• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1124-1133
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• 2000

Advanced Intelligent Network Intelligent Peripheral (AIN IP) is one of the AIN elements which consist of Service Control Point (SCP), Service Switching Point (SSP), and IP for AIN services, such as play announcement, digit collect, voice recognition/synthesis, voice prompt and receipt. This paper, featuring ISUP/INAP protocols, describes the procedures for call setup/release bearer channels between SSP/SCP and IP, todeliver specialized resources through the bearer channels, and it describes the structure and procedure for AIN services such as Automatic Collect Call (ACC), Universal Personal Telecommunication (UPT), and teleVOTing(VOT). In this environments, the delay characteristics of If system is investigated as the performance analysis, Policy establishment.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.159-166
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• 2009

In this paper, we analyse the noise robustness of MLPs(Multilayer perceptrons) through deriving the probability density function(p.d.f.) of output nodes with additive input noises and the misclassification ratio with the integral form of the p.d.f. functions. Also, we propose linear preprocessing methods to improve the noise robustness. As a preprocessing stage of MLPs, we consider ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise reduction effect using PCA or ICA in the viewpoints of SNR(Singal-to-Noise Ratio), we verify the preprocessing effects through the simulations of handwritten-digit recognition problems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.61-67
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• 2004

We propose a new algorithm to generate additional training patterns using the brain-style information processing algorithm, that is, supervised and unsupervised learning models. This will be useful in the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on. We adopt the independent component analysis as an unsupervised model for generating exempalr patterns and multilayer perceptions as supervised models for verifying usefulness of the generated patterns. After statistical analysis of the proposed pattern generation algorithm, we verify successful operations of our algorithm through simulation of handwritten digit recognition with various numbers of training patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.68-73
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• 2020

This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 ㎛ CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.67-75
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• 1998

Although the nCE(n-th order cross-entropy) error function resolves the incorrect saturation problem of conventional EBP(error back-propagation) algorithm, the performance of MLP's (multilayer perceptrons) trained using the nCE function depends heavily on the order of the nCE function. In this paper, we propose an adaptive learning rate to make the performance of MLP's insensitive to the order of the nCE error. Additionally, we propose a limited error signal of output node to prevent unstable learning due to the adaptive learning rate. The effectiveness of the proposed method is demonstrated in simulations of handwritten digit recognition and thyroid diagnosis tasks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.56-67
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• 2019

The aim of this study was to investigate the influence of rosemary oil inhalation on memory, attention and autonomic nervous system according to the concentration difference in the aged. The research design was non-equivalent control group non-synchronized design. Participants were 89 individuals aged 65 or older who live in the community. Participants inhaled almond carrier oil(control group), 10%(experimental group A) and 100%(experimental group B) rosemary oil. Memory, attention, and autonomic nervous system responses were measured. Data were analyzed by SPSS win 24.0. The differences of the group and time were analyzed through repeated measure ANOVA. There were no significant differences in immediate recall (F=.42, p =.656), delayed recall (F=.45, p=.639), recognition (F=1.45, p=.242), digit span-forward (F=1.53, p=.223), digit span-backward (F=.46, p=.636), activities of sympathetic nerve system (LF)(F=.19, p=.828), activities of parasympathetic nerve system (HF)(F=.37, p=.694), LH/HF(F=1.39, p=.256), systolic blood pressure (F=.37, p=.694), diastolic blood pressure (F=1.25, p=.291). The inhalation of 10% and 100% rosemary oil for five minutes showed no significant effects on memory, attention and automatic nervous system in the aged.