In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing’s temperature is more accurate within 3% error and lower clothing’s temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively.

Single-wall carbon nanotubes (SWNTs) are promising materials for saturable absorbers (SAs) in mode-locked lasers. However it has been widely recognized that the degradation of optical properties of film-type SWNTs used in femtosecond mode-locked lasers limits the achievable long-term stability of such lasers. In this paper, we study the degradation of optical properties of SWNT-SA fabricated as sandwich type using HiPCO SWNTs with an Er-doped all-fiber laser. The thresholds of laser pump power are examined to avoid the damage of the SWNT-SA. Based on the proposed analysis, it is shown that all-fiber laser pulses of 300 fs pulse width, 3.85 mW average output power, 211.7 MW/cm² peak intensity and 69.9 MHz repetition rate can be reliably generated without any significant damage to the SWNT-SA film.

Over the past decade, much research has been done to improve single‐fog images. However, most of these have concentrated on outdoor environments and little has been done for indoor environments. In this study, an effective method of removing fog from images both indoors and outdoors is presented. A new single image enhancement approach is based on mixture of dark channel prior (DCP) and contrast limited adaptive histogram equalisation with discrete wavelet transform (CLAHE‐DWT) algorithms. With the DCP algorithm using modified transmission map, the authors obtained fast processing speed and clean dehazed image without refining process. The CLAHE and DWT methods improved the contrast and sharpness of images. Finally, an enhanced image was produced by fusing the CLAHE and DWT images. To demonstrate the effectiveness of the proposed method, the authors performed objective image quality assessments, and so on. Through a variety of experiments for various indoor and outdoor images with fog, the proposed method was proven to be highly effective.