• Journal of the Ergonomics Society of Korea
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• v.25 no.4
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• pp.9-22
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• 2006

1/f fluctuation characteristics can be seen in various natural phenomena, such as breezes, streams, candle flames and the luminous patterns of fireflies. It is said that the 1/f fluctuation are comfortable for human beings. And they are introduced into many industrial products, such as an air conditioner, music, lighting, etc. This study focused on verifying the effects of 1/f fluctuation sound on comfort sensibility. Stimulus were divided into three groups(Group 1, 2, 3) by sound generation methodology. Fluctuation patterns of group 1, 2 were given by three types of fluctuation, 1/f0, 1/f1, 1/f2, and its of group 3 were given by two types of pure tone, 1/f1. In order to verify the effects, we measured the physiological responses of the subjects such as EEG(Electroencephalogram), ERP(Event-Related Potential), and these physiological responses were compared with subjective assessments, free answers. Consequently, we found that factor which had an effect on comfort sensibility was cognitive factor(for stimulus) rather than 1/f fluctuation sound pattern.

• Science of Emotion and Sensibility
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• v.18 no.3
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• pp.63-70
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• 2015

This study identified heart response of participants while listening to sounds which have 1/f fluctuations with exponent ${\alpha}$ gradient. The participants were engaged in emotional stress work. Prior studies related to 1/f fluctuation sound have reported that sound source can alleviate psychological and physiological state of users. Subjects of this study were exposed to sound with three levels of ${\alpha}$ gradient. Heart response of subjects were measured with Photoplethysmography(PPG) sensor simultaneously. The dependent variables of this study were beat per minute(BPM), very low frequency percent of pulse rate variability (VLF percent), the standard deviation of all normal RR intervals (SDNN), and high frequency power(HF power). Subject showed arousal response when exposed to sound with exponent ${\alpha}$ gradient of 3 whereas the sound with exponent ${\alpha}$ gradient of 1 and 2 resulted in relax effect. The characteristic of 1/f fluctuation sounds can be applied to alleviate stress for employers under emotional labor.

• Journal of the Ergonomics Society of Korea
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• v.24 no.2
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• pp.79-83
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• 2005

In order to verify the effects of sleep efficiency and sleep latency depending on the sound, Polysomnography was carried for the different sound stimulations, such as Sound A(providing R bed Co.), Sound B(1/f fluctuation sound develope by KRISS), and no sound stimulation. In case of sleep efficiency and WASO(wake after sleep onset) ratio Sound B shows more affirmative effect than no sound stimulation or Sound B. It is the result that the effect is caused because 1/f fluctuation sound has the rule and unexpectation. This research results show the possibility of application and development of the sound for sleep.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.363-375
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• 2004

In this paper, We show that the bandpass random signals of the form ∑$_{\alpha}$$\alpha$$_{\alpha}$ a Sin(2$\pi$f$_{\alpha}$t + b$_{\alpha}$) where a$_{\alpha}$ being a random number in [0,1], f$_{\alpha}$ a random integer in a given frequency band, and b$_{\alpha}$ a random number in [0, 2$\pi$], generate Gaussian white noise signals and hence they are adequate for simulating Continuous Markov processes. We apply the result to the fluctuation-dissipation formula for the Johnson noise and show that the probability distribution for the long term average of the power of the Johnson noise is a X$^2$ distribution and that the relative error of the long term average is (equation omitted) where N is the number of blocks used in the average.error of the long term average is (equation omitted) where N is the number of blocks used in the average.

• Investigative Magnetic Resonance Imaging
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• v.14 no.2
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• pp.115-120
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• 2010

Purpose : To separate and evaluate the low frequency spontaneous fluctuation BOLD signals from the functional magnetic resonance imaging data using sensorimotor active task. Materials and Methods : Twenty female archery players and twenty three control subjects were included in this study. Finger-tapping task consisted of three cycles of right finger tapping, with a subsequent 30 second rest. Blood oxygenation level-dependent (BOLD) data were collected using $T2^*$-weighted echo planar imaging at a 3.0 T scanner. A 3-D FSPGR T1-weighted images were used for structural reference. Image processing and statistical analyses were performed using SPM5 for active finger-tapping task and GIFT program was used for statistical analyses of low frequency spontaneous fluctuation BOLD signal. Results : Both groups showed the activation in the left primary motor cortex and supplemental motor area and in the right cerebellum for right finger-tapping task. ICA analysis using GIFT revealed independent components corresponding to contralateral and ipsilateral sensorimotor network and cognitive-related neural network. Conclusion : The current study demonstrated that the low frequency spontaneous fluctuation BOLD signals can be separated from the fMRI data using finger tapping paradigm. Also, it was found that these independent components correspond to spontaneous and coherent neural activity in the primary sensorimotor network and in the motor-cognitive network.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1149-1154
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• 2007

Fluctuation of control rod was experienced when plant was operating in normal operation mode in WH type NPPs. In order to cope with increased control rod fluctuation, the lead-lag controller setpoint for rod control system was optimized and resulted in increasing the margin of operation and minimizing unnecessary control rod movement. By optimization of the time constant, the margin of operation was increased by $1.5^{\circ}F$ and the control rod movement was not occurred due to mitigation of temperature fluctuation in loop. According to the mitigation of time constant, the margin of operation was increased but safety margin can be affected badly, so that the influences to FSAR design reference was evaluated. As the result of this evaluation, it satisfied the design reference of the existing safety analysis and was applied to NPP after obtaining the approval.

