스누핑 버스 다중 프로세서 시스템에서 버스 적응형 캐쉬 교체 정책의 설계

학위논문(석사)--서울대학교 대학원 :전기·컴퓨터공학부,2003.Maste

지방자치단체 위탁운영비 산정을 중심으로

학위논문(석사) -- 서울대학교대학원 : 공학전문대학원 응용공학과, 2022.2. 서은석박창우.This study is about maximizing operating efficiency by applying cost engineering to the calculation of operating costs of government- consigned municipal waste incineration facilities. Execution of 「Guidelines for Calculation of Operating Costs for Household Waste Incineration Facilities」 is due to direct management claims of incineration facility worker’s petitions to the Blue House(Cheongwadae), social issues, suggestions for system improvement in Seoul, which has been operating household waste incineration facilities for over 20 years, and unreasonable gains from the difference in labor costs for operating incineration facilities. You can see court disputes and final judgments of the Supreme Court. The revision of the guidelines in March 2018 specifies the nature of the guidelines to convert technical service standards into general services and to select a more reasonable cost calculation method for cost calculation service institutions than guidelines. This revision is a measure to improve the problem of converting technical services into general services and paying labor costs under the National Contract Act, and local governments have removed regulations on fixed costs (non-settlement) and variable costs (settlement) and labor costs. However, due to the regulations on the application of unit price for technicians by engineering technology sector, the local government is following the unreasonable unit price journalization method of labor cost. Although the revision of 「Guidelines for Calculation of Operating Costs for Household Waste Incineration Facility」 in March 2021, the application of engineering labor unit price was conflicts with the 「Guidelines for Protection of Working Conditions for Service Workers」, which is ‘cost’ matter, and the national/local contract law, the subject of general management ratio still applied to the existing other services 6% is 3% elevated and revised to facility management, expenses and cleaning services, which is ‘expenses’ matter. After the revision in March 2018, the calculation and implementation of facility operation costs of the consigned operator of the deleted large classification fixed cost and variable cost classification is an enforcement that distorts the nature of the guidelines. The result of this study is to establish a cost calculation method for the distorted implementation of the 「Guidelines for Calculation of Operating Costs for Domestic Waste Incineration Facilities」 and to substitute the market price for the fair price applying cost engineering. It is about the rational implementation and enforcement of the selection of the successful bidder who bids favorably in the country of the cost-plus as synthetic market price way.본 연구는 정부위탁 생활폐기물 소각시설 운영비 산정에 대한 원가공학(Cost Engineering)을 적용한 운영효율 극대화이다. 「생활폐기물 소각시설 운영비 산출 지침」에 의한 집행은 소각시설 근로자들의 청와대 청원의 직영주장과 사회적 이슈, 20여년 생활폐기물 소각시설을 운영해 온 서울특별시 제도개선 건의, 소각시설 운영 인건비 차액의 부당이득으로 인한 법정분쟁과 대법원 확정판결 등을 볼 수 있다. 2018년 3월 지침 개정은 기술용역 기준을 일반용역으로 전환하고, 지침보다 더 합리적인 원가계산용역기관의 원가계산방법을 선택하도록 지침의 성격을 특정하고 있다. 이 개정은 엔지니어링기술진흥법의 기술용역을 국가계약법의 일반용역 전환과 인건비 지급차이의 문제점을 개선하기 위한 대책으로 고정비(비정산)와 변동비(정산) 대분류 및 인건비 산정 노임단가의 분개 규정 등을 삭제하였지만 그대로 둔 엔지니어링 기술부문별 기술자 노임단가 적용규정 때문에 지자체에서 불합리한 노무비 단가 분개방식을 답습하고 있다. 2021년 3월의「생활폐기물 소각시설 운영비 산출 지침」개정은 원가인 엔지니어링 노임단가 적용으로 인한「용역근로자 근로조건보호지침」과 상충에 기인함도 불구하고, 국가ㆍ지방 계약법의 일반관리비율 적용대상을 기존 기타용역 6%에서 3% 상승된 시설물 관리ㆍ경비 및 청소 용역 9% 비용을 개정하였다. 이후 2018년 3월 개정 후 일부 지자체의 전력비 직불제 시행처럼 원가와 가치개선(Cost & Value Improvement)의 운영 효율화 및 삭제된 대분류 고정비와 변동비 구분의 수탁운영자의 시설운영비 산정에서 지침의 성격을 왜곡한 집행을 볼 수 있다. 본 연구결과는「생활폐기물 소각시설 운영비 산출 지침」의 왜곡된 시행에 대한 원가계산방법 정립과 원가공학(Cost Engineering)을 적용한 공정가격(Fair Price)의 시장가격 대용으로서 코스트 플러스 방식(Cost-plus as synthetic market price)의 국가에 가장 유리하게 입찰한 낙찰자를 선정하는 합리적인 시행과 집행에 대한 것이다.Ⅰ. 서론 1 1.1 연구의 배경과 목적 1 1.2 연구의 범위와 구성 2 1.3 선행 연구와 집행의 관행 4 Ⅱ. 이론적 배경 및 선행연구 고찰 5 2.1 원가공학 이론과 공정가격 5 2.2 국가·지방계약법의 원가항목 10 2.3 비목별 가격결정 원칙 10 2.4 용역근로자 보호지침 27 2.5 국가계약법 경쟁입찰 낙찰자 결정 원칙 30 2.6 행정규칙 주체의 성립·효력요건 30 2.7 생활폐기물의 관리체계와 분류 31 2.8 예정원가 낙찰율과 산출내역서 작성 및 이론 41 Ⅲ. 생활폐기물 위탁운영비 산정의 문제점 43 3.1 생활폐기물 소각시설의 운영 현황 43 3.2 지방자치단체의 위탁운영비 산정기준과 적용실태 44 3.3 지방자치단체의 위탁용역운영비 산정의 문제점 46 Ⅳ. 원가공학을 적용한 운영비 산정 개선방안 75 4.1 소각시설 운영비 원가공학 적용 극대화 개선방안 75 4.2 입찰제도-총액입찰 원가입찰제도 개선 77 4.3 경비 82 4.4 시설운영비 83 4.5 원가공학 측면의 가치공학 88 4.6 원가공학측면의 코스트-피 플러스 예정원가계산서 제안 94 4.7 원가계산의 단위가격 조사기관 95 4.8 원가계산의 단위당 가격 기준 95 4.9 원가계산용역기관 선정과 전문성 96 Ⅴ. 결론 97 5.1 결론 및 기여도 97 5.2 연구의 한계와 향후 과제 100 참고문헌 101 Abstract 103석

