• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.77-87
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• 2019

The study aimed to develop Speech Range Profile (SRP) and to examine and validate its clinical application. Forty-five participants without voice disorders aged 18-29 years were compared using SRP and Voice Range Profile (VRP). The authors developed the "Fire!" paragraph as a SRP task compromising 14 sentences including all Korean spoken phonemes and sentence types. To compare SRP and VRP results, the participants read the paragraph (reading) and counted from 21 to 30 (counting) as a part of SRP tasks, and produced a vowel /a/ from low to high frequencies (gliding) and a shortened form of the VRP as a part of VRP tasks. $F0_{max}$, $F0_{min}$, $F0_{range}$, $I_{max}$, $I_{min}$, and $I_{range}$ for each task were measured and compared, showing that $F0_{max}$, $F0_{min}$, $F0_{range}$, $I_{max}$, and $I_{range}$ were not different between reading and gliding. $I_{min}$, had the lowest value in counting. It is concluded that the newly developed SRP task, reading the "Fire" paragraph, can yield a maximum phonation range similar to that found by VRP. Therefore, it is expected that voice evaluation can be effectively performed in a relatively short time by applying SRP with the "Fire" paragraph, a functional utterance task, in place of VRP, which may be difficult to measure long term or in cases of severe voice disorders.

• Physical Therapy Korea
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• v.12 no.3
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• pp.74-83
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• 2005

The purpose of this study was to produce the regression equation from non-exercise $VO_{2max}$ of healthy young adults and to develop a maximal oxygen consumption ($VO_{2max}$) regression model. This model was based on heart rate non-exercise predictor variables (rest heart rate, maximal heart rate/rest heart rate), as an extra addition to the general regression which can reflect an individual's inherent or acquired cardiorespiratory fitness. The subjects were 101 healthy young adults aged 19 to 35 years. Exercise testing was measured by using a Balke protocol for treadmill and indirect calorimetry. The prediction equation was analyzed by using stepwise multiple regression procedures. The mean of $VO_{2max}$ was $39.02{\pm}6.72\;m{\ell}/kg/min$ (mean${\pm}$SD). The greatest variable correlated to $VO_{2max}$ was %fat. The predictor variable used in the non-exercise $VO_{2max}$ included %fat, gender, habitual physical activity and $HR_{max}/HR_{rest}$. The non-exercise $VO_{2max}$ estimation was as follows: $VO_{2max}$($m{\ell}/kg/min$)=55.58-.41(%fat)+.59(physical activity rating)-2.69($HR_{max}/HR_{rest}$)-5.36 (male=0, female=1); (R=.85, SEE=3.64, R2=.72: including heart rate variable); $VO_{2max}$($m{\ell}/kg/min$)=48.47-.41(%fat)+.45(physical activity rating)-5.12 (male=0, female=1); (R=.84, SEE=3.74, R2=.70: with the exception of heart rate variable). As an added heart rate variable, there was only a 2% coefficient of determination improved. Therefore, these results demonstrated that heart rate variable correlation with a non-exercise regression model was very low. In conclusion, for healthy young korean adults, those variables that can affect non-exercise $VO_{2max}$ estimation turned out to be only % fat, gender, and physical activity. We suggest that further research of predictor variables for non-exercise $VO_{2max}$ is necessary for different patient groups who cannot perform maximal exercise or submaximal exercise.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.63-70
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• 2010

Based on the known results of the expected busy periods for the triadic Min (N, T, D) and Max (N, T, D) operating policies applied to a controllable M/G/1 queueing model, a relation between them is constructed. Such relation is represented in terms of the expected busy periods for the simple N, T and D, and the dyadic Min (N, T), Min (T, D) and Min (N, D) operating policies. Hence, if any system characteristics for one of the two triadic operating policies are known, unknown corresponding system characteristics for the other triadic operating policy could be obtained easily from the constructed relation.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.2
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• pp.8-16
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• 2014

