• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.124-137
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• 2012

Climate analysis is important in urban planning for human comfort. Synoptic weather conditions can only resolve the 30% of local variance of wind conditions whereas 70% of the variance arise from local terrain, buildings, and other small scale thermal conditions. Climate Analysis Seoul (CAS) was developed to resolve such micro-scale climate. The Local-scale air temperature Deviation (LD) analysis map from CAS showed the co-existence of built-up and suburban areas in the study region (CR, Cold-air analysis Region) despite its small extent. Temperature, humidity, wind speed, and wind direction were monitored in CR. Hourly observed cooling rate agreed well with LD. Cold air production, transportation, and stagnation was visualized by the observed Vertical Temperature Gradient (VTG) along the small stream in CR. VTG observed at the upper-most stream can be divided into two components: radiative cooling and cold air inflow from outside. Radiative cooling exists regardless of the wind speed whereas cold air inflow occurs only with calm wind. From the regression analyses based on the wind speed, the inflow portion was determined as 84% of radiative cooling. Climate analysis in the future will be able to characterize the changes in cold air by urban development plan to support the human comfort.

• Journal of Energy Engineering
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• v.5 no.2
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• pp.160-169
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• 1996

Parametric studies are conducted for optimizing the integration design between gas turbine compressor and air separation unit (ASU) of integrated gasification combined cycle power plant. The present study adopts the ASU of double-distillation column process, from which integration conditions with compressor such as the heat exchanger condition between air and nitrogen, the amount and the pressure of extracted air are defined and mathematically formulated. The performance variations of the compressor integrated with ASU are analyzed by combining streamline curvature method and pressure loss models, and the predicted results are compared with the performance test results of actual compressors to verify the prediction accuracy. Using the present performance prediction method, the effects of pinch-point temperature difference (PTD) in the heat exchanger, the amount and the pressure of extracted air on compressor performances are quantitatively examined. As the extraction air amount or the PTD is increased, the pressure ratio and the power consumption of compressor are increased. The compressor efficiency deteriorates as the increase of the flow rate of air extracted at higher pressure level while improving at lower pressure air extraction. Furthermore, through the characteristic curve between generalized inlet condition and efficiency of compressor, optimal integration condition is presented to maximize the compressor efficiency.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.915-924
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• 2014

In an aero gas-turbine engine, a surface air-oil heat exchanger (SAOHE) is used to cool the oil system for the gearboxes and electric generators. The SAOHE is installed inside the fan casing of the engine in order to dissipate the heat from the oil system into the bypass duct stream. The purpose of this study was to develop an effective numerical method for designing an SAOHE for an aero gas-turbine engine. A two-dimensional model using a porous medium was developed to evaluate the aero-thermal performance of the fins of the heat exchanger, and a one-dimensional flow and thermal network program was developed to save time and cost in the evaluation of the heat exchanger performance. Using this network program, the pressure drop and heat transfer performance of the heat exchanger were predicted, and the results were compared with two-dimensional computational fluid dynamics results and experiment data for validation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1395-1406
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• 2022

This study investigated the effects of the urban forest and street trees on flow and temperature distribution in the Daegu National Debt Redemption Movement Memorial Park. For this, we implemented tree-drag and tree-cooling parameterization schemes in a computational fluid dynamics (CFD) model and validated the simulated wind speeds, wind directions, and air temperatures against the measured ones. We used the wind speeds, wind directions, air temperatures predicted by the local data assimilation and prediction system (LDAPS) as the inflow boundary conditions. To investigate the flow and thermal characteristics in the presence of trees in the target area, we conducted numerical experiments in the absence and presence of trees. In the absence of trees, strong winds and monotonous flows were formed inside the park, because there were no obstacles inducing friction. The temperature was inversely proportional to the wind speed. In the presence of trees, the wind speeds(temperatures) were reduced by more than 40 (5)% inside the park with a high planting density due to the tree drag (cooling) effect, and those also affected the wind speeds and temperatures outside the park. Even near the roadside, the wind speeds and temperatures were generally reduced by the trees, but the wind speeds and air temperatures increased partly due to the change in the flow pattern caused by tree drag.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.625-632
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• 2018

This study was done to reduce total energy demand based on resource shortage problems and to provide improvement points for more efficient adjustment of the high insulation standards for saving energy in Korea. The demand sensitivity was fully considered by varying the slope of each building. The energy performance of the building was maximized by the introduction of outdoor air at night. A final low-energy building model was developed with the two measures combined, and the short-term operation of the night-fuzzy ventilation system was simulated. The result showed a reduction of about 6 to 7 percent compared to the base model. The results could have many implications in terms of the need to conduct demand sensitivity analyses in architectural design.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.253-263
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• 2016

Chrysanthemum production would benefit from crop growth simulations, which would support decision-making in crop management. Chrysanthemum is a typical short day plant of which floral initiation and development is sensitive to photoperiod. We developed a model to predict phenological development and leaf appearance of chrysanthemum (cv. Baekseon) using daylength (including civil twilight period), air temperature, and management options like light interruption and ethylene treatment as predictor variables. Chrysanthemum development stage (DVS) was divided into juvenile (DVS=1.0), juvenile to budding (DVS=1.33), and budding to flowering (DVS=2.0) phases for which different strategies and variables were used to predict the development toward the end of each phenophase. The juvenile phase was assumed to be completed at a certain leaf number which was estimated as 15.5 and increased by ethylene application to the mother plant before cutting and the transplanted plant after cutting. After juvenile phase, development rate (DVR) before budding and flowering were calculated from temperature and day length response functions, and budding and flowering were completed when the integrated DVR reached 1.33 and 2.0, respectively. In addition the model assumed that leaf appearance terminates just before budding. This model predicted budding date, flowering date, and leaf appearance with acceptable accuracy and precision not only for the calibration data set but also for the validation data set which are independent of the calibration data set.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.2
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• pp.85-91
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• 2016

The Positive Temperature Coefficient (PTC) is used for cabin air heating of a battery electric vehicle, which is different from conventional vehicles. Since the PTC heater consumes a large quantity of power in a parasitic manner, many valuable studies have been reported in the field of alternative heat pumps. In this study, a model for an R134a heat pump taking into account the thermal environment of the cabin was developed for a MATLAB/SIMULINK(R) platform. Component and cabin models are validated with reference values. Results show that the heat pump is more competitive for parasitic power consumption over all ambient temperature conditions. Additionally, the method of waste heat recovery to overcome disadvantages when temperatures are below zero is applied to efficiently operate the heat pump.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.3
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• pp.65-73
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• 2004

The bolier systems of coal fired power plants are large, non-linear systems with numerous interactions between its component parts. In the analysis of such complex systems, dynamic simulation is recognized as a powerful method of keeping track of the myriad of interactions. The boiler system consists of air/gas system and water/steam system. Due to recent reinforcement of environmental regulation on pollutant discharge and requirements of design validation on properites of boiler, the commercial programs are used for the analysis of boiler system. This paper addressed to the development of model using MMS(Modular Modeling System) developed by EPRI(Electric Power Research Institute) as the simulation tool. The developed model using MMS is tested for the design and local data on boiler combustion system of korea standard coal fired power plant boiler. The simulation results show that the developed model well reproduces responses of the combustion system with less than ${\pm}$5% error under steady state and transient state conditions. The developed model for analysis of the combustion system in this paper is general and applicable to any type of coal fired power plant.