When constructing on soft ground, managing ground settlement and safety is crucial. However, there often exists a significant disparity between the actual behavior of the ground and the design plans. In this study, we aimed to compare and analyze the difference between the predicted settlement based on theoretical formulas and the measured settlement during construction, in order to predict settlement. For this purpose, we analyzed settlement data from 18 construction sites. The results indicated that the back analysis settlement values were similar to the measured settlement values, whereas the design settlement values were significantly higher compared to the measured settlement values. Specifically, the design settlement values were 1.2 to 1.4 times higher than those derived from back analysis using measured values. The RMSE analysis revealed a value of 0.6212m for the design settlement and 0.1697m for the back analysis settlement. The difference between the back analysis settlement and the measured settlement was more than 70% lower than the difference between the design settlement and the measured settlement. This indicates that the back analysis settlement values exhibit lower error rates compared to the design settlement values.
In waveguide structures, waves may be partially reflected by local non-uniformities. The effects of local non-uniformities has been previously investigated by means of a combined spectral element and finite element (SE/FE) method at relatively low frequencies. However, since the SE is formulated based on a beam theory, the SE/FE method is not appropriated for analysis at higher frequencies where complex deformation of the waveguide occurs. So it is necessary to extend this approach for high frequencies. For the wave propagation at higher frequencies, a combined spectral super element and finite element (SSE/FE) method is introduced in this paper. As an example of the application of this method, wave reflection and transmission due to a local defect in a rail are simulated at frequencies between 20kHz and 30kHz. Also numerical errors are evaluated by means of the conservation of the incident power.
In this paper, a new interpolation model for the head related transfer function (HRTF) was proposed. In the method herein, we assume that the impulse response of the HRTF for each azimuth angle is given by linear interpolation of the time-delayed neighboring impulse responses of HRTFs. The time delay of the HRTF for each azimuth angle is given by sum of the sound wave propagation time from the ears to the sound source, which can be estimated by using azimuth angle, the physical shape of the underlying head and the distance between the head and sound source, and the refinement time yielding the minimum mean square error. Moreover, in the proposed model, the interpolation intervals were not fixed but varied, which were determined by minimizing the total number of HRTFs while the synthesized signals have no perceptual difference from the original signals in terms of sound location. To validate the usefulness of the proposed interpolation model, the proposed model was applied to the several HRTFs that were obtained from one dummy-head and three human heads. We used the HRTFs that have 5 degree azimuth angle resolution at 0 degree elevation (horizontal plane). The experimental results showed that using only $30\sim40%$ of the original HRTFs were sufficient for producing the signals that have no audible differences from the original ones in terms of sound location.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
/
v.19
no.3
/
pp.109-121
/
2024
This paper adopts a resource-based approach to analyze why some universities have a greater number of faculty startups, and how this impacts on performance, in terms of indictors such as the number of employees and revenue sales. More specifically, we propose 9 hypotheses which link institutional resources to faculty startups and their performance, and compare 5 different groups of university resources for cross-college variation, using data from 134 South Korean four-year universities from 2017 to 2020. We find that the institutional factors impacting on performance of faculty startups differ from other categories of startups. The results show that it is important for universities to provide a more favorable environment, incorporating more flexible personnel policies and accompanying startup support infrastructure, for faculty startups, whilest it is more effective to have more financial resources and intellectual property for other categories of startups. Our findings also indicate that university technology-holding company and technology transfer programs are crucial to increase the number of faculty startups and their performance. Our analysis results have implications for both university and government policy-makers, endeavoring to facilitate higher particaption of professors in startup formation and ultimate commercialization of associated teachnologies.
This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.
Jo, Eun-Su;Lee, Kyu-Tae;Jung, Hyun-Seok;Kim, Bu-Yo;Zo, Il-Sung
Journal of the Korean earth science society
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v.38
no.4
/
pp.269-282
/
2017
In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.
Park, C.G.;Hyun, J.S.;Cho, D.J.;Lee, K.S.;Hah, J.K.
Korean journal of applied entomology
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v.24
no.1
s.62
/
pp.29-33
/
1985
Every plant in $990m^2$ onion field was inspected for damages by the onion maggot. Maps were constructed every ten days to show which plants were infested and which were not from April 11 to May 21, 1984. The maps were sectioned into squares one of which contains 80 onion plants and the counts of damaged onions in each square were fitted to poisson and negative binomial distribution and tested by chi-square. We argue that the satisfactory fitness of the expected negative binomial $[P(x^2)>0.05]$ provided a useful description of the spatial distribution patterns of the damaged onions. Edge effect was tested by the differences of damage ratio and variance/mean ratio (${\sigma}^2/m$) between edge and center part. The result showed that the damage ratioes and variances of all the periods, ${\sigma}^2/m$ values after May 1 were greater in edge part than in center part. Again, the maps were sectioned into four blocks and the squares (sample units) were sectioned into quadrants. By application of the variance component technique, it was suggested that $2{\sim}8$ sample units for 5% sampling error and $1{\sim}2$ sample units for 10% error should be sampled randomly to estimate the damage ratio when $2{\sim}3$ quadrants were inspected.
Yongmin Chang;Bong Soo Han;Bong Seok Kang;Kyungnyeo Jeon;Kyungsoo Bae;Yong-Sun Kim;Duk-Sik Kang
Investigative Magnetic Resonance Imaging
/
v.6
no.2
/
pp.120-128
/
2002
Purpose : To demonstrate that the relaxographic method provides additional information such as the distribution of relaxation times and water content which are poentially applicable to clinical medicine. Materials and Methods : First, the computer simulation was performed with the generated relaxation data to verify the accuracy and reliabilility of the relaxographic method (CONTINI). Secondly, in or der to see how well the CONTIN quantifies and resolves the two different ${T_1}$ environments, we calculated the oil to water peak area ratios and identified peak positions of ${T_1}-distribution$ curve of the phantom solutions, which consist of four centrifugal tubes (10 ml) filled with the compounds of 0, 10, 20, 30% of corn oil and distilled water, using CONTIN. Finally, inversion recovery MR images for a volunteer are acquired for each TI ranged from 40 to 1160 msec with TR/TE=2200/20 msec. From the 3 different ROIs (GM, WM, CSF), CONTIN analysis was performed to obtain the ${T_1}$-distribution curves, which gave peak positions and peak area of each ROI location. Results : The simulation result shows that the errors of peak positions were less in the higher peak (centered ${T_1}=600$ msec) than in the lower peak (centered ${T_1}=150$ msec) for all SNR but the errors of peak areas were larger in the higher peak than in the lower peak. The CONTIN analysis of the measured relaxation data of phantoms revealed two peaks between 20 and 60 msec and between 500 and 700 msec. The analysis gives the peak area ratio as oil 10%: oil 20%: oil 30% = 1:1.3:1.9, which is different from the exact ratio, 1:2:3. For human brain, in ROI 3 (CSF), only one component of -distributions was observed whereas in ROI 1(GM) and in ROI 2 (WM) we observed two components of ${T_1}-distribution$. For the WM and CSF there was great agreement between the observed ${T_1}-relaxation$ times and the reported values. Conclusion : we demonstrated that the relaxographic method provided additional information such as the distribution of relaxation times and water content, which were not available in the routine relaxometry and ${T_1}/{T_2}$ mapping techniques. In addition, these additional information provided by relaxographic analysis may have clinical importance.
Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.
In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.
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