• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.66-75
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• 2020

Drilling operation is the most important and costly essential work in oil and gas exploration and development. Therefore, the studies about rate of penetration have been carried out continuously to improve drilling efficiency. In recent years, data-driven models have been developed by various researchers to overcome disadvantages of traditional mathematical models. For the data-driven models, selecting proper algorithms and parameters is very important. In addition, data-driven models should be retrained in real-time during continuous drilling operations in order to improve the model performance. In this paper, the latest studies are investigated to provide information about algorithms, drilling parameters and model retraining intervals that used in drilling optimization.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2016-2021
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• 2003

This paper deals with a control technique of eliminating the transient vibration with respect to a waist axis of an articulated robot. This control technique is based on a model-based control in order to establish the damping effect on the driven mechanical part. The control model is composed of reduced-order electrical and mechanical parts related to the velocity control loop. The parameters of the control model can be obtained from design data or experimental data. This model estimates a load speed converted to the motor shaft. The difference between the estimated load speed and the motor speed is calculated dynamically, and it is added to the velocity command to suppress the transient vibration. This control method is applied to an articulated robot regarded as a time-invariant system. The effectiveness of the model-based control integrated into the position control loop is verified by simulations. Simulations show satisfactory control results to reduce the transient vibration at the end-effector.

• Journal of computational fluids engineering
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• v.8 no.2
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• pp.8-15
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• 2003

Three nonlinear κ-ε models with the wall function method are applied to the fully developed turbulent flow in a triangular subchannel of a bare rod bundle. Typical predicted quantities such as axial and secondary velocities, turbulent kinetic energy and wall shear stress are compared in details both qualitatively and quantitatively with both each other and experimental data. The nonlinear κ-ε models by Speziale[1] and Myong and Kasagi[2] are found to be capable of predicting accurately noncircular duct flows involving turbulence-driven secondary motion. The nonlinear κ-ε model by Shih et aL.[3] adopted in a commercial code is found to be unable to predict accurately noncircular flows with the prediction level of secondary flows one order less than that of the experiment.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.57.2-57.2
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• 2014

Protostellar jets and outflows are signatures of star formation and promising mechanisms for driving supersonic turbulence in molecular clouds. We quantify outflow-driven turbulence through three-dimensional numerical simulations using an isothermal version of the total variation diminishing code. We drive turbulence in real space using a simplified spherical outflow model, analyze the data through density probability distribution functions (PDFs), and investigate density and velocity power spectra. The real-space turbulence-driving method produces a negatively skewed density PDF possessing an enhanced tail on the low-density side. It deviates from the log-normal distributions typically obtained from Fourier-space turbulence driving at low densities, but can provide a good fit at high densities, particularly in terms of mass-weighted rather than volume-weighted density PDF. We find shallow density power-spectra of -1.2. It is attributed to spherical shocks of outflows themselves or shocks formed by the interaction of outflows. The total velocity power-spectrum is found to be -2.0, representative of the shock dominated Burger's turbulence model. Our density weighted velocity power spectrum is measured as -1.6, slightly less that the Kolmogorov scaling values found in previous works.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.475-478
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• 2015

The objective of this study is to apply a hybrid model for estimating solar radiation and investigate their accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series into approximation and detail components. These decomposed time series are then used as inputs of support vector machines (SVMs) modules in the WSVMs model. Results obtained indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.190-196
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• 2020

Mechanical systems using tendon-driven actuators have been widely used for bionic robot arms because not only the tendon based actuating system enables the design of robot arm to be very efficient, but also the system is very similar to the mechanism of the human body's operation. The tendon-driven actuator, however, has a drawback caused by the friction force of the sheath. Controlling the system without considering the friction force between the sheath and the tendon could result in a failure to achieve the desired dynamic behaviors. In this study, a mathematical model was introduced to determine the friction force that is changed according to the geometrical pathway of the tendon-sheath, and the model parameters for the friction model were estimated by analyzing the data obtained from dedicated tests designed for evaluating the friction forces. Based on the results, it is possible to appropriately predict the friction force by using the information on the pathway of the tendon.

• Journal of computational fluids engineering
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• v.14 no.3
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• pp.115-122
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• 2009

Numerical methods are applied to simulate the smoke behavior in a ventilated tunnel using large eddy simulation (LES) which is incorporated in FDS (Fire Dynamics Simulator) with proper combustion and radiation model. In this study, present numerical results are compared with data obtained from experiments on pool fires in a ventilated tunnel. The model tunnel is $182m(L){\times}5.4m(W){\times}2.4m(H)$. Two fire scenarios with different ventilation rates are considered with two different fire strengths. The present results are analyzed with those from LES without combustion and radiation model and from RANS ($\kappa-\epsilon$) model as well. Temperature distributions caused by fire in tunnel are compared with each other. It is found that thermal stratification and smoke back-layer can be predicted by FDS and the temperature predictions by FDS show better results than LES without combustion and radiation model. The FDS solver, however, failed to predict correct flow pattern when the high ventilation rate is considered in tunnel because of the defects in the tunnel-inlet turbulence and the near-wall turbulence.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.517-527
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• 2021

While the two principles of lean manufacturing and time-driven activity-based costing (TDABC) have been established out of multiple incentives and do not follow the same particular targets, there is substantial commonality between them. In these conditions, the supply management of a multi-product system needs a rigorous production model to minimize costs. In this sense, this paper proposes an interactive model with the consideration of optimizing product-mix decisions using both lean development tools and TDABC. This paper proposes a qualitative approach using the case study of the Iraqi state company for battery production. The suggested model decreased manufacturing time and costs, along with some substantial reduction in idle production capacity by 26 percent in 2019, based on the findings of the case study. On the other hand, the proposed model gives two side advantages: an efficient division of costs on goods due to the use of time spent as a cost factor for products and cost savings due to the introduction of the lean manufacturing approach that reduces all additional costs and increases product-mix decisions. Furthermore, the analytical data gathered here suggests that the incorporation of lean management concepts and TDABC has a strong and important influence on product-mix decisions.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.