• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.441-452
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• 2018

Purpose: The purpose of this paper is to study quantitative risk grade assessment for objective government quality assurance activities based on risk management in initial mass production for weapon systems. Methods: The Defense quality management regulations and foreign risk assessment documents are referred to analyze problems performing quality assurance actives. The failure rate data, maintainability and cost of products have been studied to quantify the risk Likelihood and impact. The analyzed data were classified as risk grade assessment through K-means Cluster Analysis method. Results: Results show that a proposed method can objectively evaluate risk grade. The analyzed results are clustered into three levels such as high, middle and low. Two products are allocated high, eleven low and seven middle. Conclusion: In this paper, quantitative risk grade assessment methods were presented by analyzing risk ratings based on objective data. The findings showed that the methods would be effective for initial mass production for weapon systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.18-23
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• 2006

This paper presents an operator training simulator for power system restoration against massive blackout. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using power flow calculation methodology, and PSS/E data is used to set up the initial state for easy setting. The proposed simulator consists of three major components-a power flow(PF), a data conversion(CONV), and, a GUI module. The PF module calculates power flow, and then checks over-voltages of buses and overloads of lines. The CONV module composes a Y-Bus array and a database at each restoration action. The initial Y-Bus array is composed from PSS/E data. A user friendly GUI module is developed including a graphic editor and a built-in operation manual. The maximum processing time for one step operation is 15 seconds, which is adequate for training purpose.

• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.87-95
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• 2012

This technical paper describes the analysis results of telemetry data for the initial activation of star trackers for an agile high accuracy low earth orbit satellite. The satellite was recently launched and is in the Launch and Early Operation Phases. It uses two SED36 star trackers manufactured by SODERN. The star tracker is separated by three parts, an optical head, an electronics box, and a baffle with maintaining optical head base plate temperature 20 degC in order to achieve the better performance in low frequency error. This paper presents the initial activation status, requirements and performance, anomaly occurrence, and noise equivalent angle performance analysis during the mission mode by processing the telemetry data.

• Journal of Environmental Impact Assessment
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• v.8 no.3
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• pp.77-90
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• 1999

The degree of green naturality (DGN) has played a key role for maintaining the environmental quality from inappropriate developments, although the quality and effectiveness of the mapping of DGN has been under debate. In this study, spatial distribution of degree of green naturality was initially estimated from forest stand maps that were produced from the aerial photo interpretation and extensive field survey. Once the boundary of initial classes of DGN were defined, it were overlaid with normalized difference vegetation index (NDVI) data that were derived from the recently obtained Landsat Thematic Mapper data. NDVI was calculated for each pixel from the radiometrically corrected satellite image. There were no significant differences in mean values of vegetation index among the initial DGN classes. However, the satellite derived vegetation index was very effective to delineate the developed and damaged forest lands and to adjust the initial value of DGN according to the distribution of NDVI within each class.

• International Journal of Highway Engineering
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• v.20 no.1
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• pp.137-146
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• 2018

PURPOSES : This study aims to analyze how the installation of advanced warning devices affects individual drivers' driving behaviors with operating record data collected from 100 vehicles. METHODS : With collected data, the changes in individual drivers' driving behaviors, such as Forward Collision Warning (FCW) and Lane Departure Warning (LDW), were investigated with respect to the cumulative distance traveled and driving time. For the analysis, operating record data collected from 100 vehicles for seven months were used. RESULTS : The results showed that individual drivers' driving behaviors could be categorized into six different types. In addition, most of the drivers showed unstable warning patterns in the initial stage after installation of an advanced warning device. Approximately 40% of vehicles equipped with advanced warning systems were found to have positive effects, indicating that the frequencies of both FCW and LDW had been continuously decreasing after installation of the system. CONCLUSIONS : The warning device might be helpful for making drivers' driving behaviors safer. Driving behaviors during the initial stage of the system installation, which might be regarded as an adaptation phase, were found to be very unstable compared with normal situations, indicating that adequate education and training should be provided to all the drivers to prevent operator disruption at the initial installation of the system.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Asian Journal of Business Environment
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• v.12 no.3
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• pp.1-12
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• 2022

Purpose: This study aims to investigate whether AIS data can be used as a supporting indicator or as an initial signal to describe Indonesia's export-import conditions in real-time. Research design, data, and methodology: This study performs several stages of data selection to obtain indicators from AIS that truly reflect export-import activities in Indonesia. Also, investigate the potential of AIS indicators in producing forecasts of the value and volume of Indonesian export-import using conventional statistical methods and machine learning techniques. Results: The six preprocessing stages defined in this study filtered AIS data from 661.8 million messages to 73.5 million messages. Seven predictors were formed from the selected AIS data. The AIS indicator can be used to provide an initial signal about Indonesia's import-export activities. Each export or import activity has its own predictor. Conventional statistical methods and machine learning techniques have the same ability both in forecasting Indonesia's exports and imports. Conclusions: Big data AIS can be used as a supporting indicator as a signal of the condition of export-import values in Indonesia. The right method of building indicators can make the data valuable for the performance of the forecasting model.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.122-127
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• 2001

Lasting machines for shoe manufacturing are continuously developed with the aid of automation and Computer Aided Manufacturing (CAM). Although automation and CAM techniques have tremendously reduced the labor in shoe manufacturing field, there still remain some parts manufactured by experts. In order to enhance the capability and efficiency of machines for labor-free shoe manufacturing, CAD data of a shoe last is indispensable. While CAD datarization takes the fundamental role in the shoe design as well as the shoe manufacturing, there has been little research for the CAD datarization of a shoe last. In this paper, a new procedure for CAD datarization of a shoe last using finite element patches is proposed and some data for the control part of the shoe lasting machine are extracted and interpolated from the CAD data. The outer line of a shoe-last sole is interpolated by a tension spline method and bonding lines are extracted from the shoe CAD data. Finally, initial setting data for the lasting machine are extracted from the last CAD data and initial setup parts of the lasting machine.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.