• Journal of computational fluids engineering
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• v.15 no.1
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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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• v.30 no.1
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• pp.1-6
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• 2017

Epoxy resin (EP) is one of the most famous thermoset materials. In general, because EP has three-dimensional random network, it possesses thermal properties like a typical heat insulator. Recently, there has been increasing interest in controlling the network structure for making new functionality from EP. Indeed, the new modified EP represented as liquid crystalline epoxy (LCE) is spotlighted as an enabling technology for producing novel functionalities, which cannot be obtained from the conventional EPs, by replacing the random network structure to oriented one. In this paper, we review current progress in the field of LCEs and their application for the highly thermal conductive composite materials.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.6 no.2
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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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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• v.31 no.2
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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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• v.3 no.2
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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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• v.62 no.1
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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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.6
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• pp.42-50
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• 2006

This paper addresses the optimized thermal modeling methodology for spacecraft board level thermal analysis. A direct thermal modeling of external and internal structure of active parts which have high power dissipation is newly proposed, based on conventional plate modeling for Printed Circuit Board(PCB). The parts thermal modeling results were compared with other generic methodologies and verified by thermal vacuum test. This parts thermal modeling was directly applied to thermal analysis of CS(Communication Subsystem) board of HAUSAT-2 small satellite. As a result, it was confirmed that the parts thermal modeling can complement other conventional modeling methodologies. A parts thermal modeling is very effective for thermal control design, since the existing thermal problems can be solved at the parts level in advance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.