• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.12
• /
• pp.154-161
• /
• 2009

A fuel cell power plant is a very complex system which has various control loops with some non-linearity. For control of a fuel cell power plant, dynamic models of fuel cell stacks have been developed and simplified process flow diagrams of a fuel cell power plant has been presented. Using such a model of a Molten Carbonate Fuel Cell (MCFC) power plant, this paper deals with development of an intelligent setpoint reference governor (I-SRG) to find the optimal setpoints and feed forward control inputs for the plant power demand. The I-SRG is implemented with neural network by using Particle Swarm Optimization (PSO) algorithm based on system constraints and performance objectives. The feasibility of the I-SRG is shown through simulation of an MCFC power plant for tracking control of its power demand.

• Nuclear Engineering and Technology
• /
• v.53 no.7
• /
• pp.2377-2386
• /
• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• Earthquakes and Structures
• /
• v.17 no.2
• /
• pp.143-162
• /
• 2019

Motivated by a simpler and more compact hybrid active tuned mass damper (ATMD) system with wide frequency spacing (i.e., high robustness) but not reducing the effectiveness using the least number of ATMD units, the active tuned tandem mass dampers (ATTMD) have been proposed to attenuate undesirable oscillations of structures under the ground acceleration. Likewise, it is expected that the frequency spacing of the ATTMD is comparable to that of the active multiple tuned mass dampers (AMTMD) or the multiple tuned mass dampers (MTMD). In accordance with the mode generalised system in the specific vibration mode being controlled (simply referred herein to as the structure), the closed-form expression of the dimensionless displacement variances has been derived for the structure with the attached ATTMD. The criterion for the optimum searching may then be determined as minimization of the dimensionless displacement variances. Employing the gradient-based optimization technique, the effects of varying key parameters on the performance of the ATTMD have been scrutinized in order to probe into its superiority. Meanwhile, for the purpose of a systematic comparison, the optimum results of two active tuned mass dampers (two ATMDs), two tuned mass dampers (two TMDs) without the linking damper, and the TTMD are included into consideration. Subsequent to work in the frequency domain, a real-time Simulink implementation of dynamic analysis of the structure with the ATTMD under earthquakes is carried out to verify the findings of effectiveness and stroke in the frequency domain. Results clearly show that the findings in the time domain support the ones in the frequency domain. The whole work demonstrates that ATTMD outperforms two ATMDs, two TMDs, and TTMD. Thereinto, a wide frequency spacing feature of the ATTMD is its highlight, thus deeming it a high robustness control device. Furthermore, the ATTMD system only needs the linking dashpot, thus embodying its simplicity.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.807-817
• /
• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of Microbiology and Biotechnology
• /
• v.29 no.9
• /
• pp.1470-1477
• /
• 2019

${\beta}_2$-adrenergic receptor (${\beta}_2-AR$) was expressed efficiently using Bac-to-Bac Baculovirus Expression System in Sf9 cells as a bio-recognition element for multianalyte screening of ${\beta}$-agonist residues in pork. Sf9 cells were selected as the expression system, and codon optimization of wild-type nucleic acid sequence and time-dependent screening of expression conditions were then carried out for enhancing expression level and biological activity. Under optimum conditions of multiplicity of infection (MOI) = 5 and 48 h post transfection, the protein yield was up to 1.23 mg/ml. After purification by chromatographic techniques, the purified recombinant protein was applied to develop a direct competitive enzyme-linked receptor assay (ELRA) and the efficiency and reliability of the assay was determined. The IC50 values of clenbuterol, salbutamol, and ractopamine were 28.36, 50.70, and $59.57{\mu}g/l$, and clenbuterol showed 47.61% and 55.94% cross-reactivities with ractopamine and salbutamol, respectively. The limit of detection (LOD) was $3.2{\mu}g/l$ and the relevant recoveries in pork samples were in the range of 73.0-91.2%, 69.4-84.6%, and 63.7-80.2%, respectively. The results showed that it had better performance compared with other present nonradioactive receptorbased assays, indicating that the genetically modified ${\beta}_2-AR$ would have great application potential in detection of ${\beta}$-agonist residues.

