• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.148-153
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• 2017

During natural ageing of HTPB propellant undergoes a series of slow physico-chemical degradation reactions. By using accelerated ageing conditions it is possible to simulate the material behaviour at different time-temperature conditions especially focused on the in-service conditions. Ageing behaviour of HTPB propellant were investigated using HFC(Micro-Heat Flow Calorimeter) is universal technique for measuring the rate of slow chemical and physical processes in long-term storage.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.5
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• pp.59-65
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• 2018

During natural aging, hydroxyl-terminated polybutadiene(HTPB) propellant undergoes a series of slow physico-chemical degradation reactions. By using accelerated ageing conditions it is possible to simulate the material behavior at different time-temperatures focusing on in-service conditions. Aging behaviors of HTPB propellant are investigated using HFC(heat flow calorimeter), a universal technique for measuring the rate of slow chemical and physical processes in long-term storage.

• Journal of Energy Engineering
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• v.2 no.1
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• pp.95-103
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• 1993

A conceptual thermoelectric heat pump(cooling mode) of small capacity is designed. Its performance is investigated through parametric analysis. COP and cooling capacity decease as the ambient temperature increases with ${\mu}$, J, T$\sub$wi/, fixed. To design a system of fixed capacity comes to calculate ${\mu}$ and J when T$\sub$wi/, and T$\sub$a/ are given. As v is fixed by semi-conductor manufacturers, optimum combination of n and I should be searched for ν. Optimum current could be calculated using ${\mu}$-J curve and optimum value of ${\mu}$. COR$\sub$R/ increases as water flow rate increases and T$\sub$a/ decreases. The effect of heat transfer coefficient at hot(heat releasing) side is more significant than that at cold(heat absorbing) side.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.472-482
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• 2008

A modulation transfer function(MTF) tree is established to estimate the overall MTF of an observation satellite and to analyze the image performance. Basic MTF models relevant to each MTF tree component are represented as mathematical relationship between optics-structural dynamics, thermal deformation, attitude and dynamic characteristics of a satellite and the effects due to the space environment. The Basic MTF models consist of diffraction limited MTF with central obscuration, aberration, defocus, line-of-sight(LOS) jitter, linear motion, detector integration, and so forth. Performance estimation is demonstrated for a virtual earth-observation satellite in order to validate the constructed modeling method. The proposed models enable the system engineers to calculate the overall system MTF and to determine the crucial design parameters that affect the image performance in the conceptual design phase of an observation satellite.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Water for future
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• v.28 no.6
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• pp.147-158
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• 1995

The purpose of this study is to establish a method of estimating the daily urban water demand using statistical model. This method will be used for the development of the efficient management and operation of the water supply facilities. The data used were the daily urban water use, the population, the year lapse and the weather conditions such as temperature, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. The raw data used in this study were rearranged either by month or by season for the purpose of analysis, and the statistical analysis was applied to the data to obtain the regression model. As a result, the multiple linear regression model was developed to estimate the daily urban water use based on the seather condition. The regression constant and the model coefficients were determined for each month of a year. The accuracy of the model was within 3% of average error and within 10% of maximum error. The developed model was found to be useful to the practical operation and management of the water supply facilities.

• Journal of Korean Home Economics Education Association
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• v.31 no.2
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• pp.113-135
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• 2019

The purpose of this study was to analyze the characteristics reflected in the 2015 revised home economics curriculum for the key competencies presented in the OECD education 2030 project. The results indicate that first, in general, about 46.5% of the competencies could be classified into the skill, attitude and value category; 17% into the learning concept framework category; 24.2% into the competency development cycle category; and 12.5% into the complex competency category. Overall, the competencies of the OECD learning framework are found to be reflected primarily in the achievement standards(59%), followed by characteristics(16.1%), teaching-learning and assessments orientation(9.4%), content system(8%), and goals(7.6%). Second, the key competencies were reflected in the middle school curriculum, more often in the descending order of action, problem-solving, communication, respect, creative thinking, conflict resolution, empathy, critical thinking, self-regulation, and student agency. In the high school curriculum, the competencies were reflected more often in the descending order of action, empathy, problem-solving, anticipation, global competence, self-regulation, student agency, literacy for sustainable development, reflection, and critical thinking. Third, the heat map shows that the competencies corresponding to the third and fourth levels are most frequently reflected in the curriculum. Therefore, it is advisable to develop effective plans to execute and support the reflection of key competencies in the curriculum. Through this study, home economics educators are expected to understand the inter-connectivity between the key competencies emphasized by the OECD learning framework and the competencies of home economics as a practical subject, and to scrutinize how to help individual students develop their overall competencies and be prepared for the future.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.3
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• pp.239-245
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• 2010

There are 11,811 LWR spent fuels stored at reactor sites, as of 2009. Source terms based on reference spent fuel which represents entire spent fuels with bounding values in the aspect of source term has been applied to a design of nuclear installations, instead of those which are generated by weighting respective source term for each spent fuel. Simplified regression models to estimate total decay heat, radioactivity, and ingestion hazard index for spent fuel from Westinghouse-type reactors were developed in this study, because it can be used as a fundamental model for weighting source term for respective spent fuel to exclude conservativeness in source terms. It was found that the estimated source terms agreed with calculated value from ORIGEN-ARP within 5%. It was also found that the conservativeness could be excluded if the weight source terms were used as reference source term in the design. Therefore, it is expected that the developed regression model could be widely used in the conceptual design process of nuclear facilities related with storage and disposal of spent nuclear fuel.