• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.15-26
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• 2004

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. The optimal regionalization of the precipitation data were classified by the above mentioned regionalization for all over the regions except Jeju and Ulleung islands in Korea. Design rainfalls following the consecutive duration were derived by the regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root mean square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared between the regional and at-site frequency analysis. It has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design rainfall. Consequently, optimal design rainfalls following the classified regions and consecutive durations were derived by the regional frequency analysis using Generalized extreme value distribution which was identified to be more optimal one than the other applied distributions. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.735-743
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• 2020

Secure coding is a technique that detects malicious attack or unexpected errors to make software systems resilient against such circumstances. In many cases secure coding relies on static analysis tools to find vulnerable patterns and contaminated data in advance. However, secure coding has the disadvantage of being dependent on rule-sets, and accurate diagnosis is difficult as the complexity of static analysis tools increases. In order to support secure coding, we apply machine learning techniques, such as DNN, CNN and RNN to investigate into finding major weakness patterns shown in secure development coding guides and present machine learning models and experimental results. We believe that machine learning techniques can support detecting security weakness along with static analysis techniques.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.39-47
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• 2023

This study compares various transformation techniques for multifocus image fusion. Multi-focus image fusion is a procedure of merging multiple images captured at unalike focus distances to produce a single composite image with improved sharpness and clarity. In this research, the purpose is to compare different popular frequency domain approaches for multi-focus image fusion, such as Discrete Wavelet Transforms (DWT), Stationary Wavelet Transforms (SWT), DCT-based Laplacian Pyramid (DCT-LP), Discrete Cosine Harmonic Wavelet Transform (DC-HWT), and Dual-Tree Complex Wavelet Transform (DT-CWT). The objective is to increase the understanding of these transformation techniques and how they can be utilized in conjunction with one another. The analysis will evaluate the 10 most crucial parameters and highlight the unique features of each method. The results will help determine which transformation technique is the best for multi-focus image fusion applications. Based on the visual and statistical analysis, it is suggested that the DCT-LP is the most appropriate technique, but the results also provide valuable insights into choosing the right approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1C
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• pp.78-83
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• 2002

Reachability analysis of finite state machines is very useful for many computer-aided design applications such as communication protocol or microprecessor design. We present new techniques to improve approximate reachability analysis. The key idea is to used an iterative approximate reachability analysis technique in which don't care sets derived from previous iterations are used to improve the approximation in subsequent iterations. Experimental results show that the new techniques can improve reachability analysis significantly compared to existing analysis techniques.

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.202-218
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• 1998

It is well-known that leading firms are more innovative than others, with far more sales from new products. This paper suggests that what kinds of creatively techniques can be a, pp.ied to new product development process for the purpose of commercial success. Both divergent and convergent techniques are considered at each stage of new product development process. Some typical creativity techniques such as boundary examination, bug list, manipulative verbs, morphological analysis, SCAMPER, and TRIZ are explained with case examples.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.41-53
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• 2002

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. According to the regions and consecutive durations, optimal design rainfalls were derived by the regional frequency analysis for L-moment in the second report of this project. Using the LH-moment ratios and Kolmogorov-Smirnov test, the optimal regional probability distribution was identified to be the Generalized extreme value (GEV) distribution among applied distributions. regional and at-site parameters of the GEV distribution were estimated by the linear combination of the higher probability weighted moments, LH-moment. Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. Relative efficiency (RE) for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.157-164
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• 2019

Big data is a field that has been utilized and developed in various fields in our society recently. Big data analysis techniques are frequently used to analyze various big data in various fields of politics, economy, and society to grasp various meanings hidden in the data. However, big data analysis is used some case studies of in fields of analysis of educational data, but analysis of the curriculum and direction is still inadequate. Therefore, this study aims to identify and analyze the core concepts of middle school informatics textbooks using big data analysis techniques. Text mining was used for big data analysis for informatics textbook analysis. Through the core concepts of middle school informatics textbooks identified using this techniques, we could confirm the concepts to be emphasized in the textbooks and the possibility of using big data in the field of education.

