• Journal of Magnetics
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• 제5권2호
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• pp.27-34
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• 2000

The structure of a series of Cu/Ni is characterized by using a program, SUPREX, to model the x-ray diffraction patterns, multilayers. The samples had nominal layer thickness of 3/3, 7/7, 13.5/13.5, 20/20, 30/30, 50/50, 80/80, 100/100, and 200/200 Angstroms. The diffraction patterns were taken around the (111) peak for the two constituent materials. A kinematical model is used to characterize the diffraction patterns and the parameters for the model are described. An initial model is calculated using initial guesses for the parameters. The model is then fit to the data by reducing $x^2$using the Levenberg-Marquardt algorithm. The samples are shown to be high quality supperlattices.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2003년도 International Meeting on Information Display
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• pp.32-35
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• 2003

Nonlinear parameterization of OLED device from measurements of bias dependence of impedance spectra and parameter extraction using Levenberg-Marquardt complex nonlinear least square regression algorithm based on resistor-capacitor equivalent circuit model enables computer simulation of OLED power driving characteristics in forms of square-wave or sinusoidal output signal at arbitrary conditions. We introduce developed OLED power driving simulation software and discuss transient responses in voltage-or current-controlled operations as well as nonlinear characteristics of OLED, by presenting both the simulation and experimental results. This OLED simulation technique using impedance spectroscopy is extremely useful in predicting performance of the nonlinear device, especially in time-domain analysis of device operation.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.849-856
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• 2003

본 논문에서는 다층 퍼셉트론의 학습을 위한 새로운 두 단계 학습방법을 제안하였다. 첫 번째 단계는 국소최적해로 빨리 수렴하기 위해 Levenberg-Marquardt 알고리즘을 이용한 국소 탐색 단계이다. 두 번째 단계는 첫 번째 단계에서 찾은 국소최적해가 원하는 수준에 미치지 못할 경우 새로운 국소최적해로 벗어나기 위한 선형탐색을 기반의 터널링 단계이다. 이 방법은 연결가중치 공간에서 전역최적해를 빠르게 찾을 수 잇는 새로운 방법을 제공한다. 4가지 벤치마크 문제에 기존의 다층 퍼셉트론의 학습 알고리즘과 비교 실험을 통해, 제안된 알고리즘이 빠른 수렴 속도와 낮은 오차값을 가짐을 알 수 있었다.

• 센서학회지
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• 제13권6호
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• pp.454-461
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• 2004

We have designed a portable electronic nose (e-nose) system using an array of commercial gas sensors and personal digital assistants (PDA) for the recognition and analysis of volatile organic compounds (VOC) in the field. Field screening of pollutants has been a target of instrumental development during the past years. A portable e-nose system was advantageous to localize the special extent of a pollution or to find pollutants source. The employment of PDA improved the user-interface and data transfer by Internet from on-site to remote computer. We adapted the Lavenberg-Marquardt algorithm based on the back-propagation and proposed the method that could predict the concentration levels of VOC gases after classification by separating neural network into two parts.

• 설비공학논문집
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• 제13권12호
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• pp.1288-1296
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• 2001

A scheme for on-line fault detection and diagnosis of an air-handling unit is presented. The fault detection scheme uses residuals which are generated by comparing each measurement with analytical redundancies computed from the reference models. In this paper, artificial neural networks (ANNs) are used to estimate analytical redundancy and to classify faults. The Lebenburg-Marquardt algorithm is used to train feed forward ANNs that provide estimates of continuous states and diagnosis results. The simulation result demonstrated that the ANNs can effectively detect and diagnose faults in the highly non-linear and complex HVAC systems.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.233-240
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• 2002

In this paper, the effects of vehicle loads on flat slab system are investigated on the basis of the previous studies for beam-girder parking structural system. The influence surfaces of flat slab for typical design section are developed for the purpose of obtaining maximum member forces under vehicle loads. In addition, the equivalent vehicle load factors for flat slab parking structures are suggested using artificial neural network. The network responses are compared with the results by numerical analyses to verify the validation of Levenberg-Marquardt algorithm adopted as training method in this paper. Many parameter studies fur the flat slab structural system show dominant vehicle load effects at the center positive moments in both column and middle strips, like the beam-girder parking structural system.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• ETRI Journal
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• 제45권6호
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• pp.1065-1078
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• 2023

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle-to-internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic-related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle-to-network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two-hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle-to-internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• ETRI Journal
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• 제22권1호
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• pp.38-46
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• 2000

The extraction of model parameters for embedded passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, a method for optimizing the extraction of these parameters using genetic algorithms is presented. The results of this method are compared with optimization using the Levenberg-Marquardt (LM) algorithm used in the HSPICE circuit modeling tool. A set of integrated resistor structures are fabricated, and their scattering parameters are measured for a range of frequencies from 45 MHz to 5 GHz. Optimal equivalent circuit models for these structures are derived from the s-parameter measurements using each algorithm. Predicted s-parameters for the optimized equivalent circuit are then obtained from HSPICE. The difference between the measured and predicted s-parameters in the frequency range of interest is used as a measure of the accuracy of the two optimization algorithms. It is determined that the LM method is extremely dependent upon the initial starting point of the parameter search and is thus prone to become trapped in local minima. This drawback is alleviated and the accuracy of the parameter values obtained is improved using genetic algorithms.