• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.371-379
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• 2009

In this paper, we proposed new de-interlacing algorithm for video data using intensity gradient filter and median filter with texture detection in the image block. We first introduce the texture detection. According to texture detection, the current region is determined into smooth region or texture region. In case that the smooth region interpolated by median filter. In addition, in case of the texture region, we calculate missing pixel value using intensity gradient filter. Therefore, we analyze the local region feature using the texture detection and classify each missing pixel into two categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results show that the proposed algorithm performs well with a variety of moving sequences compared with conventional intra-field method in the literature.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.3
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• pp.401-409
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• 2009

The shape of crotch area is very important to develop functional clothing as well as other ergonomic goods such as chair or saddle etc. However, it is inevitable that 3D scanned image of crotch would have missing part due to its folded shape including overlapping legs nearby. Therefore, the objectives of this research was to compare reconstruction methods of missing parts at crotch using seven dummies of real men's replicas. Two reconstruction methods adopted were kinds of 'fill- hole' in Rapidform 2004, one was 'smooth' and the other was 'curvature'. Each restored image was compared with the original shape of the dummies. As results, the average distance was 0.66mm between original and 'smooth' treated images and 0.59mm between original and 'curvature' treated, which was not statistically different. Average area of restored crotch region was $8740.04cm^2$ by 'smooth' method and $8405.02cm^2$ by 'curvature' method which is close to the original area of $8413.76cm^2$. Statistical difference was found between images of original and 'smooth' ones$(p=0.04^*)$. However, there was no difference between original and 'curvature' treated images, which indicates that 'curvature' method is more useful to fill the hole compared with 'smooth' method.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.83-90
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• 2009

Natural disasters, such as the recent floods in the Midwest, Hurricane Ike in the Gulf coast region (U.S.), and the earthquake in Sichuan (China), cause severe damage to the infrastructure as well as the associated industries and communities that rely on the infrastructure. The estimated damages due to Hurricane Ike in 2008 were a staggering $27 billion, the third worst in U.S. history. In addition, the worst earthquake in three decades in Sichuan resulted in about 90,000 people dead or missing and $20 billion of the estimated loss. A common observation in the analyses of these natural disaster events is the inadequacy of critical infrastructure to withstand the forces of natural calamities and the lack of mitigation strategies when they occur on the part of emergency-related organizations, industries, and communities. If the emergency-related agencies could identify and fortify the vulnerable critical infrastructure in the preparedness stage, the damage and impacts can be significantly reduced. Therefore, it is important to develop a decision support system (DSS) for identifying region-specific mitigation strategies based on the inter-relationships between the infrastructure and associated industries and communities in the affected region. To establish effective mitigation strategies, relevant data were collected from the affected areas with respect to the technical, social, and economic impact levels. The data analysis facilitated identifying the major factors, such as vulnerability, criticality, and severity, for developing a DSS. Customized mitigation strategies that will help agencies prepare, respond, and recover according to the disaster response were suggested.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.879-890
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• 2011

The GOSAT (Greenhouse gases Observing SATellite) data provide new opportunities the most regionally complete and up-to-date assessment of $CO_2$. However, in practice, GOSAT records often suffer from missing data values mainly due to unfavorable meteorological condition in specific time periods of data acquisition. The aim of this research was to identify optimal spatial interpolation techniques to ensure the continuity of $CO_2$ from samples taken in the North East Asia. The accuracy among ordinary kriging (OK), universal kriging (UK) and simple kriging (SK) was compared based on the combined consideration of $R^2$ values, Root Mean Square Error (RMSE), Mean Error (ME) for variogram models. Cross validation for 1312 random sampling points indicate that the (UK) kriging is the best geostatistical method for spatial predictions of $CO_2$ in the East Asia region. The results from this study can be useful for selecting optimal kriging algorithm to produce $CO_2$ map of various landscapes. Also, data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the GOSAT sample data.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.4
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• pp.354-361
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• 2022

The development of digital technology is causing great changes in dentistry. This digital workflow combines various 3D data in the prosthetic treatment area for diagnosis and prosthetic manufacturing. The planned diagnosis and the fabrication of prosthesis in a virtual patient formed by synthesizing digital data can simulate the results of prosthetic treatment more intuitively than conventional methods, thereby increasing the predictability of aesthetic prosthetic treatment. In this case report, functionally and aesthetically satisfied clinical results were obtained by fabricating a fixed partial dentures through a digital workflow on congenital missing teeth in the maxillary anterior region.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.7
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• pp.935-944
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• 2012

Amur grayling, Thymallus grubii, is an important economic cold freshwater fish originally found in the Amur basin. Currently, suffering from loss of habitat and shrinking population size, T. grubii is restricted to the mountain river branches of the Amur basin. In order to assess the genetic diversity, population genetic structure and infer the evolutionary history within the species, we analysised the whole mitochondrial DNA control region (CR) of 95 individuals from 10 rivers in China, as well as 12 individuals from Ingoda/Onon and Bureya River throughout its distribution area. A total of 64 variable sites were observed and 45 haplotypes were identified excluding sites with gaps/missing data. Phylogenetic analysis was able to confidently predict two subclade topologies well supported by maximum-parsimony and Bayesian methods. However, basal branching patterns cannot be unambiguously estimated. Haplotypes from the mitochondrial clades displayed local homogeneity, implying a strong population structure within T. grubii. Analysis of molecular variance detected significant differences among the different geographical rivers, suggesting that T. grubii in each river should be managed and conserved separately.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.157-166
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• 2014

If there are metals located in the X-ray scanned object, a point outside the metals has its range of projection angle at which projections passing through the point are disturbed by the metals. Roughly speaking, this implies that attenuation information at the point is missing in the blocked projection range. So conventional projection completion MAR algorithms to use the undisturbed projection data on the boundary of the metaltrace is less efficient in reconstructing the attenuation coefficient in detailed parts, in particular, near the metal region. In order to overcome this problem, we propose the algebraic correction technique (ACT) to utilize a pre-reconstructed interim image of the attenuation coefficient outside the metal region which is obtained by solving a linear system designed to reduce computational costs. The reconstructed interim image of the attenuation coefficient is used as prior information for MAR. Numerical simulations support that the proposed correction technique shows better performance than conventional inpainting techniques such as the total variation and the harmonic inpainting.