• Wind and Structures
• /
• v.38 no.4
• /
• pp.231-244
• /
• 2024

As wind farms expand into low wind speed areas, an increasing number are being established in mountainous regions. To fully utilize wind energy resources, it is essential to understand the details of mountain flow fields. Reconstructing the wind speed field in complex terrain is crucial for planning, designing, operation of wind farms, which impacts the wind farm's profits throughout its life cycle. Currently, wind speed reconstruction is primarily achieved through physical and machine learning methods. However, physical methods often require significant computational costs. Therefore, we propose a Full Convolutional Neural Network (FCNN)-based reconstruction method for mountain wind velocity fields to evaluate wind resources more accurately and efficiently. This method establishes the mapping relation between terrain, wind angle, height, and corresponding velocity fields of three velocity components within a specific terrain range. Guided by this mapping relation, wind velocity fields of three components at different terrains, wind angles, and heights can be generated. The effectiveness of this method was demonstrated by reconstructing the wind speed field of complex terrain in Beijing.

• Geomechanics and Engineering
• /
• v.37 no.4
• /
• pp.419-429
• /
• 2024

The primary goal was to assess how the addition of cement dust, a byproduct known to be harmful, could be used to stabilize clay. Various percentages of cement dust were added to soil samples, which were then subjected to triaxial testing at different rates of strain using an unconsolidated undrained triaxial machine. Six different rates of strain were applied to analyze the response of the clay under different conditions, resulting in 216 triaxial sample tests. As the percentage of cement dust in the clay samples increased, there was a noticeable increase in the strength properties of the clay, indicating a positive effect of cement dust on the clay's strength characteristics. Higher rates of strain during testing led to increased strength properties of the clay. Varying cement dust content influenced the impact of increasing the rate of strain on the clay's strength properties. Higher cement dust content reduced the sensitivity of the clay to changes in strain rate, indicating that the clay became less responsive to changes in strain rate as cement dust content increased. Potential for Clay Stabilization Cement dust proved the potential to enhance the strength properties of clay, indicating its potential utility in clay stabilization applications. Both higher percentages of cement dust and higher rates of strain were found to increase the clay's strength. It's essential to consider both the percentage of cement dust and the rate of strain when assessing the strength properties of clay in practical applications.

• Structural Engineering and Mechanics
• /
• v.89 no.3
• /
• pp.283-300
• /
• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Smart Structures and Systems
• /
• v.33 no.2
• /
• pp.105-118
• /
• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Geomechanics and Engineering
• /
• v.36 no.5
• /
• pp.441-453
• /
• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Wind and Structures
• /
• v.38 no.6
• /
• pp.461-475
• /
• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.34 no.10
• /
• pp.1599-1605
• /
• 2005

The object of this study is to develop the method for differentiating fresh meat from frozen meat by using the measurement of the mitochondrial malate dehydrogenase in the Korean native cattle. The principle of this experiment is based on the fact that the enzyme proteins associated with mitochondrial membrane could be released by freezing. The methods of differentiating fresh meat from thawed, frozen meat were studied by measurements of mitochondrial malate dehydrogenase activity of meat press juice. Fresh and frozen beef were stored at 4, -4, -18 and -77$^{\circ}C$ for 15-day storage period. A meat press machine using air pressure was manufactured especially for these experiments, and sufficient amount of drip (about 0.15 mL/g) from 1.5 g of beef sample was efficiently obtained under a pressure of 8 kg/$cm^{2}$ generated by the meat pressing machine. The mitochondrial malate dehydrogenase activities of frozen meat drip i년ices stored at -18 and -77$^{\circ}C$ were significantly higher than those of fresh and frozen meat samples at -4$^{\circ}C$ (p < 0.05) during 10-min reaction period. However, the enzyme activities of the frozen meat drip juices (-18 and -77$^{\circ}C$) disappeared after 5 minutes of the reaction, which was not observed from the fresh and -4$^{\circ}C$ frozen meats. The enzyme activity maintained until 12 minutes for the fresh and -4$^{\circ}C$ frozen meats. From these results, the mitochondrial malate dehydrogenase could be considered as an indicator to differentiate fresh beef from frozen one.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.24 no.3
• /
• pp.231-242
• /
• 2008

