• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.627-640
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• 2017

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.367-373
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• 2000

Natural and artificial land features are very dynamic, changing somewhat repidly in our lifetime. It is important that such changes are inventoried accurately so that the physical and human processes at work can be more fully understood. Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. The purpose of this research is to detect environmental changes surrounding an area of Mountain Moscow, Idaho using Landsat Thematic Maper (TM) images of (July 8, 1990 and July 20, 1991). For accurate classification, the Image enhancement process was performed for improving the image quality of each image. A SPOT image (Aug. 14, 1992) was used for image merging in this research. Supervised classification was performed using the maximum likelihood method. Accuracy assessments were done for each classification. Two images were compared on a pixel-by-pixel basis using the post-classification comparison method that is used for detecting the changes of the study area in this research. The 'from-to' change class information can be detected by post classification comparison using this method and we could find which class change to another.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.230-235
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• 2022

This study is to select an optimal object detection algorithm for designing a self-checkout counter to improve the inconvenience of payment systems for products without existing barcodes. To this end, a performance comparison analysis of YOLO v2, Tiny YOLO v2, and the latest YOLO v5 among deep learning-based object detection algorithms was performed to derive results. In this paper, performance comparison was conducted by forming learning data as an example of 'donut' in a bakery store, and the performance result of YOLO v5 was the highest at 96.9% of mAP. Therefore, YOLO v5 was selected as the artificial intelligence object detection algorithm to be applied in this paper. As a result of performance analysis, when the optimal threshold was set for each donut, the precision and reproduction rate of all donuts exceeded 0.85, and the majority of donuts showed excellent recognition performance of 0.90 or more. We expect that the results of this paper will be helpful as the fundamental data for the development of an automatic payment system using AI self-service technology that is highly usable in the non-face-to-face era.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.197-204
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• 2022

Sorting is an important data structure in many applications in the real world. Several sorting algorithms are currently in use for searching and other operations. Sorting algorithms rearrange the elements of an array or list based on the elements' comparison operators. The comparison operator is used in the accurate data structure to establish the new order of elements. This report analyzes and compares the time complexity and running time theoretically and experimentally of insertion, merge, and heap sort algorithms. Java language is used by the NetBeans tool to implement the code of the algorithms. The results show that when dealing with sorted elements, insertion sort has a faster running time than merge and heap algorithms. When it comes to dealing with a large number of elements, it is better to use the merge sort. For the number of comparisons for each algorithm, the insertion sort has the highest number of comparisons.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.1-13
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• 2023

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.238-242
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• 2005

This paper considers a routing problem on a RPR(Resilient Packet Ring). The RPR is one of the ring-type data telecommunication network. Our major problem is to find an optimal routing algorithm for a given data traffic on the network under no splitting the traffic service, where the maximum load of a link is minimized. This paper characterizes the Minmax problem and develops two heuristic algorithms. By using the numerical comparison, we show that our heuristic algorithm is valuable for efficient routing the data traffic on a RPR.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.349-361
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• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.126-132
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• 2004

The database design for the management of bridge measurement information is presented in this paper. To express the associated data generated during the whole process of ambient measurement efficiently, requirements analysis for database construction is performed. And to define objects and organize schema conceptual and logical design are performed, which convert data model into logical schema. Finally, physical design is performed using DDL(data defined language). This database is based on the object-relational data modeling approach that has rich expressive power and good reusability in comparison with the traditional entity-relational modeling.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.31-39
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• 2006

In many cases, we frequently get a desired information based on the appropriate statistical analysis of collected data sets. Lots of statistical theory rely on the assumption of the normality of the data. In this paper, we compare the empirical power of some normality tests including sample entropy quantity. Monte carlo simulation is conducted for the calculation of empirical power of considered normality tests by varying sample sizes for various distributions.