• Journal of the Korea Concrete Institute
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• v.27 no.6
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• pp.695-703
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• 2015

Acceptance criteria for chemical admixtures of cement concrete were investigated in domestic and international specifications. The reliability was verified for solid content test method of chemical admixture examined statistical analysis by round robin test. The applicability of FT-IR spectroscopy for qualitative measurement of multi-compound chemical admixtures verified. From solid content experimental results, outlier analysed using Cochran, Grubbs and Dickson's Q test. Repeatability and reproducibility standard deviation for solid content results showed 0.25 and 0.098% respectively according to KS A ISO 5725-2 procedure, it can be confirmed reliability of test methods. FT-IR spectrum of liquefied or oven-dried chemical admixtures condition showed big differences. It is needed that the FT-IR analysis is performed on dry material. However there's no difference with the applicability of FT-IR spectroscopy for multi-compound chemical admixtures. So the utility of method analysis could not identify.

• Analytical Science and Technology
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• v.28 no.6
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• pp.446-452
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• 2015

In order to evaluate and improve the ability of Korean testing laboratories to measure Polybrominated diphenyl ethers in acrylonitrile-butadiene-styrene (ABS), a proficiency test was organised by Korea Research Institute of Standards and Science (KRISS) based on ISO/IEC 17043. The proficiency test material used was 10 g of a granular ABS fortified with a mixture of congeners of PBDE (BDE-154, 183, 206, 209). Homogeneity and stability were investigated to assess the adequacy of the test material. The certified value established by KRISS based on the national reference was used for assigned value of each PBDE. The test materials were distributed to the 16 participating laboratories. The participating laboratories were requested to analyse the samples employing the methods used in their routine analysis. Each laboratory was given it’s own code to secure the anonymity. Participants results were evaluated with z-scores according to ISO/IEC 17043. The standard deviation for proficiency assessment was set by standard deviation of the participants results except for outlier. The results, the laboratory's performance and improvement of accuracy were discussed.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.353-356
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• 2007

With advances in sensor technology, current researches on the pertinent techniques are actively directed toward the way which enables the USN computing service. For many applications using sensor networks, the incoming data are by nature characterized as high-speed, continuous, real-time and infinite. Due to such uniqueness of sensor data characteristics, for some instances a finite-sized buffer may not accommodate the entire incoming data, which leads to inevitable loss of data, and requirement for fast processing makes it impossible to conduct a thorough investigation of data. In addition to the potential problem of loss of data, incoming data in its raw form may exhibit high degree of complexity which evades simple query or alerting services for capturing and extracting useful information. Furthermore, as traditional mining techniques are developed to handle fixed, static historical data, they are not useful and directly applicable for analyzing the sensor data. In this paper, (1) describe how three mining techniques (sensor data outlier analysis, sensor pattern analysis, and sensor data prediction analysis) are appropriate for the USN middleware structure, with their application to the stream data in ocean environment. (2) Another proposal is a middleware structure based on USN environment adaptive to above mining techniques. This middleware structure includes sensor nodes, sensor network common interface, sensor data processor, sensor query processor, database, sensor data mining engine, user interface and so on.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.25-34
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• 2008

By comparing two images, which are captured with the same scene at different time, we can detect a set of outliers, such as occluding objects due to moving vehicles. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, an algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergence property of the estimates of the noise variance. Using a correction constant for the estimate of the noise variance is proposed. The correction enables the detection algorithm robust to the choice of thresholds for selecting outliers. Numerical analysis using both synthetic and Teal images are also shown in this paper to show the robust performance of the detection algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.363-370
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• 2023

Due to the spread of COVID-19, the use of public transportation such as urban railroads has changed significantly since the beginning of 2020. Therefore, in this study, daily time series data for each urban railway station were collected for three years before COVID-19 and after the spread of COVID-19, and the similarity of time series analysis was evaluated through DTW (Dynamic Time Warping) distance method to derive regression centers for each cluster, and the effect of various external events such as COVID-19 on changes in the number of users was diagnosed as a time series impact detection function. In addition, the characteristics of use by cluster of urban railway stations were analyzed, and the change in passenger volume due to external shocks was identified. The purpose was to review measures for the maintenance and recovery of usage in the event of re-proliferation of COVID-19.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.8-18
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• 2024

The present study concerns reduced order modeling of a marine diesel engine, which can be used for outlier detection in status monitoring and carbon intensity index calculation. Principal Component Analysis (PCA) is introduced for the reduced order modeling, focusing on the feasibility of detecting and treating nonlinear variables. By cross-correlation, it is found that there are seven non-linear data channels among 23 data channels, i.e., fuel mode, exhaust gas temperature after the turbocharger, and cylinder coolant temperatures. The dataset is handled so that the mean is located at the nominal continuous rating. Polynomial presentation of the dataset is also applied to reflect the linearity between the engine speed and other channels. The first principal mode shows strong effects of linearity of the most data channels to show the linearity of the system. The non-linear variables are effectively explained by other modes. second mode concerns the temperature of the cylinder cooling water, which shows small correlation with other variables. The third and fourth modes correlates the fuel mode and turbocharger exhaust gas temperature, which have inferior linearity to other channels. PCA is proven to be applicable to data given in binary type of fuel mode selection, as well as numerical type data.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.7-16
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• 2016

In this paper, we propose a method for effectively detecting specific behavior. The proposed method detects outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment and add keystroke based on repeated pattern. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players was used to analyze high-dimensional game-player data for a detection effect of repeated behaviour pattern. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. In addition, Repeated behaviour pattern can be analysed possible. The proposed method can also be used for feedback and quantification about analysis of various interactive content provided in PC environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.369-375
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• 2016

Most companies are willing to spend money on security systems such as DRM, Mail filtering, DLP, USB blocking, etc., for data leakage prevention. However, in many cases, it is difficult that legal team take action for data case because usually the company recognized that after the employee had left. Therefore perceiving one's resignation before the action and building up adequate response process are very important. Throughout analyzing DRM log which records every single file's changes related with user's behavior, the company can predict one's resignation and prevent data leakage before those happen. This study suggests how to prevent for the damage from leaked confidential information throughout building the DRM monitoring process which can predict employee's resignation.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1187-1196
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• 2014

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. This study aims to analyze the cause-and-effect relationship between IRG yield and climate variables such as temperature and precipitation by using IRG data and climate data of Korea Weather Bureau. From path analysis of structural equation model under multivariate normality, we found that there was a weather effect on IRG yield that the winter grass IRG yield was directly affected by spring temperature and indirectly affected by spring rainfall. These results showed that IRG can be sown in early spring in the area where it is hard to prepare for winter due to low temperature. This paper can contribute to increase IRG yield by showing the cause-and-effect relationship and this study can be extended to various structural equation models for other crops.