• Journal of Veterinary Clinics
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• v.17 no.1
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• pp.225-233
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• 2000

The aim in this study was to obtain the basic data for estimation of optimal mating time and ovulation time by finding out the relationship between vaginal cytology and reproductive hormone profiles during the estrous cycle in 36 Korea Jin-do bitches. A characteristic features of vaginal cytology during the estorous cycle were the high proportion of superficial cell, anuclear cell and erythrocyte in proestrus, superficial cell, anuclear cell and erythroucyte in estrus, parabasal cell, small and large intermediate cell and leukocytes in diestrus, and parabasal cell and small intermediate cell in anestrus, respectively. Cornification index(CI) was the high proportion in proestrus and estrus, then it was decreaed in diestrus and anestrus. It indicated that the CI was significantly high proportion in proestrus and estrus in comparison with the other phases. Plasma progesterone concentration was below 1.0ng/ml at the first day of vulval bleeding in pregnant and non-pregnat bitches, and then it was increaed slowly. When Day 0 was timed from the day that plasma progesterone concentration was first increased above 4.0ng/ml, plasma progesterone concentrations at Day 0 in pregnant and non-pregnant above 4.0 ng/ml, plasma progesterone concentrations at Day 0 in pregnant and non-pregnant bitches were $5.2{\pm}0.3ng/ml and 5.7{\pm}0.5ng/ml$$46.5{\pm}3.3 ng/ml$ in pregnant bitches and at Day 20 with $39.8{\pm}0.1ng/ml$ in nonpregnant bitches. It indicated that plasma progesterone concentration was higher in pregnant bitches than in non-pregnant bitches. Hereafter plasma progesterone concentration was decreased steadily. At Day 63 which is parturition day, plasma progesterone concentration was decreased below 1.0ng/ml with $0.8{\pm}0.2ng/ml$ in pregnant bitches whereas in the non-pregnant bitches at Day 75 were decreased below 1.0ng/ml with 40.5{\pm}0.4ng/ml$. Plasma progesterone concentrations was maintained below 1.0ng/ml during anestrus in all of them. The plasma estradiol-17 $\beta$ concentration was increased above 1.0 pg/ml at the first day of vulval bleeding and it showed a peak Day-1 with 38.2 pg/ml. Thereafter it was sharply decreased after Day 0, which was the day that plasma progesterone concentration was first increase above 4.0ng/ml, and was maintained below basal levels. In relationship between CI and reproductive hormones, plasma estradiol-17 $\beta$concentration showed a peak at Day-3 and CI showed a peak at Day-1 which was the second day after plasma estradiol-17 $\beta$ peak, and plasma progesterone concentration was first increased above 4.0ng/ml at Day 0 which was the first day after CI peak. CI was first increased above 80% at Day-6 which was the third day before plasma estradio-17 $\beta$ peak and it was maintained above 80% between Days-6 and Day 3 during 10 days, and showed above 90% at Day-3 which was the day that plasma estradiol-17 $\beta$peak and was maintained above 90% between Day-3 and Day 3 during 7 days. In conclusion, ovulation in Koran Jin-do bitches occurred at the first day after CI peak, at the third day after plasma estradiol-17 $\beta$peak and the day when plasma progesterone concentration was first increased above 4.0 ng/ml. And it was estimated that the optimal mating time was the day when the CI was maintained above 90% and plasma progesterone concentration was between 3.0~8.0ng/ml. Therefore plasma progesterone concentration measurement was used for determination of an accurate ovulation time and the optimal mating time but also vaginal cytology, which is low-priced with equipment and is the simple examination method, was reliable method for estimating estrous cycle, optimal breeding time and ovulation time in Korea Jin-do bitches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1082-1087
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• 2008

Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.

