• Journal of Power Electronics
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• v.15 no.4
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• pp.1006-1017
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• 2015

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

• Korean Journal of Metals and Materials
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• v.48 no.9
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• pp.791-797
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• 2010

The creep crack growth rate (da/dt) of the Cr-Mo steels tested by pre-crack and the voltage (or resistance) variables were related into fracture parameter (Ct), crack growth coefficient (H), and an exponent (q) in the parts of Base, weld and HAZ. The fracture parameter (Ct) has various variables relating to the specimen and crack shape, applied stress, and creep strain curve. The H and q was inferred by OLS regression (ordinary least square method), and the H values were solved in statistics and probability assessment, which were attained fromPDF's distributions (probability density function). The HAZ part has the highest value of q by OLS regression and the widest distribution of H by PDF of WEIBULL, which means that the crack sensitivity of HAZ should be cautioned against the creep crack growth and failure.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.11a
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• pp.282-285
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• 1999

The (1-x)MgTiO$_3$-xSrTiO$_3$(x=0,0.1) ceramics were prepared by the conventional mixed oxide method. The structural properties were investigated with sintering temperature and composition ratio by XRD, SEM and DT-TGA. Increasing the sintering temperature from 130$0^{\circ}C$ to 1$600^{\circ}C$, second phase was decreased and grain size was increased. In the case of 0.9 MgTiO$_3$-0.1SrTiO$_3$ ceramics sintered at 130$0^{\circ}C$, dielectric constant, quality factor and temperature coefficient of resonant frequency were 22.61, 10,928(at 1GHz), +50.26ppm/$^{\circ}C$ , respectively.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Progress in Medical Physics
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• v.18 no.4
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• pp.214-220
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• 2007

Diffusion tensor tractorgraphy which is based on line propagation method with brute force approach is implemented and the vector correlation function is proposed in addition to the conventional fractional anisotrophy value as a criterion to select seed points. For the whole tractography, the proposed method used 41 % less seed points than the conventional brute force approach for $FA{\geq}0.3$ and most of the fiber tracks in the outer region of white matter were removed. For the corticospinal tract passing through region of interest, the proposed method has produced similar results with 50% less seed points than conventional one.

• Restorative Dentistry and Endodontics
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• v.26 no.2
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• pp.134-140
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• 2001

The polymerization shrinkage of composite resins is an important drawback although the composites have many advantages-more esthetic and conservative than metallic restoratives etc. The purposes of this research were to develop a new measurement method and to manufacture an instrument that can measure the initial dynamic volumetric shrinkage of composite resins during polymerization. The instrument was basically an electromagnetic balance that constructed with a force transducer using position sensitive photo detector(PSPD) and a negative feedback servo amplifier of proportional-derivative(PD) controller. The volumetric change of composites during polymerization was detected continuously as buoyancy change in distilled water by means of Archimedes's principle. It was converted to continuous electrical voltage signal in real time. The signal was properly conditioned and filtered and then it was stored in computer by a data acquisition(DAQ) board. By using this electronic instrument. the dynamic patterns of the polymerization shrinkage of eight commercial(Z-100, DenFil, AeliteFil, Z-250, P-60, SureFil, Synergy compact, and Tetric ceram) composite resins were measured and compared. The results were as follows. 1. From this project of developing instrument, the ability has been achieved that can acquire and process data of electrical signal transformed from various physical phenomenon by using temperature, displacement. photo. and force transducer. As a consequence, the instrumentation and measurement system used to analyze the physical characteristics of various dental materials in dental research field can be designed, manufactured and implemented in lab. 2. This instrument has some advantages. It was insensible to temperature change and could measure true dynamic volumetric shrinkage in real time without complicated process. It showed accuracy and high precision results with small standard deviation. 3. The polymerization shrinkage of composites was significantly different between brands and ranged from 2.47％ to 3.89％, The order of polymerization shrinkage was as follows, in order of increasing shrinkage, SureFil, P60, Z250, Z100, Synergy compact. DenFil, Tetric ceram, and AeliteFil. 4. The polymerization shrinkage rate per unit time, dVol％/dt, showed that the instrument can provide an indirect research method for polymerization reaction kinetics.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Journal of Chest Surgery
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• v.36 no.4
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• pp.242-254
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• 2003

Most of the studies conducted have investigated the beneficial effects of ischemic preconditioning on normothermic myocardial ischemia. However, the effect of preconditioning could be attenuated through the use of multidose cold cardioplegia as practiced in contemporary clinical heart surgical procedures. The purpose of this study was to investigate whether preconditioning improves postischemic cardiac function in a model of 25℃ moderate hypothermic ischemic heart induced by cold cardioplegia in isolated rat hearts. Material and Method: The isolated Sprague-Dawley rat hearts were randomly assigned to four groups. All hearts were perfused at 37℃ for 20 minutes with Krebs-Henseleit solution before the baseline hemodynamic data were obtained. Group 1 consisted of preconditioned hearts that received 3 minutes of global ischemic preconditioning at 37℃, followed by 5 minutes of reperfusion before 120 minutes of cardioplegic arrest (n=6). Cold (4℃) St. Thomas Hospital cardioplegia solution was infused to induce cardioplegic arrest. Maintaining the heart at 25℃, infusion of the cardioplegia solution was repeated every 20 minutes throughout the 120 minutes of ischemic period. Group 2 consisted of control hearts that underwent no manipulations between the periods of equilibrium and 120 minutes of cardioplegic arrest (n=6). After 2 hours of cardioplegic arrest, Krebs solution was infused and hemodynamic data were obtained for 30 minutes (group 1, 2: cold cardioplegia group). Group 3 received two episodes of ischemic preconditioning before 30 min of 37℃ normothermic ischemia and 30 minutes of reperfusion (n=6). Group 4 served as ischemic controls for group 3 (group 3, 4: warm ischemia group). Result: Preconditioning did not influence parameters such as left ventricular systolic pressure (LVSP), left ventricular end-diastolic pressure (LVEDP), rate-pressure product (RPP) and left ventricular dp/dt (LV dp/dt) in the cold cardioplegia group. (p=NS) However, preconditioning before warm ischemia attenuated the ischemia induced cardiac dysfunction, improving the LVSP, LVEDP, RPP, and LVdp/dt. Less leakage of CPK and LDH were observed in the ischemic preconditioning group compared to the control group (p<0.05). Conclusion: Ischemic preconditioning improved postischemic cardiac function after warm ischemia, but did not protect cold cardioplegic hearts.