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한국인에서 마취유도를 위한 Propofol과 Ketamine의 용량반응과 최면성 간섭
최영환,이승준,길호영 대한마취과학회 1999 Korean Journal of Anesthesiology Vol.36 No.2
Background : Both propofol and ketamine are useful hypnotics for induction of anesthesia, and the combination of propofol and ketamine has been used for total intravenous anesthesia. The aim of this study was to evaluate the dose response of propofol, ketamine and combination of these drug, and determine possible interaction between two drugs in patients. Methods : The effect of ketamine on the dose response curve for propofol was studied in unpremedicated 165 ASA physical status I or II patients who were scheduled for elective operation. As an endpoint of hypnosis, ability to open eyes on verbal command was checked. Dose response curves for propofol and ketamine were determined with a probit procedure and their type of pharmacologic interaction was determined by fractional and isobolographic analysis. Results : At the hypnotic endpoint, the ED50s were 1.13 mg/kg propofol, 0.66 mg/kg ketamine, and the ED95s were 1.67 mg/kg propofol, 1.09 mg/kg ketamine. The type of interaction between two drugs for hypnosis was found to be additive and ketamine was 1.7 times potent than propofol as an equieffective dose of hypnosis. Conclusions : The type of interaction between propofol and ketamine for hypnosis was additive. (Korean J Anesthesiol 1999; 36: 214∼219)
최영환,류민영 한국고분자학회 2020 Macromolecular Research Vol.28 No.4
Fossil-based PC, bisphenol-A polycarbonate (BPA-PC), is polymerized using bisphenol-A, which is derived from fossil-fuel based chemicals. Bio-based polycarbonate (bio-based PC) is polymerized using isosorbide, which is taken from plants. Accordingly, bio-based PC does not contain toxic polymerization chemicals. The rheological characteristics of fossil-based PC and bio-based PC samples, including viscosity, storage and loss moduli, and melt tension, were studied and compared. The mechanical properties of tensile behavior and impact strength were also measured and discussed. The shear viscosity curves and storage and loss moduli patterns of the bio-based PC were found to be somewhat different from those of fossilbased PC. The bio-based PC had higher tensile strength and elastic modulus than the fossil-based PC. The fossil-based PC exhibited a stress jump in the high strain region of the stress-strain curve, while the bio-based PC exhibited no stress jumps. The bio-based PC had a lower impact strength than the fossil-based PC. The cross-section of the fractured impact specimen of the bio-based PC showed only mirror regions, while that of the fossil-based PC showed both mirror regions and misted regions.
최영환,여은지,임효상 한국정보과학회 2016 데이타베이스 연구 Vol.32 No.2
GPU(Graphical Processing Unit) is a special purpose electronic circuit designed to efficiently process graphical operations such as floating point operations. The efficiency of GPU comes from the high-parallelism of operations and high-bandwidth of memory communications. Using these advantages of GPUs, recently GPU is widely utilized not only in graphic applications but also in many other applications so as to increase processing performance. In this paper, we focus on moving object applications and use the GPUs’ parallelism in order to increase the performance of encrypting a large number of moving objects generated in real time. First, we propose a data model for representing a large(1,024 bit) integer. Next, based on the data model, we provide algorithms for multiplication and modular operations which are essential in the data encryption. We then provide two kinds of parallel algorithms for these operations: 1) object level parallelism algorithm and 2) operational level parallelism. Finally, we provide experiment results to show the characteristics and the performance benefits of the proposed algorithms. GPU(Graphical Processing Unit)는 그래픽 처리 작업을 전문으로 담당하는 연산장치로, 그래픽의 필수 요소인 부동소수점 연산을 병렬적으로 처리한다는 특징을 가진다. 최근 들어 GPU는 그 높은 병렬처리 성능을활용하여 그래픽 응용뿐만 아니라 다양한 응용에서 처리 성능을 높이기 위하여 널리 활용되기 시작하였다. 본 논문에서는 다양한 응용 중에서 이동객체에 대한 연구에 초점을 맞춰, 실시간으로 생성되는 대량의 이동객체 위치 정보를 암호화하는 것의 성능을 높이기 위해 GPU의 병렬성을 이용하는 알고리즘을 제시한다. 먼저, 1,024 bit 정수를 표현하기 위해서 큰 정수 데이터 표현모델을 제안하고, 다음으로 이를 사용하여 암호화 알고리즘의 핵심 연산인 곱셈과 모듈러를 수행하는 방법을 제시한다. 제안하는 알고리즘은 병렬화 수준을 두 가지로 구분하여 1) 객체 수준 병렬화(object level parallelism) 방법과 2) 연산 수준 병렬화(operational level parallelism) 방법을 각각 제시한다. 그리고 실험을 통해 각 방법의 특징과 장단점을 분석하고 기존의 CPU기반 알고리즘과의 성능 차이를 보여 해당 알고리즘의 유용성을 보인다.