• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.264-270
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• 2011

A new method for creating a logical data model is proposed. The logical data model developed by the method defines table, primary key, foreign key, and fields. The framework of the logical data model is constructed by modeling the relationships between events and their related entity types. The proposed method consists of a series of objective and quantitative decisions such as maximum cardinality of relationships and functional dependency between the primary key and attributes. Even beginners to database design can use the methology as long as they understand such basic concepts about relational databases as primary key, foreign key, relationship cardinality, parent-child relationship, and functional dependency. The simple and systematic approach minimizes decision errors made by a database designer. In practial database design the method creates a logical data model in Boyce-Codd normal form unless the user of the method makes a critical decision error, which is very unlikely.

• Geomechanics and Engineering
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• v.29 no.2
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• pp.171-182
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• 2022

Reusing the used pile has not yet been implemented due to the unpredictability of the bearing capacity evolution. This paper presents an analytic approach to estimate the sides shear setup after the dissipation of pore pressure. Long-term evolution of adjacent soil is simulated by viscoelastic-plastic constitutive model. Then, an innovative concept of quasi-overconsolidation is proposed to estimate the strength changes of surrounding soil. Total stress method (α method) is employed to evaluate the long term bearing capacity. Measured data of test piles in Louisiana and semi-logarithmic time function are cited to validate the effectiveness of the presented method. Comparisons illustrate that the presented approach gives a reasonably prediction of the side shear setup. Both the presented method and experiment show the shaft resistance increase by 30%-50%, and this highlight the potential benefit of piles reutilization.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.359-369
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• 2024

Data-driven model for estimating fuel oil consumption in seaways is suggested using the model test results and operation data. The data-driven model requires the forecasted wind speed, direction, and the desired ship speed as inputs to predict the engine speed, power, and fuel oil consumption. The structure of the data-driven model is based on the deterministic model of added power of a similar vessel of which model test results in the calm water and head seas are accessible. For a given wind speed, the wind resistance and added resistance in irregular waves presented by Pierson-Moskowitz spectrum are computed to be applied to the propulsion performance prediction. The deterministic model takes a cubic approximation between the wind speed and engine speed. The model is tuned by actual operation data in seaways with various wind direction groups. The shaft power and fuel oil consumption estimation are added to the model to be validated with the operation data. The prediction model is validated with the operation data to confirm that the estimation of the engine speed is the most significant uncertainty source.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Smart Structures and Systems
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• v.22 no.2
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.181-184
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• 2008

The quality of orthoimages mainly depends on the elevation information and exterior orientation (EO) parameters. Since LiDAR data directly provides the elevation information over the earth's surface including buildings and trees, the concept of true orthorectification has been rapidly developed and implemented. If a LiDAR-driven digital surface model (DSM) is used for orthorectification, the displacements caused by trees and buildings are effectively removed when compared with the conventional orthoimages processed with a digital elevation model (DEM). This study sequentially utilized LiDAR data to generate orthorectified digital aerial images. Experimental orthoimages were produced using DTM and DSM. For the preparation of orthorectification, EO components, one of the inputs for orthorectification, were adjusted with the ground control points (GCPs) collected from the LiDAR point data, and the ground points were extracted by a filtering method. The orthoimage generated by DSM corresponded more closely to non-ground LiDAR points than the orthoimage produced by DTM.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.159-163
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• 2006

In this paper a data-driven method to improve the performance of the Jacobian adaptation (JA) for the noisy speech recognition is proposed. In stead of constructing the reference HMM by using the model composition method like the parallel model combination (PMC), we propose to train the reference HMM directly with the noisy speech. This was motivated from the idea that the directly trained reference HMM will model the acoustical variations due to the noise better than the composite HMM. For the estimation of the Jacobian matrices, the Baum-Welch algorithm is employed during the training. The recognition experiments have been done to show the improved performance of the proposed method over the Jacobian adaptation as well as other model compensation methods.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1559-1566
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• 2017

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

• 전기의세계
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• v.21 no.2
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• pp.9-19
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• 1972

The transient characteristics of separately excited d-c motor driven by thyristor d-c chopper is studied in this paper. The armature controlled system is applied. As a result of theoretrical analysis the following conculsions were drawn: (1) For the transient analysis, it is recognized that the state transition analysis is a more general method and powerful tool than the state equation method or signal flow graph method, although it includes iterative matrix calculations. And the system is dealt with a finite width sampled-data system in the state transition analysis. (2) The transient characteristics of the motor angular velocity and its torque to the sampling duration variation are compared with those due to the amplitude variation of d-c chopper voltage as follows. The attenuation rate of the transient characteristics is equal in both cases, but the initial value of the transient characteristics in former case is greater than in latter case. (3) The roots of characteristics equation of the system lie inside the unit circle of the Z-plane. Therefor the system is stable. Further it is found that as the sampling duration is decreased the relative stability is lessened.

• Wind and Structures
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• v.5 no.5
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• pp.441-462
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• 2002

This paper presents a practical numerical method to determine both the spatial and temporal distribution of driving rain on buildings. It is based on an existing numerical simulation technique and uses the building geometry and climatic data at the building site as input. The method is applied to determine the 3D spatial and temporal distribution of wind-driven rain on the facade a low-rise building of complex geometry. Distinct wetting patterns are found. The important causes giving rise to these particular patterns are identified : (1) sweeping of raindrops towards vertical building edges, (2) sweeping of raindrops towards top edges, (3) shelter effect by various roof overhang configurations. The comparison of the numerical results with full-scale measurements in both space and time for a number of on site recorded rain events shows the numerical method to yield accurate results.