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2채널 트랜스오럴 필터를 이용한 최적 청취영역 확대에 관한 연구
안찬식 광운대학교 정보과학기술대학원 2002 국내석사
본 논문은 2채널 스피커를 사용하여 청취자에게 보다 입체적인 음향 효과를 제시하기 위해 크로스토크 현상을 제거하고 청취자의 보다 자유로운 청취를 위해 최적 청취영역 확대를 위한 실험과 시스템 구현에 관한 것이다. 정면에 위치한 두 스피커로부터 교차경로인 크로스토크를 제거하기 위해 음질의 왜곡을 최소화하는 자유음장 모델을 이용하여 구현한 트랜스오럴 필터를 사용하였다. 최적 청취영역의 확대를 위해 스피커는 BPF(Band Pass Filter)를 이용하여 저주파 영역을 제외한 중?고주파 영역을 이용하는 스피커 재생 시스템을 구성하였다. 기존 크로스토크제거 시스템을 사용하여 고정된 한 점의 청취영역에서 좌?우로 5Cm씩 이동하며 100Cm까지 음의 분리도를 측정한 결과 30Cm, 55Cm, 75Cm, 90Cm, 100Cm의 청취위치에서 음의 분리도가 5dB이상 나타났다. 각 측정 위치에 자유음장 모델을 이용하여 트랜스오럴 필터링 하였고, 각 필터들 간에 간섭현상을 막기 위해 1/3-Octave Band Pass Filter를 사용하여 음질을 보상하였다. 2채널 시스템에서 제시하는 음원과 각 위치의 음원을 비교하여 음질 평가를 실시하였다. 기존의 트랜스오럴 시스템과 비교평가를 실시한 결과 크로스토크 제거률이 38.5dB ~ 43dB로 비슷한 성능을 보였다. 확산감에 대해 전문가를 대상으로 한 주관 평가는 5점 평가를 기준으로 0.4점이 향상되었다.
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Today we are observing a lot of injuries, casualties, and property losses that are mainly caused by the defects of products. In order to derive safety designs, which minimize the possibility of such product liability-related accidents, we need to take into account the user-product interaction as an important part of the danger factor analysis. Existing risk analysis techniques, however, have some limitations in detecting comprehensive danger factors that are peculiarly involved in human errors and the functional defects of products. Researches on danger factor analysis regarding the user-product interaction have been carried out actively in ergonomics. in this paper, we suggest a novel productrisk analysis technique, which is more objective and systematic compared to the previous ones, by combining a modified TAFEI (Task Analysis For Error Identification) technique with SASA (Systematic Approach to Accident Scenario Analysis) technique. By applying this technique to the product design practice in industry, corporations will be able to improve the product safety, consequently strengthening the competitiveness.
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안찬식 Graduate School, Yonsei University 2014 국내박사
Purpose: Hepatic resection is a primary treatment modality for hepatocellular carcinoma (HCC), but the high incidence of recurrence after hepatic resection remains its major drawback. Especially early recurrence within the first 2 years after resection of HCC is known to be associated with worse prognosis. The purpose of this study was to develop and validate a nomogram based on a multivariate model to preoperatively predict the risk of early recurrence after curative resection of single HCC, using various imaging findings of dynamic gadoxetic acid-enhanced and diffusion-weighted MRI.Materials and Methods: A total of 268 patients were included who underwent curative hepatic resection for single HCCs followed by gadoxetic acid-enhanced MRI from January 2008 to August 2011. We allocated 187 patients who underwent hepatic resection before September 2010 into the training cohort to derive a prediction model, and the remaining 81 patients were allocated into the validation cohort. All MR images of the training cohort were retrospectively reviewed by two radiologists by consensus, who determined whether the following eight MRI features were found in each HCC: ring-like arterial enhancement, peritumoral arterial enhancement, capsule, peritumoral hypointensity in the hepatobiliary phase (HBP), non-smooth tumor margin in the HBP, satellite nodules, intratumoral fat, and gross vascular invasion. Based on the multivariate logistic regression analysis, a nomogram was constructed. To validate a prediction model established from the results of the first review, the same radiologists analyzed the MR images of the validation cohort and assessed MRI features that had been independently associated with early recurrence in the training cohort.Results: Univariate and multivariate logistic regression analyses with 187 patients in the training set showed significant associations between early HCC recurrence and the following factors: ring-like enhancement (odds ratio [OR], 3.83; 95% confidence interval [CI], 1.39-10.52; P=0.01), peritumoral enhancement (OR, 2.64; 95% CI, 1.27-5.46; P<0.009), satellite nodule (OR, 4.07; 95% CI, 1.09-15.21; P=0.037), and tumor size (OR, 1.66; 95% CI, 1.31-2.09; P<0.001). The prediction model based on this result showed an area under the curve (AUC) of 0.791 in the training set. Internal validation with bootstrap sampling was performed to determine calibration accuracy, and the corrected AUC remained high (0.788) after bootstrapping. For the validation data set, discrimination was as good as for the training set, with an AUC of 0.783. The calibration was also good, with no significant difference (P=0.091) between the nomogram-predicted and the observed early recurrence rates.Conclusion: We developed and validated a nomogram that can be used to preoperatively estimate the risk of recurrence within 2 years after resection of single HCCs.
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