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Lateral Osteotomy with Sawing Technique in Open Rhinoplasty
김연환,이학승,김정태,Kim, Youn-Hwan,Lee, Hak-Sung,Naidu, Shenthilkumar,Kim, Jeong-Tae Korean Society of Plastic and Reconstructive Surge 2010 Archives of Plastic Surgery Vol.37 No.4
Purpose: Lateral osteotomy is an essential step in the correction of nasal bony asymmetry. Direct visualization allows accurate repositioning of the nasal bones compared to blind techniques, which require precision and manual dexterity. We propose direct visualization procedures in open corrective rhinoplasty. Methods: The technique was used on 16 patients. All patients underwent open rhinoplasty with a columellar incision. The marginal incisions were extended on either side to allow access to the piriform aperture. A double hook was used to caudally retract the lower lateral cartilages and the fibrous connections between the upper and lower lateral cartilages were released until the piriform aperture was visualized. Through the incision, lateral osteotomy was performed using a reciprocating saw at that time with direct visualization. Additional procedures including augmentation rhinoplasty, hump resection, septoplasty and tip plasty were performed simultaneously. Results: This method provided excellent exposure to the lateral nasal bones and allowed the lateral osteotomy to be carried out precisely using the reciprocating saw. Conclusion: This extended open rhinoplasty method is suitable for most individuals, allowing a wide surgical field.
김연환,김동환,박선휘,Kim, Yeonwhan,Kim, Donghwan,Park, SunHwi 한국전력공사 2018 KEPCO Journal on electric power and energy Vol.4 No.2
본 논문에서는 가스 터빈 축 진동 신호 비정상 상태 분석의 사례 연구를 위해 커널 회귀 모델을 적용한다. 원격으로 전송되는 발전소 가스터빈의 진동데이터에 커널 회귀 모델을 적용하여 설비를 실시간으로 감시 및 분석 외에도, 축진동 신호의 비정상 상태를 분석하기 위하여 활용될 수 있다. 정상운전 중에 측정한 가스터빈의 정상적인 축진동 데이터 기반의 훈련데이터를 사용하여 생성한 자동연관커널회귀의 경험적 모델을 생성하고 적용할 수 있다. 이 데이터 기반 모델의 예측치를 실시간 데이터와 비교하여 신호의 상태를 분석하고 잔차를 감시하여 이상상태에 대한 분석 정보를 제공할 수 있다. 이상상태에서 발생하는 잔차는 비정상적으로 변화됨으로서 비정상 상태를 분석 할 수 있다. 본 논문에서 커널회귀모델은 축진동 센서의 신호 이상의 원인 분석 사례에서 고장을 구분할 수 있는 정보를 제공한다. In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.