38 research outputs found
마이크로 구조 표면에서의 최소막비등 온도 증진: 실험 및 해석
DoctorWe investigated the minimum film-boiling temperature, TMFB, on microstructured surfaces using a quenching experiment conducted in saturation temperature water at atmospheric pressure. Firstly, two microstructured quench spheres, Brass and SUS316L (d = 10 mm), were prepared using a chemical etching and anodic oxidation method: Microstructured Surface in Brass (MS_Brass) and MS_SUS316L. Secondly, three types of CuO surface structured quench spheres (MS_CuO, Nanostructured Surface NS_CuO and Micro/Nanostructured Surface MNS_Brass/CuO) on brass (d = 15 mm), were prepared using a chemical etching and electrochemical deposition (ECD) method. Especially, a periodic CuO microstructure is obtained using ECD of 1 µm diameter particles forming unit-cell porous cones of average height L = 100 µm and base diameter D = 20 µm in MS_CuO. Surface parameters, including the contact angle, L, D, and chemical composition, were measured. TMFB increased only at MS_SUS316L, but not on MS_Brass. Also, significant increase in TMFB (> 600oC) is achieved with MS_CuO. These results attributed to local cooling (fin effect) by the microstructure causing liquid–solid contact. The increase in TMFB was affected by the surface microstructures, and was determined by characteristic variables of microstructure: thermal conductivity k, L, D and its shape. Based on fin theory, we analyzed that the dimensionless temperature difference θ* of microstructure depends on the hybrid Biot number, Bih = (mL)2 = hL2/(kD). Fin analysis predicts the cone tip cooling to the homogeneous nucleation temperature of water (~ 330oC), while the base temperature is at 600oC in MS_CuO: Bih = 3.6 with h = 800 W/(m2-K), L = 100 µm, D = 20 µm and = 0.5 W/(m-K). This causes liquid–solid contact during quenching and analysis suggests the fin effective thermal conductivity and geometric ratio L2/D are key to this liquid–solid contact. By including the temperature difference between the fin-base and the fin-tip in the model suggested by K.J. Baumeister and F.F. Simon (1973), we develop a model of TMFB on microstructured surfaces. This approach improves our understanding of the effects of microstructured surfaces in liquid-solid contact during quenching, and may contribute to the development of advanced high temperature cooling systems
Representational Changes of Associative Information in the PrefrontalCortex during Memory Updating
Hierarchical Carbon Nanosheet Derived from Metal Organic Framework on Graphene Oxide for Room Temperature NO2 Gas Sensors
Perovskite La0.75Sr0.25Cr0.5Mn0.5O3 sensitized SnO2 Fiberin-Tube Scaffold: Highly Selective and Sensitive Formaldehyde
Highly Sensitive Acetone Sensor Derived by Nanoscale PtO2 Catalysts-Loaded SnO2 Multichannel Nanofibers
Dual Sensitization of Pt-ZnO Nanoparticles in Hierarchical Reduced Graphene Oxide for NO2 Chemiresistors
의미 컨텍스트로서 형용사를 활용한 의미 기반 명사 유사도 계산 방법
학위논문(석사) - 한국과학기술원 : 전산학부, 2016.2
,[v, 35 p. :]Noun similarity measures the semantic likeness between two nouns, and it generally means semantic similarity. Measuring semantic similarity requires an information resource such as a corpus or knowledge base. In this thesis, we focus on methods for using corpus data. Previous research on computing semantic similarity using corpus data still has some critical limitations. First, the target nouns should directly or indirectly co-occur in the corpus. Also, the words that are semantically unrelated to the target words in the context can be incorrectly used as representing the meaning. To overcome these limitations, we propose a method of utilizing the modifying adjectives in the context of a target noun. By using adjectives for a target noun, we can extract contextual information regardless of whether or not it co-occur with the other noun being compared in the corpus. To effectively make use of adjective information, we adopt the adjective classification method from past research. With the method we form vectors, each representing attributes of each adjective. We evaluate the proposed method with existing benchmarks and compare the performance with past studies. The result shows that adjective information has a positive impact on measuring noun similarity.한국과학기술원 :전산학부
