• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.68-68
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• 2011

As feature size is smaller, new technology are needed in semiconductor factory such as gap-fill technology for sub 100nm, development of ALD equipment for Cu barrier/seed, oxide trench etcher technology for 25 nm and beyond, development of high throughput Cu CMP equipment for 30nm and development of poly etcher for 25 nm and so on. We are focus on gap-fill technology for sub-30nm. There are many problems, which are leaning, over-hang, void, micro-pore, delaminate, thickness limitation, squeeze-in, squeeze-out and thinning phenomenon in sub-30 nm gap fill. New gap-fill processes, which are viscous oxide-SOD (spin on dielectric), O3-TEOS, NF3 Based HDP and Flowable oxide have been attempting to overcome these problems. Some groups investigated SOD process. Because gap-fill performance of SOD is best and process parameter is simple. Nevertheless these advantages, SOD processes have some problems. First, material cost is high. Second, density of SOD is too low. Therefore annealing and curing process certainly necessary to get hard density film. On the other hand, film density by Flowable oxide process is higher than film density by SOD process. Therefore, we are focus on Flowable oxide. In this work, dielectric film were deposited by PECVD with TSA(Trisilylamine - N(SiH3)3) and NH3. To get flow-ability, the effect of plasma treatment was investigated as function of O2 plasma power. QMS (quadruple mass spectrometry) and FTIR was used to analysis mechanism. Gap-filling performance and flow ability was confirmed by various patterns.

• Fibers and Polymers
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• v.7 no.2
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• pp.129-138
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• 2006

Using a strain-controlled rheometer, the steady shear flow properties of aqueous xanthan gum solutions of different concentrations were measured over a wide range of shear rates. In this article, both the shear rate and concentration dependencies of steady shear flow behavior are reported from the experimentally obtained data. The viscous behavior is quantitatively discussed using a well-known power law type flow equation with a special emphasis on its importance in industrial processing and actual usage. In addition, several inelastic-viscoplastic flow models including a yield stress parameter are employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models is also examined in detail. Finally, the elastic nature is explained with a brief comment on its practical significance. Main results obtained from this study can be summarized as follows: (1) Concentrated xanthan gum solutions exhibit a finite magnitude of yield stress. This may come from the fact that a large number of hydrogen bonds in the helix structure result in a stable configuration that can show a resistance to flow. (2) Concentrated xanthan gum solutions show a marked non-Newtonian shear-thinning behavior which is well described by a power law flow equation and may be interpreted in terms of the conformational status of the polymer molecules under the influence of shear flow. This rheological feature enhances sensory qualities in food, pharmaceutical, and cosmetic products and guarantees a high degree of mix ability, pumpability, and pourability during their processing and/or actual use. (3) The Herschel-Bulkley, Mizrahi-Berk, and Heinz-Casson models are all applicable and have equivalent ability to describe the steady shear flow behavior of concentrated xanthan gum solutions, whereas both the Bingham and Casson models do not give a good applicability. (4) Concentrated xanthan gum solutions exhibit a quite important elastic flow behavior which acts as a significant factor for many industrial applications such as food, pharmaceutical, and cosmetic manufacturing processes.

• Journal of the Korean Magnetics Society
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• v.20 no.2
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• pp.75-82
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• 2010

A recent progress on the prediction of the thermal stability of a nanostructured exchange-coupled trilayer is reviewed. An analytical/numerical combined method is used to calculate its magnetic energy barrier and hence the thermal stability parameter. An important feature of the method is the use of an analytical equation for the total energy that contains the magnetostatic fields. Under an assumption of the single domain state, the effective values of all the magnetostatic fields can be obtained by averaging their nonuniform values over the entire magnetic volume. In an equilibrium state, however, it is not easy to calculate the magnetostatic fields at the saddle point due to the absence of suitable methods of the accessing its magnetic configuration. This difficulty is overcome with the use of equations that link the magnetostatic fields at the saddle point and critical fields. Since the critical fields can readily be obtained by micromagnetic simulation, the present method should provide accurate results for the thermal stability of a nanostructured exchange-coupled trilayer.

• Journal of KIISE
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• v.42 no.2
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• pp.227-234
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• 2015

This paper proposes a modified MI-SVM algorithm by considering data distribution. The previous MI-SVM algorithm seeks the margin by considering the "most positive" instance in a positive bag. Positive instances included in positive bags are located in a similar area in a feature space. In order to reflect this characteristic of positive instances, the proposed method selects the "most positive" instance by calculating the distance between each instance in the bag and a pivot point that is the intersection point of all positive instances. This paper suggests two ways to select the "most positive" pivot point in the training data. First, the algorithm seeks the "most positive" pivot point along the current predicted parameter, and then selects the nearest instance in the bag as a representative from the pivot point. Second, the algorithm finds the "most positive" pivot point by using a Diverse Density framework. Our experiments on 12 benchmark multi-instance data sets show that the proposed method results in higher performance than the previous MI-SVM algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.78-88
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• 2009

