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이기준,Sather,N F 한국화학공학회 1971 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.9 No.4
This paper presents a statistical mechanical description of the random motion of particles in suspension and some results derived there-from for the viscosity of concentrated suspensions of solid spherical particles. In statistical mechanics the information about the probability of dynamical states of the particles of a fluid (i. e., their positions, translational velocities and rotational velocities) is embedded in a particle distribution function, and all of the macroscopic properties of the fluid are specified as integral moments of the distribution function. In particular, the local stress in a suspension is defined as the integral over all particle states of the distribution weighted with the stress at a point for a given instantaneous total particle state. Thus, the calculation of the rheological properties of the suspension comes down to a determination of the distribution function and this local stress function. If the overall shear rate in the suspension is steady and not too large and the particles are neutrally buoyant solid spheres, the net forces and torques on the particles are zero and the local fluid motion is governed by the Stokes equations. It is shown that under these conditions 1) the particle distribution function is independent of the linear and angular velocities of the particles and so is uniquely determined by the particle positions, 2) the viscosity of the suspension must be independent of the shear rate for all concentrations, and 3) the viscosity can be written in a power series in the concentration of particles in which the term of the nth power is the incremental contribution to the viscosity from interactions involving particles. In order to calculate the coefficients of these terms the fluid velocity near the surface of a sphere moving with its neighbors in a, shear field is required. Since an exact determination of the flow field is impractical to obtain for groups of more than two particles, an approximate solution of the Stokes equations obtained by the method of weighted residuals is used in the calculations of the suspension viscosity reported here. The particle configuration is assumed to be simple cubic, and the distribution of interparticle distances is obtained from an approximate statistical model. The viscosity-concentration relationship calculated under these assumptions is found to agree closely with the compilations of experimental data reported by Rutgers and Thomas.
에지 방향성 히스토그램과 HMM을 이용한 제스처 인식에 관한 연구
이기준,Lee, Kee-Jun 한국정보통신학회 2011 한국정보통신학회논문지 Vol.15 No.12
In this paper, the algorithm that recognizes the gesture by configuring the feature information obtained through edge orientation histogram and principal component analysis as low dimensional gesture symbol was described. Since the proposed method doesn't require a lot of computations compared to the existing geometric feature based method or appearance based methods and it can maintain high recognition rate by using the minimum information, it is very well suited for real-time system establishment. In addition, to reduce incorrect recognition or recognition errors that occur during gesture recognition, the model feature values projected in the gesture space is configured as a particular status symbol through clustering algorithm to be used as input symbol of hidden Markov models. By doing so, any input gesture will be recognized as the corresponding gesture model with highest probability. 본 논문에서는 에지 방향성 히스토그램과 주성분 분석을 통해서 얻어진 특징 정보를 저차원 제스처 심볼로 구성하여 제스처를 인식하는 알고리즘에 대해 기술한다. 제안된 방법은 기존의 기하학적인 특징 기반 방법이나 외관기반 방법에 비해 많은 계산 량이 요구 되지 않고 최소한의 정보를 사용하고도 높은 인식률을 유지 할 수 있기에 실시간 시스템 구축에 매우 적합하다. 또한 제스처 인식 시 발생하는 잘못된 인식이나 인식 오차를 줄이기 위해 객체 공간상에 투영된 모델 특징 값을 은닉마르코프 모델의 입력 기호로 이용되기 위해서 군집화 알고리즘을 통해 특정한 상태 기호로 구성한다. 이렇게 함으로써 임의의 입력 제스처는 확률 값이 가장 높은 해당 제스처 모델로 인식하게 된다.