• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.715-720
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• 2020

In Big data analysis, text data is mostly unstructured and large-capacity, so analysis was difficult because analysis techniques were not established. Therefore, this study was conducted for the possibility of commercialization through verification of usefulness and problems when applying the big data word cloud technique, one of the text data analysis techniques. In this paper, the limitations and problems of this technique are derived through visualization analysis of the "President UN Speech" using the R program word cloud technique. In addition, by proposing an improved model to solve this problem, an efficient method for practical application of the word cloud technique is proposed.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.237-240
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• 2002

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. RRMSE, RBIAS and 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. 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 the Korea Society of Computer and Information
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• v.15 no.2
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• pp.75-81
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• 2010

Feeling of shooting is one of the most important features of shooting games. Game developers have tried to improve feeling of shooting by using various techniques, such as visual/sound effects, rumble effects, animations, and camera techniques. In this paper, we introduce the results of the empirical analysis of the several techniques in a 2D shooting game. We carried out two experiments in which levels of feeling of shooting were measured in a simple 2D shooting game. The first experiment was configured with 16 combinations of the four techniques (visual, animation, sound, and rumble effects) applied to a shooting object (a cannon), and the second was configured with 16 combinations of the two techniques (visual and sound effects) applied to both or either side of a shooting object and exploding objects (enemy ships). The analysis results of the experiments showed that all of each techniques were statistically significant factors. We could also found that sound effects and rumble effects are more effective than visual effects and animations and that exploding objects are more important that a shooting object.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.43-51
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• 2021

Incident investigation is one of the most important processes among various other safety management methods to prevent industrial accidents. Finding the root causes of accidents, eliminating hazards, and improving safety are the most important purposes of investigating accidents. During the investigation process, root cause analysis (RCA) techniques are used to effectively identify RCA. Over the past few decades, over 30 RCA methods have been developed. These techniques are being widely used in some industries, such as the nuclear and aircraft industries; however, most of the RCA techniques require professional knowledge and special training, making it difficult for safety managers in their respective fields to understand and apply them. Therefore, managers of general industrial sites are rarely present at the scene of actual accident investigations, and they cannot contribute much to the purpose and effectiveness of these investigations. In this study, to address these issues, we developed an RCA technique to facilitate root cause investigation of accidents in real-world industrial sites. To develop new techniques, Systematic Cause Analysis Technique (SCAT), one of the RCA techniques, was used to investigate incidents in the enterprise over three years. We also utilized feature analysis and other papers from existing RCA techniques. To verify its effectiveness, the technique proposed was also applied to the accident case. The technique developed can easily identify and analyze the root cause of an accident and help industrial managers. It can also identify the root cause category where accidents are concentrated and use this data to establish guidelines for preventing future accidents and, thus, focus on prioritizing improvement initiatives.

• Korean Journal of Environmental Agriculture
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• v.25 no.4
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• pp.371-381
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• 2006

New proteomic techniques have been pioneered extensively in recent years, enabling the high-throughput and systematic analyses of cellular proteins in combination with bioinformatic tools. Furthermore, the development of such novel proteomic techniques facilitates the elucidation of the functions of proteins under stress or disease conditions, resulting in the discovery of biomarkers for responses to environmental stimuli. The ultimate objective of proteomics is targeted toward the entire proteome of life, subcellular localization biochemical activities, and the regulation thereof. Comprehensive analysis strategies of proteomics can be classified into three categories: (i) protein separation via 2-dimensional gel electrophoresis (2-DE) or liquid chromatography (LC), (ii) protein identification via either Edman sequencing or mass spectrometry (MS), and (iii) proteome quantitation. Currently, MS-based proteomics techniques have shifted from qualitative proteome analysis via 2-DE or 2D-LC coupled with off-line matrix assisted laser desorption ionization (MALDI) and on-line electrospray ionization (ESI) MS, respectively, toward quantitative proteome analysis. In vitro quantitative proteomic techniques include differential gel electrophoresis with fluorescence dyes. protein-labeling tagging with isotope-coded affinity tags, and peptide-labeling tagging with isobaric tags for relative and absolute quantitation. In addition, stable isotope-labeled amino acids can be in vivo labeled into live culture cells via metabolic incorporation. MS-based proteomics techniques extend to the detection of the phosphopeptide mapping of biologically crucial proteins, which ale associated with post-translational modification. These complementary proteomic techniques contribute to our current understanding of the manner in which life responds to differing environment.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.169-169
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• 2001

With the aid of fast computing power, resampling techniques are being introduced for simulation output analysis (SOA). Autocorrelation among the output from discrete-event simulation prohibit the direct application of resampling schemes (Threshold bootstrap, Binary bootstrap, Stationary bootstrap, etc) extend its usage to time-series data such as simulation output. We present a new method for inference from a regenerative process, regenerative bootstrap, that equals or exceeds the performance of classical regenerative method and approximation regeneration techniques. Regenerative bootstrap saves computation time and overcomes the problem of scarce regeneration cycles. Computational results are provided using M/M/1 model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.143-146
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• 2005

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.1
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• pp.1-8
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• 2009

Recently microscale biofluid flows have been receiving large attention in various research areas. However, most conventional imaging techniques are unsatisfactory due to difficulties encountered in the visualization of microscale biological flows. Recent advances in optics and digital image processing techniques have made it possible to develop several advanced micro-PIV/PTV techniques. They can be used to get quantitative velocity field information of various biofluid flows from visualized images of tracer particles. In this paper, as new advanced micro-PIV techniques suitable for biofluid flow analysis, the basic principle and typical applications of the time-resolved micro-PIV and X-ray micro-PIV methods are explained. As a 3D velocity field measurement technique for measuring microscale flows, holographic micro-PTV method is introduced. These advanced PIV/PTV techniques can be used to reveal the basic physics of various microscale biological flows and will play an important role in visualizing veiled biofluid flow phenomena, for which conventional methods have many difficulties to analyze.

• Journal of Ocean Engineering and Technology
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• v.9 no.2
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• pp.7-19
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• 1995

Various laybarge methods for offshore pipeline installation are introduced. Pipe stresses and strains during the installation are discussed with linear and nonlinear analysis methods. Several operational modes of offshore pipeline installation are described. Computer modelling techniques of the pipeline installation analyses are suggested.