• 대한인간공학회지
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• 제30권6호
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• pp.715-725
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• 2011

Objective: This study is aimed at developing Articulated Human Models(AHM) using superquadrics to improve the geometric accuracy of the body shape. Background: The previous work presents the AHM with geometrical simplification such as ellipsoids to improve analysis efficiency. However, because of the simplicity, their physical properties such as a center of mass and moment of inertia are computed with errors compared to their actual values. Method: This paper introduces a three steps method to present the AHM with superquadrics. First, a 3D whole body scan data are divided into 17 body segments according to body joints. Second, superquadric fitting is employed to minimize the Euclidean distance between body segments and superquadrics. Finally, Fee-Form Deformation is used to improve accuracy over superquadric fitting. Results: Our computational experiment shows that the superquadric models give better accuracy of dynamic analysis than that of ellipsoid ones. Conclusion: We generate the AHM composed of 17 superquadrics and 16 joints using superquadric fitting. Application: The AHM using superquadrics can be used as the base model for dynamics and ergonomics applications with better accuracy because it presents the human motion effectively.

• 제어로봇시스템학회논문지
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• 제11권4호
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• pp.385-391
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• 2005

As the Internet real-time multimedia applications increases, 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 satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip 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 in 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.

• 한국정보통신학회논문지
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• 제4권1호
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• pp.97-103
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• 2000

본 논문에서는 고주파 발생기로부터 발생되는 원하지 않는 고조파 성분을 차단하기 위해 동축선로형 출력단의 내부에 대역억제필터를 삽입하고, 이필터를 등가회로 개념으로 해석하고 최적화하고자 한다. 동축선로 내부에 삽입되는 대역억제필터는 $\fraction ane-quarters\lambda$초크구조로서, 초크의 길이는 고조파 성분에 해당하는 파장의 $\fraction ane-quarters$로 초기화한다. 이때 초크구조의 불연속 경계에서 나타나는 가장자리효과는 초크의 길이를 등가적으로 증가시켜 차단주파수를 감소시키므로 고조파 성분의 정확한 차단을 어렵게 한다. 여기서는 가장자리 효과를 보상하기 위해 최적화 알고리즘(LMA)을 이용하여 대역억제필터를 최적화하고자 한다.

• 센서학회지
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• 제13권1호
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• pp.41-46
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• 2004

There is currently much interest in the development of instruments that emulate the senses of humans. Increasingly, there is demand for mimicking the human sense of smell, which is a sophisticated chemosensory system. An electronic nose system is applicable to a large area of industries including environmental monitoring. We have designed a protable electronic nose system using an array of commercial chemical gas sensors for recognizing and analyzing the various odours. In this paper, we have implemented a portable electronic nose system using an array of gas sensors for recognizing and analyzing VOCs (Volatile Organic Compounds) in the field. The accuracy of a portable electronic nose system may be lower than an instrument such as GC/MS (Gas Chromatography/Mass Spectrometer). However, a portable electronic nose system could be used on the field and showed fast response to pollutants in the field. Several different algorithms for odours recognition were used such as BP (Back-Propagation) or LM-BP (Levenberq-Marquardt Back-Propagation). We applied RBF (Radial Basis Function) Network for recognition and quantifying of odours, which has simpler and faster compared to the previously used algorithms such as BP and LM-BP.

• 대한수학회논문집
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• 제16권3호
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• pp.445-458
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• 2001

The shape and the distribution of material construction of the scatterer may be obtained from its scattered fields by the iterative inversion in the spectral domain. The illposedness, the resolution, and the uniqueness of the inversion are the key problems in the inversion and inter-related. The illposedness is shown to be caused by the evanescent modes which carries and amplifies exponentially the measurement errors in the back-propagation of the measured scattered fields. By filtering out all the evanescent modes in the cost functional defined as the squared difference between the measured and the calculated spatial spectrum of the scattered fields from the iteratively chosen medium parameters of the scatterer, one may regularize the illposedness of the inversion in the expense of the resolution. There exist many local minima of the cost functional for the inversion of the large and the high-contrast scatterer and the hybrid algorithm combining the genetic algorithm and the Levenberg-Marquardt algorithm is shown to find efficiently its global minimum. The resolution of reconstruction obtained by keeping all the propating modes and filtering out the evanescent modes for the regularization becomes 0.5 wavelength. The super resolution may be obtained by keeping the evanescent modes when the measurement error and instance, respectively, are small and near.

• 한국콘텐츠학회논문지
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• 제6권6호
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• pp.109-116
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• 2006

이 연구는 한국의 경찰관 841명에게 설문조사를 실시하여 말콰드의 학습조직이론을 실증적으로 검증하였다. 23개의 경찰서와 경찰종합학교에서 설문을 수집하였고, 경찰서의 규모와 지역, 계급과 경과를 고려하여 표본을 선정하였다. 이 연구를 통해서 학습조직의 하위체계인 첨단기술의 활용이 학습조직이 실현되는 학습의 역동성에 직접적인 영향을 미친다고 검증되었다. 경찰 학습조직을 구축하기 위해서 경찰관리자가 첨단기술의 활용능력을 향상시켜 경찰조직을 학습조직으로 만들어야 한다. 학습조직이 되면 경찰관들은 문제해결을 위해 보다 넓은 분야에서 능력을 향상시킬 수 있고, 그런 발전이 있어야 경찰활동을 잘 수행할 수 있다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2012년도 제38회 춘계학술대회논문집
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• pp.397-402
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• 2012

본 논문에서는 미지의 두 열물성 값인 열전도율과 열확산율을 구하기 위하여 Levenberg-Marquardt 방법에 의한 역해석 기법을 도입하였다. 일차원 열전도 문제에 대하여 연산식을 유도하였으며, 시편에 대하여 두 지점의 온도 및 입력유동의 열유속 측정값을 적용하였다. 예측된 열전도율 및 열확산율은 알려진 그라파이트 시편에 대한 열물성 값과 비교하였으며 그 결과 본 논문에서 제시된 역해석 예측 기법 실험의 유효성이 파악되었다.

• Structural Engineering and Mechanics
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• 제13권2호
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.

• Structural Engineering and Mechanics
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• 제35권4호
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• pp.431-457
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• 2010

This article provides a discussion of the mathematic modeling of connections for designing and qualifying structures, systems, and components subject to monotonic or cyclic loading. To characterize the force-deformation behavior of connections under monotonic loading, a review of the Ramberg-Osgood, Richard-Abbott, and Menegotto-Pinto models is conducted, and it is shown that these nonlinear functions can be mathematically derived by scaling up or down a linear force-deformation function. A generalized four-parameter model for simulating connection behavior is investigated to facilitate nonlinear regression analysis. In order to perform seismic analysis of frameworks, a hysteretic model accounting for loading, unloading, and reloading is described using the established monotonic model. For preliminary analysis, a method is provided to quickly determine the model parameters that fit approximately with the observed data. To reach more accurate values of the parameters, the methods of nonlinear regression analysis are investigated and the modified Levenberg-Marquardt and separable nonlinear least-square algorithms are applied in determining the model parameters. Example case studies illustrate the procedure for the computation through the use of experimental/analytical data taken form the literature. Transformation of connection curves from the three-parameter model to the four-parameter model for structural analysis is conducted based on the modeling of connections subject to fire.

• Computers and Concrete
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• 제20권6호
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.