• Korean Institute of Interior Design Journal
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• v.23 no.5
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• pp.76-85
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• 2014

When conscious and unconscious exploring information of space-visitors which is contained in the information acquired in the process of seeing any space is analyzed, it can be found what those visitors pick up as factors in the space for its selection as visual information in order to put it into action. This study, with the object of the space reproduced in three dimensions from the cafe which was visited for conversation, has analyzed the process of acquiring space-information by sex to find out the features of scanning path, findings of which are the followings. First, the rate of scanning type of males was "Combination (50.5%)- Circulation (31.0%) and that of females "Horizontal (32.5%) - Combination (32.1%)", which shows that there was a big difference by sex in the scanning path which took place in the process of observing any space. Second, when the features of continuous observation frequency by sex is looked into, the trends of increased "horizontal" scanning and decreased "Combination" scanning of both showed the same as the frequency of continuous observations increased, while in case of "Circulation" scanning, that of females was found to decrease but that of males showed the aspect of confusion. Third, the 'Combination' scanning of males was found strong at the short observation time with three times of continuous observation frequency defined as "Attention Concentration" while the distinct feature was seen that the scanning type was dispersed to "combination-circulation" as the frequency of continuous observation increased. Females start the information acquirement with "combination-circulation" but in the process of visual appreciation they showed a strong "Horizontal" These scanning features can be defined as those by sex for acquiring space information and therefore are very significant because they are fundamental studies which will enable any customized space-design by sex.

• Smart Structures and Systems
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• v.22 no.2
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.4
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• pp.537-542
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• 2010

The goal of this study was to investigate how spectrographic features of ultrasonic vocalizations (USVs) in rats vary among individuals and behaviors. Eighteen pairs of rats were allocated to individual pair cages. Each pair's behaviors and vocalizations were recorded during the 900s a known cage-mate was returning to the cage. The effects of individuals, behaviors, and the interaction between individuals and behaviors ($individuals{\times}behaviors$) were tested on the duration and peak frequencies. There was difference in the duration and peak frequency: i) among individuals (p<0.0001 and p<0.0001, respectively); ii) among behaviors (p = 0.0667 and p<0.0001, respectively); iii) among individuals${\times}$behaviors (p<0.0001 and p<0.0001, respectively). The frequency of ultrasonic vocalizations changed with a frequency ranging from 40 to 71 kHz which were emitted by individuals, whereas the frequency of ultrasonic vocalizations changed with a frequency ranging from 60 to 70 kHz which were emitted by behaviors. The peak frequency of call on 'contact' behavior was lower than that of call on other behaviors, but call duration of call on 'contact' was longer than on other behaviors. Especially, 40 kHz calls were found on 'contact' and 'other' behaviors. We suggest that ultrasonic vocalizations need to be subdivided and the effects of individuals and behaviors must be considered to assess emotional state of rats because these may influence the features of ultrasonic vocalizations.

• Journal of Families and Better Life
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• v.7 no.2
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• pp.95-107
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• 1989

In this study one aspect of consumer behavior in household equipment utilization was investigated the pattern, frequency, rate of washer use and their relation to the following factors a) Washer related factors : extent of the presence of desired characteristics, the evaluation of washer's intrinsic features and related household facilities. b) Psycho-social factors : attitude of energy conservation, preference & ability to wash by hand, standard of washing of the respondent homemaker. c) Socio-demographic factors : age, education level and employment status of homemaker, house-hold income, the presence of children under seven years, size of family, the presence of a paid help. The subjects of this study were 286 homemakers with washer in Seoul. Analysis methods were used to fuequency, one-way ANOVA, Gamma test, Pearson's Correlation Coefficient, t-test and multiple regression of SPSS program. The major findings are the following; 1) The pattern, frequency, rate of washe use appeared various in every household. 2) Extent of the presence of desired characteristics was very low and respondents evaluated their washer's intrinsic features moderate. 3) The pattern of washer use was affected by the evaluation of washer's intrinsic features, preference & ability to wash by hand, wife's employment and household income. The frequency of washer use was affected by family size and preference & ability to wash by hand. The rate of washer use was affected by extent of the presence of desired characteristics, the evaluation of washer's intrinsic features and preference & ability to wash by hand. Therefore, washing by hand is major substitute for washer. If more desired characteristics are added to washer, intrinsic features are improved, and maintenance costs are reduced or household income is raised, every houshold with washer will use washer more than washing by hand in washing ask so that it may gain more utility from washer.

