• Journal of the Korean Ceramic Society
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• v.38 no.4
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• pp.331-336
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• 2001

TiOCl$_2$수용액과 암모니아수의 반응으로부터 얻은 titanium hydroxide 침전물에 대해 각각 NaOH 또한 HCl 처리를 통하여 비교적 낮은 온도에서 결정성이 우수하고, 높은 비표면적을 갖는 결정형 TiO$_2$나노분말을 제조하였다. 저온처리 조건을 변화시킴으로써 TiO$_2$분말의 결정상과 입자 모양을 제어할 수 있었는데, NaOH 처리한 다음 산처리한 분말에는 아나타제와 루틸상이 함께 형성되었으며 구형입자와 긴 스핀들 모양의 입자들로 이루어져 있었고, 비등수 처리 시에는 열처리를 통해 아나타제상의 구형 입자들을 얻을 수 있었다. Titanium hydroxide를 0.1M과 0.5M에 산처리한 분말은 모두 아나타제상을 나타내었는데, 0.1M의 분말은 구형입자로 이루어져 있었고, 0.5M의 경우는 대부분 구형입자들로 이루어져 있었지만 스핀들 모양의 입자들도 소량 관찰되었으며, 2M에 산처리한 분말은 루틸상으로서 모두 스핀들 모양의 입자들로 이루어져 있었고, 제조된 분말의 비표면적은 약 240-250$m^2$/g을 나타내었다.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.105-109
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• 2014

Structure and electrical properties of $0.85NaNbO_3-0.15LiNbO_3$ ($(Li_{0.15}Na_{0.85})NbO_3$) ceramics were investigated as a function of sintering temperature. $(Li_{0.15}Na_{0.85})NbO_3$ ceramics were prepared by conventional solid state processing. A main phase of the orthorhombic perovskite structure and secondary phase of $LiNbO_3$ were confirmed for all sintered specimens. Dense $(Li_{0.15}Na_{0.85})NbO_3$ ceramics were obtained at sintering temperature above $1050^{\circ}C$. With increasing sintering temperature, the electromechanical coupling factor ($k_p$), piezoelectric constant ($d_{33}$) and relative dielectric constant (${\varepsilon}_r$) of the sintered specimens increased, while the mechanical quality factor ($Q_m$) decreased. These results are due to the increase of grain size and crystallite size of orthorhombic perovskite structure. Based on the temperature dependence of ${\varepsilon}_r$, stable piezoelectric properties were expected because no phase transition found up to $300^{\circ}C$. Typically, kp of 18%, $d_{33}$ of 34.7 pC/N, ${\varepsilon}_r$ of 135, and $Q_m$ of 62.8 were obtained for the specimens sintered at $1200^{\circ}C$ for 5 h.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.842-844
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• 1993

We present a F.L.C. (Fuzzy Logic Controller) to control of the oscillation frequency of a V.C.X.O. (Voltage Controled Crystal Oscillator). This F.C.X.O. maintains stable its oscillation frequency inside a range of 1 ppm (one part per millon), with temperature between -55$^{\circ}C$ to＋75$^{\circ}C$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.53-56
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• 2007

Symmerge is a stable minimum storage algorithm for merging that needs $O(mlog\frac{n}{m})$ element comparisons, where m and n are the sizes of the input sequences with m ${\leqq}$ n. According to the lower bound for merging, the algorithm is asymptotically optimal regarding the number of comparisons. The objective of this paper is to consider the relationship between m and n for the spanning case with the recursion level m-1.

• Korean Journal of Crystallography
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• v.11 no.2
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• pp.102-107
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• 2000

20vol% Al2O3-dispersed 2Y-TZP ceramics was prepared by mixing of 2Y-TZP and Al2O3 powder with different particle sizes, and investigated the influence of Al2O3 particle size and sintering condition on the microstructure and fracture toughness. Sintering conditions of the Al2O3-dispersed 2Y-TZP specimens showed high density at sintering condition of 1350℃ and 1500℃ for 1∼5h, and homogeneous microstructure. The grain size of tetragonal zirconia and the fracture toughness increased with the size of dispersed Al2O3 particle. The highest fracture toughness (∼17.2MPa·m1/2) of all specimens was obtained in the specimens with dispersed Al2O3 particle size of 1.0㎛ and sintered at 1500℃ for 2h.

• Journal of the Korean Ceramic Society
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• v.37 no.7
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• pp.700-704
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• 2000

Titanium hydroxide precipitate was obtained by the reaction of 0.5M TiOCl2 and 5M NH4OH solutions, then anatase TiO2 powder with nanotubes was prepared by the digestion of the heat-treated powder in 5M NaOH solution. Nanotube was formed for anatase TiO2 powder digested at 10$0^{\circ}C$ above, and the amount and length of nanotube increased with the digestion temperature. In the case of the powder digested at 15$0^{\circ}C$ for 12h, the formed nanotube was 100~150nm in length, 10~20 nm in diameter, and 2nm in width of the walls on both sides of the nanotube. The powder digested at 15$0^{\circ}C$ for 12h showed the highest specific surface area of 270$m^2$/g.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.272-277
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• 2008

Symmerge is a stable minimum storage merging algorithm that needs $O(m{\log}{\frac{n}{m}})$ element comparisons, where in and n are the sizes of the input sequences with $m{\leq}n$. Hence, according to the lower bound for merging, the algorithm is asymptotically optimal regarding the number of comparisons. The Symmerge algorithm is based on the standard recursive technique of "divide and conquer". The objective of this paper is to consider the relationship between m and n for the degenerated case where the recursion depth reaches m-1.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.96-101
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• 2014

Wind power is the fastest growing renewable energy source in the world and it is expected to remain so for some times. Recently, there is a constant need for the reduction of Operational and Maintenance(O&M) costs of Wind Energy Conversion Systems(WECS). The most efficient way of reducing O&M cost would be to utilize CMS(Condition Monitoring System) of WECS. CMS allows for early detection of the deterioration of the wind generator's health, facilitating a proactive action, minimizing downtime, and finally maximizing productivity. There are two types of faults such as mass unbalance and aerodynamic asymmetry which are related to wind turbine's rotor faults. Generally, these faults tend to generate various vibrations. Therefore, in this work a simple fault detection algorithm based on spectrums of vibration signals and simple max-min decision logic is proposed. Furthermore, in order to verify its feasibility, several simulation studies are carried out by using GH-bladed software.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.107-123
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• 2006

We propose the Generalized Binary Second-order Recurrent Neural Networks(GBSRNNf) being equivalent to regular grammars and ?how the implementation of lexical analyzer recognizing the regular languages by using it. All the equivalent representations of regular grammars can be implemented in circuits by using GSBRNN, since it has binary-valued components and shows the structural relationship of a regular grammar. For a regular grammar with the number of symbols m, the number of terminals p, the number of nonterminals q, and the length of input string k, the size of the corresponding GBSRNN is $O(m(p+q)^2)$ and its parallel processing time is O(k) and its sequential processing time, $O(k(p+q)^2)$.