• 한국전산유체공학회지
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• 제15권1호
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• pp.24-30
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• 2010

A preliminary thermal analysis is performed for the optical payload system of a geostationary satellite. The optical payload considered in this paper is GOCI(Geostationary Ocean Color Imager) of COMS of Korea. The radiative and conductive thermal models are employed in order to predict thermal responses of the GOCI on the geostationary orbit. The results of this analysis are as follows: 1) the GOCI instrument thermal control is satisfactory to provide the temperatures for the GOCI performances, 2) the thermal control is defined and interfaces are validated, and 3) the entrance baffle temperature and shutter wheel motor gradient are found slightly out their specification, therefore further detailed analyses should be continued on these elements.

• Composites Research
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• 제30권1호
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• pp.1-6
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• 2017

에폭시 수지는 3차원 네트웍 구조를 갖는 대표적인 열경화성 수지이다. 최근 에폭시 수지의 네트웍 구조를 제어하여 새로운 기능성 에폭시를 개발하는 연구가 활발히 진행되고 있다. 특히, 액정성 에폭시를 대표로 하는 새로운 개질 에폭시는 랜덤한 형태의 네트웍 구조를 배향 구조로 변경함으로써, 기존의 에폭시로부터 얻을 수 없는 새로운 기능성 발현에 성공하고 있다. 본 논문에서는 액정성 에폭시 수지의 합성과 고방열성 복합재료로의 응용에 관하여 설명하였다.

• 설비공학논문집
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• 제6권2호
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• pp.78-92
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• 1994

Thermal performance of pipe network of continuous heating system controlled by thermostat and flow control valve was simulated in consideration of radiation heat transfer and solved by linear analysis method. Thermal performance of real apartment building with radiant floor heating system was simulated by equivalence heat resistance-capacity method. This method enables to simulate the unsteady variation of temperature or each element of building. Heat transfer characteristics of each element were also investigated.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.15-24
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• 1995

This paper describes a system that can be used to recognize an unknown material regardless of the fuzzy neural network(FNN). There are some problems to realize the recognition system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient temperature. So, this paper proposes a practical method using curve fitting to remove above problems of memories and noise. and FNN is proposed to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient. Temperatures and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be recognized by the thermal conductivity.

• Nuclear Engineering and Technology
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• 제31권2호
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.133-138
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• 2001

This paper describes a system that can used to recognize an unknown material regardless of the change of ambient tem-perature using temperature response curve fitting and fuzzy neural network(FNN). There are some problems to realize the recogni-tion system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient tempera-ture. So, this paper proposes a practical method using curve fitting the remove above problems of memories and nose. And FNN is propose to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient temperature and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperature. So the material can be recognized by the thermal conductivity.

• Journal of Korean Neurosurgical Society
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• 제62권1호
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• pp.10-26
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• 2019

Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging new technology with considerable potential to treat various neurological diseases. With refinement of ultrasound transducer technology and integration with magnetic resonance imaging guidance, transcranial sonication of precise cerebral targets has become a therapeutic option. Intensity is a key determinant of ultrasound effects. High-intensity focused ultrasound can produce targeted lesions via thermal ablation of tissue. MRgFUS-mediated stereotactic ablation is non-invasive, incision-free, and confers immediate therapeutic effects. Since the US Food and Drug Administration approval of MRgFUS in 2016 for unilateral thalamotomy in medication-refractory essential tremor, studies on novel indications such as Parkinson's disease, psychiatric disease, and brain tumors are underway. MRgFUS is also used in the context of blood-brain barrier (BBB) opening at low intensities, in combination with intravenously-administered microbubbles. Preclinical studies show that MRgFUS-mediated BBB opening safely enhances the delivery of targeted chemotherapeutic agents to the brain and improves tumor control as well as survival. In addition, BBB opening has been shown to activate the innate immune system in animal models of Alzheimer's disease. Amyloid plaque clearance and promotion of neurogenesis in these studies suggest that MRgFUS-mediated BBB opening may be a new paradigm for neurodegenerative disease treatment in the future. Here, we review the current status of preclinical and clinical trials of MRgFUS-mediated thermal ablation and BBB opening, described their mechanisms of action, and discuss future prospects.

• 한국항공우주학회지
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• 제34권6호
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• pp.42-50
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• 2006

본 논문에서는 위성의 전장보드 열해석을 위한 최적의 열모델링 방법을 제안하였다. 플레이트 모델링 방법을 통한 보드 모델링에 고전력 소산 소자의 외부 및 내부 구조를 직접 모델링하는 방법을 새롭게 제안하였다. 이러한 모델링 방법을 다른 모델링과 비교 분석하여 효율성을 검토하였고 열진공 시험을 통해 검증하였다. 제시한 소자 모델링 방법으로 HAUSAT-2의 발열이 큰 통신보드의 열해석을 수행한 결과, 노드 네트워크 모델링 방법과 플레이트 모델링 방법의 단점을 모두 보완할 수 있었다. 또한, 소자 모델링 방법은 열적인 문제에 따른 소자 수준의 해결방안을 모색 후, 그에 따른 열해석을 수행하여 효과를 예측할 수 있으므로 열제어계 설계에도 효율적이다.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• 한국지능시스템학회논문지
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• 제14권1호
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• pp.1-8
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• 2004

본 논문에서는 실내대기 가스모니터링 시스템에서의 센서 고장 진단을 위한 신경회로망 기반 고장진단방법을 제안한다. 제안한 고장진단 방법에서는 신호패턴추출을 위해 센서히터 온도조절방법을 이용하였으며, 분류를 위해서는 ART2 신경회로망을 이용하였다. 그리고 가스모니터링 시스템의 실제 데이터를 이용한 시뮬레이션을 통해 제안한 ART2 신경회로망 기반 센서고장진단방법의 성능과 유용성을 확인하였다.