Purpose: The purpose of this study was to investigate the bond strength of the core-veneer interface in all ceramic systems. Material and Methods: The all ceramic systems tested with their respective veneer were IPS Empress 2 with IPS Eris, IPS e.max Press with IPS e.max Ceram and IPS-e.max ZirCAD with IPS e.max Ceram. Cores (N=36, N=12/group, diameter: 10mm, thickness: 3mm) were fabricated according to the manufacturer's instruction and cleaned with ultrasonic cleaner. The veneer(diameter: 3mm, thickness: 2mm) were condensed in stainless steel mold and fired on to the core materials. After firing, they were again ultrasonically cleaned and embedded in acrylic resin. The specimens were stored in distilled water at $37^{\circ}C$ for 1 week. The specimens were placed in a mounting jig and subjected to shear force in a universal testing machine(Z020, Zwick, Germany). Load was applied at close to the core-veneer interface as possible with crosshead speed of 1.00mm/min until failure. Average shear bond strengths(MPa) were analyzed with a one-way analysis of variance and the Tukey test(${\alpha}=.05$). The failed specimens were examinated by scanning electron microscopy(JSM-6360, JEOL, Japan). The pattern of failure was classified as cohesive in core, cohesive in veneer, mixed or adhesive. Results: The mean shear bond strength($MPa{\pm}SD$) were IPS e.max Press $32.85{\pm}6.75MPa$, IPS Empress 2 $29.30{\pm}6.51MPa$, IPS e.max ZirCAD $28.10{\pm}4.28MPa$. IPS Empress 2, IPS e.max Press, IPS e.max ZirCAD were not significantly different from each others. Scanning electron microscopy examination revealed that adhesive failure did not occur in any all ceramic systems. IPS Empress 2 and IPS e.max Press exhibited cohesive failure in both the core and the veneer. IPS e.max ZirCAD exhibited cohesive failure in veneer and mixed failure.

• Food Science and Preservation
• /
• v.22 no.6
• /
• pp.845-850
• /
• 2015

The influence of different roasting temperatures, times and extraction methods on the quality characteristics of Omija (Schizandra chinensis) seed oils was investigated. Roasted Omija seeds were divided into five groups based on roasting temperature-time conditions: no roasting (Raw) and roasting [R11: $150^{\circ}C$, 10 min, R12: $150^{\circ}C$, 20 min, R21: $250^{\circ}C$, 10 min, R22: $250^{\circ}C$, 20 min (R22)]. Oils from each of the raw and roasted Omija seeds were obtained by solvent (n-hexane) and press (machine) extraction. The $L^*$ values decreased, but the $a^*$ and $b^*$ values increased with increasing the roasting temperature and time. The $L^*$ values were lower in the press-extracted oils than in the solvent-extracted oils. The peroxide value (POV) of Omija seed oils decreased with increasing the roasting temperature-time values. The POV value was higher in the press-extracted oils than in the solvent-extracted oils. ABTS (2, 2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid)) radical inhibition of Omija seed oils was higher in the solvent-extracted oils than in the press-extracted oils, but there were no significant differences between the two oils. The four major kinds of fatty acid methyl esters detected in Omija seed oils were methyl butyrate, methyl hexanoate, methyl arachidate, and methyl eicosanoate. In conclusion, Omija seed oils obtained by solvent extraction and at higher roasting temperature-time values were more effective antioxidants.

• Journal of Life Science
• /
• v.25 no.8
• /
• pp.932-935
• /
• 2015

The purpose of this study was the effect of complex training of obese men in their 30s on inflammatory markers and homocysteine, a risk factor for cardiovascular disease. The subjects consisted of obese men (n=12) with the body fat ratio of 25% or above in their mid 30s who had no medical conditions and can follow the exercise routine required by this study. To achieve the purpose of this study measured the maximum oxygen intake (VO_2max) and 1RM of 5 kinds of machine(bench press, lat pull down, arm curl, leg press, squat) as a preliminary test; based on the results, performed 8 weeks complex training (aerobic exercise + weight training); and then analyzed the variation in body composition (body weight, body fat and BMI), blood inflammatory markers (IL-6, TNF-α and CRP), and homocysteine between before and after training. The results exhibited that 8 weeks complex training reduced weight, body fat and BMI significantly (p<0.01) and also reduced inflammatory marker CRP and homocysteine, a risk factor for cardiovascular disease, significantly (p<0.05). In conclusion, 8 weeks complex training confirmed the variation in body composition, and this variation has a positive effect on the inflammatory marker and the risk factor for cardiovascular disease.