• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• 정보통신설비학회논문지
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• 제10권4호
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• pp.143-147
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• 2011

The system using augmented reality can save the time and cost. It is verified in various fields under the possibility of a technology by solving unrealistic feeling in the virtual space. Therefore, augmented reality has a variety of the potential to be used. Generally, multimodal senses such as visual/auditory/tactile feed back are well known as a method for enhancing the immersion in case of interaction with virtual object. By adapting tangible object we can provide touch sensation to users. a 3D model of the same scale overlays the whole area of the tangible object; thus, the marker area is invisible. This contributes to enhancing immersive and natural images to users. Finally, multimodal feedback also creates better immersion. In this paper, sound feedback is considered. By further improving immersion learning augmented reality for children with the initial step learning content is presented. Augmented reality is in the intermediate stages between future world and real world as well as its adaptability is estimated more than virtual reality.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 F
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• pp.2668-2670
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• 1999

In the past, the PWM converter had a large switching loss by hard switching and difficult to high frequency operation. The resonance converter to decrease the switching loss and EMI is required the frequency control and needed to reduce the voltage or current stress at each parts. So, this paper propose the 3-phase boost converter and the method to compensated input power factor by control the amplitude - an instantaneous value of the DC inductor current -and control the switching frequency that a modulation error by the ripple of the DC inductor current. The proposed 3-phase PWM boost converter of single phase control type can takes higher capacity and compensate the power factor by using Feed back controller at each phase for the existing 3-phase bridge rectifier type. Moreover the 3-phase full bridge type using the rectifier at each 3-phase circuit will be small size reactor and compensate input power factor by minimize harmonic components of each phase.

• Transactions on Control, Automation and Systems Engineering
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• 제2권4호
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• pp.274-284
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• 2000

In this paper, the design methodology of digital fuzzy controller(DFC) for the systems with time-delay is presented and the qualitative effects of the quantizers in digital implementation of a fuzzy controllers are investigated. We propose the fuzzy feed-back controller whose output is delayed with unit sampling period and period and predicted. the analysis and the design problem considering time-delay become very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy system with time-delay is solved by linear matrix inequality(LMI) theory. Furthermore, we analyze the stability of the quantized fuzzy system. Our results prove that when quantization os taken into account, one only has convergence to some small neighborhood about origin. We develop a fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of time-delay and quantization effect. By using the proposed method, we analyze the quantization effect to the system and design a DFC which guarantees the stability of the control system in the presence of time-delay.

• 대한정형도수물리치료학회지
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• 제19권1호
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• pp.41-47
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• 2013

Background: The purpose of this study is to examine the effects of visual feedback squat exercise on the young adults with Patellofemoral pain syndrome. Methods: The subjects of this study were 22 female and 8 male who were equally and randomly allocated to a visual feedback exercise group (VSEG), an experimental group, and squat exercise group (SEG), a control group. Both did so for 30 minutes three times per week over a six-week period. Using Dartfish, their static Q-angle (SQA) and dynamic Q-angle (DQA) were evaluated. Results: The static Q-angle was significantly reduced in both groups of VSEG and SEG and in the comparison of difference values before and after exercise between groups, VSEG had more significant effect than SEG but in the comparison of the effects of dynamic Q-angle both VSEG and SEG had significant effects and in the comparison of difference values before and after exercise between groups, VSEG had more significant effect than SEG. Conclusion: Visual feedback squat exercise may be applied as a method to correct the lower extremity alignment with PFPS.

• 한국표면공학회지
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• 제24권3호
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• pp.119-124
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• 1991

Recently, the trend of research in hard coating is concentrate on developing the process of ionization rate under low operating pressure, to get the thin film with high adhesion and dense microstructures. In this study ionization rate enhancement type PVD process using permanent magnet is developed, which enhances the ionization rate by confining the plasma suppressing the wall loss of electron. By the result to investigate the characteristic of glow discharge, the ionization rate of this process is enhanced about twice as high as that of triod BARE process (about 26%), and more dense TiN microstructures are obtained in this process. Cylindrical ion energy analyzer is made and attached in front of a quadrupole mass filter for the analysis of the energy distribution of reactive gas and activated gas ions from the plasma zone. To analyze the operation mechanism of ion energy analyzer, computer simulation is performed by calculation the electric field environment using finite element method. By these analyses of ion energy distribution of outcoming ions from the plasma zone, it is found that magnetic field enhances ion kinetic energy as well as ionization rate. The other results of this study is that the foundation of feed-back system is constructed, which automatically control the partial pressure of reactive gas. In can be possible by recording the data of mass spectrum and ion energy analysis using A-D converter.

