The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.
Journal of the Korean Institute of Intelligent Systems
/
v.8
no.2
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pp.60-69
/
1998
This paper proposes a speech synthesis method using Fuzzy VQ, and then study how to make
choice of fuzziness value which optimizes (controls) the performance of FVQ in order to obtain the
synthesized speech which is closer to the original speech. When FVQ is used to synthesize a speech,
analysis stage generates membership function values which represents the degree to which an input
speech pattern matches each speech patterns in codebook, and synthesis stage reproduces a
synthesized speech, using membership function values which is obtained in analysis stage, fuzziness
value, and fuzzy-c-means operation. By comparsion of the performance of the FVQ and VQ
synthesizer with simmulation, we show that, although the FVQ codebook size is half of a VQ
codebook size, the performance of FVQ is almost equal to that of VQ. This results imply that, when
Fuzzy VQ is used to obtain the same performance with that of VQ in speech synthesis, we can reduce
by half of memory size at a codebook storage. And then we have found that, for the optimized FVQ
with maximum SQNR in synthesized speech, the fuzziness value should be small when the variance
of analysis frame is relatively large, while fuzziness value should be large, when it is small. As a
results of comparsion of the speeches synthesized by VQ and FVQ in their spectrogram of frequency
domain, we have found that spectrum bands(formant frequency and pitch frequency) of FVQ
synthesized speech are closer to the original speech than those using VQ.
Journal of the Korean Association of Geographic Information Studies
/
v.12
no.4
/
pp.136-145
/
2009
Site characteristic is an important input parameter in the geologic hazard assessments including, but not limited to, earthquakes, liquefaction and landslides. Although it is a routine to use data collected by boreholes or seismic prospecting for site classifications, we used indirect methods using the geologic and the topographic maps. A site classification map in the Gyeongsang Province has been produced by GIS tools based on geologic age, rock types, and elevations from the geologic map and the topographic map of Korea. Site B (rock site) is dominant in the study area, although softer soils are observed along rivers and in reclaimed lands. We have found that 73% of the site classification results in the study are in concordance with those obtained from borehole data. Observed discrepancies are attributed to errors in the geologic and the topographic maps. For some sites, the origin of the differences is not clear, which requires a further field study or a drilling. Site classification from this study provides essential information for reliable hazard assessments of earthquakes, floods, landslides and liquefaction. Results obtained in the study also play a crucial role in land use planning for developing areas.
Oral diseases may hinder people from living a healthy life by causing obstacles in the nutrition supply of the human body. This study aims at the found out the eating habits and recognition factors of people who are currently suffering from denial pain, and made a predictive modeling using neural network, which is a data mining. The oral health condition for maintaining and improving oral health has been examined and analyzed through a survey and the groups were divided based on the presence and the absence of dental pain. This study observed on eating habits, exercise and oral habits. The study results of neural network modeling input parameter was selected significant survival factors. As a result of making a predictive modeling using the neural network, the fitness of the predictive modeling of dental pain factors was 88.7%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.
Journal of Korean Tunnelling and Underground Space Association
/
v.3
no.3
/
pp.15-29
/
2001
For the pertinent use of NMT method, both characteristics of joints (JRC, JCS and ${\phi}_r$) and characteristics of rock mass (Q-Value) must be investigated carefully. The main objective of the study presented is to investigate how sensitive the predicted behaviour of an underground excavation is to various realistic assumptions about some input parameter for the jointed rock mass. Joint pattern in the tunnel is predicted by statistical approach (chi-square test). In this paper, sensitivity studies involving in joint characteristics were carried out. The parametric studies involving change in Barton-Bandis joint model have shown that JCS is relatively insensitive to JRC and ${\phi}_r$. An increase in JRC value may not, according to the Barton-Bandis model, necessarily lead to a decrease in displacement. The importance of dilation in predicting the behaviour of a rock mass around an excavation is emphasized from a comparison of the Barton-Bandis joint behaviour model with the Mohr-Coulomb model. The Barton-Bandis model predicted higher stress, which allow for the build-up of stress caused by dilatant behaviour.