Biomaker for diagnosing epilepsy

본 발명은 뇌전증을 진단하기 위한 바이오마커 및 이의 이용에 관한 것이다. 보다 구체적으로, 본 발명은 염기서열의 변이가 일어난 mTOR(Mammalian target of rapamycin) 유전자 또는 염기서열의 변이로 인해 아미노산 서열의 변이가 일어난 mTOR 단백질에 관한 것이다. 또한, 상기 유전자 또는 단백질을 검출할 수 있는 제제를 포함하는 뇌전증 진단용 조성물 및 키트에 관한 것이다. 또한, 뇌전증 진단에 필요한 정보를 제공하기 위하여, 환자의 시료로부터 뇌전증 진단용 바이오마커인 상기 유전자 및 단백질을 검출하는 방법에 관한 것이다

A study on the valuation of credit default swap

학위논문(석사) - 한국과학기술원 : 테크노경영전공, 2003.2, [ vi, 75 p. ]한국과학기술원 : 테크노경영전공

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction

Background: Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose: To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods: CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results: A total of 308 patients (mean age 6 standard deviation, 62 years 6 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years 6 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm). Conclusion: Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. (C) RSNA, 202

CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma

Purpose: To develop and validate a CT-based radiomic model to simultaneously diagnose anaplastic lymphoma kinase (ALK) rearrangements and epidermal growth factor receptor (EGFR) mutation status of lung adenocarcinoma and to assess whether peritumoural radiomic features add value in the prediction of mutation status. Methods: 503 patients with pathologically proven lung adenocarcinoma containing information on the mutation status were retrospectively included. Intratumoural and peritumoural radiomic features of the primary lesion were extracted from CT. We proposed two-level stepwise binary radiomics-based classification models to diagnose ALK (step1) and EGFR mutation status (step2). The performance of proposed models and added value of peritumoural radiomic features were evaluated by using the areas under receiver operating characteristic curves (AUC) and Obuchowski index in the development and validation sets. Results: Regarding the prediction of ALK rearrangement, the diagnostic performance of the intratumoural radiomic model showed the AUC of 0.77 and 0.68 for the development and validation sets, respectively. As for EGFR mutation, the diagnostic performance of the intratumoural radiomic model showed the AUCs of 0.64 and 0.62 for the development and validation sets, respectively. The radiomics added value to the model based on clinical features (development set [radiomics + clinical model vs. clinical model]: Obuchowski index, 0.76 vs. 0.66, p < 0.001; validation set: 0.69 vs. 0.61, p = 0.075). Adding peritumoural features resulted in no improvement in terms of model performance. Conclusion: The CT radiomics-based model allowed the simultaneous prediction of the presence of ALK and EGFR mutations while adding value to the clinical features

