• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.1
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• pp.20-25
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• 1999

This experiment was conducted to elucidate the relation-ship between vertical distribution of rice roots and yield traits under field conditions. Eight IRRI's new plant type rices (NPTRs) were tested in a volcanic ash soil paddy field under dense (IO 10 cm) and common (20 20 cm) planting densities. These lines were evaluated to have more spikelet numbers per panicle (SNP), lower filled grain rate (FGR), and lower rough grain weight per hill (RGWH). In dense planting, rough grain weight per stem (RGWS) was increased due to heavier culm and leaf dry weight (CLDW), and both RGWS and CLDW were related with the percentage of root distribution (%RWI) in the 10~30 cm soil layer, while in common planting, RGWS was not closely related with CLDW. SNP was highly related with root dry weight (RDW) in the 0~10cm soil layer. FGR was mainly affected by ROW in the 10~30 cm soil layer under both planting densities. RGWS was positively correlated with top dry weight (TDW) and harvest index (HI), and TDW was positively correlated with RWI under common planting or %RWI under dense planting, and HI was positively correlated with RWI in the 10~30 cm soil layer only under dense planting. RGWS was closely related with root weight index by dry weight (RWI) in the 10~30 cm soil layer and %RWI in the 0~30 cm or 10~30 cm soil layer under dense planting, and with only RWI in the 10~30 cm soil layer under common planting. But RGWH showed the close positive relationship with RDW and RWI in the 10~30 cm soil layer under dense planting, while under common planting, it showed the close positive relationship with RWI and %RWI in the 10~30 cm soil layer or %RWI in the 0~30 cm soil layer. The deeper root system in rice, especially under dense planting, is important for high yield of NPTRs focusing on the increment of top mass production and harvest index.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.19-27
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• 2019

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.237-242
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• 1993

The bearing capacity of the sand foundation including a thin dense sand layer depends on the stiffiness, thickness and the location of the dense sand layer. In this paper was the influence of the dense sand layer on both the bearing capacity and the failure configuration is studied by means of K.E.M(Kinematic Element Method). K.E.M was implemented to get the excat solution starting from the upper bound of the analysis. The result show that the bearing capacity of the foundation and the failure configuration is greatly influenced by the dense sand layer, when the layer is located not deeper than 3/5 of the foundation width.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.143-148
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• 2022

In this paper, we employ deep neural network (DNN) to discern Android user interaction data distribution from artificial data distribution. We utilize real Android user interaction trace dataset collected from [1] to evaluate our DNN design. In particular, we use sequential model with 4 dense hidden layers and 1 dense output layer in TensorFlow and Keras. We also deploy sigmoid activation function for a dense output layer with 1 neuron and ReLU activation function for each dense hidden layer with 32 neurons. Our evaluation shows that our DNN design fulfills high test accuracy of at least 0.9955 and low test loss of at most 0.0116 in all cases of artificial data distributions.

• Bulletin of the Korean Chemical Society
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• v.32 no.10
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• pp.3629-3633
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• 2011

The effect of a dense $TiO_2$ blocking layer prepared using the sol-gel method on the performance of dye-sensitized solar cells was studied. The blocking layer formed directly on the working electrode, separated it from the electrolyte, and prevented the back transfer of electrons from the electrode to the electrolyte. The dyesensitized solar cells were prepared with a working electrode of fluorine-doped tin oxide glass coated with a blocking layer of dense $TiO_2$, a dye-attached mesoporous $TiO_2$ film, and a nano-gel electrolyte, and a counter electrode of Pt-deposited FTO glass. The gel processing conditions and heat treatment temperature for blocking layer formation affected the morphology and performance of the cells, and their optimal values were determined. The introduction of the blocking layer increased the conversion efficiency of the cell by 7.37% for the cell without a blocking layer to 8.55% for the cell with a dense $TiO_2$ blocking layer, under standard illumination conditions. The short-circuit current density ($J_{sc}$) and open-circuit voltage ($V_{oc}$) also were increased by the addition of a dense $TiO_2$ blocking layer.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.1
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• pp.6-12
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• 2013

