• 한국작물학회지
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• 제44권1호
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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.

• 한국컴퓨터정보학회논문지
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• 제24권3호
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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.

• 대한토목학회논문집
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• 제13권2호
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• pp.237-242
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• 1993

기초 지반이 몇개의 토층으로 구성된 경우 그 지지력은 각 토층의 강성도와 두께 및 위치 등에 의해 영향을 받는다. 따라서 기초 설계시에 기초의 지지력을 정확히 구하고 파괴면을 예측하기 위해서는 토층의 상태를 지지력 계산에 고려해야 한다. 본 연구에서는 기초 지반이 사질지반이고 중간에 상대적으로 조밀한 지층을 포함하는 경우에 조밀한 층의 두께나 위치가 기초의 지지력 및 파괴모양에 미치는 영향을 극한 해석의 상한한계(upper bound)쪽에서 완전해에 가까운 해를 구하는 K.E.M(Kinematical Element Method)을 이용하여 검토 하였으며, 기초 폭(B)의 3/5 B보다 깊지 않은곳에 조밀한 층이 위치하는 경우에는 기초의 지지력 과 파괴모양이 조밀한 층의 영향을 받는 것으로 나타났다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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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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• 제32권10호
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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.

• 한국전기전자재료학회논문지
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• 제26권1호
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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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• 제25권1호
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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.

• 공업화학
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• 제8권5호
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• pp.853-859
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• 1997

표면층에 치밀층 구배 구조를 갖는 비대칭 폴리에테르이미드막에 대하여 치밀 구조층의 화학적 개질을 통한 막 형태의 변화와 물/이소프로판올 혼합액의 투과증발 거동을 연구하였다. 치밀 정도는 증발 시간에 의해 조절되었고, 증발 시간이 0분에서 4분으로 증가되었을 때, 투과량은 감소하였고, 선택도는 증가하였다. 또한, 수산화나트륨 용액으로 개질 된 폴리에테르이미드막을 통한 투과증발 거동은, 치밀 표면층의 개질 시간이 증가될수록 선택성은 증가하였고, 투과량은 감소하였다. 전자현미경으로 확인된 형태 변화는 개질 시간의 증가에 따라 치밀 표면층의 density 증가를 보여주었고, 이러한 결과는 위의 관찰들과 일치하였다.

• 한국막학회:학술대회논문집
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• 한국막학회 2002년도 춘계 총회 및 학술발표회
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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.

• 한국멀티미디어학회논문지
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• 제21권5호
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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.