Performance of radiomics models for survival prediction in non-small-cell lung cancer: influence of CT slice thickness

Objectives: To investigate whether CT slice thickness influences the performance of radiomics prognostic models in non-small-cell lung cancer (NSCLC) patients. Methods: CT images including 1-, 3-, and 5-mm slice thicknesses acquired from 311 patients who underwent surgical resection for NSCLC between May 2014 and December 2015 were evaluated. Tumor segmentation was performed on CT of each slice thickness and total 94 radiomics features (shape, tumor intensity, and texture) were extracted. The study population was temporally split into development (n = 185) and validation sets (n = 126) for prediction of disease-free survival (DFS). Three radiomics models were built from three different slice thickness datasets (Rad-1, Rad-3, and Rad-5), respectively. Model performance was assessed and compared in three slice thickness datasets and mixed slice thickness dataset using C-indices. Results: In corresponding slice thickness datasets, the C-indices of Rad-1, Rad-3, and Rad-5 for prediction of DFS were 0.68, 0.70, and 0.68 in the development set, and 0.73, 0.73, and 0.76 in the validation set (p = 0.40?0.89 and 0.27?0.90, respectively). Performance of the models was not significantly changed when they were applied to different slice thicknesses data in the validation set (C-index, 0.73?0.76, 0.72?0.73, 0.75?0.76; p = 0.07?0.92). In the mixed slice thickness dataset, performances of the models were similar to or slightly lower than their performances in the corresponding slice thickness datasets (C-index, 0.72?0.75 vs. 0.73?0.76) in the validation set. Conclusions: The performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness. Key Points: ? Three radiomics models based on 1-, 3-, and 5-mm CT datasets showed C-indices for predicting disease-free survival of 0.68?0.70 in the development set and 0.73?0.76 in the validation set, without statistical differences (p = 0.27?0.90). ? Application of the radiomics models to different slice thickness datasets showed no significant differences in performance between the values in the prediction of disease-free survival (p = 0.07?0.99). ? Three radiomics models based on 1-, 3-, and 5-mm CT datasets performed well in mixed slice thickness datasets, showing similar or slightly lower performances

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules

Objectives: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. Methods: Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. Results: The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. Conclusions: The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists' measurements of solid portion size

Novel Resectable Myocardial Model Using Hybrid Three-Dimensional Printing and Silicone Molding for Mock Myectomy for Apical Hypertrophic Cardiomyopathy

Objective: We implemented a novel resectable myocardial model for mock myectomy using a hybrid method of threedimensional (3D) printing and silicone molding for patients with apical hypertrophic cardiomyopathy (ApHCM). Materials and Methods: From January 2019 through May 2020, 3D models from three patients with ApHCM were generated using the end-diastolic cardiac CT phase image. After computer-aided designing of measures to prevent structural deformation during silicone injection into molding, 3D printing was performed to reproduce anatomic details and molds for the left ventricular (LV) myocardial mass. We compared the myocardial thickness of each cardiac segment and the LV myocardial mass and cavity volumes between the myocardial model images and cardiac CT images. The surgeon performed mock surgery, and we compared the volume and weight of the resected silicone and myocardium. Results: During the mock surgery, the surgeon could determine an ideal site for the incision and the optimal extent of myocardial resection. The mean differences in the measured myocardial thickness of the model (0.3, 1.0, 6.9, and 7.3 mm in the basal, midventricular, apical segments, and apex, respectively) and volume of the LV myocardial mass and chamber (36.9 mL and 14.8 mL, 2.9 mL and-9.4 mL, and 6.0 mL and-3.0 mL in basal, mid-ventricular and apical segments, respectively) were consistent with cardiac CT. The volume and weight of the resected silicone were similar to those of the resected myocardium (6 mL [6.2 g] of silicone and 5 mL [5.3 g] of the myocardium in patient 2; 12 mL [12.5 g] of silicone and 11.2 mL [11.8 g] of the myocardium in patient 3). Conclusion: Our 3D model created using hybrid 3D printing and silicone molding may be useful for determining the extent of surgery and planning surgery guided by a rehearsal platform for ApHCM