• Journal of the Korean Institute of Gas
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• v.21 no.5
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• pp.95-103
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• 2017

A lot of hazardous materials have been used for product processing and utility plant. Many accidents including toxic release, fire and explosions occur in the ammonia related facility and plant. Various safety and environment management program including PSM, SMS, ORA etc. are being implemented for risk management and accident prevention in the production industry. Also much study and research have been carried about risk assessment of accident scenario in the academic and research area. In this paper, firstly risk level was assessed by using a typically used KORA program and LOPA PFD method for the selected ammonia unloading and storage facility. And then risk reduction measures for the risk assessed facility were studied in 3 aspects and some measures were proposed. Those Risk Reduction measures are including a leak detection and emergency isolation, water spray, dilution tank, dike and trench, scattering protection in hardware impovement aspect, and a applicable risk criteria, conditional modifier for existing LOPA PFD, alternative supporting modeling program in risk estimation methodology aspect, and last RBPS(Risk Based Process Safety) program, re-doing of process hazard analysis, management system compliance audit in managerial activity aspect.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.723-730
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• 2010

Recently, diverse 3D image processing technologies have been applied in industries. Among them, stereoscopic conversion is a technology to generate a stereoscopic image from a conventional 2D image. The technology can be applied to movie and broadcasting contents and the viewer can watch 3D stereoscopic contents. Further the stereoscopic conversion is required to be applied to other fields. Following such trend, the aim of this paper is to apply the stereoscopic conversion to medical fields. The medical images can deliver more detailed 3D information with a stereoscopic image compared with a 2D plane image. This paper presents a novel methodology for converting a 2D medical image into a 3D stereoscopic image. For this, mean shift segmentation, edge detection, intensity analysis, etc are utilized to generate a final depth map. From an image and the depth map, left and right images are constructed. In the experiment, the proposed method is performed on a medical image such as CT (Computed Tomograpy). The stereoscopic image displayed on a 3D monitor shows a satisfactory performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.394-399
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• 2017

In Korean AC power railway substations, SCOTT winding transformers are under operation to have a single phase power supply together with a phase angle of $90^{\circ}$ on the secondary side of the main transformer. In the case of an internal fault of the transformer, the transformer protection relay should be cut off on the primary side, the transformer should be inoperative to the external fault of the transformer or to the normal train operation. Reducing the malfunction of the relay through an exact fault determination is very important for securing a stable power system and improving its reliability. The main transformers are protected using Buchholtz's relay and a differential relay as the internal fault detection devices, but there are some cases of the main transformer operation under the deactivation of this protection function due to a malfunction of the differential relay. In this paper, the characteristics of the SCOTT transformer and differential relay as well as the malfunctioning of the protection relays are presented. The modeling of the SCOTT transformer protection relay was accomplished by the power system analysis program and the Comtrade file from 'A substation', which was used as the input data for the fault wave, and the harmonics were analyzed to determine if the relay operates or not. In addition, an improvement plan for malfunctioning cases through wave form analysis is suggested.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.3
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• pp.185-197
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• 2001

Recently, the design method by VHDL coding and synthesis has been used widely. As the integration ratio increases, the amount design by VHDL at a time also increases so many coding errors occur in a design. Thus, lots of time and effort is dissipated to detect those coding errors. This paper proposed a method to verify the coding errors in VHDL behavioral-level designs. As the methodology, we chose the method to detect the coding error by applying the generated set of verifying patterns and comparing the responses from the error-free case(gold unit) and the real design. Thus, we proposed an algorithm to generate the verifying pattern set for the coding errors. Verifying pattern generation is peformed for each code and the coding errors are classified as two kind: condition errors and assignment errors. To generate the patterns, VHDL design is first converted into the corresponding CDFG(Control & Data Flow Graph) and the necessary information is extracted by searching the paths in CDFG. Path searching method consists of forward searching and backward searching from the site where it is assumed that coding error occurred. The proposed algorithm was implemented with C-language. We have applied the proposed algorithm to several example VHDL behavioral-level designs. From the results, all the patterns for all the considered coding errors in each design could be generated and all the coding errors were detectable. For the time to generate the verifying patterns, all the considered designed took less than 1 ［sec］ of CPU time in Pentium-II 400MHz environments. Consequently, the verification method proposed in this paper is expected to reduce the time and effort to verify the VHDL behavioral-level designs very much.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.22-34
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• 2004