• Journal of Electrochemical Science and Technology
• /
• v.12 no.1
• /
• pp.82-91
• /
• 2021

This study was conducted to investigate the effect of initial pH, current density, and electrolysis time on process performance in terms of decolorization and chemical oxygen demand (COD) removal from disperse dyebath wastewater (DDW) by mono-polar parallel laboratory scale electrocoagulation (EC) process. COD reduction of 51.3% and decolorization of 92.8% were obtained with operating cost of 0.19 €/㎥ treated wastewater for Al-Al electrode pair, while 90.5% of decolorization and 49.2% of COD reduction were obtained with operating cost of 0.20 €/㎥ treated wastewater for an Fe-Fe electrode pair. The amount of sludge production were highly related to type of the electrode materials. At the optimum conditions, the amount of sludge produced were 0.18 kg/㎥ and 0.28 kg/㎥ for Al-Al and Fe-Fe electrode pairs, respectively. High decolorization can be explained by the hydrophobic nature of the disperse dye, while limited COD removal was observed due to the high dissolved organic matter of the DDW based on auxiliary chemicals. Energy, electrode, and chemical consumptions and sludge handling were considered as major cost items to find a cost-effective and sustainable solution for EC. The contribution of each cost items on operating cost were determined as 10.0%, 51.1%, 30.5% and 8.4% for Al-Al, and they were also determined as 9.0%, 38.0%, 40.5% and 12.5% for Fe-Fe, respectively. COD reduction and decolorization were fitted to first-order kinetic rule.

• Structural Engineering and Mechanics
• /
• v.84 no.1
• /
• pp.17-33
• /
• 2022

An experimental study is carried out to investigate the performance of the cutting tool regarding the insert wear, surface roughness, cutting forces, cutting power and material removal rate of three coated carbides GC2015 (TiCN-Al₂O₃-TiN), GC4215 (Al₂O₃-Ti(C,N)) and GC1015 (TiN) during the dry turning of AISI4140 steel. For this purpose, a Taguchi design (L₉) was adopted for the planning of the experiments, the effects of cutting parameters on the surface roughness (R_a), tangential cutting force (F_z), the cutting power (P_c) and the material removal rate (MRR) were studied using analysis of variance (ANOVA), the response surface methodology (RSM) was used for mathematical modeling, with which linear mathematical models were developed for forecasting of R_a, F_z, P_c and MRR as a function of cutting parameters (V_c, f, and a_p). Then, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented for multi-objective optimization which allows manufacturers to enhance the production performances of the machined parts. Furthermore, in order to characterize and quantify the flank wear of the tested tools, some machining experiments were performed for 5 minutes of turning under a depth of 0.5 mm, a feed rate of 0.08 mm/rev, and a cutting speed of 350 m/min. The wear results led to a ratio (V_B-GC4215/V_B-GC2015) of 2.03 and (V_B-GC1015/V_B-GC2015) of 4.43, thus demonstrating the efficiency of the cutting insert GC2015. Moreover, SEM analysis shows the main wear mechanisms represented by abrasion, adhesion and chipping.

• Geomechanics and Engineering
• /
• v.31 no.6
• /
• pp.545-556
• /
• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Steel and Composite Structures
• /
• v.52 no.2
• /
• pp.145-163
• /
• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Journal of the Society of Naval Architects of Korea
• /
• v.29 no.1
• /
• pp.1-13
• /
• 1992

The objective of this study is to develop knowledge-based system for the preliminary design and midship section design of bulk carrier and to enhance the applicability of knowledge engineering in the field of Naval Architecture. First, expert system shell called E.1 is developed in C language. E.1 supports backward-chaining, automatic iteration procedure and reiterative inference mechanism for efficient application of knowledge-based system in structural design. Knowledge representation in E.1 includes IF-THEN rules, 'facts'and 'tables'. Second, knowledge bases for the principal particulars and midship section design are developed by experimental formula, design standard and experiential knowlege. Third, hybrid system combined this knowledge-based system with the optimization program of midship section is developed. Finally, the simplified design method utilizing the regression analysis of the optimum results of stiffened plate is developed for facilitating the design process. Using this knowledge-based system, the design process and results for Bulk carrier and stiffened plates are discussed. It is concluded that knowledge-based system is efficient for preliminary design and midship section design of the ship. It is expected that the performance of the CAD system would be enhanced if the better knowledge-base is accumulated in the E.1 tool.