• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2745-2748
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• 2003

In this paper, we implement the photoplethysmo-graphy system that have three channel pulse wave detects and one channel ECG amplifier. In order to detect the artery state, we measured the pulse waves at different positions, simultaneously. In general, arterial vascular system suffers the decrease of compliance, increase of resistance, and decrease of distensibility through aging. Therefore, we compared and analyzed variation of tile Pulse waves parameter both in time and frequency domains that is concerned with the changes of arterial characteries. And then evaluated the correlation coefficients between the parameters variation and the age group. As the result of experiment, we found that the Parameters have a significant correlation about aging.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.04a
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• pp.168-171
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• 1997

TFT2DS was developed to provide the usefulness as an analytic and design tool. The static characteristics of a-Si:H TFTs demonstrated a good agreement between simulated and measured data. This paper shows that TFT2DS can optimize the physical parameters of a-Si:H through sensitivity simulations and compute the static characteristics of a-Si:H TFTs. Moreover, through the sensitivity study of the parameters, it is shown that the optimizations of both the physical parameters of a-Si:H and the parameters of a-Si:H deposition, which must be inter-related, might be possibl.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.3
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• Proceedings of the KIEE Conference
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• 1998.07d
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• pp.1475-1477
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• 1998

In this paper, breakdown strength and time to breakdown are experimented under semiconducting layer/needle electrode in XLPE which is used for power cable insulator. Shape and scale parameters of obtained data are estimated using 2-parameters Weibull distribution. Life-time coefficient(n-value) using shape parameters for breakdown strength and time to breakdown tests is estimated. n-value of 1000 hour aged XLPE showed higher value than that of virgin XLPE. Increase of n-value is estimated by the stability due to removal of by-product and residue gas in XLPE by heating.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.481-486
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• 2002

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-5) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control. Based on the derived design method, the parameter estimation for controllable canonical T-S fuzzy model is also Presented.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.11
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• pp.954-958
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• 2000

The characterization of zinc diffusion processes applied for high-speed avalanche photodiodes has been examined. The different diffusion process conditions for InP test structures were explored. The zinc diffusion profiles, such as the diffusion depth and the zinc dopant concentration, were examined using secondary ion mass spectrometry with varying the process variables and material parameters. It is observed that the diffusion profiles are severly impacted on the process parameters, such as the amount of Zn$_3$P$_2$ source and the diffusion time, as well as material parameters, such as doping concentration of diffusion layer. These results can be utilized for the high-speed avalanche photodiode fabrication.

• Journal of Sensor Science and Technology
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• v.22 no.6
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• pp.387-392
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• 2013

Camera rigs for shooting 3D video are classified as manual, motorized, or fully automatic. Even in an automatic camera rig, the process of Stereoscopic 3D (S3D) video capture is very complex and time-consuming. One of the key time-consuming operations is capturing the distance parameters, which are near distance, far distance, and convergence distance. Traditionally these distances are measured by tape measure or triangular indirect measurement methods. These two methods consume a long time for every scene in shot. In our study, a compact laser distance sensing system with long range distance sensitivity is developed. The system is small enough to be installed on top of a camera and the measuring accuracy is within 2% even at a range of 50 m. The shooting time of an automatic camera rig equipped with the laser distance sensing system can be reduced significantly to less than a minute.

• ETRI Journal
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• v.45 no.4
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• pp.666-677
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• 2023

In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.