• Economic and Environmental Geology
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• v.34 no.1
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• pp.1-12
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• 2001

The crystal structure of biotite-1M from Bancroft, Ontario, was determined by Rietveld refinement method using high-resolution neutron powder diffraction data at -26.3$^{\circ}C$, 2$0^{\circ}C$, 30$0^{\circ}C$, $600^{\circ}C$, 90$0^{\circ}C$. The crystal structure has been refined to a R sub(B) of 5.06%-11.9% and S (Goodness of fitness) of 2.97-3.94. The expansion rate of a, b, c unit cell dimensions with elevated temperature linearly increase to $600^{\circ}C$. The expansivity of the c dimension is $1.61{\times}10^{40}C^{-1}$, while $2.73{\times}10^{50}C^{-1}$ and $5.71{\times}10^{-50}C^{-1}$ for the a and b dimensions, respectively. Thus, the volume increase of the unit cell is dominated by expansion of the c axis as increasing temperature. In contrast to the trend, the expansivity of the dimensions is decreased at 90$0^{\circ}C$. It may be attributed to a change in cation size caused by dehydroxylation-oxidation of $Fe^{2+}$ to $Fe^{3+}$ in vacuum condition at such high temperature. The position of H-proton was determined by the refinement of diffraction pattern at low temperature (-2.63$^{\circ}C$). The position is 0.9103${\AA}$ from the O sub(4) location and located at atomic coordinates (x/a=0.138, y/b=0.5, z/c=0.305) with the OH vector almost normal to plane (001). According to the increase of the temperature, $\alpha$* (tetrahedral rotation angle), $t_{oct}$ (octahedral sheet thickness), mean distance increase except 90$0^{\circ}C$ data. But the trend is less clearly relative to unit cell dimension expansion because the expansion is dominant to the interlayer. Also, ${\Psi}$ (octahedral flattening angle) shows no trends as increasing temperature and it may be because the octahedron (M1, M2) is substituted by Mg and Fe.

• Korean Journal of Animal Reproduction
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• v.25 no.4
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• pp.339-347
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• 2001

Sperm-mediated DNA transfer has a potential to markedly simplify techniques for the generation of transgenic animals. The exogenous DNA transfer by intracytoplasmic sperm injection (ICSI) procedure has been recently introduced in the production of transgenic animals. In this study, the developmental competence and tile expression rates of transgene were investigated after injection of spermatozoon or sperm head with enhanced green fluorescent protein (EGFP) gene into the mature porcine oocytes. The porcine oocytes were injected with intact sperm, membrane-disrupted sperm or sperm head. After injection. embryos were cultured in NCSU23 medium up to the blastocyst stage, and the developmental competence and expression rates were studied. The developmental rate (67.0%) of sperm injection group was higher than that (59.7%) of sperm head injection group, and the rates of EGFP expression were also significantly different between sperm injection and sperm head injection groups (42.1 vs 20.0%) (F＜0.05). In the porcine oocytes injected with sperm treated with different methods of membrane disruption, the removal of sperm membrane did not alter the developmental competence of embryos. The rate of blastocysts at 7 days after injection with intact and membrane disrupted sperm were 15.0 and 14.2%, respectively. The EGFP expression rates, 38.4% in embryos injected with frozen-thawed sperm was higher than that, 22.4% of embryos injected with the Triton X-100 treated sperm. Prior to injection, sperm were cultured in different EGFP gene concentrations from 0.Ol to 1ng/u${mu}ell$. However, no significant difference in developmental rates of embryos among different concentrations of EGFP gene were observed. The highest expression rate of EGFP gene, 37.4% was obtained from the embryos injected with spermatozoa treated with 0.1 ng/${mu}ell$ EGFP gene. These results suggested that exogenous DNA could be attached to the membrane disrupted sperm, and that these sperm could be used as a vector carrying foreign DNA into embryos.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.455-465
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• 2017

Understanding of the two-dimensional velocity field is crucial in terms of analyzing various hydrodynamic and fluvial processes in the riverine environments. Until recently, many numerical models have played major roles of providing such velocity field instead of in-situ flow measurements, because there were limitations in instruments and methodologies suitable for efficiently measuring in the broad range of river reaches. In the last decades, however, the advent of modernized instrumentations started to revolutionize the flow measurements. Among others, acoustic Doppler current profilers (ADCPs) became very promising especially for accurately assessing streamflow discharge, and they are also able to provide the detailed velocity field very efficiently. Thus it became possible to capture the velocity field only with field observations. Since most of ADCPs measurements have been mostly conducted in the cross-sectional lines despite their capabilities, it is still required to apply appropriate interpolation methods to obtain dense velocity field as likely as results from numerical simulations. However, anisotropic nature of the meandering river channel could have brought in the difficulties for applying simple spatial interpolation methods for handling dynamic flow velocity vector, since the flow direction continuously changes over the curvature of the channel shape. Without considering anisotropic characteristics in terms of the meandering, therefore, conventional interpolation methods such as IDW and Kriging possibly lead to erroneous results, when they dealt with velocity vectors in the meandering channel. Based on the consecutive ADCP cross-sectional measurements in the meandering river channel. For this purpose, the geographic coordinate with the measured ADCP velocity was converted from the conventional Cartesian coordinate (x, y) to a curvilinear coordinate (s, n). The results from application of A-VIM showed significant improvement in accuracy as much as 41.5% in RMSE.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.169-181
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• 2004

SK Telecom has already constructed GIMS system as the base common framework of LBS/GIS service system based on OGC(OpenGIS Consortium)'s international standard for the first mobile vector map service in 2002, But as service content appears more complex, renovation has been needed to satisfy multi-purpose, multi-function and maximum efficiency as requirements have been increased. This research is for preparation ion of GML3-based platform to upgrade service from GML2 based GIMS system. And with this, it will be possible for variety of application services to provide location and geographic data easily and freely. In GML 3.0, it has been selected animation, event handling, resource for style mapping, topology specification for 3D and telematics services for mobile LBS multimedia service. And the schema and transfer protocol has been developed and organized to optimize data transfer to MS(Mobile Stat ion) Upgrade to GML 3.0-based GIMS system has provided innovative framework in the view of not only construction but also service which has been implemented and applied to previous research and system. Also GIMS channel interface has been implemented to simplify access to GIMS system, and service component of GIMS internals, WFS and WMS, has gotten enhanded and expanded function.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.