• Journal of Plant Biotechnology
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• v.37 no.2
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• pp.196-204
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• 2010

With the completion of genome sequencing of several organisms, attention has been focused to determine the function and functional network of proteins by proteome analysis. The recent techniques of proteomics have been advanced quickly so that the high-throughput and systematic analyses of cellular proteins are enabled in combination with bioinformatics tools. Furthermore, the development of proteomic techniques helps to elucidate the functions of proteins under stress or diseased condition, resulting in the discovery of biomarkers responsible for the biological stimuli. Ultimate goal of proteomics orients toward the entire proteome of life, subcellular localization, biochemical activities, and their regulation. Comprehensive analysis strategies of proteomics can be classified as three categories: (i) protein separation by 2-dimensional gel electrophoresis (2-DE) or liquid chromatography (LC), (ii) protein identification by either Edman sequencing or mass spectrometry (MS), and (iii) quanitation of proteome. Currently MS-based proteomics turns shiftly from qualitative proteome analysis by 2-DE or 2D-LC coupled with off-line matrix assisted laser desorption ionization (MALDI) and on-line electrospray ionization (ESI) MS, respectively, to quantitative proteome analysis. Some new techniques which include top-down mass spectrometry and tandem affinity purification have emerged. The in vitro quantitative proteomic techniques include differential gel electrophoresis with fluorescence dyes, protein-labeling tagging with isotope-coded affinity tag, and peptide-labeling tagging with isobaric tags for relative and absolute quantitation. In addition, stable isotope labeled amino acid can be in vivo labeled into live culture cells through metabolic incorporation. MS-based proteomics extends to detect the phosphopeptide mapping of biologically crucial protein known as one of post-translational modification. These complementary proteomic techniques contribute to not only the understanding of basic biological function but also the application to the applied sciences for industry.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.2
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• pp.144-156
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• 2016

Soil is a dynamic biological system, in which it is difficult to determine the composition of microbial communities. Knowledge of microbial diversity and function in soils are limited because of the taxonomic and methodological limitations associated with studying the organisms. In this review, approaches to measure microbial diversity in soil were discussed. Research on soil microbes can be categorized as structural diversity, functional diversity and genetic diversity studies, and these include cultivation based and cultivation independent methods. Cultivation independent technique to evaluate soil structural diversity include different techniques such as Phospholipid Fatty Acids (PLFA) and Fatty Acid Methyl Ester (FAME) analysis. Carbon source utilization pattern of soil microorganisms by Community Level Physiological Profiling (CLPP), catabolic responses by Substrate Induced Respiration technique (SIR) and soil microbial enzyme activities are discussed. Genetic diversity of soil microorganisms using molecular techniques such as 16S rDNA analysis Denaturing Gradient Gel Electrophoresis (DGGE) / Temperature Gradient Gel Electrophoresis (TGGE), Terminal Restriction Fragment Length Polymorphism (T-RFLP), Single Strand Conformation Polymorphism (SSCP), Restriction Fragment Length Polymorphism (RFLP) / Amplified Ribosomal DNA Restriction Analysis (ARDRA) and Ribosomal Intergenic Spacer Analysis (RISA) are also discussed. The chapter ends with a final conclusion on the advantages and disadvantages of different techniques and advances in molecular techniques to study the soil microbial diversity.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.170-189
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• 2017

Purpose: This paper presents Raman chemical imaging technology for inspecting food and agricultural products. Methods The paper puts emphasis on introducing and demonstrating Raman imaging techniques for practical uses in food analysis. Results & Conclusions: The main topics include Raman scattering principles, Raman spectroscopy measurement techniques (e.g., backscattering Raman spectroscopy, transmission Raman spectroscopy, and spatially offset Raman spectroscopy), Raman image acquisition methods (i.e., point-scan, line-scan, and area-scan methods), Raman imaging instruments (e.g., excitation sources, wavelength separation devices, detectors, imaging systems, and calibration methods), and Raman image processing and analysis techniques (e.g., fluorescence correction, mixture analysis, target identification, spatial mapping, and quantitative analysis). Raman chemical imaging applications for food safety and quality evaluation are also reviewed.