• Proceedings of the ESK Conference
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• 2004.10a
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• pp.47-47
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• 2004

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.132-139
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• 1995

Turbulent boundary layer pressure fluctuation exerted on the surface of a structure can give rise to a elastic stress wave on the surface of the structure. The stress wave so called surface wave, will not only propagate along the surface of structure but also penerate into the structure. To reduce the transmission of stress wave into the structure the elastomer layer is usually attactched on the surface of structure. The transfer function, which is defined herein as the ratio of stress waves at the surface and bottom of the elastomer layer, is derved by use of the cylindrical coordinates system. The elastodynamics of the elastomer layer subjected to the turbulent boundary layer pressure fluctuation is represented by the simplified one degree-of-freedom model for easy prediction of the stress wave transmission as well as efficient design of the elastomer layer.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.122-126
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• 2010

Purpose: F-18-FPCIT that shows strong familiarity with DAT located at a neural terminal site offers diagnostic information about DAT density state in the region of the striatum especially Parkinson's disease. In this study, we altered the iteration and subset and measured SUV${\pm}$SD and Contrasts from phantom images which set up to specific iteration and subset. So, we are going to suggest the appropriate range of the iteration and subset. Materials and Methods: This study has been performed with 10 normal volunteers who don't have any history of Parkinson's disease or cerebral disease and Flangeless Esser PET Phantom from Data Spectrum Corporation. $5.3{\pm}0.2$ mCi of F-18-FPCIT was injected to the normal group and PET Phantom was assembled by ACR PET Phantom Instructions and it's actual ratio between hot spheres and background was 2.35 to 1. Brain and Phantom images were acquired after 3 hours from the time of the injection and images were acquired for ten minutes. Basically, SIEMENS Bio graph 40 True-point was used and True-X method was applied for image reconstruction method. The iteration and Subset were set to 2 iterations, 8 subsets, 3 iterations, 16 subsets, 6 iterations, 16 subsets, 8 iterations, 16 subsets and 8 iterations, 21 subsets respectively. To measure SUVs on the brain images, ROIs were drawn on the right Putamen. Also, Coefficient of variance (CV) was calculated to indicate the uniformity at each iteration and subset combinations. On the phantom study, we measured the actual ratio between hot spheres and back ground at each combinations. Same size's ROIs were drawn on the same slide and location. Results: Mean SUVs were 10.60, 12.83, 13.87, 13.98 and 13.5 at each combination. The range of fluctuation by sets were 22.36%, 10.34%, 1.1%, and 4.8% respectively. The range of fluctuation of mean SUV was lowest between 6 iterations 16 subsets and 8 iterations 16 subsets. CV showed 9.07%, 11.46%, 13.56%, 14.91% and 19.47% respectively. This means that the numerical value of the iteration and subset gets higher the image's uniformity gets worse. The range of fluctuation of CV by sets were 2.39, 2.1, 1.35, and 4.56. The range of fluctuation of uniformity was lowest between 6 iterations, 16 subsets and 8 iterations, 16 subsets. In the contrast test, it showed 1.92:1, 2.12:1, 2.10:1, 2.13:1 and 2.11:1 at each iteration and subset combinations. A Setting of 8 iterations and 16 subsets reappeared most close ratio between hot spheres and background. Conclusion: Findings on this study, SUVs and uniformity might be calculated differently caused by variable reconstruction parameters like filter or FWHM. Mean SUV and uniformity showed the lowest range of fluctuation at 6 iterations 16 subsets and 8 iterations 16 subsets. Also, 8 iterations 16 subsets showed the nearest hot sphere to background ratio compared with others. But it can not be concluded that only 6 iterations 16 subsets and 8 iterations 16 subsets can make right images for the clinical diagnosis. There might be more factors that can make better images. For more exact clinical diagnosis through the quantitative analysis of DAT density in the region of striatum we need to secure healthy people's quantitative values.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.1
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• pp.38-42
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• 2006

We report 1/f noise PSD(Power Spectrum Density) of sub-100 nm MOSFETs as a function of various parameters such as HCS (Hot Carrier Stress), bias condition, temperature, device size and types of MOSFETs. The noise spectra of sub-100 nm devices showed Lorentzian-like noise spectra. We could check roughly the position of a dominant noise source by changing $V_{DS}$. With increasing measurement temperature, the 1/f noise PSD of 50 nm PMOS device decreases, but there is no decrease in the noise of NMOS device. RTN (Random Telegraph Noise) was measured from the device that shows clearly a Lorentzian-like noise spectrum in 1/f noise spectrum.