• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.165-174
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• 2010

The numerical modeling of a coal gasification reaction occurring in an entrained flow coal gasifier is presented in this study. The purposes of this study are to develop a reliable evaluation method of coal gasifier not only for the basic design but also further system operation optimization using a CFD(Computational Fluid Dynamics) method. The coal gasification reaction consists of a series of reaction processes such as water evaporation, coal devolatilization, heterogeneous char reactions, and coal-off gaseous reaction in two-phase, turbulent and radiation participating media. Both numerical and experimental studies are made for the 1.0 ton/day entrained flow coal gasifier installed in the Korea Institute of Energy Research (KIER). The comprehensive computer program in this study is made basically using commercial CFD program by implementing several subroutines necessary for gasification process, which include Eddy-Breakup model together with the harmonic mean approach for turbulent reaction. Further Lagrangian approach in particle trajectory is adopted with the consideration of turbulent effect caused by the non-linearity of drag force, etc. The program developed is successfully evaluated against experimental data such as profiles of temperature and gaseous species concentration together with the cold gas efficiency. Further intensive investigation has been made in terms of the size distribution of pulverized coal particle, the slurry concentration, and the design parameters of gasifier. These parameters considered in this study are compared and evaluated each other through the calculated syngas production rate and cold gas efficiency, appearing to directly affect gasification performance. Considering the complexity of entrained coal gasification, even if the results of this study looks physically reasonable and consistent in parametric study, more efforts of elaborating modeling together with the systematic evaluation against experimental data are necessary for the development of an reliable design tool using CFD method.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.126-138
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• 2017

Recently, the national interest in environment is increasing and for dealing with water environment-related issues swiftly and accurately, the demand to facilitate the analysis of water environment data using a GIS is growing. To meet such growing demands, a spatial network data-based stream network analysis map(Korean Reach File; KRF) supporting spatial analysis of water environment data was developed and is being provided. However, there is a difficulty in delineating catchment areas, which are the basis of supplying spatial data including relevant information frequently required by the users such as establishing remediation measures against water pollution accidents. Therefore, in this study, the development of a computer program was made. The development process included steps such as designing a delineation method, and developing an algorithm and modules. DEM(Digital Elevation Model) and FDR(Flow Direction) were used as the major data to automatically delineate catchment areas. The algorithm for the delineation of catchment areas was developed through three stages; catchment area grid extraction, boundary point extraction, and boundary line division. Also, an add-in catchment area delineation module, based on ArcGIS from ESRI, was developed in the consideration of productivity and utility of the program. Using the developed program, the catchment areas were delineated and they were compared to the catchment areas currently used by the government. The results showed that the catchment areas were delineated efficiently using the digital elevation data. Especially, in the regions with clear topographical slopes, they were delineated accurately and swiftly. Although in some regions with flat fields of paddles and downtowns or well-organized drainage facilities, the catchment areas were not segmented accurately, the program definitely reduce the processing time to delineate existing catchment areas. In the future, more efforts should be made to enhance current algorithm to facilitate the use of the higher precision of digital elevation data, and furthermore reducing the calculation time for processing large data volume.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.75-89
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• 2007

This study has its aim at suggesting new direction of our education to search different ways in housing contents by comparing the necessity perception and demands between teachers and students for housing contents in Technology Home Economics curriculum of middle school. To achieve this aim, I chose middle school teachers in charge of Technology Home Economics and male and female students who are in the first grade in high school in Ulsan. I sent e-mail, mail, and visited researcher to gather the data. I used SPSS +12 statistical package for frequency, percentage, mean, standard deviation, and t-test to analyze the data. Here is the result. First, In the part of application of living place, teachers had necessity perception in use and placement of furniture, and arrangement of objects. Students had necessity perception in the use and placement of furniture, the kind and choice of furniture. Also in the indoor environment and equipment part, both teachers and students had necessity perception in controlling of ventilation, temperature, and humidity. In the part of maintenance repair of housing, teachers had necessity perception in the need for maintenance management but students had necessity perception in house equipments and repair had high necessity perception Second, In housing-related general part, teachers demanded housing for elderly, disabled people, information about future housing and students demanded environmentally friendly living environment, housing for elderly, disabled people. In interior design part, teachers demanded in the expression of interior places through computer, the kind and characteristic of housing material and students demanded the way to reuse old furniture, kind and characteristic of housing material. In the part of housing preparation and occupation, teachers demanded the kind of housing-related occupation and students demanded the housing tax and the process of house purchase or concerned matter. Third, there were some difference of necessity perception and degree of demand between teachers and students. Teachers had higher necessity perception and demand in all part except in demand for housing equipment, maintenance, and environmentally friendly living environment.

