http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Olfactory Evaluation by UPSIT before and after Endoscopic Sinus Surgery
홍석찬,김진국,강성호 대한비과학회 1996 Journal of rhinology Vol.3 No.2
It is well known that olfactory dysfunction is related to many etiologies such as obstructive nasal or sinus disease, upper respiratory tract infection, head trauma, aging and congenital anomaly. The most common cause is chronic paranasal sinusitis. Endoscopic sinus surgery has been reported to improve olfactory function. Fifteen patients with chronic paranasal sinusitis were evaluated by UPSIT (University of Pennsylvania Smell Indentification Test) before and after endoscopic sinus surgery. Preoperatively, no one bad normosmia, 10 patients had hyposmia and 5 had anosmia. Postoperatively, 2 patients had normosmia, 11 had hyposmia and 2 patients had anosmia. In the control group (N=5) who did not complain of olfactory dysfunction, 2 subjects had normosmia and 3 had hyposmia. It is concluded that endoscopic sinus surgery may be beneficial for the relied of olfactory disturbance caused by chronic sinusitis.
홍석찬,안재윤,조재훈,임대준,박가현 대한이비인후과학회 2008 대한이비인후과학회지 두경부외과학 Vol.51 No.8
This study examined the causes and epidemiologic factors of smell loss in Koreans using the Korean Version of the Sniffin’ Sticks Test and compared the results with cases of foreign countries. Subjects and Method:The data of 386 patients who visited clinics complaining of smell loss were retrospectively analyzed with medical charts. Results: Idiopathic, upper respiratory tract infection, trauma, nasal and paranasal sinus disease were the major causes of smell loss in this study. The distribution of gender and age, severity of smell loss, association of smell loss with allergies and nasal polyps were discussed in detail. Conclusion:The proportion of each cause observed in this study was different compared with the results of other domestic reports of nasal and paranasal sinus disease, but came out similar to the results of foreign studies. (Korean J Otorhinolaryngol-Head Neck Surg 2008;51:717-21)
홍석찬,이송원,장철,신현수 대한이비인후과학회 2003 대한이비인후과학회지 두경부외과학 Vol.46 No.1
The extramedullary plasmacytoma is a neoplastic proliferation of plasmacytes in reticuloendothelial tissues, and it occurs most commonly in the head and neck area, especially in the upper respiratory tract and the oral cavity. The most frequent sites are the paranasal sinuses, nasal cavity and nasopharynx. If histologic diagnosis of plasmacytoma is confirmed, all screening tests are necessary to rule out multiple myeloma. The treatment and prognosis are different according to sites where the extramedullary plasmacytoma is originated. We experienced a case of extramedullary plasmacytoma that originated from the septum in a 60- year-old man. After the embolization, endoscopicgical removal of the mass was carried out. Its histopathologic finging revealed the plasmacytoma of lambda type. Systemic evaluations were done immediately and there was no evidence of systemic involvement. Additional radiotherapy (50 Gy) was performed. After ten months of treatment, there was no evidence of recurrence. So we report this case with a review of the literature. (Korean J Otolaryngol 2003;46:81-4)
딥러닝 학습을 이용한 주파수 도메인에서의 승차감 진동 수치 회귀 예측 기법 개발
배승환(Seunghwan Bae),한동운(Dongun Han),박성근(Seongkeun Park),조한준(Hanjun Cho),김상원(Sangwon Kim) 한국자동차공학회 2020 한국자동차공학회 학술대회 및 전시회 Vol.2020 No.11
In this paper, we implemented a network that predicts the vibration value of the driver’s seat, which is the vibration value of riding comfort. The input feature used to predict the ride comfort transfer function uses a total of 15 factors, x, y, and z axes, respectively, for the vibration values of the four wheels and the engine vibration. The target value is the vibration value of the x and z axis of the driver’s seat. Through the following characteristic factors, we could experimentally find out that prediction through learning is possible even with the method using the deep learning technique, which is beyond the existing analysis method through mechanical modeling. In addition, network evaluation and verification were performed based on the degree of similarity of the comparison graph between the MSE and the actual result at the time of prediction, and the network with a deep design of the MLP showed the best performance through experiments on various network configurations.