• Journal of Veterinary Clinics
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• v.25 no.2
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• pp.79-84
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• 2008

For estimating the ovulation time in Miniature Schnauzer dogs during the estrous cycle, radioimmunoassay of plasma estradiol-$17{\beta}$ and progesterone concentrations was conducted on blood samples in 21 pregnant and 13 non pregnant dogs. When Day 0 was that plasma progesterone concentrations exceeded 4.0 ng/ml, on Day 64, parturition day, progesterone declined below 1.0 ng/ml with $0.92\;{\pm}\;0.29\;ng/ml$ and when Day 0 was that plasma progesterone concentrations declined below 1.0 ng/ml, on Day -64, progesterone increased above 4.0 ng/ml with $4.56\;{\pm}\;0.87\;ng/ml$. Gestational length was $63.71\;{\pm}\;1.35$ (Mean${\pm}$S.D.) days from plasma progesterone concentrations exceeded 4.0 ng/ml and was $66.29\;{\pm}\;1.98$ days from first male acceptance. The plasma estradiol-$17{\beta}$ concentrations reached maximum value with $28.20\;{\pm}\;2.86\;pg/ml$ on Day .2, and plasma progesterone concentrations reached $5.90\;{\pm}\;0.36 ng/ml, 5.18\;{\pm}\;0.32 ng/ml on Day 0, and the maximum of 61.58\;{\pm}\;10.47 ng/ml on Day 19 and 56.05\;{\pm}\;8.86\;ng/ml$ on Day 16 in pregnant and non pregnant dogs, respectively. Afterward, plasma progesterone concentrations declined below 1.0 ng/ml on Day 64 with $0.92\;{\pm}\;0.29\;ng/ml$ in pregnant cycles and on Day 58 with $0.95\;{\pm}\;0.63\;ng/ml$ in non pregnant dogs. No difference were found pregnant and non pregnant dogs in plasma estradiol-$17{\beta}$ and progesterone concentrations (p<0.01). Based on first male acceptance (Day 0), the maximum of plasma estradiol-$17{\beta}$ concentrations ($29.31\;{\pm}\;3.61\;pg/ml$) occurred on Day -1 and plasma progesterone concentrations exceeded 4.0 ng/ml on Day 2 in pregnant ($5.37\;{\pm}\;0.76\;ng/ml$) and non pregnant ($4.25\;{\pm}\;0.80\;ng/ml$) dogs. These results suggest that in Miniature Schnauzers, the ovulation occurred when plasma progesterone concentrations exceeded 4.0 ng/ml, 3 days after plasma estradiol-$17{\beta}$ peak and 2 days after first male acceptance.

• Journal of Veterinary Clinics
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• v.36 no.6
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• pp.364-367
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• 2019

The aim of this study was to compare the plasma progesterone concentrations during gestation in 4 breeds of companion bitches. When Day 0 was timed the first male acceptance, plasma progesterone were 1.8 ± 0.4 ng/ml (Mean ± SD) at Day 0 and increased constantly with 58.5 ± 8.9 ng/ml a peak at Day 20. Thereafter plasma progesterone concentrations was decreased below 1.0 ng/ml with 0.7 ± 0.2 g/ml at Day 65. There were no statistically significant differences of plasma progesterone concentrations in the each day of gestation and in litter size among the companion bitches (p < 0.01). These results indicated that the plasma progesterone concentrations was useful method for estimating optimal breeding time, ovulation time, the time of onset of parturition, properly planned elective cesarean section and abortion time in companion bitches.

• Biomolecules & Therapeutics
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• v.15 no.2
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• pp.123-126
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• 2007

A sensitive, simple and highly selective liquid chromatography method of determination for extraction of pseudoephedrine hydrochloride from Allegra D tablet was developed. The chief benefit of the present method is the minimal sample preparation, as the procedure is only filtering through pore syringe filter. Two drugs (pseudoephedrine hydrochloride, fexofenadine) were separated on a C$_{18}$ column and analyzed by high performance liquid chromatography (HPLC). The method had a chromatographic run time of 8.0 min. 1 ml of pseudoephedrine hydrochloride solution (1 mg/ml) was filtered through 0.22 um pore syringe filter. 50 ul of filtering solution was injected to HPLC pump and we knew the retention time (1.85 min) of separating of pseudoephedrine hydrochloride using UV detector at 280 nm. We used C$_{18}$ column (4.6 mm${\times}$250 mm), mobile phase solution (<0.05 mol/L NaH$_2$PO$_4$, 2 ml/L H$_3$PO$_4$>/CH$_3$CN / sodium dodesyl sulfate = 60 ml / 40 ml / 1 g). We separated psedoephedrine hydrochloride at run time of 1.85 min from Allegra D tablet solution (1 mg/ml) filtered through 0.22 um pore syringe filter using UV detector at 280 nm. Flow rate was set at 1.0 ml/min and the column temperature was set at 40$^{\circ}C$. Psedoephedrine hydrochloride solution (1 mg/ml) separated from Allegra D tablet was filtered through 0.22 um pore syringe filter and injected 50 ul. We confirmed the peak of psedoephedrine hydrochloride at same retention time and the separating solution was freeze-dried. In conclusion, A simple isocratic reverse-phase HPLC method has been developed that provides excellent separation of pseudoephedrine from Allegra D tablet.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.358-358
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• 2023