In many face recognition problems, the number of available images is limited compared to the dimension of the input space which is usually equal to the number of pixels. This problem is called as the 'small sample size' problem and regularization methods are typically used to solve this problem in feature extraction methods such as LDA. By using regularization methods, the modified within class matrix becomes nonsingu1ar and LDA can be performed in its original form. However, in the process of adding a scaled version of the identity matrix to the original within scatter matrix, the scale factor should be set heuristically and the performance of the recognition system depends on highly the value of the scalar factor. By using the proposed resampling method, we can generate a set of images similar to but slightly different from the original image. With the increased number of images, the small sample size problem is alleviated and the classification performance increases. Unlike regularization method, the resampling method does not suffer from the heuristic setting of the parameter producing better performance.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.699-704
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• 2001

ABSTRACT In this paper, a new hybrid modular recurrent TDNN (time-delay neural network)-HMM (hidden Markov model) architecture for speech recognition has been studied. In TDNN, the recognition rate could be increased if the signal window is extended. To obtain this effect in the neural network, a high-level memory generated through a feedback within the first hidden layer of the neural network unit has been used. To increase the ability to deal with the temporal structure of phonemic features, the input layer of the network has been divided into multiple states in time sequence and has feature detector for each states. To expand the network from small recognition task to the full speech recognition system, modular construction method has been also used. Furthermore, the neural network and HMM are integrated by feeding output vectors from the neural network to HMM, and a new parameter smoothing method which can be applied to this hybrid system has been suggested.

• Smart Structures and Systems
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• v.17 no.2
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• pp.167-194
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• 2016

This paper investigates the Synergetics based Damage Detection Method (SDDM) for frame structures by using surface-bonded PZT (Lead Zirconate Titanate) patches. After analyzing the mechanism of pattern recognition from Synergetics, the operating framework with cooperation-competition-update process of SDDM was proposed. First, the dynamic identification equation of structural conditions was established and the adjoint vector (AV) set of original vector (OV) set was obtained by Generalized Inverse Matrix (GIM).Then, the order parameter equation and its evolution process were deduced through the strict mathematics ratiocination. Moreover, in order to complete online structural condition update feature, the iterative update algorithm was presented. Subsequently, the pathway in which SDDM was realized through the modified Synergetic Neural Network (SNN) was introduced and its assessment indices were confirmed. Finally, the experimental platform with a two-story frame structure was set up. The performances of the proposed methodology were tested for damage identifications by loosening various screw nuts group scenarios. The experiments were conducted in different damage degrees, the disturbance environment and the noisy environment, respectively. The results show the feasibility of SDDM using piezoceramic sensors and actuators, and demonstrate a strong ability of anti-disturbance and anti-noise in frame structure applications. This proposed approach can be extended to the similar structures for damage identification.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1681-1689
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• 2010

Several studies on the use of Support Vector Machines (SVMs) for diagnosing rotating machinery have been successfully carried out, but the fault classification depends on the input features as well as a multi-classification scheme, binary optimizer, kernel function, and the parameter to be used in the kernel function. Most of the published papers on multiclass SVM applications report the use of the same features to classify the faults. In this study, simple statistical features are determined on the basis of time domain vibration signals for various fault conditions, and the optimal features for each fault condition are selected. Then, the optimal features are used in the SVM training and in the classification of each fault condition. Simulation results using experimental data show that the results of the proposed stepwise classification approach with a relatively short training time are comparable to those for a single multi-class SVM.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.35-39
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• 2020

DNN error is small compared to the conventional speech recognition system, DNN is difficult to parallel training, often the amount of calculations, and requires a large amount of data obtained. In this paper, we generate a phoneme unit to estimate the GMM parameters with each phoneme model parameters from the GMM to solve the problem efficiently. And it suggests ways to improve performance through clustering for a specific vocabulary to effectively apply them. To this end, using three types of word speech database was to have a DB build vocabulary model, the noise processing to extract feature with Warner filters were used in the speech recognition experiments. Results using the proposed method showed a 97.9% recognition rate in speech recognition. In this paper, additional studies are needed to improve the problems of improved over fitting.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.4
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• pp.379-388
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• 2007

Ever increasing fuel prices and environmental concerns are forcing commercial vessel operators and designers to re-assess current vessel designs with an emphasis on their propulsion systems. The most important parameter determining propulsive efficiency is the diameter of propeller. Many investigations have been carried out to adapt a large and slow turning propeller known as one of the most robust and effective way of achieving high efficiency in ship propulsion system. However, for the same vessel a further increase of propeller diameter would require the modification of the aft end while still paying attention to the hull clearance to prevent excessive propeller excited vibrations. In order to take the advantage of this approach small workboats (e.g. tug boats, fishing vessels etc.) operate in service with a significant increase of aft draught and hence resulting "inclined keel" configuration can be observed. Although it is not unusual to see large vessels sometimes to operate with stern trim to improve their operational performance and fuel efficiency, it is rare to see a such vessel purposely built with an inclined keel feature to fit a large diameter propeller for power saving. This paper investigates the application of the inclined keel configuration to a 3600TEU container vessel with the aim of fitting an 11 % larger diameter propeller (and hence resulting 17.5 % lower rpm) to gain further power saving over the similar size basis container ship with conventional "level keel" configuration.