• 융합신호처리학회논문지
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• 제5권4호
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• pp.263-270
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• 2004

최근 웨이블릿 변환은 많은 분야에서 다양하게 적용된다. 본 논문에서 tasks뇌파의 중요한 몇가지 특성파 검출을 위한 다비치 웨이블릿은 뇌파분석에 필요하다. 우리가 제안한 시스템은 다른 방법보다는 특성파 검출에 높은 성능을 가졌다. 본 연구의 뉴럴시스템의 구조는 하나의 은닉층과 3계층 피드포워드층은 오류 BP 학습알고리즘을 적용하였다. 4명의 피험자에게 알고리즘을 적용하여 92% 분류율을 보였다. 제안된 시스템은 웨이블릿과 신경망으로 tasks 뇌파의 보다 정확하게 분석함을 보였다. 모의실험결과 tasks 뇌파는 의사의 노동력을 줄일수 있고 정량적 해석이 가능함을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.195-200
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• 1998

Since the neural network was introduced, significant progress has been made on data handling and learning algorithms. Currently, the most popular learning algorithm in neural network training is feed forward error back-propagation (FFEBP) algorithm. Aside from the success of the FFEBP algorithm, polynomial neural networks (PNN) learning has been proposed as a new learning method. The PNN learning is a self-organizing process designed to determine an appropriate set of Ivakhnenko polynomials that allow the activation of many neurons to achieve a desired state of activation that mimics a given set of sampled patterns. These neurons are interconnected in such a way that the knowledge is stored in Ivakhnenko coefficients. In this paper, the PNN model has been developed using the plasma enhanced chemical vapor deposition (PECVD) experimental data. To characterize the PECVD process using PNN, SiO$_2$films deposited under varying conditions were analyzed using fractional factorial experimental design with three center points. Parameters varied in these experiments included substrate temperature, pressure, RF power, silane flow rate and nitrous oxide flow rate. Approximately five microns of SiO$_2$were deposited on (100) silicon wafers in a Plasma-Therm 700 series PECVD system at 13.56 MHz.

• 한국전자파학회논문지
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• 제18권1호
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• pp.90-95
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• 2007

LDR(Low Date Rate) UWB(Ultra Wide Band) 시스템에서 chaotic 신호를 이용한 OOK(On Off Keying) 통신 방법이 선택적 사항으로 채택되어 있다. 이 시스템에서 중요 회로 중에 하나인 chaotic 신호 발생 회로는 현재 대부분 아날로그 형태의 피드백 구조를 이용하고 있으나, 아날로그 형태의 피드백 구조는 공정 변화에 대한 취약점이 있어 양산성에 어려움이 있다. 이를 해결하기 위해 본 논문에서는 0.18 um CMOS 공정을 이용하여 디지털 구조의 PN-sequence를 응용하여 중심 주파수 4 GH에서 2 GHz의 대역폭을 갖는 Quasi-chaotic 신호 발생 회로를 설계 및 검증하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.210-213
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• 1999

본 논문에서는 로봇 매니퓰레이터 제어를 위해 새로운 신경회로망을 제안한다. 제안한 신경회로망구조는 은닉층과 출력층의 출력이 피드백 층을 거쳐 다시 은닉층과 출력층으로 피드백되는 구조이다. 피드백 층은 한번의 시간 지연을 갖는다. 제안한 신경회로망의 학습은 일반적인 오차 역전파 알고리즘을 사용한다. 로봇 매니퓰레이터를 대상으로 시뮬레이션과 실험을 통해서 제안한 신경회로망 구조의 유용성을 확인한다.