• The Korean Journal of Community Living Science
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• v.16 no.4
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• pp.83-93
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• 2005

This study was conducted to provide information about the behavioral intentions to diet in adolescent females. To explain the behavior intention to diet, a conceptual framework based on the ' Social Support, Control and the Stress Process Model ' and the ' Theory of Reasoned Actio ' was used. The survey was carried out by self-questionnaires with 463 female high school and college students in Daegu. Analysis of data was done by using mean, correlation and multiple regression analysis with the SAS computer program. Subjects were divided into 3 groups-underweight, normal weight, and overweight-according to their current body size. The most powerful influencing factor related to perceived stress -that is dissatisfaction with body image- was the current figure, regardless of current body size. The fatter the current body size, the higher the score for the behavioral intention to diet. In attitude toward the behavior of dieting, the fatter the current figure, the higher the attitude score, and the belief of behavioral outcome was the main decision variable. For the score of the subjective norm, the overweight group was significantly higher than other groups. The influencing factors for the behavioral intention to diet were perceived stress and attitude toward dieting behavior, especially beliefs of behavioral outcome.

• Progress in Medical Physics
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• v.17 no.1
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• pp.24-31
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• 2006

Automated analysis software was developed to measure the magnitude of the intrafractional and interfractional errors during breast radiation treatments. Error analysis results are important for determining suitable planning target volumes (PTV) prior to Implementing breast-conserving 3-D conformal radiation treatment (CRT). The electrical portal imaging device (EPID) used for this study was a Portal Vision LC250 liquid-filled ionization detector (fast frame-averaging mode, 1.4 frames per second, 256X256 pixels). Twelve patients were imaged for a minimum of 7 treatment days. During each treatment day, an average of 8 to 9 images per field were acquired (dose rate of 400 MU/minute). We developed automated image analysis software to quantitatively analyze 2,931 images (encompassing 720 measurements). Standard deviations ($\sigma$) of intrafractional (breathing motion) and intefractional (setup uncertainty) errors were calculated. The PTV margin to include the clinical target volume (CTV) with 95% confidence level was calculated as $2\;(1.96\;{\sigma})$. To compensate for intra-fractional error (mainly due to breathing motion) the required PTV margin ranged from 2 mm to 4 mm. However, PTV margins compensating for intefractional error ranged from 7 mm to 31 mm. The total average error observed for 12 patients was 17 mm. The intefractional setup error ranged from 2 to 15 times larger than intrafractional errors associated with breathing motion. Prior to 3-D conformal radiation treatment or IMRT breast treatment, the magnitude of setup errors must be measured and properly incorporated into the PTV. To reduce large PTVs for breast IMRT or 3-D CRT, an image-guided system would be extremely valuable, if not required. EPID systems should incorporate automated analysis software as described in this report to process and take advantage of the large numbers of EPID images available for error analysis which will help Individual clinics arrive at an appropriate PTV for their practice. Such systems can also provide valuable patient monitoring information with minimal effort.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.9
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• pp.25-33
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• 2007

In this paper, the high isolation and wideband 94 GHz MIMIC(Millimeter-wave Monolithic Integrated Circuit) single balanced cascode mixer was designed and fabricated. Also, we designed and fabricated a 3 dB tandem coupler which has a high isolation and wideband characteristic. The single balanced resistive mixer which does not require an external IF balun was designed using the 0.1 ${\mu}m$ InGaAs/InAlAs/GaAs metamorphic HEMT(High Electron Mobility Transistor). The DC characteristics of MHEMT's are 665 mA/mm of drain current density, 691 mS/mm of maximum transconductance. The current gain cut-off frequency($f_T$) is 189 GHz and the maximum oscillation frequency($f_{max}$) is 334 GHz. A 94 GHz single balanced cascode mixer was fabricated using our 0.1 ${\mu}m$ MHEMT MIMIC process. From the measurements, the fabricated couplers showed wideband characteristics. The conversion loss of single balanced cascode mixer was 9.8 dB at an LO power of 10.9 dBm. The LO to RF isolation of single balanced cascode mixer was 29.5 dB at 94 GHz. We obtained in this study a higher LO-RF isolation compared to some other single balanced mixers.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.1 s.116
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• pp.62-75
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• 2007

