• The KSFM Journal of Fluid Machinery
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• v.17 no.1
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• pp.35-40
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• 2014

A multi-objective optimization of a regenerative fan for enhancing the aerodynamic and aeroacoustic performance was carried out using an integrated fan design system, namely, Total FAN-Regen$^{(R)}$. The Total FAN-Regen$^{(R)}$ was developed for non-specialists to carry out a series of design process, viz., computational preliminary design, three-dimensional aerodynamic and aeroacoustic analyses, and design optimization, for a regenerative fan. An aerodynamic analysis of the regenerative fan was conducted by solving three-dimensional Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. And, an aeroacoustic analysis of the regenerative fan was implemented in a finite/infinite element method by solving the variational formulation of Lighthill's analogy based on the results of the unsteady flow analysis. An optimum shape obtained by Total FAN-Regen$^{(R)}$ shows the enhanced efficiency and decreased sound pressure level as much as 1.5 % and 20.0 dB, respectively, compared to those of the reference design. The performance test was carried out for an optimized regenerative fan to validate the performance of the numerically predicted optimal design.

• Speech Sciences
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• v.5 no.1
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• pp.121-139
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• 1999

Our experimental subject was a laryngectomee who had undergone total laryngectomy with $PROVOX^{(R)}$ insertion, and learned esophageal speech after the surgery, so he could produce both $PROVOX^{(R)}$ voice and esophageal voice. With this subject's production of $PROVOX^{(R)}$ and esophageal voice, we are to compare the acoustic and aerodynamic characteristics of the two voices, under the same physical conditions of the same person. As a result, the fundamental frequency of esophageal voice was 137.2 Hz, and that of $PROVOX^{(R)}$ was 97.5 Hz. $PROVOX^{(R)}$ voice showed lower jitter, shimmer and NHR than esophageal voice, which means that $PROVOX^{(R)}$ voice showed better voice quality than esophageal voice. In spectrographic analysis, the formation of formants and pseudoformants were more distinct in esophageal voice and several temporal aspects of acoutic features such as VOT and closure duration were more similar with normal voice in $PROVOX^{(R)}$ voice. During the sentence utterance, esophageal voice showed longer pause or silence duration than $PROVOX^{(R)}$ voice. Maximum phonation time and mean flow rate of $PROVOX^{(R)}$ voice were much longer and larger than esophageal voice, but mean and range of sound pressure level, subglottic pressure and voice efficiency were similar in the two voices. Glottal resistance of esophageal voice was much larger than $PROVOX^{(R)}$ voice which showed still larger glottal resistance than normal voice.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.8 no.4
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• pp.202-207
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• 1975

During the term of June, 7 to August 11, the noises in the maine engine room in terms of the r. p. m. of the Pung-Yang Ho (4,500 H. P.), the Chuk-Yang Ho (3,800 H. P.), the Dong-Bang Ho (3,000 H. P.), the Oh-Dae San Ho (2,690 H, P.), the Kwan-Ak-San Ho (1,000 H. P.) and the Back-Kyung Ho (850 H. P.) (Refer to Table 1) were measured with the use of sound level meter, which has measuring range 37-140 dB and the results obtained are as follows : 1. Capacity of the engine room becomes large according to the total H. P. of the main engine, but the vessels are using of a type of engine, i.e., 6 cylinder, and thus the noise, pressure has shown a tendency to become lower except Kwan-Ak-San Ho, Chuk-Yang Ho and Dong Bang Ho where the noise pressure was higher by 3 dB than curve of mean value. 2. The maximum noise pressure appeared even before the main engine reached the maximum r. p. m. and while the percentage of the r. p. m. varied depending on the vessel, the maximum noise appeared at around the $67-75\%$ of the r. p. m. 3. The maximum of noise pressure in the respective engine room ranged between 93.5-105 dB while it was between 72-81 dB at the fish process room in the stern trawl vessel where the oral communications were possible.