본 연구에서는 그동안 수자원분야 강우유출 해석분야에 활용되었던 대표적인 머신러닝&딥러닝(ML&DL) 모델을 활용하여 모델의 하이퍼파라미터 튜닝뿐만 아니라 모델의 특성을 고려한 기상 및 수문데이터의 조합과 전처리(lag-time, 이동평균 등)를 통하여 데이터 특성과 ML&DL모델의 조합시나리오에 따른 일 유입량 예측성능을 비교 검토하는 연구를 수행하였다. 이를 위해 소양강댐 유역을 대상으로 1974년에서 2021년까지 축적된 기상 및 수문데이터를 활용하여 1) 강우, 2) 유입량, 3) 기상자료를 주요 영향변수(독립변수)로 고려하고, 이에 a) 지체시간(lag-time), b) 이동평균, c) 유입량의 성분분리조건을 적용하여 총 36가지 시나리오 조합을 ML&DL의 입력자료로 활용하였다. ML&DL 모델은 1) Linear Regression(LR), 2) Lasso, 3) Ridge, 4) SVR(Support Vector Regression), 5) Random Forest(RF), 6) LGBM(Light Gradient Boosting Model), 7) XGBoost의 7가지 ML방법과 8) LSTM(Long Short-Term Memory models), 9) TCN(Temporal Convolutional Network), 10) LSTM-TCN의 3가지 DL 방법, 총 10가지 ML&DL모델을 비교 검토하여 일유입량 예측을 위한 가장 적합한 데이터 조합 특성과 ML&DL모델을 성능평가와 함께 제시하였다. 학습된 모형의 유입량 예측 결과를 비교·분석한 결과, 소양강댐 유역에서는 딥러닝 중에서는 TCN모형이 가장 우수한 성능을 보였고(TCN>TCN-LSTM>LSTM), 트리기반 머신러닝중에서는 Random Forest와 LGBM이 우수한 성능을 보였으며(RF, LGBM>XGB), SVR도 LGBM수준의 우수한 성능을 나타내었다. LR, Lasso, Ridge 세가지 Regression모형은 상대적으로 낮은 성능을 보였다. 또한 소양강댐 댐유입량 예측에 대하여 강우, 유입량, 기상계열을 36가지로 조합한 결과, 입력자료에 lag-time이 적용된 강우계열의 조합 분석에서 세가지 Regression모델을 제외한 모든 모형에서 NSE(Nash-Sutcliffe Efficiency) 0.8이상(최대 0.867)의 성능을 보였으며, lag-time이 적용된 강우와 유입량계열을 조합했을 경우 NSE 0.85이상(최대 0.901)의 더 우수한 성능을 보였다.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Journal of radiological science and technology
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• v.20 no.2
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• pp.68-72
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• 1997

Small bowel series using methylcellulose are considered a better technique than using other contrast media considering a significant decrease of transit time of $BaSO_4$ and that of the necessary time for the examination. We investigated the mean transit time of $BaSO_4$, maximum luminal diamenter of small bowel, optical density and flocculation frequency after adminstratting 100 ml of 120% $BaSO_4$ to 20 pts), 150 ml of 70% $BaSO_4$ to 20 pts and 200 ml of $BaSO_4$ with 600 ml of mechylcellulose. It was shown that the technique using 150 ml of 70% $BaSO_4$ had the best result. When we apply a adequate amount of density(w/v%), dosage to pts for small bowel series using MC, we can decrease an examination time and have the better image due to double contrast. It is considered that a more study to lower the density of 70% $BaSO_4$ is necessary.

