• Earthquakes and Structures
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• v.19 no.2
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• pp.129-144
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• 2020

In this study, the idea of damping force linearly proportional to horizontal isolation displacement is implemented into sloped rolling-type bearings in order to meet different seismic performance goals. In addition to experimentally demonstrating its practical feasibility, the previously developed analytical model is further modified to be capable of accurately predicting its hysteretic behavior. The numerical predictions by using the modified analytical model present a good match of the shaking table test results. Afterward, several sloped rolling-type bearings designed with linearly variable damping force are numerically compared with a bearing designed with conventional constant damping force. The initial friction damping force adopted in the former is designed to be smaller than the constant one adopted in the latter. The numerical comparison results indicate that when the horizontal isolation displacement does not exceed the designed turning point (or practically when subjected to minor or frequent earthquakes that seldom have a great displacement demand for seismic isolation), the linearly variable damping force design can exhibit a better acceleration control performance than the constant damping force design. In addition, the former, in general, advantages the re-centering performance over the latter. However, the maximum horizontal displacement response of the linearly variable damping force design, in general, is larger than that of the constant damping force design. It is particularly true when undergoing a horizontal isolation displacement response smaller than the designed turning point and designing a smaller value of initial friction damping force.

• Geomechanics and Engineering
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• v.18 no.3
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• pp.299-313
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• 2019

This research investigated the nonlinear compressibility, permeability, the yielding due to structural degradation and their effects on consolidation behavior of structured soft soils. Based on oedometer and hydraulic conductivity test results of natural and reconstituted soft clays, linear log (1+e) ~ $log\;{\sigma}^{\prime}$ and log (1+e) ~ $log\;k_v$ relationships were developed to capture the variations in compressibility and permeability, and the yield stress ratio (YSR) was introduced to characterize the soil structure of natural soft clay. Semi-analytical solutions for one-dimensional consolidation of soft clay under time-dependent loading incorporating the effects of soil nonlinearity and soil structure were proposed. The semi-analytical solutions were verified against field measurements of a well-documented test embankment and they can give better accuracy in prediction of excess pore pressure compared to the predictions using the existing analytical solutions. Additionally, parametric studies were conducted to analyze the effects of YSR, compression index (${\lambda}_r$ and ${\lambda}_c$), and permeability index (${\eta}_k$) on the consolidation behavior of structured soft clays. The magnitude of the difference between degree of consolidation based on excess pore pressure ($U_p$) and that based on strain ($U_s$) depends on YSR. The parameter ${\lambda}_c/{\eta}_k$ plays a significant role in predicting consolidation behavior.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2709-2716
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• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

• Ecology and Resilient Infrastructure
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• v.5 no.1
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• pp.25-34
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• 2018

This study presents prediction and historical analysis of the long-term bed elevation change in the Mankyung-gang River. The study area is a 25 km long reach including middle and lower courses of the Mankyung-gang River. HEC-RAS program was used for numerical predictions, and values of roughness coefficients were calibrated. Then, predictions were made in the two periods, seven years from 1986 to 1993 and twelve years from 1993 to 2005. Simulation results were compared with two sets of measured data for bed elevations. Four sediment transport formulas, namely MPM's, Toffaleti's, MPM-Toffaleti's, and Yang's formula, were tested. Simulation results showed that none of the four sediment transport formulas predicted the bed elevation change in the period of 1986 - 1993. This is related to the fact that dredging work was performed in the upstream reach in the period of 1986 - 1993, and sediment was deposited in this part severely later. However, it was found that MPM-Toffaleti's formula predicted properly the bed elevation change for the period of 1993 - 2005.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• International Journal of Automotive Technology
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• v.6 no.6
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• pp.579-584
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• 2005

Unsteady state free natural gas jets injected from several types of injectors were numerically simulated. Simulations showed good agreements with the schlieren experimental results. Moreover, injections of natural gas in intake manifolds of a single-valve engine and a double-valve engine were predicted as well. Predictions revealed that large volumetric injections of natural gas in intake manifolds led to strong impingement of natural gas with the intake valves, which as a result, gave rise to pronounced backward reflection of natural gas towards the inlets of intake manifolds, together with significant increase in pressure in intake manifold. Based on our simulations, we speculated that for engines with short intake manifolds, reflections of the mixture of natural gas and air were likely to approach the inlets of intake manifolds and subsequently be inbreathed into other cylinders, resulting in non-uniform mixture distributions between the cylinders. For engines with long intake manifolds, inasmuch as the degrees of intake interferences between the cylinders were not identical in light of the ignition sequences, non-uniform intake charge distributions between the cylinders would occur.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.707-727
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• 2022

Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.

• Journal of Microbiology and Biotechnology
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• v.17 no.9
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• pp.1437-1444
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• 2007

A response surface model was developed for predicting the growth rates of Staphylococcus aureus in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10, 20, 30, and $40^{\circ}C$. In all experimental variables, the primary growth curves were well ($r^2=0.9000$ to 0.9975) fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. aureus were generally decreased by basic (pH 9-10) or acidic (pH 5-6) conditions and higher NaCl concentrations. The response surface model was identified as an appropriate secondary model for growth rates on the basis of correlation coefficient (r=0.9703), determination coefficient ($r^2=0.9415$), mean square error (MSE=0.0185), bias factor ($B_f=1.0216$), and accuracy factor ($A_f=1.2583$). Therefore, the developed secondary model proved reliable for predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. aureus in TSB medium.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.156-161
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• 2003

It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.79-85
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• 2003

One of unique characteristics of guided waves is a dispersive behavior that guided wave velocity changes with an excitation frequency and mode. In practical applications of guided wave techniques, it is very important to identify propagating modes in a time-domain waveform for determination of defect location and size. Mode identification can be done by measurement of group velocity in a time-domain waveform. Thus, it is preferred to generate a single or less dispersive mode But in many cases, it is difficult to distinguish a mode clearly in a time-domain waveform because of superposition of multi modes and mode conversion phenomena. Time-frequency analysis is used as efficient methods to identify modes by presenting wave energy distribution in a time-frequency. In this study, experimental guided wave mode identification is carried out in a steel plate using time-frequency analysis methods such as wavelet transform. The results are compared with theoretically calculated group velocity dispersion curves. The results are in good agreement with analytical predictions and show the effectiveness of using the wavelet transform method to identify and measure the amplitudes of individual guided wave modes.