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.124-132
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• 2023

When the building becomes high, the number of households increases and they are adjacent to the elevator. So, frequency of use of elevators will increase. Elevator noise is bound to increase in the future. However, there are currently no legal standards for elevator noise or measurement and evaluation methods that can clearly measure elevator noise in Korea. Although some methods for measuring elevator noise are presented in KS F ISO 16032, this standard is not a standard established for elevator noise. It is a standard that integrates the overall measurement method of building equipment and equipment, and the position of the microphone is selected by the experimenter during measurement. Elevator noise is characterized by a low sound pressure level as the noise in the mid-low frequency band is important. However, even today, complaints from residents about elevator noise are increasing. In this study, the position of the microphone that can most sensitively pick up the elevator noise when measuring the elevator noise was studied. According to the distance from the wall and the height from the floor, a total of 9 microphone positions were measured and analyzed. As a result of the experiment, it was confirmed that the elevator noise has a very high influence in the 63 Hz band. The measured value at the center point was identified as a factor that lowered the overall elevator noise level value.

• Journal of Korean Academy of Nursing
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• v.13 no.1
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• pp.7-21
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• 1983

For the flexible and rational distribution of limited existing health resources based on measurements of individual risk, the socalled Risk Approach is being proposed by the World Health Organization as a managerial tool in maternal and child health care program. This approach, in principle, puts us under the necessity of developing a technique by which we will be able to measure the degree of risk or to discriminate the future outcomes of pregnancy on the basis of prior information obtainable at prenatal care delivery settings. Numerous recent studies have focussed on the identification of relevant risk factors as the Prior infer mation and on defining the adverse outcomes of pregnancy to be dicriminated, and also have tried on how to develope scoring system of risk factors for the quantitative assessment of the factors as the determinant of pregnancy outcomes. Once the scoring system is established the technique of classifying the patients into with normal and with adverse outcomes will be easily de veloped. The scoring system should be developed to meet the following four basic requirements. 1) Easy to construct 2) Easy to use 3) To be theoretically sound 4) To be valid In searching for a feasible methodology which will meet these requirements, the author has attempted to apply the“Likelihood Method”, one of the well known principles in statistical analysis, to develop such scoring system according to the process as follows. Step 1. Classify the patients into four groups: Group $A_1$: With adverse outcomes on fetal (neonatal) side only. Group $A_2$: With adverse outcomes on maternal side only. Group $A_3$: With adverse outcome on both maternal and fetal (neonatal) sides. Group B: With normal outcomes. Step 2. Construct the marginal tabulation on the distribution of risk factors for each group. Step 3. For the calculation of risk score, take logarithmic transformation of relative proport-ions of the distribution and round them off to integers. Step 4. Test the validity of the score chart. h total of 2, 282 maternity records registered during the period of January 1, 1982-December 31, 1982 at Ewha Womans University Hospital were used for this study and the“Questionnaire for Maternity Record for Prenatal and Intrapartum High Risk Screening”developed by the Korean Institute for Population and Health was used to rearrange the information on the records into an easy analytic form. The findings of the study are summarized as follows. 1) The risk score chart constructed on the basis of“Likelihood Method”ispresented in Table 4 in the main text. 2) From the analysis of the risk score chart it was observed that a total of 24 risk factors could be identified as having significant predicting power for the discrimination of pregnancy outcomes into four groups as defined above. They are: (1) age (2) marital status (3) age at first pregnancy (4) medical insurance (5) number of pregnancies (6) history of Cesarean sections (7). number of living child (8) history of premature infants (9) history of over weighted new born (10) history of congenital anomalies (11) history of multiple pregnancies (12) history of abnormal presentation (13) history of obstetric abnormalities (14) past illness (15) hemoglobin level (16) blood pressure (17) heart status (18) general appearance (19) edema status (20) result of abdominal examination (21) cervix status (22) pelvis status (23) chief complaints (24) Reasons for examination 3) The validity of the score chart turned out to be as follows: a) Sensitivity: Group $A_1$: 0.75 Group $A_2$: 0.78 Group $A_3$: 0.92 All combined : 0.85 b) Specificity : 0.68 4) The diagnosabilities of the“score chart”for a set of hypothetical prevalence of adverse outcomes were calculated as follows (the sensitivity“for all combined”was used). Hypothetidal Prevalence : 5％ 10％ 20％ 30％ 40％ 50％ 60％ Diagnosability : 12% 23% 40% 53% 64% 75% 80%.