• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.792-793
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• 2008

This paper deals with the temperature calculation using equivalent thermal network for surface mounted permanent magnet synchronous motor(SPMSM) under the steady-state condition. In the equivalent thermal network, heat sources are generated from copper loss and iron loss. Heat transfer consists of conduction, convection and radiation. However, radiation is neglected in this paper because its effect is much smaller than others. Although the heat transfer coefficient in conduction use material property, heat transfer coefficient in convection is difficult to measure due to the atmosphere and ambient condition. Temperatures of each region in SPMSM are measured by thermocouple in operating condition and the thermal resistances of convection are calculated by kirchhoff's current law(KCL) and experimental result. In order to verify the validation and reliability of the proposed equivalent thermal network, temperature which is calculated other load condition is compared with experimental results. Accordingly, temperatures of each region in other SPMSMs will be easily predicted by the proposed equivalent thermal network.

• 전자공학회논문지CI
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• 제48권2호
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• pp.96-105
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• 2011

유비쿼터스 센서 네트워크(USN: Ubiquitos Sensor Network)는 우리 생활 전반적으로 다양하게 적용되고 있는 기술이다. 본 논문은 온도와 습도를 일정하게 유지해야 할 필요가 있는 공간을 효율적으로 관리할 수 있는 USN 기술 기반의 시스템에 대한 설계 및 이의 구현 사례를 소개한다. 본 시스템은 온 습도 센서가 내장된 다수의 센서 모듈을 이용하여 대상 공간에 무선 센서 네트워크를 구성하고, 각 모듈의 센서로부터 감지된 온 습도 정보를 실시간으로 수집하여 Database에 기록함과 동시에 사용자가 그래픽 인터페이스를 통해 대상 공간의 온 습도 현황을 Monitoring 할 수 있는 시스템이다. 또한, 각 온 습도 센서 모듈의 관리 대상 공간에 대하여 특정 온 습도 값을 지정하고 이 값과 일정 수준 이상의 오차가 발생하면 경고를 발생토록 한다. 본 논문에서 소개하는 시스템은 수동적이었던 기존의 데이터 수집 및 관리 방식을 자동화함으로서 보다 편리하고 효율적으로 대상 공간의 온 습도를 관리할 수 있으며, 저장된 데이터를 이용하여 온 습도로 인해 발생된 문제점에 대한 사후 분석 및 환경의 개선에 필요한 정보를 얻을 수 있다.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.465-469
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• 2001

In injection molding short shot is one of the frequent and fatal defects. Experts of Injection molding usually adjust process conditions such as injection time, mold temperature, and melt temperature because it is most economic way in time and cost. However, it is difficult task to find appropriate process conditions for troubleshooting of short shot as injection molding process is a highly nonlinear system and process conditions are coupled. In this paper, a modular fuzzy neural network (MFNN) has been applied to injection molding process to shorten troubleshooting time of short shot. Based on melt temperature and fill time, a reasonable initial mo이 temperature is recommenced by the NFNN, and then the mold temperature is inputted to injection molding process. Depending on injection molding result, specifically the insufficient quantity of an injection molded part. and appropriate mold temperature is recommend repeatedly through the NFNN.

• E2M - 전기 전자와 첨단 소재
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• 제7권6호
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• pp.527-533
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• 1994

In this study, it was studied on dielectric breakdown strength and thennostability properties due to the structure variation of matrix resin and treatment of coupling agent of epoxy insulating materials. The interpenetrating network structure was formed by simultaneous heating curing the epoxy resin with single network structure and the methacrylic acid resin. Also inner structure was observed and the glass transition temperature was measured on these three type specimens. Dielectric breakdown properties were investigated by applying DC, AC and impulse voltage. As a result, the glass transition temperature and the dielectric breakdown strength of specimen with interpenetrating network structure was more higher than another two type specimens.

