• 한국정밀공학회지
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• 제12권12호
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• pp.30-37
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• 1995

The mechanical seals, which are installed in rotating machines like pump and compressor, are gengrally used as sealing devices in the many fields of industries. The failure of mechanical seals such as leakage,fast and severe wear, excessive torque, and squeaking results in big problems. To monitor the failure of mechanical seals and to propose the proper monitoring techniques with artificial neural network, sliding wear experiments were conducted. Torque and temperature of the mechanical seals were measured during experiments. Optical microstructure was observed for the wear processing after every 10 minute sliding at rotation speed of 1750 rpm and scanning electron microscopy was also observed. During the experiment, the variation of torque and temperature that meant an abnormal phenomenon, was observed. That experimental data recorded were applied to the developed monitoring system with artificial neural network. This study concludes that torque and temperature of mechanical seals wil be used to identify and to monitor the condition of sliding motion of mechanical seals. An availability to monitor the mechanical seal failure with artificial neural network was confirmed.

• Journal of information and communication convergence engineering
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• 제13권1호
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• pp.21-26
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• 2015

A wireless sensor network (WSN) is designed and implemented for a freeze dryer. Freeze-drying technology is widely used in the fields of pharmacy and biotechnology as well as the food and agriculture industries. Taking into account the demand for high-resolution pressure and temperature measurements in a freeze dryer, the proposed WSN has a significant advantage of creating a monitoring environment in a freeze dryer. The proposed WSN uses a ZigBee/IEEE 802.15.4 network with an altimeter module that contains a high-resolution pressure and temperature sensor with a serial digital data interface. The ZigBee network is suitable for low-energy and low-data-rate applications in the field of wireless communication. The altimeter module is capable of sensing pressure in the range of 7.5-975 Torr (10-1300 mbar) and temperature in the range of $-40^{\circ}C$ to $125^{\circ}C$ with a DC power consumption of $3{\mu}W$. The implemented WSN is installed in a commercial laboratory freeze dryer in order to demonstrate its functionality and efficiency. A comparison with the temperature profile measured by a thermocouple installed in the freeze dryer reveals that the resolution of the temperature profile measured by WSN is superior to that measured by the thermocouple.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2004년도 추계학술대회 논문집
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• pp.52-55
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• 2004

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. $1K\times1K$ CCD color camera and Xenon Lamp(500W) were used for the visualization of a Hele-Shaw cell. The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity. A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.

• 대한기계학회논문집B
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• 제27권11호
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• pp.1572-1578
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• 2003

Particle Image Thermometry(PIT) with liquid crystal tracers is used for visualizing and analysis of the bubbly flow in a vertical temperature gradient. Quantitative data of the temperature were obtained by applying the color-image processing to a visualized image, and neural-network was applied to the color-to-temperature calibration. This paper describes the method, and presents the transient mixing temperature patterns of the bubbly flow.

• Advances in environmental research
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• 제6권4호
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• 한국해안·해양공학회논문집
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• 제27권1호
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• pp.56-62
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• 2015

지구 온난화의 영향으로 해수면과 기온이 상승하고, 이의 직접적인 영향으로 수온이 증가하고 있다. 지구 온난화가 하천의 수질과 생태 환경에 미치는 영향을 추정하기 위해서는 수온에 대해 이해하고 수온의 변화를 예측할 필요가 있다. 이 연구에서는 수온의 변화를 예측하기 위하여 기온과 수온자료를 입력자료로 하여 수온의 예측을 실시하였다. 2012년에서 2014년까지 환경부의 수질환경관측소에서 관측한 새만금호내의 신시, 가력, 만경, 동진 4개 지점의 수온자료와 기상청에서 같은 기간에 관측한 부안의 자동관측 기온 자료를 활용하였다. 신경망이론을 이용하여 최고 및 최저 수온을 예측한 결과 4개 지점의 모든 결과에서 아주 높은 상관계수를 가지고 있다.

• 소성∙가공
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• 제27권4호
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• 한국전자통신학회논문지
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• 제18권5호
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• pp.841-852
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• 2023

국내 전력계통의 주파수 조정용 발전기로 사용되고 있는 가스터빈은 탄소중립 정책과 더불어 신속한 기동·정지 및 높은 열효율 등으로 인해 이용률이 증가하고 있다. 가스터빈은 고온의 화염을 이용하여 터빈을 회전시키기 때문에 터빈 입구온도가 기기의 성능과 수명을 좌우하는 핵심요소로 작용하고 있다. 하지만 입구온도는 직접적인 측정이 불가능함에 따라 제작사가 산출한 온도를 이용하거나, 현장 경험을 토대로 하여 예측된 온도를 적용하고 있어서 가스터빈의 안정적인 운전 및 유지관리에 많은 어려움을 겪고 있다. 이에 본 연구에서는 인공신경망에서 많이 사용되고 있는 DNN(: Deep Neural Network) 기반으로 하는 재열 가스터빈의 입구온도를 예측할 수 있는 모델을 제시하고 실측 데이터를 기반으로 제안된 DNN의 성능을 검증하고자 한다.

• 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.