• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.181-184
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• 2000

The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.9-16
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• 2016

In this paper, we present a performance evaluation agent based on fuzzy cluster analysis and validity measures. The proposed agent is consists of three modules, fuzzy cluster analyzer, performance evaluation measures, and feature ranking algorithm for feature selection step in CAD system. Feature selection is an important step commonly used to create more accurate system to help human experts. Through this agent, we get the feature ranking on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. Also we design a CAD system incorporating the agent and apply five different feature combinations to the system. Experimental results proposed approach has higher classification accuracy and shows the feasibility as a diagnosis supporting tool.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.93-104
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• 2009

Modular implementation of a feature is a first step toward feature-oriented product line engineering, which develops and then utilizes core assets to configure products in terms of features. Aspect-oriented programming provides effective mechanisms for improving the modularity of feature implementations. However, as features in general are not independent of each other, changes in the implementation of one feature may cause changes to or side effects in the implementation of other features. Moreover, since the time at which a feature is incorporated into products, called feature binding time, may be various from compile time through load time to run time, a feature may have to be implemented differently depending on when the feature is bound into a product. To make each feature implementation module as independent as possible, this paper proposes aspectual implementation patterns that can effectively separate feature dependencies as well as feature binding time from feature implementation modules. These patterns enable flexible composition of feature implementation modules without affecting other modules according to feature selection. The approaches are demonstrated and evaluated based on a product line of scientific calculator applications.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.133-149
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• 1996

In order to reduce the trial-and-errors in design and production of injection molded plastic parts, there has been much research effort not only on CAE systems which simulate the injection molding process, but also on CAD systems which support initial design and re-design of plastic parts and their molds. The CAD systems and CAE systems have been developed independently with being built on different basis. That is, CAD systems manipulate the part shapes and the design features in a complete solid model, while CAE systems work on shell meshes generated on the abstract sheet model or medial surface of the part. Therefore, it is required to support the two types of geometric models and feature information in one environment to integrate CAD and CAE systems for accelerating the design speed. A feature-based non-manifold geometric modeling system has been developed to provide an integrated environment for design and analysis of injection molding products. In this system, the geometric models for CAD and CAE systems are represented by a non-manifold boundary representation and they are merged into a single geometric model. The suitable form of geometric model for any application can be extracted from this model. In addition, the feature deletion and interaction problem of the feature-based design system has been solved clearly by introducing the non-manifold Boolean operation based on 'merge and selection' algorithm. The sheet modeling capabilities were also developed for easy modeling of thin plastic parts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.618-627
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• 1990

In this paper a new algorithm for separation of contacting subpattern and connective feature extraction of strokes is proposed. This algorithm is able to classification of the type of contacting parts, connective feature extreaction of strokes, separate the phoneme of contacting parts between strokes, classify the character types by feature classification of connecting parts and analysis of connecting attribute. Also, shape normalize into formal patterns and decide on the input pattern from position value of bending feature of this normalized shape and make an recognition experiment by neural network using BEP learining algorithm. This algorithm represents the good achievement ratio by separation of phoneme, classification of character type, connective feature extraction of stroke and recognition experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.51-63
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• 2001

In supervised machine learning, an induction algorithm, which is able to extract rules from data with learning capability, provides a useful tool for data mining. Practical induction algorithms are known to degrade in prediction accuracy and generate complex rules unnecessarily when trained on data containing superfluous features. Thus it needs feature subset selection for better performance of them. In feature subset selection on the induction algorithm, wrapper method is repeatedly run it on the dataset using various feature subsets. But it is impractical to search the whole space exhaustively unless the features are small. This study proposes a heuristic method that uses sensitivity analysis of neural networks to the wrapper method for generating rules with higher possible accuracy. First it gives priority to all features using sensitivity analysis of neural networks. And it uses the wrapper method that searches the ordered feature space. In experiments to three datasets, we show that the suggested method is capable of selecting a feature subset that improves the performance of the induction algorithm within certain iteration.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.27-35
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• 2008

With the proliferation of wireless devices, mobile ad-hoc networking (MANETS) has become a very exciting and important technology. However, MANET is more vulnerable than wired networking. Existing security mechanisms designed for wired networks have to be redesigned in this new environment. In this paper, we discuss the problem of anomaly detection in MANET. The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new or unseen attacks. We propose a new anomaly detection method for MANETs. The proposed method performs cross-feature analysis on the basis of Rough sets to capture the inter-feature correlation patterns in normal traffic. The performance of the proposed method is evaluated through a simulation. The results show that the performance of the proposed method is superior to the performance of Huang method that uses cross-feature based on the probability of feature attribute value. Accordingly, we know that the proposed method effectively detects anomalies.