• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.61-73
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• 2007

As a part of Load and Resistance Factor Design(LRFD) code development in Korea, in this paper an intensive reliability analysis was performed to evaluate reliability levels of the two static bearing capacity methods for driven steel pipe piles adopted in Korean Standards for Structure Foundations by the representative reliability methods of First Order Reliability Method(FORM) and Monte Carlo Simulation(MCS). The resistance bias factors for the two static design methods were evaluated by comparing the representative measured bearing capacities with the design values. In determination of the representative bearing capacities of driven steel pipe piles, the 58 data sets of static load tests and soil property tests were collected and analyzed. The static bearing capacity formula and the Meyerhof method using N values were applied to the calculation of the expected design bearing capacity of the piles. The two representative reliability methods(FORM, MCS) based computer programs were developed to facilitate the reliability analysis in this study. Mean Value First Order Second Moment(MVFOSM) approach that provides a simple closed-form solution and two advanced methods of FORM and MCS were used to conduct the intensive reliability analysis using the resistance bias factor statistics obtained, and the results were then compared. In addition, a parametric study was conducted to identify the sensibility and the influence of the random variables on the reliability analysis under consideration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1162-1165
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• 2001

Operational Modal Analysis also known as Output Only Modal Analysis has in the recent years been used for extracting modal parameters of civil engineering structures and is now becoming popular for mechanical structures. The advantage of the method is that no artificial excitation need to be applied to the structure or force signals to be measured. All the parameter estimation is based upon the response signals, thereby minimising the work of preparation for the test. This test case is a controlled lab set-up enabling different parameter estimation methods techniques to be used and compared to the Operational Modal Analysis. For Operational Modal Analysis two different estimation techniques are used： a non-parametric technique based on Frequency Domain Decomposition (FDD), and a parametric technique working on the raw data in time domain, a data driven Stochastic Subspace Identification (SS!) algorithm. These are compared to other methods such as traditional Modal Analysis.

• Journal of Korean Society of Rural Planning
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• v.22 no.2
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• pp.31-43
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• 2016

In existing method, research for landscape resource is driven by professional (or with the participation of local people at Tokenism level), and usually hinder local residents from reflecting their appreciations on the landscape resources in their own ways and eventually ends up with indistinguishable landscape planning. To avoid this, a profound understanding of what landscape they experience in their daily life and how they perceive it should be empirically analysed carefully. The purpose of this study is to apply walking behavior as a method to examine local residents' subjective perceptions and consider its usability. The researcher walked the site(Sucheongri) with the residents, carrying a GPS device, taking photographs of the landscape objects they described, and recording the relevant explanations. After gathering photographs and explanations which represent the research participants' individual subjective perception, the researcher analysed the explanation using open coding, based on grounded theory. By the analysis, 117 landscape objectives are identified and 18 reason factors for landscape perception were deduced from the explanation. Those factors could be classified as 'positive feeling inducing' and 'negative feeling inducing', and also as 'personal emotion based' and 'community based emotion'. By comparison between feeling map by conventional method and feeing map by new method, usability of new method was empirically reveled. Walking behavior makes it easier for researcher to get more abundant data in quantitative aspect and profound understanding with affection of respondent by allowing them to 'go beyond' the perceptions they remember. Finally new method with walking gives professionals a contextual understanding of a place and more resident-oriented plans and management on sites.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.121-130
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• 2007

in a software development, the design or architecture prior to implementing the software is essential for the success. This paper presents a case that we successfully designed a software architecture of radiation monitoring system (RMS) for HANARO research reactor currently operating in KAERI by applying the quality attribute-driven design method which is modified from the attribute-driven design (ADD) introduced by Bass[1]. The quality attribute-driven design method consists of following procedures: eliciting functionality and quality requirements of system as architecture drivers, selecting tactics to satisfy the drivers, determining architectures based on the tactics, and implementing and validating the architectures. The availability, maintainability, and interchangeability were elicited as duality requirements, hot-standby dual servers and weak-coupled modulization were selected as tactics, and client-server structure and object-oriented data processing structure were determined at architectures for the RMS. The architecture was implemented using Adroit which is a commercial off-the-shelf software tool and was validated based on performing the function-oriented testing. We found that the design method in this paper is an efficient method for a project which has constraints such as low budget and short period of development time. The architecture will be reused for the development of other RMS in KAERI. Further works are necessary to quantitatively evaluate the architecture.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.764-769
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• 2003

The paper introduces a development result for displacement measurement system of multiple moving objects based on image processing technique. The image processing method adopts inertia moment theory for obtaining the centroid of the targets and basic processing algorithms of gray, binary, closing, labeling and etc. To get precise displacement measurement in spite of multiple moving targets, a CCD camera with zoom is used and the position of camera is changed by a pan/tilt system. The fiducial marks on the fixed positions are used as the sensing points for the image processing to recognize the position errors in directions of X -Y coordinates. The precise alignment device is pan /tilt of X - Y type and the pan/tilt is controlled by DC servomotors which are driven by 80c196kc microprocessor based controller. The centers of the fiducial marks are obtained by a inertia moment method. By applying the developed precise position control system for multiple targets, the displacement of multiple moving targets are detected automatically and are stored in the database system in a real time. By using database system and internet, displacement data can be confirmed at a great distance and analyzed. The developed system shows the effectiveness such that it realizes the precision about 0.12mm in the position control of X -Y coordinates.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.3
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• pp.216-224
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• 2011

In This study develops a quasi-three dimensional numerical model of wave driven coastal currents with accounting the effects of the wave-current interaction and the surface rollers. In the wave model, the current effects on wave breaking and energy dissipation are taken into account as well as the wave diffraction effect. The surface roller associated with wave breaking was modeled based on a modification of the equations by Dally and Brown (1995) and Larson and Kraus (2002). Furthermore, the quasi-three dimensional model, which based on Navier-Stokes equations, was modified in association with the surface roller effect, and solved using frictional step method. The model was validated by data sets obtained during experiments on the Large Scale Sediment Transport Facility (LSTF) basin and the Hazaki Oceanographical Research Station (HORS). Then, a model test against detached breakwater was carried out to investigate the performance of the model around coastal structures. Finally, the model was applied to Akasaki port to verify the hydrodynamics around coastal structures. Good agreements between computations and measurements were obtained with regard to the cross-shore variation in waves and currents in nearshore and surf zone.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.1-8
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• 2012

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.