• Journal of Applied Reliability
• /
• v.2 no.2
• /
• pp.63-83
• /
• 2002

Degradation data can provide more reliability information than traditional failure-time data, especially products with few or no failures. This paper is concerned with a method of estimating lifetime distribution from field data with supplementary information on degradation data and covariates. When a distribution of degradation rate obtained by follow-up study for a portion of products that survive after-warranty follows a reciprocal-Weibull or lognormal distribution. A time-to-failure distribution of the product follows Weibull or lognormal distribution, respectively. A method of estimating lifetime parameters for this kind of data and their asymptotic properties are studied. Effects of after-warranty report probability, follow-up rate, and proportion of degradation data on pseudo maximum likelihood estimators of these parameters are investigated.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.162-164
• /
• 2003

The multi-disciplinary research project Strengthening Local Authorities in Risk Management (SLARIM), initiated by ITC, includes three case study cities in Asia. An important question is: what are the essential data for risk management and how to access such data. The role of common sources (e.g. census data), data derived from remote sensing (high-resolution satellite imagery, aerial photos), and data from close sensing (field observation, including mobile GIS) to acquire essential risk management data will be discussed. Special attention is given to the question of the minimum area and to disaggregating population data. A few examples are given of Kathmandu / Lalitpur, Nepal.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.36-40
• /
• 2003

The considered satellite is supposed to operate in the earth-point mode and sun-point mode in accordance with the mission requirements. The magnetic field correction is based on the orbit geometry using a set of measured magnetic field data from the three-axis-magnetometer and its algorithm excludes the earth’s magnetic field model. Moreover, the usefulness of the proposed method is investigated throughout the simulation of KOMPSAT-1.

• Wind and Structures
• /
• v.17 no.2
• /
• pp.123-133
• /
• 2013

Pressure field and velocity profiles in a thunderstorm downburst are significantly different from that of an atmospheric boundary layer wind. A model of the pressure field in a downburst is presented in accordance with the experimental and numerical results. Large eddy simulation method is employed to investigate transient pressure field on impingement ground of a downburst. In addition, velocity profiles of the downburst are studied, and good agreement is achieved between the present results and the data obtained from empirical models.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1998.11a
• /
• pp.183-186
• /
• 1998

Axial magnetic field generated by special electrode construction in vacuum interrupters is used to extinguish electric plasma arcs. This investigation by FDM should prove to what extent the magnetic field might influence on the arc expansion. The calculated results show that the stronger magnetic field induced the lesser radius of arc plasma. This study will help to offer good data in design of vacuum interrupters.

• Journal of the Korean Geotechnical Society
• /
• v.26 no.7
• /
• pp.147-159
• /
• 2010

The settlement prediction is very important in preloading method for a construction site on the soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may differ by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method which is a direct regression method, were applied with various data groups. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Regardless of the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.129-140
• /
• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of Astronomy and Space Sciences
• /
• v.39 no.4
• /
• pp.159-167
• /
• 2022

Because of the small number of spacecraft available in the Earth's magnetosphere at any given time, it is not possible to obtain direct measurements of the fundamental quantities, such as the magnetic field and plasma density, with a spatial coverage necessary for studying, global magnetospheric phenomena. In such cases, empirical as well as physics-based models are proven to be extremely valuable. This requires not only having high fidelity and high accuracy models, but also knowing the weakness and strength of such models. In this study, we assess the accuracy of the widely used Tsyganenko magnetic field models, T96, T01, and T04, by comparing the calculated magnetic field with the ones measured in-situ by the GOES satellites during geomagnetically disturbed times. We first set the baseline accuracy of the models from a data-model comparison during the intervals of geomagnetically quiet times. During quiet times, we find that all three models exhibit a systematic error of about 10% in the magnetic field magnitude, while the error in the field vector direction is on average less than 1%. We then assess the model accuracy by a data-model comparison during twelve geomagnetic storm events. We find that the errors in both the magnitude and the direction are well maintained at the quiet-time level throughout the storm phase, except during the main phase of the storms in which the largest error can reach 15% on average, and exceed well over 70% in the worst case. Interestingly, the largest error occurs not at the Dst minimum but 2-3 hours before the minimum. Finally, the T96 model has consistently underperformed compared to the other models, likely due to the lack of computation for the effects of ring current. However, the T96 and T01 models are accurate enough for most of the time except for highly disturbed periods.

• Journal of Korean Library and Information Science Society
• /
• v.51 no.2
• /
• pp.103-131
• /
• 2020

This study was started with the aim of grasping the current status of research data management of forestry engineering researchers. In order to achieve the research purpose, the survey was conducted using a tool called DAF (Data Asset Framework). DAF is an investigative tool that provides a means to identify, position, describe and evaluate how the agency manages research data. Using this DAF, the research data management status was analyzed for researchers in the field of forestry engineering at the National Institute of Forest Science. As a result of analysis, the current status and problems of the five categories such as the method and type of research data creation, sharing, storage, preservation, and reuse were identified, and solutions were presented in relation to the problems. This study is a basic investigation using a systematic tool such as DAF, and can be used as a reference for analyzing the current status and problems of research data when designing RDM system in a specific field.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.99-109
• /
• 2020

This paper proposes a platform construction plan for the use of spectrum big data, collects and analyzes the big data in the radio wave field, establishes a linkage plan, and presents a support system scheme for linking and using the spectrum and public sector big data. It presented a plan to build a big data platform in connection with the spectrum public sector. In a situation where there is a lack of a support system for systematic analysis and utilization of big data in the field of radio waves, by establishing a platform construction plan for the use of big data by radio-related industries, the preemptive response to realize the 4th Industrial Revolution and the status and state of the domestic radio field. The company intends to contribute to enhancing the convenience of users of the big data platform in the public sector by securing the innovation growth engine of the company and contributing to the fair competition of the radio wave industry and the improvement of service quality. In addition, it intends to contribute to raising the social awareness of the value of spectrum management data utilization and establishing a collaboration system that uses spectrum big data through joint use of the platform.