• Science of Emotion and Sensibility
• /
• v.20 no.2
• /
• pp.161-170
• /
• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.1
• /
• pp.6-11
• /
• 2011

This paper proposes a new collision risk assessment system for pedestrians's safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method.

• International Journal of Concrete Structures and Materials
• /
• v.2 no.2
• /
• pp.91-98
• /
• 2008

Curved bridges are important components of a highway transportation network for connecting local roads and highways, but very few data have been collected in terms of their field performance. This paper presents two-years monitoring and system identification results of a curved concrete box-girder bridge, the West St. On-Ramp, under ambient traffic excitations. The authors permanently installed accelerometers on the bridge from the beginning of the bridge life. From the ambient vibration data sets collected over the two years, the element stiffness correction factors for the columns, the girder, and boundary springs were identified using the back-propagation neural network. The results showed that the element stiffness values were nearly 10% different from the initial design values. It was also observed that the traffic conditions heavily influence the dynamic characteristics of this curved bridge. Furthermore, a probability distribution model of the element stiffness was established for long-term monitoring and analysis of the bridge stiffness change.

• Journal of Advanced Navigation Technology
• /
• v.21 no.4
• /
• pp.347-352
• /
• 2017

In this paper, we propose a method that reduces mobile user's handover call dropping probability by using cognitive radio technology(CR) in cellular - based wireless cognitive radio networks. The proposed method predicts a cell to visit by Ziv-Lempel algorithm, and then supports mobile user with prediction of spectrum holes based on CR technology when allocated channels are short in the cell. We make neural network predict spectrum hole resources, and make handover calls use the resources before initial calls. Simulation results show CR technology has the capability to reduce mobile user handover call dropping probability in cellular mobile communication networks.

• Journal of Korea Water Resources Association
• /
• v.32 no.1
• /
• pp.3-13
• /
• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• Journal of KIISE:Software and Applications
• /
• v.30 no.3_4
• /
• pp.326-338
• /
• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.11 no.5
• /
• pp.105-117
• /
• 2001

As we live in the 21st century, so called the "Information Age", network has become a basic establishment. However, we have found the different face that it also has been used as a tool of a unauthorized outflow and destruction of information. In recent years, beginner could easily get a hacking and weakness reference tools from internet. The menace of the situation has increased; the intellectual diverse offensive technique has become increasingly dangerous. The purpose of the thesis is to detect a abnormal packet for networking offense. In order to detect the packet, it gathers the packets and create inspection information that tells abnormality by using probability of special quality, then decision of intrusion is made by using a neural network.l network.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.2924-2944
• /
• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Geomechanics and Engineering
• /
• v.2 no.1
• /
• pp.57-69
• /
• 2010

A major consideration in the design of tunnels in urban areas is the prediction of the ground movements and surface settlements associated with the tunneling operations. Excessive ground movements can damage adjacent building and utilities. In this paper, a neural network model is used to predict the maximum surface settlement, based on instrumented results from three separate EPB tunneling projects in Singapore. This paper demonstrates that by coupling the trained neural network model to a spreadsheet optimization technique, the reliability assessment of the settlement serviceability limit state can be carried out using the first-order reliability method. With this method, it is possible to carry out sensitivity studies to examine the effect of the level of uncertainty of each parameter uncertainty on the probability that the serviceability limit state has been exceeded.

• Journal of Biomedical Engineering Research
• /
• v.10 no.3
• /
• pp.343-350
• /
• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.