• Journal of Embryo Transfer
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• v.21 no.2
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• pp.109-119
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• 2006

This study was conducted to examine relationship between vaginal cytology and reproductive hormones during the estrous cycle and to provide basic data to estimate for ovulation time and optimal mating time in 6 beagle dogs The duration of proestrus, estrus and diestrus were $8.5{\pm}1.4,\;10.0{\pm}1.4\;and\;54.0{\pm}2.8$ days at pregnant respectively, and $7.9{\pm}2.1,\;9.5{\pm}0.7\;and\;62.0{\pm}11.3$ days at non-pregnant respectively. The duration of interestrous intervals were $246.2{\pm}24.5$ days at pregnancy, and $175.3{\pm}34.5$ days at non-pregnancy. The duration of interestrous intervals at pregnancy was longer than that of non-pregnancy. A characteristic features of vaginal cytology during the estrous cycle were the high proportion of superficial cell, anuclear cell and erythrocyte in proestrus and estrus, parabasal cell, small intermediate cell and leukocyte in diestrus, and parabasal cell and small intermediate cell in anestrus, respectively. Cornification index (CI) in proestrus and estrus were significantly higher than that of CI in diestrus and anestrus. Plasma progesterone concentration was below 1.0 ng/ml at the first day of vulval bleeding at pregnancy and non-pregnancy, and then it was above 2.0 ng/ml at Day -2 in all bitches. When plasma progesterone concentration was first increased above 4.0 ng/ml, it was the second day after the first day of male acceptance. Plasma progesterone concentration showed above 40 ng/ml on Day $20{\sim}22$ in all bitches, and then it was gradually decreased until Day 35. Plasma progesterone concentration at pregnancy was higher than that of non-pregnancy from Day 35 to Day 63. Plasma estradiol-$17\;{\beta}$ concentration was above 9.0 pg/ml at the first day of vulval bleeding, and it showed 26.4 pg/ml on Day -2. When it was timed from the first day of male acceptance (Day 0), plasma estradiol-$17{\beta}$ concentration showed a peak on Day 0 and plasma progesterone concentration was first increased above 4.0 ng/ml on Day 2 which was the third day after plasma estradiol-$17{\beta}$ peak. CI was first increased above 80 and 90% on Day -1 and Day 1, respectively. CI was maintained above 80% from Day -1 to Day 8 (10 days) and above 90% from Day 1 to Day 6 (6 days), respectively. CI was maintained above 80% from Day 0 to Day 8 (9 days) and above 90% from Day 1 to Day 6 (6 days), respectively. Plasma progesterone concentration was first increased above 4.0 ng/ml on the second day after the day which CI was first increased above 90%. In conclusion, beagle bitches ovulated on the second day after the day which CI was first increased above 90% and on the day which plasma progesterone concentration was first increased 4.0 ng/ml, and it was estimated that the optimal mating time was the day which the second day after CI was first increased above 90% and plasma concentration was between $2{\sim}25ng/ml$. The measurement of plasma progesterone was used to determine of and accurate ovulation time and the optimal mating time, but vaginal cytology is low-priced and simple method to estimate estrous cycle, optimal mating time and ovulation time.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.6
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• pp.44-51
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• 1999

The HMM-Net is a neural network architecture that implements the computation of output probabilities of a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria of maximum likehood(ML) and minimization of mean squared error(MMSE) are used for learning HMM-Net classifiers. The criterion MMSE is better than ML when initial learning condition is well established. However Ml is more useful one when the condition is incomplete[3]. Therefore we propose an efficient learning method of HMM-Net classifiers using a hybrid criterion(ML/MMSE). In the method, we begin a learning with ML in order to get a stable start-point. After then, we continue the learning with MMSE to search an optimal or near-optimal solution. Experimental results for the isolated numeric digits from /0/ to /9/, a training and testing time-series pattern set, show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.