• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.2
• /
• pp.47-55
• /
• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.28 no.5
• /
• pp.277-283
• /
• 2016

Tetrapod is one of the most widely used concrete armor units for rubble mound breakwaters. The calculation of the stability number of Tetrapods is necessary to determine the optimal weight of Tetrapods. Many empirical formulas have been developed to calculate the stability number of Tetrapods, from the Hudson formula in 1950s to the recent one developed by Suh and Kang. They were developed by using the regression analysis to determine the coefficients of an assumed formula using the experimental data. Recently, software engineering (or machine learning) methods are introduced as a large amount of experimental data becomes available, e.g. artificial neural network (ANN) models for rock armors. However, these methods are seldom used probably because they did not significantly improve the accuracy compared with the empirical formula and/or the engineers are not familiar with them. In this study, we propose an explicit method to calculate the stability number of Tetrapods using the weights and biases of an ANN model. This method can be used by an engineer who has basic knowledge of matrix operation without requiring knowledge of ANN, and it is more accurate than previous empirical formulas.

• Journal of the Korean Geotechnical Society
• /
• v.16 no.1
• /
• pp.75-81
• /
• 2000

The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.

• Journal of Broadcast Engineering
• /
• v.24 no.4
• /
• pp.623-632
• /
• 2019

Many researches have been carried out for brightness and contrast enhancement, illumination reduction and so forth. Recently, the aforementioned hand-crafted approaches have been replaced by artificial neural networks. This paper proposes a convolutional neural network that can replace the method of generating a reflectance image where illumination component is attenuated. Experiments are carried out on 102 low-light images and we validate the feasibility of the replacement by producing satisfactory reflectance images.

• Journal of KIISE
• /
• v.45 no.1
• /
• pp.22-29
• /
• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Annual Conference on Human and Language Technology
• /
• 1989.10a
• /
• pp.126-131
• /
• 1989

본 논문에서는 Error Back Propagation 학습을 이용해 한글 문자를 On-Line 인식하는 시스템을 제안한다. Pointing device의 궤적을 추적해 입력 패턴의 특징(feature)을 추출해 신경 회로망 입력으로 준다. 이때 사용하는 특징은 기본 획 (stroke)의 종류 및 획간의 상대적 위치 관계이다. 학습과정에서는 자소의 정의를 읽어 초성, 중성, 종성에 대해 각 획수마다 정의된 신경회로망의 weight를 조정한다. 인식 과정에서는 초성, 중성, 종성의 순으로 에러가 최소인 획수의 신경회로망 출력을 택하여 2 바이트 조합형 코드로 완성한다. 이로써 Intelligent Man-Machine Interface 시스템중 위치 및 크기에 무관한 전필 입력 시스템을 구현한다.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.1182-1185
• /
• 1995

This paper presents a novel technology for unattented continuous monitoring of radioactive material in hot cell environments. In this monitoring system, the surveillance camera data and NDA data are time synchronized and integrated into the same dimension through data processing. The integrated information is then fed into a neural network to generate diagnostics through data processing. the integrated information of the concept is tested for a spent nuclear fuel transprotation in an operational hot cell at KAERI. The presented integral part of the multi-sensory system and the analytical paradigm may provide an effective technologyical alternative for safeguarding new conceptual hot cell facilities, namely the Dupic facility.

• Journal of the Korean Society for Precision Engineering
• /
• v.33 no.12
• /
• pp.985-992
• /
• 2016

Bearings have various uses in industrial equipment. The lifetime of bearings is often lesser than anticipated at the time of purchase, due to environmental wear, processing, and machining errors. Bearing conditions are important, since defects and damage can lead to significant issues in production processes. In this study, we developed a method to diagnose faults in the bearing conditions. The faults were determined using kurtosis, average, and standard deviation. An intrinsic mode function for the data from the selected axis was extracted using empirical mode decomposition. The intrinsic mode function was obtained based on the frequency, and the learning data of ANN (Artificial Neural Network) was concluded, following which the normal and fault conditions of the bearing were classified.

• Computational Structural Engineering
• /
• v.10 no.2
• /
• pp.273-281
• /
• 1997

In the structural analysis, artificial neural networks as a parallel computational model that is similar to the human brain and can self-organize complex nonlinear relationships without making assumptions is introduced. The purpose of this paper is to develop the Neural Network for Approximate Structural Analysis(NNASA) to predict the behaviour of the stub-girder system. As an initial stage, the paper presents the development of the prototype of NNASA based on the problem related to the deflection of a simple beam, and shows the verification of this model by two examples.

• Journal of Broadcast Engineering
• /
• v.23 no.1
• /
• pp.3-10
• /
• 2018

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.