Purpose The quantitative analysis of gallbladder emptying is very important in diagnosis of motility disorder of gallbladder and in biliary physiology. The GBEF obtain the statics aquisition method or the dynamic acquisition method in two ways. The purpose of this study is to compare the GBEF value of statics acquisition method and the dynamic acquisition method. And we find the best way for calculate GBEF. Materials and Methods The quantitative hepatobiliary scan with $^{99m}Tc$-mebrofenin was performed of 27 patients. Initial images were acquired statically, for 60 min after injection of the radioactive tracer. And if the gallbladder is visualized to 60 min, performed stimulation of gallbladder (1egg, 200 mL milk). After that, started acquisition of dynamic image for 30 min. After that, image of after fatty meal of the statics method were acquired on equal terms with 60 min image. The statics GBEF was calculated using the images of before fatty meal and post fatty meal by the statics method. The dynamic GBEF was calculated using the images of time of maximum bile juice uptake ($T_{max}$) and time of minimum bile juice uptake ($T_{min}$) images from the gallbladder time-activity curve. A bile juice is secreted from gallbladder while eating a fatty meal. that is named early GBEF and that was calculated using before fatty meal image of the statics method and 1 min image of the dynamic method. Results The result saw very big difference between two according to $T_{max}$. The result, were as follows. 1) In case of less than 1 min, the dynamic mean GBEF was $40.1{\pm}21.7%$, the statics mean GBEF was $51.5{\pm}23.6%$ in 16 cases. The early mean GBEF was $14.0{\pm}29.1%$. The GBEF of statics method was higher because that include secreted bile juice while performed stimulation of gallbladder. A difference of GB counts according to acquisition method and the early bile juice counts was $17.6{\pm}14.8%$ and $13.5{\pm}15.3%$. 2) In case of exceed than 1 min, the dynamic mean GBEF was $31.0{\pm}19.7%$, the statics mean GBEF was $21.3{\pm}19.4%$ in 7 cases. The early GBEF was $-6.9{\pm}4.9%$. The GBEF of dynamic method was higher because that include concentrated bile juice to $T_{max}$. A difference of GB counts according to acquisition method and the early bile juice counts was $14.3{\pm}7.3%$ and $5.9{\pm}3.9%$. Conclusion The statics method is very easy and simple, but in case of $T_{max}$ delay, the GBEF can be lower. The dynamic method is able to calculate accurately in case of $T_{max}$ delay, but in case of $T_{max}$ is less than 1 min, the GBEF can be lower because dynamic GBEF exclude secreted bile juice while performed stimulation of gallbladder. The best way to calculate GBEF is to scan with dynamic method preferentially and to choose suitable method between the two way after conform $T_{max}$ on the T-A curve of the dynamic method.

• 한국체육학회지인문사회과학편
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• v.51 no.3
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• pp.373-382
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• 2012

The purpose of this study is to predict VO₂max with body index and submaximal metabolic responses. The subjects are consisted of 250 male aging from 18 to 34 and we separated them into two groups randomly; 179 for a sample, 71 for a cross-validation group. They went through maximal exercise testing with Bruce protocol, and we measured the metabolic responses in the end of the first(3 minute) and second stage(6 minute). To predict VO₂max, we applied multiple regression analysis to the sample with stepwise method. Model 1's variables are weight, 6 minute HR and 6 minute VO₂(R=0.64, SEE=4.74, CV=11.7%, p<.01), and the equation is VO₂max(ml/kg/min)= 72.256-0.340(Weight)-0.220(6minHR)+0.013(6minVO₂). Model 2's variables are weight, 6 minute HR, 6 minute VO₂, and 6 minute VCO₂(R=0.67, SEE=4.59, CV=11.3%, p<.01), and the equation is VO₂max(ml/kg/min)= 68.699-0.277(Weight) -0.206(6minHR)+0.020(6minVO₂)-0.009(6minVCO₂). And the result did not show multicolinearity for both models. Model 2 demonstrated more correlation compared to Model 1. However, when we conducted cross-validation of those models with 71 men, measured VO₂max and estimated VO₂ Max had statistical significance with correlation (R=0.53, 0.56, P<.01). Although both models are functional with validity considering their simplicity and utility, Model 2 has more accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.243-250
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• 2014

In this paper, I devise a balance-swap optimization (BSO) algorithm to solve economic load dispatch with a quadratic fuel cost function. This algorithm firstly sets initial values to $P_i{\leftarrow}P_i^{max}$, (${\Sigma}P_i^{max}$ > $P_d$) and subsequently entails two major processes: a balance process whereby a generator's power i of $_{max}\{F(P_i)-F(P_i-{\alpha})\}$, ${\alpha}=_{min}(P_i-P_i^{min})$ is balanced by $P_i{\leftarrow}P_i-{\alpha}$ until ${\Sigma}P_i=P_d$; and a swap process whereby $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_i+{{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}$ = 1.0, 0.1, 0.1, 0.01, 0.001 is set at $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$. When applied to 15, 20, and 38-generators benchmark data, this simple algorithm has proven to consistently yield the best possible results. Moreover, this algorithm has dramatically reduced the costs for a centralized operation of 73-generators - a sum of the three benchmark cases - which could otherwise have been impossible for independent operations.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.155-165
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• 2012