Kim, Sun-Min;Yorozu, Kazuaki;Tachikawa, Yasuto;Shiiba, Michiharu
Proceedings of the Korea Water Resources Association Conference
/
2011.05a
/
pp.15-15
/
2011
The radar observation system in Japan is operated by two governmental groups: Japan Meteorological Agency (JMA) and the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan. The JMA radar observation network is comprised of 20 C-band radars (with a wavelength of 5.6 cm), which cover most of the Japan Islands and observe rainfall intensity and distribution. And the MLIT's radar observation system is composed of 26 C-band radars throughout Japan. The observed radar echo from each radar unit is first modified, and then sent to the National Bureau of Synthesis Process within the MLIT. Through several steps for homogenizing observation accuracy, including distance and elevation correction, synthesized rainfall intensity maps for the entire nation of Japan are generated every 5 minutes. The MLIT has recently launched a new radar observation network system designed for flash flood observation and forecasting in small river basins within urban areas. It is called the X-band multi parameter radar network, and is distinguished by its dual polarimetric wave pulses of short length (3cm). Attenuation problems resulting from the short wave length of radar echo are strengthened by polarimetric wavelengths and very dense radar networks. Currently, the network is established within four areas. Each area is observed using 3-4 X-band radars with very fine resolution in spatial (250 m) and temporal (1 minute intervals). This study provides a series of utilization procedures for the new input data into a real-time forecasting system. First of all, the accuracy of the X-band radar observation was determined by comparing its results with the rainfall intensities as observed by ground gauge stations. It was also compared with conventional C-band radar observation. The rainfall information from the new radar network was then provided to a distributed hydrologic model to simulate river discharges. The simulated river discharges were evaluated again using the observed river discharge to estimate the applicability of the new observation network in the context of operations regarding flood forecasting. It was able to determine that the newly equipped X-band polarimetric radar network shows somewhat improved observation accuracy compared to conventional C-band radar observation. However, it has a tendency to underestimate the rainfall, and the accuracy is not always superior to that of the C-band radar. The accuracy evaluation of the X-band radar observation in this study was conducted using only limited rainfall events, and more cases should be examined for developing a broader understanding of the general behavior of the X-band radar and for improving observation accuracy.
Journal of the Korea Institute of Information and Communication Engineering
/
v.12
no.12
/
pp.2349-2356
/
2008
In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.
Soil hydraulic properties such as hydraulic conductivity or water retention which are costly to measure can be indirectly generated by soil pedotransfer function (PTF) using easily obtainable soil data. The field soil structure description which is routinely recorded could also be used in PTF as an input to reduce the uncertainty. The purposes of this study were to use qualitative morphological soil structure descriptions and soil structural index into PTF and to evaluate their contribution in the prediction of soil hydraulic properties. We transformed categorical morphological descriptions of soil structure into quantitative values using categorical principal component analysis (CATPCA). This approach was tested with a large data set from the US National Pedon Characterization database with the aid of a categorical regression tree analysis. Six different PTFs were used to predict the saturated hydraulic conductivity and those results were averaged to quantify the uncertainty. Quantified morphological description was successively used in multiple linear regression approach to predict the averaged ensemble saturated conductivity. The selected stepwise regression model with only the transformed morphological variables and structural index as predictors predicted the $K_{sat}$ with $r^2$ = 0.48 (p = 0.018), indicating the feasibility of CATPCA approach. In a regression tree analysis, soil structure index and soil texture turned out to be important factors in the prediction of the hydraulic properties. Among structural descriptions size class turned out to be an important grouping parameter in the regression tree. Bulk density, clay content, W33 and structural index explained clusters selected by a two step clustering technique, implying the morphologically described soil structural features are closely related to soil physical as well as hydraulic properties. Although this study provided relatively new method which related soil structure description to soil structure index, the same approach should be tested using a datasets containing the actual measurement of hydraulic properties. More insight on the predictive power of soil structure index to estimate hydraulic properties would be achieved by considering measured the saturated hydraulic conductivity and the soil water retention.
Journal of the Microelectronics and Packaging Society
/
v.26
no.1
/
pp.29-33
/
2019
With shrinking form factor and improving performance of electronic packages, high input/output (I/O) density is considered as an important factor. Fan out wafer-level packaging (FO-WLP) has been paid great attention as an alternative. However, FO-WLP is vulnerable to warpage during its manufacturing process. Minimizing warpage is essential for controlling production yield, and in turn, package reliability. While many studies investigated the effect of process and design parameters on warpage using finite element analysis, they did not take uncertainty into consideration. As parameters, including material properties, chip positions, have uncertainty from the point of manufacturing view, the uncertainty should be considered to reduce the gap between the results from the field and the finite element analysis. This paper focuses on the effect of uncertainty of Young's modulus of chip on fan-out wafer level packaging warpage using finite element analysis. It is assumed that Young's modulus of each chip follows the normal distribution. Simulation results show that the uncertainty of Young's modulus affects the maximum von Mises stress. As a result, it is necessary to control the uncertainty of Young's modulus of silicon chip since the maximum von Mises stress is a parameter related to the package reliability.
Input ratio of electricity to other production inputs in the Korean manufacturing sector has been higher than for the other OECD countries. In addition, electricity prices in Korea has been relatively lower than the average of OECD countries. Moreover, electricity sector is responsible for most CO2 emissions in Korea as coal and natural gas account 41.9% and 26.8% of electricity production as of 2018. Therefore, it looks inevitable to raise the electricity tariff for the manufacturing sector in Korea, but there is a concern that increase in the electricity tariff might affect small and medium enterprises (SMEs) more than large firms. This study estimates electricity demand's price and output elasticities for large firms and SMEs in steel industry by employing a time varying parameter model (Kalman filter). The analysis shows that changes in output levels regardless of firms' size affect electricity demands more significantly than do changes in electricity prices. Second, large firms have higher variances for both price and output elasticities of electricity demand. Third, large firms have higher price elasticity but lower output elasticity of electricity demand relative to SMEs. Policy implications are suggested in association with how to reduce electricity demands in the energy-intensive industry.
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