This paper describes a shaped-beam antenna for increasing the antenna gain of a radiating element. The proposed antenna structure is composed of an exciting element and a multi-layered disk array structure(MDAS). The stack micro-strip patch elements were used as the exciter for effectively radiating the electromagnetic power to the MDAS over the broadband, and finite metallic disk array elements - which give the role of a director for shaping the antenna beam with the high gain - were finitely and periodically layered onto it. The efficient power coupling between the exciter and the MDAS should be carried out in such a way that the proposed antenna has a high gain characteristic. The design parameters of the exciter and the MDAS should be optimized together to meet the required specifications to meet the required specifications. In this study, a shaped-beam antenna with high gain was optimally designed under the operating conditions with a linear polarization and the frequency band of $9.6{\sim}10.4\;GHz$. Two methods constructed using thin dielectric film and dielectric foam materials respectively were also proposed in order to implement the MBAS of the antenna. In particular, through the computer simulation process, the electrical performance variations of the antenna with the MDAS realized by the thin dielectric film materials were shown according to the number of disk array elements in the stack layer. Two kinds of antenna breadboard with the MDAS realized with the thin dielectric film and dielectric foam materials were fabricated, but experimentation was conducted only on the antenna breadboard(Type 1) with the MDAS realized with the thin dielectric film materials according to the number of disk array elements in the stack layer in order to compare it with the electrical performance variations obtained during the simulation. The measured antenna gain performance was found to be in good agreement with the simulated one, and showed the periodicity of the antenna gain variations according to the stack layer number of the disk array elements. The electrical performance of the Type 1 antenna was measured at the center frequency of 10 GHz. As the disk away elements became the ten stacks, a maximum antenna gain of 15.65 dBi was obtained, and the measured return loss was not less than 11.4 dB within the operating band. Therefore, a 5 dB gain improvement of the Type 1 antenna can be obtained by the MDAS that is excited by the stack microstrip patch elements. As the disk array elements became the twelve stacks, the antenna gain of the Type 1 was measured to be 1.35 dB more than the antenna gain of the Type 2 by the outer dielectric ring effect, and the 3 dB beam widths measured from the two antenna breadboards were about $28^{\circ}$ and $36^{\circ}$ respectively.

• Archives of design research
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• v.18 no.3 s.61
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• pp.85-94
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• 2005

Most of product designers use 3D CAD system as a inevitable design tool nowadays and many new products are developed through a concurrent engineering process. However, it is very difficult for novice designers to get the sense of reality from modeling objects shown in the computer screens. Such a intangibility problem comes from the lack of haptic interactions and contextual information about the real space because designers tend to do 3D modeling works only in a virtual space of 3D CAD system. To address this problem, this research investigate the possibility of a interactive quantified structure simulation for product design using AR(augmented reality) which can register a 3D CAD modeling object on the real space. We built a quantified structure simulation system based on AR and conducted a series of experiments to measure how accurately human perceive and adjust the size of virtual objects under varied experimental conditions in the AR environment. The experiment participants adjusted a virtual cube to a reference real cube within 1.3% relative error(5.3% relative StDev). The results gave the strong evidence that the participants can perceive the size of a virtual object very accurately. Furthermore, we found that it is easier to perceive the size of a virtual object in the condition of presenting plenty of real reference objects than few reference objects, and using LCD panel than HMD. We tried to apply the simulation system to identify preference characteristics for the appearance design of a home-service robot as a case study which explores the potential application of the system. There were significant variances in participants' preferred characteristics about robot appearance and that was supposed to come from the lack of typicality of robot image. Then, several characteristic groups were segmented by duster analysis. On the other hand, it was interesting finding that participants have significantly different preference characteristics between robot with arm and armless robot and there was a very strong correlation between the height of robot and arm length as a human body.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.60-64
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• 2017

Purpose Westgard multi-rules application based on test quality improvement and commercialized international standard has been widely used in quality control. However, it is difficult to applicate the Westgard multi-rules in nuclear medicine in vitro tests due to the larger sample sizes and the simultaneous measurement of quality control material and patient sample. This study investigated the usefulness of Westgard multi-rules application in nuclear medicine in vitro tests. Materials and Methods A total of 282 systematic error multi-rules (22s, 101s) recorded in the samsung medical center computer system from January 2013 to June 2016 along with 117 cases of corrective measure record was analyzed. The Quality control implementation is recorded in Hospital information system were divided into 4 high-level areas including quality control material error, experimental procedural error, Kit lot number management error, and others. To prevent quality control material error, the existing method that each staff used their own method was changed. The staff who in charge of managing the quality control material was designated and daily consumption amount of every test was strictly controlled by one person. To prevent other errors, every test step was standardized so that the entire test procedures are identically implemented. Results The total quality control implementation was 117 cases; As a result, 62 quality control material errors were 62 cases, experimental process errors were 24 cases, Kit lot number control errors were 18 cases, and other errors were 13 cases. The quality control material error was corrected and could be used fresh materials within 2 days after thawing. The cases of systemic error were decreased to causes as quality control material error. The quality control materials were reduced above 10 vials to a monthly average. In addition, these errors of experimental processing and Kit lot number were improved by test standardization. Consequently, the cases of 101s and 22s in systematic error rules decreased at least 2 cases to a monthly average. Conclusion To confirm of systematic error through multi-rules application quickly, it is necessary to base on management of the QC material, target values and standard deviation. Moreover, in the event of a systematic error, it was found important to record measures based on test cause analysis. The experiment results are expected to contribute to internal quality control improvement and prompt and accurate result reporting through error recording and causal analysis based on Westgard multi-rules analysis.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.