• Bulletin of the Korean Chemical Society
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• 제3권1호
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• pp.9-13
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• 1982

Sulfametrole, $C_9H_{10}N_4O_3S_2$, crystallizes in the monoclinic system, space group $P2_1/n$ , with a = 8.145(2), b = 16.505(4), c = 9.637(1)${\AA},{\beta}=103.72(1)^{\circ},D_m=1.52gcm^{-3}$,Z=4.Intensities for 3594(2143 observed) unique reflections were measured on a four-circle diffractometer with Mo $K{\alpha}$ radiation $({\lambda}=0.71069{\AA})$. The structure was solved by direct method and refined by full-matrix least squares to a final R of 0.070. The geometrical features of the thiadiazole ring indicate some ${pi}$-electron delocalization inside the ring. The least squares planes defined by the benzene and thiadiazole rings are nearly perpendicular to each other(dihedral angle; $93.9^{\circ}$ ). All the potential hydrogen-bond donor atoms in the molecule, N(1) and N(2), are included in the hydrogen bonding. The molecules through hydrogen bonding form three dimensional network.

• Advances in aircraft and spacecraft science
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• 제9권1호
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• pp.1-15
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• 2022

This paper presents calibration of flush air data sensing systems during ascent period of a satellite launch vehicle. Aerodynamic results are numerically computed by solving three-dimensional time dependent compressible Euler equations over a payload shroud of a satellite launch vehicle. The flush air data system consists of four pressure ports flushed on a blunt-cone section of the payload shroud and connected to on board differential pressure transducers. The inverse algorithm uses calibration charts which are based on computed and measured data. A controlled random search method coupled with neural network technique is employed to estimate pitch and yaw angles from measured transient differential pressure history. The algorithm predicts the flow direction stepwise with the function of flight Mach numbers and can be termed as an online method. Flow direction of the launch vehicle is compared with the reconstructed trajectory data. The estimated values of the flow direction are in good agreement with them.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제6권1호
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• pp.13-24
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• 1988

The determination of three-dimensional positions on a circular or cylindrical surface covers a variety of applications. As an example, consider the monitoring of structures, which is an important topic in the broad category of deformation analysis. The use of convergent photography in determination of these positions has the many advantages over survey based procedures. This paper illustrates results from bundle adjustments derived from convergent photography of a cylindrical object, with both metric and non-metric cameras utilized in the test. In addition to standard error comparisons resulting from the error analysis provided by the bundle adjustments, object space coordinates resulting from metric and non-metric camera network geometries will also be compared.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.411-426
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• 2012

The Canton Tower (formerly named Guangzhou New TV Tower) of 610 m high has been instrumented with a long-term structural health monitoring (SHM) system consisting of over 700 sensors of sixteen types. Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), an SHM benchmark problem for high-rise structures has been developed by taking the instrumented Canton Tower as a host structure. This benchmark problem aims to provide an international platform for direct comparison of various SHM-related methodologies and algorithms with the use of real-world monitoring data from a large-scale structure, and to narrow the gap that currently exists between the research and the practice of SHM. This paper first briefs the SHM system deployed on the Canton Tower, and the development of an elaborate three-dimensional (3D) full-scale finite element model (FEM) and the validation of the model using the measured modal data of the structure. In succession comes the formulation of an equivalent reduced-order FEM which is developed specifically for the benchmark study. The reduced-order FEM, which comprises 37 beam elements and a total of 185 degrees-of-freedom (DOFs), has been elaborately tuned to coincide well with the full-scale FEM in terms of both modal frequencies and mode shapes. The field measurement data (including those obtained from 20 accelerometers, one anemometer and one temperature sensor) from the Canton Tower, which are available for the benchmark study, are subsequently presented together with a description of the sensor deployment locations and the sensor specifications.

