• 한국광학회:학술대회논문집
• /
• 한국광학회 2003년도 하계학술발표회
• /
• pp.60-61
• /
• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• Journal of Radiation Protection and Research
• /
• 제33권1호
• /
• pp.13-20
• /
• 2008

석유 및 정유관련 산업에서 다중상(multi-phase flow) 유체의 배관 내 흐름은 일반적인 현상의 하나이다. 그러나 각각의 상에 대한 정확한 유량측정은 항상 정확한 결과획득을 얻는데 장애의 근원으로 작용하였다. 일반 상업용 유량계는 일정 이상의 기포가 포함된 유체 흐름의 경우 유량계측에 상당한 오차를 유발한다. 본 연구에서는 ${\gamma}$-ray attenuation 기법을 이용하여 clamp-on 타입으로 배관 외부에서 다중상 유체흐름의 유량 측정을 수행하였다. 사용된 밀봉 감마선원으로는 $^{137}Cs$ 20 mCi와 17 mCi 두 개의 동위원소를 사용하였으며, 감마선 검출기로는 $2"{\times}2"$ NaI(Tl) 섬광계수관을 이용하였다. 방사선 검출기로부터 데이터를 수집하고 각각의 데이터에 대해 푸리에 변환과 필터링을 통해 노이즈를 최소화하였다. 복원된 신호에 대해 상호상관함수(cross correlation function)를 적용하여 두 검출기 사이의 통과시간(transit time)을 측정함으로써 유량을 산정하였다. 배관 내 기포함량 측정을 통해 유량을 보정해줌으로써 측정유량의 정확도를 높였다. 두 선원간의 거리가 4D(D; inner diameter) 그리고 본 실험의 측정조건(N/S: $0.12{\sim}0.15$, sampling time ${\Delta}\;t$: 4msec) 하에서 기포량(단면적 대비 $6.1\;%{\sim}9.2\;%$) 보정을 통해 산정된 유량은 계측오차가 실제 평균유량 대비 1.7 % 이하인 정확도를 보였다. 또한 두 밀봉 감마선원 간의 거리가 가까울수록 통과시간 측정에 정확도가 향상되므로 보다 정확한 유량측정이 가능하였다. 본 연구를 통해 다중상 혼합유체의 유량을 밀봉감마선원과 상호상관 기법으로 이용하여 계측할 수 있음을 확인하였다. 방사성동위원소의 선택 및 계측시스템의 최적화 조건 등에 대한 추가연구가 수행된다면 석유화학 산업과 같은 장치산업의 유지관리 측면에 경제적으로 크게 기여할 수 있을 것으로 판단된다.