In this paper, the key assembly of the recently developed light machine gun-Ⅱ sighting device was identified as prone to experiencing horizontal noise caused by unstable voltage fluctuations under high-temperature conditions during the defense environmental stress screening test. The primary objective ot this study is to address the issue of horizontal noise through circuit analysis and device parameter tuning, aiming to eliminate its presence.

Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. In most of the safety assessment and fracture mechanic applications proposed that these cracks and defects eventually will grow and will have potential lead to in-service failure. Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. Currently, non-destructive methods are getting popular in the field of inspecting defects in structure and one of them in trends is that using the thermographic image to detect hidden effects. However, the accuracy of the thermal camera, also called resolution, is highly dependent on camera variables such as lens, detector, sensitivity etc. Also, the most important question that needs to be answered for this research is what happens to the image in fog, rain or other climatic conditions where the camera detects crack which exceptionally smaller than most thermographic applications detects. This paper investigates the accuracy of thermal images obtained by the thermal camera under various weather condition and aims at providing information about optimum choice of environmental condition where the more favorable thermal images can be obtained and increase survey reliability and accuracy of the analysis.

This paper presents a vision-based fall detection system to automatically monitor and detect people’s fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

Recently, Maintenance and inspection of plant are now being actively studied with the development of plant industry. In this paper, A leak detection in piping facilities using thermal imaging camera is proposed. This method was verified by laboratory experiment. In future, Appropriate algorithm will be applied to this method for real time detection and finally applied to the plant that is the ultimate goal of this study.

We conducted thermal analyses and cooling tests of the space observation camera (SOC) of the multi-purpose infrared imaging system (MIRIS) to verify passive cooling. The thermal analyses were conducted with NX 7.0 TMG for two cases of attitude of the MIRIS: for the worst hot case and normal case. Through the thermal analyses of the flight model, it was found that even in the worst case the telescope could be cooled to less than 206°K. This is similar to the results of the passive cooling test (~200.2°K). For the normal attitude case of the analysis, on the other hand, the SOC telescope was cooled to about 160°K in 10 days. Based on the results of these analyses and the test, it was determined that the telescope of the MIRIS SOC could be successfully cooled to below 200°K with passive cooling. The SOC is, therefore, expected to have optimal performance under cooled conditions in orbit.

터널에서 외기에 접촉되는 부분은 입, 출구부와 환기구가 있으며 이러한 부위의 열화와 그 진행속도는 외기의 영향을 받지 않는 구간과는 다른 차이를 보여준다. 실제로 터널에서 외기에 접촉되는 부분뿐만 아니라 외기의 영향을 받는 구간은 일반적인 구간과는 다른 관점에서 접근해야 할 필요가 있고 보수나 보강공법 적용시에도 우선 고려가 되어야 한다. 그럼에도 불구하고 터널의 유지관리시, 점검 및 정밀안전진단시 외부온도의 영향을 받는 구간의 결함 및 열화는 그 범위가 광범위함에도 불구하고 구조체의 안전성에 미치는 영향이 미소하다는 이유로 소홀히 되고 있는 실정이며 일시적인 계절영향, 온도 변화요인에 의한 것으로 판단하여 특별한 관리가 되지 않고 있는 실정이다. 이에 본 논문은 외부온도, 기온의 터널내 영향범위를 산출하기 위해 2개소의 도심지 터널에 대해서 적외선 열화상카메라를 활용하여 터널내의 외부온도 영향범위를 결정하고 그 영향범위에서 발생되는 콘크리트라이닝 또는 콘크리트 구조체의 결함 및 열화원인을 분석하고 유지관리시 중점을 두고 시행해야 할 사항을 제시하였다.