• ETRI Journal
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• v.26 no.3
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• pp.273-276
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• 2004

We propose a novel feature processing technique which can provide a cepstral liftering effect in the log-spectral domain. Cepstral liftering aims at the equalization of variance of cepstral coefficients for the distance-based speech recognizer, and as a result, provides the robustness for additive noise and speaker variability. However, in the popular hidden Markov model based framework, cepstral liftering has no effect in recognition performance. We derive a filtering method in log-spectral domain corresponding to the cepstral liftering. The proposed method performs a high-pass filtering based on the decorrelation of filter-bank energies. We show that in noisy speech recognition, the proposed method reduces the error rate by 52.7% to conventional feature.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.1
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• pp.105-115
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• 1991

This paper describes an integrated CAD and CAPP system for prismatic parts of injection mold which generates a complete process plan automatically from CAD data of a part without human intervention. This system employs Auto CAD as a CAD model and GS-CAPP as an automatic process planning system for injection mold. The proposed CAD/CAPP system consists of three modules such as CAD data conversion module, manufacturing feature recognition module, and CAD/CAPP interface module. CAD data conversion module transforms design data of AutoCAD into three dimensional part data. Manufacturing feature recognition module extracts specific manufacturing features of a part using feature recognition rule base. Each feature can be recognized by combining geometry, position and size of the feature. CAD/CAPP interface module links manufacturing feature codes and other head data to automatic process planning system. The CAD/CAPP system can improve the efficiency of process planning activities and reduce the time required for process planning. This system can provide a basis for the development of part feature based design by analyzing manufacturing features.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.26-32
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• 2017

VOD (Video-On Demand) Services are considered to be one of the most successful data broadcasting services, along with Electronic Program Guides (EPGs). In particular, VOD services provide supplementary revenue for broadcasting companies in addition to the existing subscription fees and advertisement-based revenue. Therefore, each broadcasting company has developed its own VOD service and constantly seeks to improve it. This leads to the development of new VOD services, so developers are considering ways to effectively handle the frequent development needs. In this background, we conducted underlying research to apply the feature-oriented analysis model to the development of VOD services. The feature-oriented analysis model used in this study is the Feature-Oriented Domain Analysis (FODA) one developed by SEI of Carnegie Mellon University. FODA provides a tool for specifying the feature model of a software domain, based on which the developers can determine the configuration of the software with the customers. This study developed a feature model of the VOD service domain and devised the functionalities and test cases in an integrated manner with the feature model. Additionally, we proposed a VOD service development process utilizing the feature model, function specification, and test cases.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.58-68
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• 2005

In this paper, we propose a scheme to improve discriminative property in the feature compensation method for robust speech recognition under noisy environments. The estimation of noisy speech model used in existing feature compensation methods do not guarantee the computation of posterior probabilities which discriminate reliably among the Gaussian components. Estimation of Posterior probabilities is a crucial step in determining the discriminative factor of the Gaussian models, which in turn determines the intelligibility of the restored speech signals. The proposed scheme employs minimum classification error (MCE) training for estimating the parameters of the noisy speech model. For applying the MCE training, we propose to identify and determine the 'competing components' that are expected to affect the discriminative ability. The proposed method is applied to feature compensation based on parallel combined mixture model (PCMM). The performance is examined over Aurora 2.0 database and over the speech recorded inside a car during real driving conditions. The experimental results show improved recognition performance in both simulated environments and real-life conditions. The result verifies the effectiveness of the proposed scheme for increasing the performance of robust speech recognition systems.

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.115-143
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• 1994

In this paper,I present an extended two-level model using LR parsing techniques.The LR-based two-level model not only guarantees effcient morphological processing but also achieves a higher degree of descriptive adequacy than Koskenniemi's original model.The two-level model is augmented with an independent morphosyntactic module based on feature-based CF word grammar.By adopting a CF word grammar,our model is capable of dealing with complex words with discontinuous dependencies without having duplicate lexicons.It is shown how LR predictions manifested in the parsing table can help the morphological processor to minimize the dictionary lookup process.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.267-271
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• 2000

A part definition must not only provide shape information of a nominal part but also contain non-shape information such as tolerances, surface roughness and material attributes. Although machining features are useful for suitable shape information for process reasoning in the CAPP, they need to be integrated with tolerance information for effective process planning. We develop the tolerance modeler that efficiently integrates machining features with tolerance information for feature-based CAPP It is based on the association of machining features, tolerance features. and tolerances Tolerance features, where tolerances are assigned, are classified into two types; one is the face that is a topological entity on a solid model and the other is the functional geometry that is not referenced to topological entities. The functional geometry is represented by using machining features All the data for representing tolerance information with machining features are stored completely and unambiguously in the independent tolerance structure. The developed tolerance modeler is implemented as a module of a comprehensive feature-based CAPP system.