• Journal of the Korean Statistical Society
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• 제36권2호
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• pp.209-228
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• 2007

Given scattered surface air temperatures observed by a network of weather stations, it is an important problem to estimate the entire temperature field for every location on the globe. Recently, a multi-scale spherical basis function (SBF) representation was proposed by Li (1999) for representing scattered data on the sphere. However, for a successful application of Li (1999)'s method, some practical issues such as network design, bandwidth selection of SBFs and initial coefficients are to be resolved. This paper proposes automatic procedures to design network and to select bandwidths. This paper also considers a preprocessing problem to obtain a stable initial coefficients from scattered data. Experiments with real temperature data demonstrate the promising empirical properties of the proposed approaches.

• 대한기계학회논문집
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• 제19권8호
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• pp.1907-1914
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• 1995

An on-line measurement of the workpiece hardened depth in laser surface hardening processes is very much difficult to achieve, since the hardening process occurs in depth wise direction. In this paper, the hardened depth is estimated using a multilayered neural network. Input data of the neural network are the surface temperatures at arbitrary chosen five surface points, laser power and traveling speed of laser beam torch. To simulate the actual hardening process, a finite difference method(FDM) is used to model the process. Since this model yields the calculation results of the temperature distribution around the workpiece volume in the vicinity of the laser torch, this model is used to obtain the network's training data and laser to evaluate the performance of the neural network estimator. The simulation results show that the proposed scheme can be used to estimate the hardened depth with reasonable accuracy.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.946-949
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• 2007

무선 센서네트워크는 온도나 소리, 진동, 압력, 움직임 등의 환경 조건을 감시할 수 있는 장치가 다른 위치에 분산되어 있는 무선 네트워크이다. 환경 모니터링은 센서 네트워크의 응용분야로 과학적으로 많은 이점을 가지고 있다. 본 논문에서는 온도, 습도, 조도 센서의 모니터링에 사용되는 센서 네트워크 시스템을 설계 ${\cdot}$ 구현한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.798-800
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• 1995

The neural network predictive controller(NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output(Neural Network Predictor) and the other one is for control the plant(Neural Network Controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and prediction error. The NNP forecasts the future output based upon the current control input and the estimated control output. The method is applied to the control of temperature in boiler systems. The proposed NNPC is compared with the other conventional control methods such as PID controller, neural network controller with specialized learning architecture, and one-step-ahead controller. The computer simulation and experimental results show that the proposed method has better performances than the other methods.

• 한국화재소방학회논문지
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• 제10권2호
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• pp.28-39
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• 1996

A recently developed electro-thermal simulation methodology is used to analyze the behavior of a PWM(Pulse-Width-Modulated) voltage source inverter which uses IGBT(Insulated Gate Bipolar Transistor) as the switching devices. In the electro-thermal network simulation methdology, the simulator solves for the temperature distribution within the power semiconductor devices(IGBT electro-thermal model), control logic circuitry, the IGBT gate drivers, the thermal network component models for the power silicon chips, package, and heat sinks as well as the current and voltage within the electrical network. The thermal network describes the flow of heat form the chip surface through the package and heat sink and thus determines the evolution of the chip surface temperature used by the power semiconductor device models. The thermal component model for the device silicon chip, packages, and heat sink are developed by discretizing the nonlinear heat diffusion equation and are represented in component from so that the thermal component models for various package and heat sink can be readily connected to on another to form the thermal network.

• 전기학회논문지
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• 제68권1호
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• pp.153-158
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• 2019

In this paper, we analyze the infrared feature for the small coast targets according to the surrounding environment for autonomous flight device equipped with an infrared imaging sensor and we propose Cross Duality of Neural Network (CR-DuNN) method which can classify the target and clutter in coastal environment. In coastal environment, there are various property according to diverse change of air temperature, sea temperature, deferent seasons. And small coast target have various infrared feature according to diverse change of environment. In this various environment, it is very important thing that we analyze and classify targets from the clutters to improve target detection accuracy. Thus, we propose infrared feature learning algorithm through LSTM neural network and also propose CR-DuNN algorithm that integrate LSTM prediction network with Du-CNN classification network to classify targets from the clutters.