• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.287-288
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• 2007

In this paper, we propose a fake-iris detection method. In order to detect the fake-iris, we measure physiological features which are the reflectance ratio of the iris to the sclera at 750 nm and that at 850nm. In order to classify live and fake iris features, we use support vector data description (SVDD). From our experimental results, it is clear that our fake-iris detection method achieves high performance when distinguishing between a live-iris and a fake-iris.

• The Korean Journal of Mycology
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• v.47 no.2
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• pp.121-124
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• 2019

An endophytic fungus, Rhizosphaera pini strain NIE7426, was isolated from the sub-alpine coniferous tree Abies nephrolepis in Mt. Nochu of Gangwon Province. It was characterized by macroscopic and microscopic features, as well as the internal transcribed spacer (ITS) 1, 2 and 5.8S sequences. All morphological and molecular features support the first recognition of this taxon in Korea. In addition, this study adds A. nephrolepis as a host plant R. pini.

• Journal of Communications and Networks
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• v.2 no.1
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• pp.5-17
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• 2000

cdma2000 offers several enhancement as compared to TIA/EIA-95, although it remains fully compatible with TIA/EIA-95 systems and allows for a smooth migration from one to the other-Major new capability include:1)connectivity to GSM-MAP in addition to IP and IS-41 networks; 2) new layering with new LAC and MAC architectures for improved service multiplexing and QoS management and efficient use of radio resource ;3) new bands and band widths of operation in support of various operator need and constraints, as well as desire for a smooth and progressive migration to cdma 2000; and 4) flexible channel structure in support of multiple services with various QoS and variable transmission rates at up to 1 Mbps per channel and 2 Mbps per user. Given the phenomenal success of wireless services and desire for higher rate wireless services. improved spectrum efficiency was a major design goal in the elaboration of cdma2000. Major capacity enhancing features include; 1) turbo coding for data transmission: 2)fast forward link power control :3) forward link transmit diversity; 4) support of directive antenna transmission techniques; 5) coherent reverse link structure; and 6) enhanced access channel operation. As users increasingly rely on their cell phone at work and at home for voice and data exchange, the stand-by time and operation-time are essential parameters that can influence customer's satisfaction and service utilization. Another major goal of cdma2000 was therefore to enable manufacturers to further optimize power utilization in the terminal. Major battery life enhancing features include; 1) improved reverse link performance (i.e., reduced transmit power per information bit; 2) new common channel structure and operation ;3) quick paging channel operation; 4) reverse link gated transmission ; and 5) new MAC stated for efficient and ubiquitous idle time idle time operation. this article provides additional details on those enhancements. The intent is not to duplicate the detailed cdma2000 radio access network specification, but rather to provide some background on the new features of cdma2000 and on the qualitative improvements as compared to the TIA/EIA-95 based systems. The article is focused on the physical layer structure and associated procedures. It therefore does not cover the MAC, LAC, radio resource management [1], or any other signaling protocols in any detail. We assume some familiarity with the basic CDMA concepts used in TIA/EIA-95.

• 대한공업교육학회지
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• v.34 no.2
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• pp.304-320
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• 2009

This study was performed to grasp relations between different sub-factors of youths' leadership living skills and between the sub-factors of youths' leadership living skills and personal features (support by parents, support by peers, sense of self-respect, sense of self-effectiveness) in order to generally understand the characteristics of youths. The result and conclusion of this study are as follows. First, the sub-factors of youths' leadership living skills showed various kinds of correlations and especially, measures to improve learning ability skill, self-understanding skill and group activity skill are necessary for healthy and general growth in adolescence. Second, the sense of self-respect showed positive correlations with decision making skill and self-understanding skill and programs are to improve leadership living skill gradually and positively through the enhancement of the sense of self-respect. Third, the degree of support by peers showed relations with decision making skill and group activity skill meaning the importance of peer groups in adolescence and diverse measures to form peer groups are necessary.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.337-351
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• 2021

Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.

• ETRI Journal
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• v.44 no.3
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• pp.462-475
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• 2022

Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1032-1035
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• 2003

The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.71-74
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• 2014

A new classification framework called 'support feature machine' was introduced in [2] for analyzing medical data. Contrary to authors' claim, however, the proposed method is not designed to guarantee minimizing the use of the spatial feature variables. This paper mathematically remedies this drawback and provides comments on models from [2].