that reflected the dynamic nature of the network. As a result, the 11 main variables acting on building a cooperative relationship between winning companies were derived and the effect of each variables on the probability value of cooperation between individual links was identified.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.