• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Management Science and Financial Engineering
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• v.15 no.1
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• pp.51-69
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• 2009

A network model and a Genetic Algorithm (GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing a non-linear objective function with the linear constraints. In the model, the flow-conservation constraints are utilized to restrict the solution space and to force the link flows become consistent to the traffic counts. The objective of the model is to minimize the discrepancies between two sets of link flows. One is the set of link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links. The other is the set of link flows those are estimated through the trip distribution and traffic assignment using the path flow estimator in the logit-based SUE. In the proposed GA, a chromosome is defined as a real vector representing a set of Origin-Destination Matrix (ODM), link flows and route-choice dispersion coefficient. Each chromosome is evaluated by the corresponding discrepancies. The population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment technique is used during the crossover and mutation.

• The KSFM Journal of Fluid Machinery
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• v.10 no.5
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• pp.9-19
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• 2007

Fluid temperature fluctuations in a mixing tee pipe were numerically analyzed by LES model in order to clarify internal turbulent flows and to develope an evaluation method for high-cycle thermal fatigue. Hot and cold water with an temperature difference $40^{\circ}C$ were supplied to the mixing tee. Fluid temperature fluctuations in a mixing tee pipe is analysed by using the computational fluid dynamics code, FLUENT, Temperature fluctuations of the fluid and pipe wall measured as the velocity ratio of the flow in the branch pipe to that in the main pipe was varied from 0.05 to 5.0. The power spectrum method was used to evaluate the heat transfer coefficient. The fluid temperature characteristics were dependent on the velocity ratio, rather than the absolute value of the flow velocity. Large fluid temperature fluctuations were occurred near the mixing tee, and the fluctuation temperature frequency was random. The ratios of the measured heat transfer coefficient to that evaluated by Dittus-Boelter's empirical equation were independent of the velocity ratio, The multiplier ratios were about from 4 to 6.

• Steel and Composite Structures
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• v.39 no.3
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• pp.275-290
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• 2021

The main purpose of this research work is to investigate the critical buckling load of functionally graded (FG) porous plates with graphene platelets (GPLs) reinforcement using generalized differential quadrature (GDQ) method at thermal condition. It is supposed that the GPL nanofillers and the porosity coefficient vary continuously along the plate thickness direction. Generally, the thermal distribution is considered to be nonlinear and the temperature changing continuously through the thickness of the nanocomposite plates according to the power-law distribution. To model closed cell FG porous material reinforced with GPLs, Halpin-Tsai micromechanical modeling in conjunction with Gaussian-Random field scheme are used, through which mechanical properties of the structures can be extracted. Based on the third order shear deformation theory (TSDT) and the Hamilton's principle, the equations of motion are established and solved for various boundary conditions (B.Cs). The fast rate of convergence and accuracy of the method are investigated through the different solved examples and validity of the present study is evaluated by comparing its numerical results with those available in the literature. A special attention is drawn to the role of GPLs weight fraction, GPLs patterns through the thickness, porosity coefficient and distribution of porosity on critical buckling load. Results reveal that the importance of thermal condition on of the critical load of FGP-GPL reinforced nanocomposite plates.

• The Journal of Engineering Geology
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• v.21 no.3
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• pp.247-257
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• 2011

In this study the infinite slope model, one of the physical landslide models has been suggested to evaluate the susceptibility of the landslide. However, applying the infinite slope model in regional study area can be difficult or impossible because of the difficulties in obtaining and processing of large spatial data sets. With limited site investigation data, uncertainties were inevitably involved with. Therefore, the probabilistic analysis method such as Monte Carlo simulation and the GIS based infinite slope stability model have been used to evaluate the probability of failure. The proposed approach has been applied to practical example. The study area in Boeun area been selected since the area has been experienced tremendous amount of landslide occurrence. The geometric characteristics of the slope and the mechanical properties of soils like to friction angle and cohesion were obtained. In addition, coefficient of variation (COV) values in the uncertain parameters were varied from 10% to 30% in order to evaluate the effect of the uncertainty. The analysis results showed that the probabilistic analysis method can reduce the effect of uncertainty involved in input parameters.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.323-329
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• 2002

