• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1032-1038
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• 2021

Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.11-18
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• 2023

In this paper, we evaluate deep learning time series forecasting models. Recent studies show that those models perform better than the traditional prediction model such as ARIMA. Among them, recurrent neural networks to store previous information in the hidden layer are one of the prediction models. In order to solve the gradient vanishing problem in the network, LSTM is used with small memory inside the recurrent neural network along with BI-LSTM in which the hidden layer is added in the reverse direction of the data flow. In this paper, we compared the performance of Informer by comparing with other models (LSTM, BI-LSTM, and Transformer) for real Nitrogen dioxide (NO2) data. In order to evaluate the accuracy of each method, mean square root error and mean absolute error between the real value and the predicted value were obtained. Consequently, Informer has improved prediction accuracy compared with other methods.

• Smart Structures and Systems
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• v.32 no.5
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• pp.281-295
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• 2023

The purpose of this study is to demonstrate and verify the application of phase-control absolute-acceleration-feedback active tuned mass dampers (PCA-ATMD) to multiple-degree-of-freedom (MDOF) building structures. In addition, servo speed control technique has been developed as a replacement for force control in order to mitigate the negative effects caused by friction and inertia. The essence of the proposed PCA-ATMD is to achieve a 90° phase lag for a structure by implementing the desired control force so that the PCA-ATMD can receive the maximum power flow with which to effectively mitigate the structural vibration. An MDOF building structure with a PCA-ATMD and a real-time filter forming a complete system is modeled using a state-space representation and is presented in detail. The feedback measurement for the phase control algorithm of the MDOF structure is compact, with only the absolute acceleration of one structural floor and ATMD's velocity relative to the structure required. A discrete-time direct output-feedback optimization method is introduced to the PCA-ATMD to ensure that the control system is optimized and stable. Numerical simulation and shaking table experiments are conducted on a three-story steel shear building structure to verify the performance of the PCA-ATMD. The results indicate that the absolute acceleration of the structure is well suppressed whether considering peak or root-mean-square responses. The experiment also demonstrates that the control of the PCA-ATMD can be decentralized, so that it is convenient to apply and maintain to real high-rise building structures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.355-369
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• 2018

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.7
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• pp.350-358
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• 2000

Based on the results in the first-part paper, PSS tuning methods including the determination of PSS gain and compensation of phasor lagging are investigated in this second part. In the phase compensation, PSS should compensate phase lagging, resulted from power system-generator-excitation system loop, to provide damping torque in equal phase with the generator speed. Also, PSS gain should be determined to provide maximum damping torque as much as possible without the other modes unstable. In this paper, gain tuning method using the root-locus and the phase compensation of PSS at the one machine with infinite bus system are presented. The differences between PSS tuning at the tuning condition and at the least-stable condition are also discussed.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.473-473
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• 2009

There have been many academic researches on the aerodynamic design of wind turbine based on blade element method (BEM) and momentum theory (MT, or actuating disk theory). However, in the real world, the turbine blade design requires many additional constraints more than theoretical analysis. The standard procedure is studied in the present paper to design new blades for the wind turbine system ranged from the small size from 1 to 10 kW. From the experience of full design of a 10 kW blade, the authors tried to set up a standard procedure for the aerodynamic design based on IEC 61400-2. Wind-turbine scale, rotating speed, and geometrical chord/twist distribution at the segmented span positions are calculated with a suitable BEM/MT code, and the geometrical shape of tip and root should be modified after considering various parameters: wing-tip vortex, aerodynamic noise, turbine efficiency, structural safety, convenience of fabrication, and even economic factor likes price, etc. The evaluated data is passed to the next procedure of structural design, but some of them should still be corresponded with each other: the fluid-structure interaction is one of those problems not yet solved, for example. Consequently, the design procedure of small wind-turbine blades is set up for the mass production of commercial products in this research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5888-5903
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• 2019

The Relational ranking method applies authority-based ranking in relational dataset that can be modeled as graphs considering also their tuples' values. Authority directions from tuples that contain the given keywords and transfer to their corresponding neighboring nodes in accordance with their values and semantic connections. From our previous work, ObjectRank extends to ValueRank that also takes into account the value of tuples in authority transfer flows. In a maked difference from ObjectRank, which only considers authority flows through relationships, it is only valid in the bibliographic databases e.g. DBLP dataset, ValueRank facilitates the estimation of importance for any databases, e.g. trading databases, etc. A relational keyword search paradigm Object Summary (denote as OS) is proposed recently, given a set of keywords, a group of Object Summaries as its query result. An OS is a multilevel-tree data structure, in which node (namely the tuple with keywords) is OS's root node, and the surrounding nodes are the summary of all data on the graph. But, some of these trees have a very large in total number of tuples, size-l OSs are the OS snippets, have also been investigated using ValueRank.We evaluated the real bibliographical dataset and Microsoft business databases to verify of our proposed approach.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.165-180
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• 2015

In this paper lead zirconate titanate transducers (PZT) are employed for damage detection of four reinforced concrete (RC) column specimens retrofitted with carbon fiber reinforced polymer (CFRP) jackets. A major disadvantage of FRP jacketing in RC members is the inability to inspect visually if the concrete substrate is damaged and in such case to estimate the extent of damage. The parameter measured during uniaxial compression tests at random times for known strain values is the real part of the complex number of the Electromechanical Admittance (Conductance) of the sensors, obtained by a PXI platform. The transducers are placed in specific positions along the height of the columns for detecting the damage in different positions and carrying out conclusions for the variation of the Conductance in relation to the position the failure occurred. The quantification of the damage at the concrete substrate is achieved with the use of the root-mean-square-deviation (RMSD) index, which is evaluated for the corresponding strain values. The experimental results provide evidence that PZT transducers are sensitive to damage detection from an early stage of the experiment and that the use of PZT sensors for monitoring and detecting the damage of FRP-retrofitted reinforced concrete members, by using the Electromechanical Admittance (EMA) approach, can be a highly promising method.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.27-34
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• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.