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Image Reconstruction from Point Cloud Data by CIP-Level Set Method
Ishimoto Hironori,Ryuzaburo Sugino,Noboru Morizumi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Level set method(LSM) is the efficient computation method for the interface capturing with corresponding of advection equation attend topological change of interface. LSM is consisted of the generation of signed distance function from initial interface and the convergence computation of the interface evolution for the target shape using the advection equation in conjunction with the mean curvature flow. We can get the image source from the actual object through the some kinds of distance measurement. The many measured points form the point cloud as the pointing sketch of aobject surface. The precision surface model of the object requires that we extract the suitable interface for the purpose from the point cloud data. The extraction is one of image reconstruction. LSM is very useful method to extract the fine surface model from the point cloud data. However, the conventional LSM has some difficalties in the practical computing. In the long time computing, the distance function will be broken up without the frequent reinitialization of it. In this study, we proposed the new computation algorithm in which it is combined the LSM with the CIP scheme. The CIP presents very good performance to keep the shape profile in the advection computing. The obtained results are the various of examples of image reconstruction such as the convex sharp profile, then on-convex profile and the topological changing of profiles. We show the applicability and the effectiveness through the comparison of the CIP scheme with Up-Wind scheme.
Dialogical Design of Fuzzy Controller Using Rough Grasp of Process Property
Naoyuki ISHIMARU,Tutomu ISHIMOTO,Kageo AKIZUKI 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process.<br/> The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order.<br/> The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response.<br/> The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.