This paper proposes a frame reliability weighting method to compensate for a time-selective noise that occurs at random positions of speech signal contaminating certain parts of the speech signal. Speech frames have different degrees of reliability and the reliability is proportional to SNR (signal-to noise ratio). While it is feasible to estimate frame Sl? by using the noise information from non-speech interval under a stationary noisy situation, it is difficult to obtain noise spectrum for a time-selective noise. Therefore, we used statistical models of clean speech for the estimation of the frame reliability. The proposed MFR (model-based frame reliability) approximates frame SNR values using filterbank energy vectors that are obtained by the inverse transformation of input MFCC (mal-frequency cepstral coefficient) vectors and mean vectors of a reference model. Experiments on various burnt noises revealed that the proposed method could represent the frame reliability effectively. We could improve the recognition performance by using MFR values as weighting factors at the likelihood calculation step.

• Journal of Korean Society of Rural Planning
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• v.18 no.3
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• pp.149-164
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• 2012

The purpose of the present study is to construct a rural development strategy from the nexus between spatial changes in specialized crops and suitable cultivation area of the crops. This paper pays particular attention to identify product life cycle of specialized crops in rural areas and estimate the impact of climate change on alterations in spatial distribution of the crops. In order to do so, first of all, this study applies multi-level model (Random coefficient model) to estimate the regional coefficient of five orchard crops. It utilizes the data 1995 to 2010 Korea Agricultural Census. Futhermore, it also adopts overlay analysis by ArcGIS to identify the development path of the crops and the relationship with climate change. Based on the results, it suggests a mechanism activating regional agriculture. The findings propose re-searching and relocating specialized regions of the crops. Especially, it proves each rural area can drive the new agricultural strategy to strengthen regional agriculture by estimating the relationship between development of specialized crops and suitable cultivation areas. For instance, shifting specialized crops in particular regions and enriching genetic or species varieties can be primary measures and it will contribute to improve the reliable base for income sources in the rural communities. This paper also offers specific policy implications regarding rural development plans in response to crops' life cycle and climate changes.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.449-463
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• 2015

Video object tracking is important for variety of applications, such as security, video indexing and retrieval, video surveillance, communication, and compression. This paper proposes an object tracking method in HEVC bitstreams. Without pixel reconstruction, motion vector (MV) and size of prediction unit in the bitstream are employed in an Spatio-Temporal Markov Random Fields (ST-MRF) model which represents the spatial and temporal aspects of the object's motion. Coefficient-based object shape adjustment is proposed to solve the over-segmentation and the error propagation problems caused in other methods. In the experimental results, the proposed method provides on average precision of 86.4%, recall of 79.8% and F-measure of 81.1%. The proposed method achieves an F-measure improvement of up to 9% for over-segmented results in the other method even though it provides only average F-measure improvement of 0.2% with respect to the other method. The total processing time is 5.4ms per frame, allowing the algorithm to be applied in real-time applications.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.327-339
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• 2021

This study examines the impact of bank restructuring on the financial performance of commercial banks in Vietnam. The data for this study was obtained from the audited financial statements of 30 Vietnamese commercial banks from 2007 to 2019. Multiple regression analysis was used for investigation. Financial performance, as evaluated by ROAA, ROEA, and NIM, is the dependent variable. Financial restructuring, ownership restructuring, and operational restructuring are the independent variables. Pooled least squares (Pooled OLS), fixed effects model (FEM), random effects model (REM), and system generalized moment regression model (System GMM) are the estimate methods used to increase the accuracy of the regression coefficient. The research results show that the variables of financial restructuring activities such as government intervention and the ratio of equity to total assets; variables of ownership restructuring such as capital adequacy ratio, privatization of state-owned commercial banks, mergers, and acquisitions; variables of operational restructuring such as employees, branches, the cost to total assets; GDP variables and the second restructuring period have a positive impact on financial performance. Variables such as debt-to-capital ratio, bad debt ratio, state ownership ratio, expense-income ratio, and inflation have a negative effect on financial performance.