• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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• pp.247-253
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• 1999

In this paper predictive PID control system using neural network (NNPPID) is proposed to control temperature system. NNPPID is composed of neural network predictor forecasts the future output of plant based on the present input and output of plant. Neural self-tuner yields parameters of PID controller. Experiments prove that NNPPID temperature control system has better performance than conventional PID control.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.479-480
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• 2007

It is study about implementation of the home network system that used an ubiquitous sensor network and an embedded system in this paper. PXA270 and CC2420 were used, and the home server of a wireless sensor home network system composed it. A wireless control system is composed of a gas valve, a DC motor, a lamp and a door rock. A wireless detection system is composed of a gas detection sensor, a movement detection sensor, an extension detection sensor The wireless detection system that was an environment sensing system was composed of temperature, humidity, mic, illuminance, a speed-up, infrared rays temperature sensing module, and modular, other RFID established an USB camera, and an ubiquitous home network was composed.

• International Journal of Concrete Structures and Materials
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• 제6권3호
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• pp.177-186
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• 2012

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

• Journal of Advanced Marine Engineering and Technology
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• 제27권7호
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• pp.921-929
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• 2003

An experimental investigation was performed to study the characteristics of turbulent water flow in a horizontal circular tube by using liquid crystal. To determine some characteristics of the turbulent flow, 2D PIV technique is employed for velocity measurement and liquid crystal is used for heat transfer experiments in water. Temperature visualization was made quantitatively by calibrating the color of the liquid crystal versus temperature using various approaches (TLC technique: Thermochromic Liquid Crystal), and a neural-network algorithm was applied to the color-to-temperature calibration. This study shoud the temperature and time-mean velocity distribution for Re = 2,436, 2,500 and 2,724 along longitudinal sections and the results appear to be physically reasonable.

• 제어로봇시스템학회논문지
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• 제9권12호
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• pp.1048-1054
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• 2003

In laser surface hardening processes, the geometrical parameters such as the depth and the width of a hardened layer can be utilized to assess the hardened layer quality. However, accurate monitoring of the geometrical parameters for on-line process control as well as for on-line quality evaluation is very difficult because the hardened layer is formed beneath a material surface and is not visible. Therefore, temperature monitoring of a point of specimen surface has most frequently been used as a process monitoring method. But, a hardened layer depends on the temperature distribution and the thermal history of a specimen during laser surface hardening processing. So, this paper describes the estimation results of the geometric parameters using multi-point surface temperature monitoring. A series of hardening experiments were performed to find the relationships between the geometric parameters and the measured temperature. Estimation results using a neural network show the enhanced effectiveness of multi-point surface temperature monitoring compared to one-point monitoring.

• 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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1994년도 춘계학술대회 논문집
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• pp.30-33
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• 1994

This paper studies on the effect of DSC(Differential Scanning Calorimeter) analysis condition on the glass transition temperature of silica filled epoxy network polymer used for ultra-high voltage apparatus. The effects of temperature scanning rate specimen size and gas flow rate on measured glass transition temperature have been studied in order to select optimum thermal analysis condition.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.151-155
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• 2024

This project is designed with the aim to facilitate the farmer or gardener to engage in green house systems and to improve agricultural technology. In order to reduce continuous monitoring of the soil parameters, excess time consumption for the farmers and excessive usage of water, "Smart irrigation and temperature control for a greenhouse system" has been developed. There are two different ways to irrigate the land namely traditional irrigation methods and modern irrigation methods.

• 센서학회지
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• 제15권5호
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• pp.362-370
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• 2006

The wireless sensor network (WSN) based ECG and body temperature measuring system for ubiquitous health-care were designed and developed. The system was composed of a wireless sensor network node, base station and server computer for the continuous monitoring of ECG signals and body temperatures of patients at home or hospital. ECG signal and body temperature data, important vital signals which are commonly used in clinical and trauma care, were displayed on a graphical user interface (GUI). The data transfer from sensor nodes on patients' body to server computer was accomplished through a base-station connected to a server computer using Zigbee compatible IEEE802.15.4 standard wireless communication. Real-time as well as historical, ECG data of elderly persons or patients, can also be retrieved and played back to assist the diagnosis. The ubiquitous health care system presented in this study can effectively reduce social medical expenses, which will be increased greatly in the coming aging society.

• Transactions on Control, Automation and Systems Engineering
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• 제2권4호
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• pp.268-273
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• 2000

Plasma models are crucial to equipment design and process optimization. A radial basis function network(RBFN) in con-junction with statistical experimental design has been used to model a process plasma. A 2$^4$ full factorial experiment was employed to characterized a hemispherical inductively coupled plasma(HICP) in characterizing HICP, the factors that were varied in the design include source power, pressure, position of shuck holder, and Cl$_2$ flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature. and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBEN are 0.409(10(sup)12/㎤), 0.277(eV), and 0.699(V), for electron density, electron temperature, and Plasma potential, respectively. For spatial uniformity data, they are 2.623(10(sup)12/㎤), 5.704(eV) and 3.481(V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network(GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBEN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.