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Phase-shifting fringe projection profilometry (PSFPP) is a three-dimensional (3D) measurement technique widely adopted in industry measurement. It recovers the 3D profile of measured objects with the aid of the fringe phase. The phase accuracy is among the dominant factors that determine the 3D measurement accuracy. Evaluation of the phase accuracy helps refine adjustable measurement parameters, contributes to evaluating the 3D measurement accuracy, and facilitates improvement of the measurement accuracy. Although PSFPP has been deeply researched, an effective, easy-to-use phase accuracy evaluation method remains to be explored. In this paper, methods based on the uniform-phase coded image (UCI) are presented to accomplish phase accuracy evaluation for PSFPP. These methods work on the principle that the phase value of a UCI can be manually set to be any value, and once the phase value of a UCI pixel is the same as that of a pixel of a corresponding sinusoidal fringe pattern, their phase accuracy values are approximate. The proposed methods provide feasible approaches to evaluating the phase accuracy for PSFPP. Furthermore, they can be used to experimentally research the property of the random and gamma phase errors in PSFPP without the aid of a mathematical model to express random phase error or a large-step phase-shifting algorithm. In this paper, some novel and interesting phenomena are experimentally uncovered with the aid of the proposed methods.

In this paper, a disturbance-observer-based composite anti-disturbance control method is proposed for landing control of the carrier-based UAV subject to both external and internal disturbances. By the virtue of the composite control scheme, two nested loops are simultaneously designed. In the inner loop, disturbance observers are employed to estimate the effect of disturbances. On the other hand, in the outer loop, the sliding mode control (SMC) is designed to attenuate both the disturbance estimation error and guarantee the control performance. Then, with the disturbance estimation provided by the disturbance observer, the composite anti-disturbance controller is designed to achieve disturbance compensation and attenuation using both the feed-forward and feed-back channels. Besides, the stability of overall closed-loop is analyzed via Lyapunov theory. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

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Fringe projection profilometry (FPP) has been widely used in three-dimensional (3D) profile measurements for its efficiency and high accuracy. As an offshoot of FPP, color fringe projection profilometry (CFPP) has the advantage of being dynamic, non-contact, and full-field, which makes it one of the most valuable 3D profile measurement techniques. However, phase errors may arise in CFPP when the phase is retrieved by phase-shifting algorithms. This is due to the gamma nonlinear response, color coupling, and color imbalance of electro-optical devices. In this paper, a multi-confusion matrices (MCM) correction technique is proposed to reduce the phase error by compensating the captured color fringe pattern intensity. This can be accomplished in three steps. Firstly, project 30 ~220 (with the step of ten gray levels) uniform intensity patterns on a calibration plate, and then extract the intensity information to construct multi-confusion matrices (MCM) and LUTs (look up tables). Secondly, search optimal confusion matrix (OCM) from MCM to correct the fringe pattern. Finally, retrive the unwrapping phase using the corrected fringe pattern. The simulation and experimental results indicate that the proposed approach can reduce phase errors and use one color fringe pattern to reconstruct 3D topography.

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-Project stakeholders are individuals or organizations that are closely related to the project. In agricultural development projects, there are multiple stakeholders that have complex interests. It is necessary to recognition the key stakeholders correctly and understand the role and power of the key stakeholders in agricultural development projects. This paper was firstly applied fuzzy inference system to identify the key stakeholders in agricultural development project, and put forward the appropriate management strategy to ensure the success of agricultural development project.

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