• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.446-452
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• 2016

The aircraft has to be considered for safety very importantly because of peculiarity of flight in the air, so it should be retained through proper inspection and maintenance not only in production phase but also in operating phase. Recently, it is using the latest technology as engineering approach not depending on human factor to determine on maintenance needs, and domestic production rotary aircraft also has the health & usage monitoring system to measure and to monitor major components. However, continued vibration exceedance phenomenon occurred in production and operation phase because of inappropriate thresholds, and it confirmed as false alarm which is not necessary to repair. In this paper, it is described that operational concept of HUMS, and especially it contains a study result for efficiency of aircraft operation and ultimately the improvement of flight safety by optimizing HUMS thresholds to determine efficiently necessity of maintenance under limited conditions and by establishing inspection/maintenance procedures when the re-designated thresholds exceedance occurred.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.579-584
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• 2017

The damper oil seal of a high-speed railway vehicle is made from nitrile butadiene rubber (NBR) in order to prevent lubricant from leaking into the damper and to stop harmful contaminants from entering the external environment while in service. Oil leakage through the seal primarily occurs from fatigue failure of the damper. Cumulative damage of the seal occurs due to the contact force between the rod and the rubber during movement due to track irregularities and cants, among other factors. Thus, the design of the oil seal should minimize the maximum principal strain at weak points. In this study, the optimal cross section of the damper oil seal was found using the multi-island genetic algorithm method to improve the durability of the damper. The optimal shape of the oil seal was derived using process automation and design optimization software. Nonlinear material properties for finite element analysis (FEA) of the rubber were determined by Marlow's model. The nonlinear FEA confirmed that the maximum principal strain at the oil leakage point was decreased 24% between the initial design and the optimum design.

• Journal of Institute of Convergence Technology
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• v.6 no.2
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• pp.35-40
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• 2016

Ignitor tip is a component of burner to start the burning process in power plant. This is used to ignite the coal to a constant operating state by fuel mixed with air and kerosene. This component is composed of three components so that air and kerosene are mixed in the proper ratio and injected uniformly. Because the parts with the designed shape are manufactured in the machining process, they have to be made of three parts. These parts are designed to have various functions in each part. The mixing part mixes the supplied air and kerosene through the six holes and sends it to the injecting part at the proper ratio. The inject part injects mixed fuel, which is led to have a constant rotational direction in the connecting part, to the burner. And the connecting plate that the mixed fuel could rotate and spray is assembled so that the flame can be injected uniformly. But this part causes problems that are worn by vibration and rotation because it is mechanically assembled between the mixing part and the inject part. In this study, 3D printing method is used to integrate a connecting plate and an inject part to solve this wear problem. The 3D printing method could make this integrated part because the process is carried out layer by layer using a metal powder material. The part manufactured by 3D printing process should perform the post process such as support removal and surface treatment. However, while performing the 3D printing process, the material properties of the metal powders are changed by the laser sintering process. This change in material properties makes the post process difficult. In consideration of these variables, we have studied the optimization of manufacturing process using 3D printing method.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.174-182
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• 2002

The object-oriented design methods and their CASE tools are widely used in practice by many real-time software developers. However, object-oriented CASE tools require an additional step of identifying tasks from a given design model. Unfortunately, it is difficult to automate this step for a couple of reasons: (1) there are inherent discrepancies between objects and tasks; and (2) it is hard to derive tasks while maximizing real-time schedulability since this problem makes a non-trivial optimization problem. As a result, in practical object-oriented CASE tools, task identification is usually performed in an ad-hoc manner using hints provided by human designers. In this paper, we present a systematic, schedulability-aware approach that can help mapping real-time object-oriented models to multi-threaded implementations. In our approach, a task contains a group of mutually exclusive transactions that may possess different periods and deadline. For this new task model, we provide a new schedulability analysis algorithm. We also show how the run-time system is implemented and how executable code is generated in our frame work. We have performed a case study. It shows the difficulty of task derivation problem and the utility of the automated synthesis of implementations as well as the Inappropriateness of the single-threaded implementations.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.5
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• pp.364-370
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• 2009

With time, the stable management of turbidity is becoming more important in the water treatment process. So optimization of flocculation is important for the improvement of the sedimentation efficiency. we evaluated the hydrodynamic behavior in the rotation direction (clock-wise, counterclock-wise) of the flocculator in the flocculation basin using Computational Fluid Dynamics (CFD). The results of the CFD simulation, in cases where flocculators rotate in a clockwise direction, a stronger flow is formed near the surface of the water where the rotating direction and current of flow correspond. The variance and standard deviation of the flux are about 8.5 and 2.9 respectively. In contrast, in the case of a counterclockwise direction, a stronger flow is formed near the bottom of the basin. The variance and standard deviation of the flux are about 5.3 and 2.3, respectively. The effluent flux is affected more by the third flocculator spin than the first and second flocculator spins. The third flocculator spinning in the counterclockwise direction is better for the uniform flow of the sedimentation basin than the third flocculator spinning in the clockwise direction

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• Journal of Korea Water Resources Association
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• v.41 no.4
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• pp.413-422
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• 2008

It is essential to locally adjust the Hargreaves parameter for estimating reference evapotranspiration with short data as a substitute of Penman-Monteith equation. In this study, evaluation of daily-based reference evapotranspiration is computed with Hargreaves equation. in Gyeonggi bay area including Ganghwa, Incheon, Suwon, Seosan, and Cheonan station for the time period of 1997-2004. Hargreaves coefficient is adjusted to give the best fit with Penman-Monteith evapotranspiration, being regarded as a reference. Then, the preferred parameters are validated for the same stations for the time period of 2005-2006. The optimization-based correction in calibration for 1997-2004 shows improved performance of the Hargreaves equation, giving 0.68-0.77 to 0.92-0.98 in Nash-Sutcliffe coefficient of efficiency (NSC) and 14.63-23.30 to 5.23-11.75 in RMSE. The validation for 2005-2006 shows improved performance of the Hargreaves equation, giving 0.43-0.85 to 0.93-0.97 in NSC and 14.43-26.81 to 6.48-9.09 in RMSE.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1105-1116
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• 2014

A stochastic rainfall model, NSRPM (Neyman-Scott Rectangular Pulse Model), is able to reflect the cluster characteristics of rainfall events which is unable in the RPM (Rectangular Pulse Model). Therefore NSRPM has advantage in the hydrological applications. The NSRPM consists of five model parameters and the parameters are estimated using optimization techniques such as DFP (Davidon-Fletcher-Powell) method and genetic algorithm. However the DFP method is very sensitive in initial values and is easily converge to local minimum. Also genetic algorithm has disadvantage of long computation time. Nelder-Mead method has several advantages of short computation time and no need of a proper initial value. In this study, the applicability of parameter estimation methods was evaluated using rainfall data of 59 national rainfall networks from 1973-2011. Overall results demonstrated that accuracy in parameter estimation is in the order of Nelder-Mead method, genetic algorithm, and DFP method.