• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.193-196
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• 1995

An algorithm using topological mapping has been developed for a real-time detection of the QRS complexes of ECG signals. As a measurement of QRS complex energy, we used topological mapping from one dimensional sampled ECG signals to two dimensional vectors. These vectors are reconstructed with the sampled ECG signals and the delayed ones. In this method, the detection rates of CRS complex vary with the parameters such as R-R interval average and peak detection threshold coefficient. We use mean, median, and iterative method to determint R-R interval average and peak estimation. We experiment on various value of search back coefficient and peak detection threshold coefficient to find optimal rule.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.468-472
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• 2004

This paper presents an intelligent iris recognition system which consists of quality check, iris localization, feature extraction, and verification. For the quality check, the local statistics on the pupil boundary is used. Gaussian mixture model is used to segment and localized the iris region. The feature extraction method is based on an optimal waveform simplification. For the verification, we use an intelligent variable threshold.

• International Journal of Contents
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• v.8 no.4
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• pp.7-11
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• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.114-125
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• 1992

A policy of periodic replacement with minimal repair at failure is considered for a complex system. Under such a policy the system is replaced at periodic times. iT(i=1,2, $\ldots$), while minimal repair is performed at any intervening system failures. The cost of the j-th minimal repair to the component which fails at age t is g(C(t). $c_j$ (t)), where C(t) is the age-dependent random part, $c_j$(t) is the deterministic part which depends on the age and the number of the minimal repair to the component, and g is a positive nondecreasing continuous function. The cost of replacement is expensive when the number of failures occurring in (0. T) is greater than a threshold level. The problem of determining the optimal replacement period, $T^{\ast}$, which minimizes the total expected cost per unit time over an infinite time horizon is considered. Various special cases are considered.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.2
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• pp.75-78
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• 2016

This research was designed 700 level power MOSFET for smart LED driver ICs package. And we analyzed electrical characteristics of the power MOSFET as like breakdown voltage, on-resistance and threshold voltage. Because this research is important optimal design for smart LED ICs package, we designed power MOSFET with design and process parameter. As a result of this research, we obtained $60{\mu}m$ N-drift layer depth, 791.29 V breakdown voltage, $0.248{\Omega}{\cdot}cm^2$ on resistance and 3.495 V threshold voltage. We will use effectively this device for smart LED driver ICs package.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.790-798
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• 1991

The congestion control methods of an integrated heterogeneous traffic multiplexer are studied. The real-time traffics have preemptive priority over nonreal-time traffics in capturing the common output link. Also real-time traffics are transmitted with bandwidth reduction when the nonreal-time traffics are over the buffer threshold. The proposed model formulated the system as a continous time Markov process and is analysed using matrix equation. Time delay and average number of used channel are applied for performance parameters. In this study, a new control method with the sizable buffer threshold is introduced and the optimal congestion control can be obtained.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.862-864
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• 2003

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum threshold has remained a challenge over decades. Many image segmentation techniques are developed using information about image in other space rather than the image space itself. Most of the technique based on histogram analysis information-theoretic approaches. In this paper, the criterion function for finding optimal threshold is developed using an intensity-based classuncertainty (a histogram-based property of an image) and region-homogeneity (an image morphology-based property). The theory of the optimum thresholding method is based on postulates that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The performance of the proposed method is illustrated on experimental data obtained by W-band millimeter-wave radiometer image under different noise level.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.631-641
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• 2004

In this paper, a model for an inventory whose stock decreases with time is considered. When a deliveryman arrives, if the level of the inventory exceeds a threshold $\alpha$, no stock is delivered, otherwise a delivery is made. It is assumed that the size of a delivery is a random variable Y which is exponentially distributed. After assigning various costs to the model, we calculate the long-run average cost and show that there exist unique value of arrival rate of deliveryman $\alpha$, unique value of threshold $\alpha$ and unique value of average delivery m which minimize the long-run average cost.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.