• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.393-398
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• 2010

Early pregnancy diagnosis of bovine is an essential component for efficient reproductive plan in farms because long term of non-pregnancy results in economic losses by failure of offspring production and low milk yield in dairy cattle. The major steroid hormones related with reproduction are known to be progesterone and estrogen in bovine pregnancy. To evaluate detection level of hormones in milk, plasma and milk progestrone and estrogen of Holstein cows was analyzed during artificial insemination (AI) and embryo transfer (ET). Progesterone concentration at 21 days postestrus was significantly different in plasma and milk between pregnant and non-pregnant cows. Estrogen concentration at estrus was higher in pregnant recipients than that in non-pregnant recipients. To analyze correlation between hormone levels and conception rates in Holstein, the conception and return rates were checked following AI, and the returned cows were on the track of pregnancy after consecutive AI. Pregnant cows following first AI were considered as high conception group while pregnant cows following third AI were rated as low conception group. Proportion of high and low conception groups in this study was 78.2% and 9.1%, respectively. Hormone analysis indicated that high conception group had higher estrogen level during estrus than low conception group ($26.45{\pm}3.32$ vs $19.017{\pm}2.97$). Progesterone level was not different between high and low conception groups during estrus but increased significantly after 21 days postestrus (21 day: $4.95{\pm}1.12$ vs $0.95{\pm}0.23$, 35 day: $12.47{\pm}3.82$ vs $2.41{\pm}1.21$). In conclusion, the pattern of progesterone and estrogen secretion in Holstein milk samples could be a good candidate for early pregnancy detection and selection of recipients during ET.

• Korean Journal of Radiology
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• v.23 no.3
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.2
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• pp.73-78
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• 2022

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.

• Journal of Oral Medicine and Pain
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• v.18 no.2
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• pp.81-96
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• 1993

In order to evaluate the prognosis of conservative treatment for Craniomandibular Disorders, 127 patients were subjected at the Dept. of Oral Medicine, Pusan National University Hospital from 1983 through 1991. All the changes of patients' symptoms and related factors were analyzed before treatment, after treatment and at follow-up examination by means of subjective and objective symptom indicies (Ai, AAI, SDS, Di, CDS, MCO). 1. All the indices were reduced and MCO became increased at follow-up examination (p<0.01). 2. As the duration after treatment became longer, all the indices became reduced and MCO became increased. 3. There were no significant differences in index scores according to sex and age. 4. Long-term patients showed higher index scores and lower MCO than short-term patients(p<0.01). 5. Chronic group showed higher index scores and lower MCO than acute group (p<0.05). 6. Macrotrauma group showed higher index scores and lower MCO than microtrauma group (p<0.05) 7. While muscle group showed the lowest MCO, muscle and joint group showed the highest index scores (p<0.01). 8. The long-term success rate of conservative treatment was more than 80% (p<0.01)

• Smart Media Journal
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• v.12 no.5
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• pp.81-87
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• 2023

In this paper, accidents in high-voltage overhead distribution lines, which provide stable power supply in the power system, cause inconvenience in life and disruption of production of companies. 22.9 [kV] high-voltage overhead power distribution lines aim to improve reliability and stability, such as damage caused by rain, snow, wind, etc., or electric shock prevention. Therefore, in order to prevent wire disconnection accidents due to deterioration of electrical conductivity or tensile strength due to corrosion of overhead distribution lines, it is necessary to prevent unexpected accidents in the future through regular inspection and repair. In order to diagnose deterioration due to corrosion of distribution lines, a diagnostic system (measuring instrument) is installed on the wires to monitor the condition of the wires. The manager on the ground receives the measured data through ZigBee wireless communication, controls the diagnosis system through the diagnosis system program, and grasps the condition of the overhead distribution line through the measured data and photographed photos, and predicts the life of the wire along with the visual inspection method. developed a program.

• The Journal of Korean Medicine
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• v.44 no.4
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• pp.136-149
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• 2023

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.425-437
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• 2001

This reserch was to develop database software in order to handle a lot of clinical nursing data with nursing diagnoses, related factors, defining characteristics, nursing interventions, nursing activities and nursing outcomes. MS Access2000 and SQL was selected to use a general purpose database logic with an efficiency. MS Visual Basic 6.0 was used to construct the circumstance of Graphic User Interface. The Linkage Database of abdominal surgery patients was constructed from the clinical data and questionnaire. This database system could add related factors, defining characteristics, nursing activities in the database and analyze the statistical results through Access query. In the final stage, end-users satisfaction analysis using 5 points Likert scale was dong with the response of using the database system. The accuracy/trustworthiness of the database system was verified with the highest average scores as 4.42 and also, the efficiency as 4.21, user friendly function as 4.1.

• Advances in pediatric surgery
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• v.7 no.1
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• pp.64-67
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• 2001

It has been widely accepted that complete surgical resection of hepatoblastoma is essential for long-term survival. But unfortunately less than 50 % of hepatic tumors in children can be totally removed at the time of diagnosis. This report is to present the experience of successful resection of hepatoblastoma after concurrent radiotherapy with transarterial chemoinfusion in a child. We believe this modality of treatment enables complete resection of unresectable hepatoblastoma. which is resistant to the systemic chemotherapy.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1382-1383
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• 2008

In this paper, we have studied for analysis of the partial discharge(PD) signal based on fuzzy algorism. Partial discharge signal detector is difficult because of partial discharge signal is very non-linear. Also, it is very difficult work that separate partial discharge signal from noise. We constructed partial discharge accumulation detection system that use Labview for detection of non-linear partial discharge signal. And analyzed Partial discharge signal that is detected by Labview system utilizing Fuzzy model.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.216-222
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• 2021

Recently, various robots are being used for the purpose of structural inspection or safety diagnosis, and their needs are also rising rapidly. Among the structural inspection using robots, a lot of researches has recently been conducted on inspection of various facilities and structures using an unmanned aerial vehicle (UAV). However, since GNSS (Global Navigation Satellite System) signals cannot be received in an environment near or below structures, the operation of UAVs has been done manually. For a stable autonomous flight without GNSS signals, additional technologies are required. This paper proposes the autonomous flight system for structural inspection consisting of simultaneous localization and mapping (SLAM), path planning, and controls. The experiments were conducted on an actual large bridge to verify the feasibility of the system, and especially the performance of the proposed SLAM algorithm was compared through comparative analysis with the state-of-the-art algorithms.