• Applied Biological Chemistry
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• 제34권4호
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• pp.379-385
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• 1991

Morphological changes of starch granules from 12 different varieties of rice were examined by scanning electron microscopy during heating at 2.5% (w/v) concentration. Rice starch granules proceeded through a similar pattern of progressive morphological changes daring heating, regardless of variety. Rice starch granules began to swell radially in the initial stage of gelatinization and then undergo radial contraction and random tangential expansion to form complex structures in the latter stage of gelatinization temperature range. At higher temperatures, starch granules softened and melted into thin flat discs, and then stretched into thin filaments to form three-dimensional networks. These progressive morphological changes were reflected in the changes of swelling power, solubility and amylograph viscosity of starch. During the transition of melting or softening, swelling power, solubility and amylograph viscosity increased rapidly. The time of loss of granular structure of starch depended on gelatinization temperature range. The ratio of amylose to amylopectin was largely responsible fur the rate of melting or softening and the fineness of a three-dimensional filamentous network above the gelatinization temperature range. Therefore, both the gelatinization temperature range and amylose content of starch affect the rate of cooking, and amylose content of starch affects the final texture of cooked starch paste.

• Journal of Surface Science and Engineering
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• 제56권1호
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• pp.104-114
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• 2023

Three dimensional (3D) porous structures consisting of Cu@CoO core-shell-type nano-dendrites were synthesized and tested as the anode materials in lithium secondary batteries. For this purpose, first, the 3D porous films comprising Cu@Co core-shell-type nano-dendrites with various thicknesses were fabricated through the electrochemical co-deposition of Cu and Co. Then the Co shells were selectively anodized to form Co hydroxides, which was finally dehydrated to get Cu@CoO nanodendrites. The resulting electrodes exhibited very high reversible specific capacity almost 1.4~2.4 times the theoretical capacity of commercial graphite, and excellent capacity retention (~90%@50^th cycle) as compared with those of the existing transition metal oxides. From the analysis of the cumulative irreversible capacity and morphology change during charge/discharge cycling, it proved that the excellent capacity retention was attributed to the unique structural feature of our core-shell structure where only the thin CoO shell participates in the lithium storage. In addition, our electrodes showed a superb rate performance (70.5%@10.8 C-rate), most likely due to the open porous structure of 3D films, large surface area thanks to the dendritic structure, and fast electron transport through Cu core network.

• Journal of Cadastre & Land InformatiX
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• 제48권2호
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.523-526
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• 2002

The crosstalk among coupled microstrip lines is the major limiting factors of signal qualities in the high-speed digital and communication equipment. In this paper, a three-dimensional finite difference time domain (FDTD) method is applied to analyze the crosstalk between the coupled microstrip lines. The proposed structures of the coupled microstrip lines are investigated to reduce the coupling in a simple way by modifying their ground plane with an optimum gap. The examples of these structures with the different sizes of the gaps on their ground plane are studied. These structures are considered as the four-port network to evaluate transmission efficiency, near- and far-end crosstalk. Gaussian pulse is excited to evaluate the frequency characteristics from dc to 30 ㎓. The transmission efficiency, near- and far-end crosstalk of each structure of the coupled microstrip lines are demonstrated. The numerical results of this study show that the majority of crosstalk is the far-end crosstalk. The usage of the optimum gap on the ground plane can reduce the far-end crosstalk of the coupled microstrip lines while the transmission efficiency is nearly equal.

• Computers and Concrete
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• 제8권2호
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• pp.125-142
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• 2011

In the present paper, a numerical simulation method based on mesoscopic composite structure of concrete, the truss network model, is developed to evaluate the diffusivity of concrete in order to account for the microstructure of concrete, the binding effect of chloride ions and the chloride concentration dependence. In the model, concrete is described as a three-phase composite, consisting of mortar, coarse aggregates and the interfacial transition zones (ITZs) between them. The advantage of the current model is that it can easily represent the movement of mass (e.g. water or chloride ions) through ITZs or the potential cracks within concrete. An analytical method to estimate the chloride diffusivity of mortar and ITZ, which are both treated as homogenious materials in the model, is introduced in terms of water-to-cement ratio (w/c) and sand volume fraction. Using the newly developed approaches, the effect of cracking of concrete on chloride diffusion is reflected by means of the similar process as that in the test. The results of calculation give close match with experimental observations. Furthermore, with consideration of the binding capacity of chloride ions to cement paste and the concentration dependence for diffusivity, the one-dimensional nonlinear diffusion equation is established, as well as its finite difference form in terms of the truss network model. A series of numerical analysises performed on the model find that the chloride diffusion is substantially influenced by the binding capacity and concentration dependence, which is same as that revealed in some experimental investigations. This indicates the necessity to take into account the binding capacity and chloride concentration dependence in the durability analysis and service life prediction of concrete structures.

• Smart Structures and Systems
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• 제29권4호
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.