The use of reflective optical systems is essential to acquiring high-resolution image quality in aerospace applications that observe distant objects. The geometric shapes of large-aperture reflective optical systems can be deformed depending on various operating and space environments, which deformation consequently affects optical performance. In this study, we predict the image quality of a reflective aerospace optical system according to various environmental changes. In particular, the shape deformation due to vibration and heat generated from the launch vehicle was mainly observed, and the effect on gravity was also considered. The variations of image quality, such as Modulation Transfer Function (MTF) and wave-front error (WFE), were also observed by importing the deformed shapes into the optical simulation tool. This study is intended to provide approaches to reduce the cost and lead time to develop aerospace optical systems.

The utilization of aerospace technology is growing to meet various requirements of climate change sensing, communications, and the military. Large aperture and precise reflective optical systems such as mirrors are needed to acquire high-quality data, and the requirements of lightness, low cost, and low deformation should also be met to operate in the space environment. In this review, we highlight the recent progress of reflective optical systems for aerospace applications. In particular, optical systems for artificial satellites and homing optics are mainly introduced, and optical and mechanical simulations are discussed according to operating environments. We also discuss various reflective optic designs, materials, and operating principles for aerospace applications, such as a homing head and optical payload. We hope that this review provides approaches for developing surveillance systems, exploring space, and addressing the climate crisis.

The fourth industrial revolution is rapidly emerging as a new innovation trend for industrial automation. Accordingly, the demand for inspection equipment is highly increasing and vision sensor technologies are continuously evolving. Machine vision algorithms applied to deep learning are also being rapidly developed to maximize the performance of inspection equipment. In this review, we highlight the recent progress of vision sensor technology for the industrial inspection system. In particular, inspection principles and industrial applications of a vision sensor are classified according to the vision scanning methods. We also discuss machine vision-based inspection techniques containing rule- and deep learning-based image processing algorithms. We believe that this review provides novel approaches for various inspection fields of agriculture, medicine, and manufacturing industries.