• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.363-370
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• 2024

This study is about the organizational justice of fire service agency for organizational commitment and motivation of firefighters. The research data were collected from 355 firefighters nationwide using the On-nara Administrative Work Management System 2.0. The statistical processing method of the research data was analyzed using the statistical program SPSS 28.0. The study results showed that the overall level of firefighters' perception of organizational justice was slightly lower than normal, with an average score of 2.85. Regarding gender, male firefighters were more likely to believe that the organization was injustice than female firefighters. In addition, organizational justice was found to be lower among fire sergeants by rank and first aiders by responsibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• Transactions of Materials Processing
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• v.19 no.8
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• pp.523-528
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• 2010

The forging industry is composed of those plants that make parts to order for customers; or make parts for their own company's internal use; or make standard parts for resale. Also, the forging industry is closely related with automobile industry and ship building industry - Korea's major export industry. But, it is hard to find the Korea's forging industry's statistical analysis because it is not revealed with final product. In this paper, we perform statistical analysis using the micro data service provided by the Statistics Korea. We focus on the analysis of production costs as well as the status of forging company and their employee.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.4 no.2
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• pp.1-9
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• 1986

The marathon course on road surfaces varies frequently in alignment horizontally and /or vertically. This fact compelled use to use a calibrated bicycle method for the course measurement, and to be required statistical approaches for data processing. This paper deals with the computation of the Seoul Olympic Marathon course lengths measured on May 18, 1986. The concept of safety factor against short course is analyzed for certification, and statistical methods are presented to compute an appropriate safety factor. The results of computation show that the best (lowest) actual measurement provides about 99.95% confidence that the combined lengths of all intervals will not be found short upon an equally accurate remeasurement.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.701-710
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• 2012

Retinitis Pigmentosa(RP) is a common hereditary disease. While they have been normally living, those who have this symptom feel frustration and pain by the damage of visual acuity. At the national level, the loss of the economic activity due to the reduction of economically active population will be also greater. There is an urgent need for the base study that can provide the clinical prognosis information of RP disease. In this study, we suggest that it is possible to predict prognosis through the pattern classification of RP data. Statistical processing results through statistical software like SPSS(Statistical Package for the Social Service) were mainly applied for the conventional study in data analysis. However, machine learning and automatic pattern classification was applied to this study. SVM(Support Vector Machine) and other various pattern classifiers were used for it. The proposed method confirmed the possibility of prognostic prediction based on the result of automatically classified RP data by SVM classifier.

• Atmosphere
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• v.34 no.1
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• pp.23-34
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• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.425-440
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• 2012

As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.95-101
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• 2015

The backup site operation of the Image Data Acquisition and Control System (IDACS) for Communication Ocean Meteorological Satellite (COMS) is discussed in terms of the ground station configuration, image data processing, and the characteristics of backup activities for both the meteorological image data and the ocean image data. The well-performed backup operation of the COMS IDACS is also confirmed with the first three years normal operation results from April, 2011 to March, 2014. The operation results are analyzed through statistical approach to provide the achieved operational performance of the image data reception, preprocessing, and broadcast.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.49-56
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• 2017

In this paper we propose the approach for big data integration so as to analyze, visualize and predict the future of the trend of the market, and that is to get the integration data model using the R language which is the future of the statistics and the Hadoop which is a parallel processing for the data. As four approaching methods using R and Hadoop, ff package in R, R and Streaming as Hadoop utility, and Rhipe and RHadoop as R and Hadoop interface packages are used, and the strength and weakness of four methods are described and analyzed, so Rhipe and RHadoop are proposed as a complete set of data integration model. The integration of R, which is popular for processing statistical algorithm and Hadoop contains Distributed File System and resource management platform and can implement the MapReduce programming model gives us a new environment where in R code can be written and deployed in Hadoop without any data movement. This model allows us to predictive analysis with high performance and deep understand over the big data.