This paper suggests traveling salesman problem algorithm that have been unsolved problem with NP-Hard. The proposed algorithm is a heuristic with edge-swap method. The classical method finds the initial solution starts with first node and visits to mostly adjacent nodes then decides the traveling path. This paper selects minimum weight edge for each nodes, then perform Min-Min method that start from minimum weight edge among the selected edges and Min-Max method that starts from maximum weight edges among it. Then we decide tie initial solution to minimum path length between Min-Min and Min-Max method. To get the final optimal solution, we apply previous two-opt to initial solution. Also, we suggest extended 3-opt and 4-opt additionally. For the 7 actual experimental data, this algorithm can be get the optimal solutions of state-of-the-art with fast and correct.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.392-392
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• 2004

• Tuberculosis and Respiratory Diseases
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• v.47 no.1
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• pp.26-34
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• 1999

Background : Generally $VO_2$ max is higher in treadmill exercise than cycle ergometer exercise. According to Hassen and Wasserman, $VO_2$ max with treadmill exercise is higher at ratio of 1.11 than that with cycle ergometer. $VO_2$ max also is influenced by race, sociocultural background, exercise habit In this study, $VO_2$ max and AT were evaluated between Treadmill and cycle exercise in male Korean college students. Method: Study subjects were 44 male college students. We randomized them into 2 groups; 24 students did treadmill exercise at first and 1 week later did cycle ergometer. Another 20 students did in opposite method. They made symptom limited maximal exercise. Author defined maximal exercise as followings: 1) respiratory exchange ratio(RER)> 1.1, 2) plateau>30 sec, 3) heart rate reserve(HRR) <15%, or 4) breathing reserve (BR)<30%. Otherwise their results are excluded as submaximal exercise. Anaerobic threshold(AT) was estimated by V-slope method. Results: $VO_2$ max and AT was $45.1{\pm}6.66m\ell$/kg/min and $26.0{\pm}6.78m\ell$/kg/min in treadmill and $34.9{\pm}5.89m\ell$/kg/min, $19.5{\pm}4.77m\ell$/kg/min in Cycle Ergometer. The measured-$VO_2max$/pred-$VO_2max$ was $98.8{\pm}13.24%$ in treadmill; $84.4{\pm}13.42%$ in cycle ergometer. Comparing $VO_2$ max in treadmill with that obtained by Hassen's method, there were significant differences.(p<0.01). At maximal exercise there were differences in HRR, $O_2$/pulse, BR, $V_E$/MVV, $V_E/VCO_2$ between treadmill and cycle but not in $V_E/VO_2$, Vd/Vt, Ti/Ttot. At AT there were differences in $O_2$/pulse, BR, $V_E$/MVV, Ti/Ttot between treadmill and cycle, otherwise not. Conclusion: According to the result of this study, there are larger gap between treadmill and cycle ergometer in normal Korean adults than foreign data, and it needs further study to obtain reference value of Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.36-39
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• 2019

데이터 마이닝에서 클러스터링은 서로 유사한 특징을 갖는 데이터들을 동일한 클래스로 분류하는 방법이다. 클러스터링에는 다양한 방법이 존재하지만 대표적으로 집합으로 표현된 데이터들의 유사도를 측정하기 위해서는 자카드 유사도(Jaccard Similarity)를 이용한다. 자카드 유사도는 서로 다른 집합 간의 공통된 부분을 상대적으로 평가하여 유사도를 측정하는 방법이다. 그러나 최근에는 데이터를 저장할 수 있는 기술과 매체의 발전으로 표현할 수 있는 데이터의 영역과 범위는 발전되고 있기 때문에 많은 연산과 시간의 비용이 발생하게 된다. 이를 해결하기 위해서 두 데이터의 표본의 유사도를 통해 실제 데이터들의 유사도를 추정할 수 있는 Min-Hash 가 제안되었다. 본 논문에서는 이를 활용하여 집합의 영역을 다중 집합(Multiset)으로 확장하여 중복되는 값을 가질 수 있는 두 데이터 간의 유사도를 효율적으로 추정할 수 있는 Min-Max Hash 를 제안한다.