An artificial disturbance like fragmentation in watershed forest has impacted a sustainability of watershed ecosystem, therefore this research quantified the landscape structure in the Geumho river watershed using landscape indices and GIS. Landscape indices were calculated from the forest distributed maps for 24 subwatersheds. Three common factors, which explained about 85% of the variation in the original data, were extracted by a factor analysis. The fragmentation gradient in forest landscape, which was calculated from the factor scores, was correlated with proportion of urban land (r=0.827, p<0.01, $R^2$=0.685), elevation (r=-0.637, p<0.01) and slope gradient (r=-0.593, p<0.01). The result of the study presented that the methodology and the values of landscape indices to assess the structural patterns of forest landscape for the Geumho river watershed management. Future research will be directed towards the detection of impacts of landscape patterns and their changes on the integrity of watershed environments.

• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.28-38
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• 2018

Purpose: To evaluate the medical expenditures for prostate cancer patients, including out-of-pocket costs, and compared the costs between androgen deprivation therapy and radical prostatectomy treatment. Methodology: This study combined clinical data from 357 prostate cancer patients from the Smart Prostate Cancer Database and the medical expenditure data from the claims and cost databases. We used the independent two-sample t-tests to compare androgen deprivation therapy and radical prostatectomy. Multivariable logistic regression analysis was conducted to identify determining factors for androgen deprivation therapy and radical prostatectomy treatments. Findings: The medical costs of androgen deprivation therapy treatment were much lower than radical prostatectomy treatment at the one year and remained lower until the fourth-year. However, after four years, the accumulated medical expenditures of androgen deprivation therapy become significantly higher than radical prostatectomy treatment. Patients with a higher cancer stage and older age had higher chances of being treated using androgen deprivation therapy treatment than radical prostatectomy treatment. Practical Implications: Our results show that early detection of cancer reduces the treatment cost for both patients and insurance payers. It also demonstrates that cost comparisons should be conducted over long periods of time in order to most accurately assess the costs.

• Journal of Environmental Science International
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• v.15 no.1
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• pp.85-94
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• 2006

The simultaneous analysis of multi-residual pesticides was developed using a gas chromatography (GC) method. In this study, a simple and reliable methodology was improved to detect 154 kinds of pesticides in sinseng extract sample by using a liquid-liquid extraction procedure, open column chromagraphy and chromatographic analysis by CC electron capture detector (ECD) and GC nitrogen-phosphorus detector (NPD). The 154 kinds of pesticides were classified in 4 groups according to the chemical structure. The extraction of pesticides was experimented with $70\%$ acetone and dichloromethane/petroleum ether in order, and cleaned up via open column chromatography $(3\times30cm)$ packed with florisil $(30g,\;130^{\circ}C,\;12hrs)$. The final extract was concentrated in a rotator evaporator at $40^{\circ}C$ until dryness. Then the residue was redissolved to 2ml with acetone, and analyzed by GC-ECD and GC-NPD. The applied concentration of pesticides was over $1\~10{\mu}g/ml$. The recovery tests were ranged from $70.7\%$ to $115.2\%$ with standard deviations between 0.3 and $5.7\%$ of the standard spiked to the ginseng extract sample (Group $I\~IV$). The limit of detection (LOD) ranged from 0.001 to $0.099{\mu}g/ml$ (Group $I\~IV$). The 9 kinds of pesticides were not detected. The developed method was applied satisfactory to the determination of the 154 kinds of pesticides in the ginseng extract with good reproducibility and accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.47-54
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• 2017

Recently, there are many studies related aerial mapping project and 3 dimensional shape and model reconstruction using UAV(unmanned aerial vehicle) system and images. In this study, we create 3D reconstruction point data using image matching technology of the UAV overlap images, detect shape change of structure and perform accuracy assessment of area($m^2$) and volume($m^3$) value. First, we build the test structure model data and capturing its images of shape change Before and After. Second, for post-processing the Before dataset is convert the form of raster format image to ensure the compare with all 3D point clouds of the After dataset. The result shows high accuracy in the shape change of more than 30 centimeters, but less is still it becomes difficult to apply because of image matching technology has its own limits. But proposed methodology seems very useful to detect illegal any structures and the quantitative analysis of the structure's a certain amount of damage and management.