• Journal of Power Electronics
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• v.8 no.3
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• pp.228-238
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• 2008

Many induction motor broken bar diagnosis methods are based on evaluating special components in machine signals spectrums. Current, power, flux, etc are among these signals. Frequencies related to a broken rotor fault are slip dependent, therefore, correct diagnosis of fault - especially when obtrusive frequency components are present - depends on accurate determination of motor velocity and slip. The traditional methods typically require several sensors that should be pre-installed in some cases. This paper presents a diagnosis method based on only a vibration sensor. Motor velocity oscillation due to a broken rotor causes frequency components at twice slip frequency difference around speed frequency in vibration spectrum. Speed frequency and its harmonics as well as twice supply frequency, can easily and accurately be found in a vibration spectrum, therefore th motor slip can be computed. Now components related to rotor fault can be found. It is shown that a trained neural network - as a substitute for an expert person - can easily categorize the existence and the severity of a fault according to the features extracted from the presented method. This method requires no information about th motor internal and has been able to diagnose correctly in all the laboratory tests.

• Speech Sciences
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• v.15 no.1
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• pp.63-72
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• 2008

Various acoustic features were extracted and analyzed to estimate the inter- and intra-speaker variability of emotional speech. Tokens of vowel /a/ from sentences spoken with different modes of emotion (sadness, neutral, happiness, fear and anger) were analyzed. All of the acoustic features (fundamental frequency, spectral slope, HNR, H1-A1 and formant frequency) indicated greater contribution to inter- than intra-speaker variability across all emotions. Each acoustic feature of speech signal showed a different degree of contribution to speaker discrimination in different emotional modes. Sadness and neutral indicated greater speaker discrimination than other emotional modes (happiness, fear, anger in descending order of F-ratio). In other words, the speaker specificity was better represented in sadness and neutral than in happiness, fear and anger with any of the acoustic features.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.9-16
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• 2015

In this paper, several experiments are performed according to deep neural network (DNN) based features for the performance comparison of speaker verification (SV) systems. To this end, input features for a DNN, such as mel-frequency cepstral coefficient (MFCC), linear-frequency cepstral coefficient (LFCC), and perceptual linear prediction (PLP), are first compared in a view of the SV performance. After that, the effect of a DNN training method and a structure of hidden layers of DNNs on the SV performance is investigated depending on the type of features. The performance of an SV system is then evaluated on the basis of I-vector or probabilistic linear discriminant analysis (PLDA) scoring method. It is shown from SV experiments that a tandem feature of DNN bottleneck feature and MFCC feature gives the best performance when DNNs are configured using a rectangular type of hidden layers and trained with a supervised training method.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.77-81
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• 2006

This paper presents a technique of imposing the prosodic features of a native speaker's utterance onto the same sentence uttered by a non-native speaker. Three acoustic aspects of the prosodic features were considered: the fundamental frequency (F0) contour, segmental durations, and the intensity contour. The fundamental frequency contour and the segmental durations of the native speaker's utterance were imposed on the non-native speaker's utterance by using the PSOLA (pitch-synchronous overlap and add) algorithm [1] implemented in Praat[2]. The intensity contour transfer was also done in Praat. The technique of transferring one or more of these prosodic features was elaborated and its implications in the area of language education were discussed.

• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.592-595
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• 2009

We propose an automatic spam mail classifier for e-mail data using Support Vector Machines (SVM). We use a lexical form of a word and its part of speech (POS) tags as features. We select useful features with ${\chi}^2$ statistics and represent each feature using text frequency (TF) and inversed document frequency (IDF) values for each feature. After training SVM with the features, SVM classifies each email as spam mail or not. In experiment, we acquired 82.7% of accuracy with e-mail data collected from a web mail system.