Cavity ring-down spectroscopy (CRDS) is an ultra-sensitive direct absorption technique that offers unique advantages compared to other spectroscopic techniques. It can measure cooperative enhanced absorption for weakly absorbing species at ultra-low concentrations. This is achieved by leveraging the concept of a stable optical cavity, which allows for an effective optical path length of several kilometers within a small physical sample length. One advantage of CRDS technology is that it is unaffected by fluctuations in the intensity of the light source. Another advantage is its applicability to the detection of atoms, molecules, and radicals in the atmosphere. Additionally, the equipment associated with this technology is compact and robust. This paper will first introduce the fundamental principles and setup of CRDS technology. It will then provide an overview of the characteristics of the fabrication equipment and the high reflectivity mirror coating process used in cavity ring-down spectroscopy.

Self-Powered neutron detectors (SPND) detect current generated from interaction of neutron flux with emitter materials. They are inside a reactor in NPP (Nuclear Power Plant), and currently used to monitor the output and control the operation. Since the signal level of prompt-response SPND is very small (-nA), an analog circuit is necessary to amplify the current signal, and to convert it into voltage. This circuit needs to perform an anti-aliasing function to reduce distortion at the stage of digital conversion. In this paper, a systematic design process for high-gain amplification circuit is presented. Based on error analysis of the circuit, parameters are selected to satisfy design requirements. A third-order anti-aliasing filter is designed. A prototype circuit is built. Measured performance of the circuit confirms that the circuit satisfies all design requirements and validates the efficacy of the design to be used in practical environments.

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.

Hands perform various functions. There are many inconveniences in life without the use of hands. People without the use of hands wear prostheses. Recently, there have been many developments and studies about robotic prosthetic hands performing hand functions. Grasping motions of robotic prosthetic hands are integral in performing various functions. Grasping motions of robotic prosthetic hands are required recognition of grasping targets. A path toward using images to recognize grasping targets exists. In this study, object recognition in images for grasping motions are performed by using object detection based on deep-learning. A suitable model for the grasping motion was examined through three object detection models. Also, we present a method for selecting a grasping target when several objects are recognized. Additionally, it will be used for grasping control of robotic prosthetic hands in the future and possibly enable automatic control robotic prosthetic hands.