Aerosol deposition(AD) coating that enable fabricate films at low temperature have begun to be widely researched for the integration of ceramics as well to realize high-speed deposition rates. For application of ceramic thick film by AD to display and electronic ceramic industry, fabrication of dense structure with a no cracking is required. In this study, to fabricate dense ceramic thick film, the effect of crystal phase of starting powder was investigated. For this study, amorphous and crystalline $SiO_2$ powders were used as starting powders. Two types of $SiO_2$ powders were deposited on glass substrate by AD. In the case of amorphous $SiO_2$ powder, the deposited films had extremely incompact and opaque layer, irrespective of particle size. In contrast to amorphous powder, in the case of crystalline powder, porous structure layer and dense microstructure with no cracking layer were fabricated depending on the particle size. The optimized starting powder size for dense coating layer was $1{\sim}2{\mu}m$. The transmittance of film reached a maximum of 76% at 800 nm.

• Tribology and Lubricants
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• v.25 no.1
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• pp.7-12
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• 2009

Coating brittle intermetallic compounds on metal can enlarge the range of their use. It is found that intermetallic compound coating layers made by only combustion synthesis in an electric furnace have porous multi-phase structures containing several intermediate phases, even though the coating layers show good wear resistance. In this study, dense $Ni_{3}Al$ single phase layer corresponding to the initial composition of the mixed powder is coated on two different ferrous materials by the diffusing treatment after combustion synthesis. After- ward, sliding wear behaviors of the coating layer are evaluated in comparison with that of the coating layer with porous multi-phase structure made by only combustion synthesis. As a result, the wear properties of the coating layer composed of dense $Ni_{3}Al$ single phase are considerably improved at the range of low sliding speed com- pared with that of the coating layer with porous multi-phase structure, particularly in the running-in wear region. This is attributed to the fact that wear of the coating layer is progressed by shearing as a sequence of adhesion, not by occurring of pitting on the worn surface due to having dense structure without pores.

• Applied Chemistry for Engineering
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• v.8 no.5
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• pp.853-859
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• 1997

For asymmetric polyetherimide membranes having a dense layer gradient structure on the skin layer, the morphology change and pervaporation behaviors of water/isopropanol mixture through chemical modification of dense skin layer were investigated. The extent of the density was controlled by the evaporation, time, and when the evaporation time was increased from 0 min to 4 min, the permeation flux was decreased, the separation factor was increased. Also, the pervaporation behaviors of the polyetherimide membranes modified with sodium hydroxide solution, as the modification time of dense skin layer increased, the selectivity increased, and the permeation flux decreased. The morphology change identified by SEM shows that the density of dense skin layer tends to increase with increasing modification time, this result is consistent with above observations.

• Proceedings of the Membrane Society of Korea Conference
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• 2002.05a
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• pp.35-41
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• 2002

Dense oxygen ionic conducting materials can be used for oxygen separation membranes at high temperatures. However, they show relatively low permeation flux because of their large resistances. To reduce resistances and improve the oxygen permeation flux, thin dense yttria-stabilized-zirconia (YSZ)/Pd composite dual-phase membranes were fabricated by a new approach that combines the reservoir method and chemical vapor deposition (CVD). A thin porous YSZ layer was coated on a porous alumina support by dip-coating the YSZ suspension. A continuous Pd phase was formed inside pores of the YSZ layer by the reservoir method. The residual pores of the YSZ/Pd layer were plugged with yttria/zirconia by CVD to ensure the gas tightness of the membranes. The oxygen permeation fluxes through these composite membrane were 2.0$\times$10$^{-8}$ mol/cm$^2$.s and 4.8$\times$10$^{-8}$ mol/cm$^2$.s at 105$0^{\circ}C$ when air and oxygen were used as the permeate gases, respectively. These oxygen permeation values are about 1 order of magnitude higher than those of pure YSZ membranes prepared under similar conditions.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.571-582
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• 2018

Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.