In mechanical braking systems, there are hot spots on the surface of a braking disc due to thermal deformation with a high thermal gradient. Controlling such hot spots is important for extending the life of a braking disc. In this study, surface temperatures of railway brake discs were monitored using infrared (IR) thermal imaging technique. A highspeed infrared camera with a maximum speed of 380 Hz was used to monitor surface temperature changes of the braking disc. Braking tests were performed with a full-scale dynamometer. During the braking test, the surface temperature change of the braking disc were monitored using a high-speed infrared camera. Hot spots and thermal damage observed on the surface of railway brake discs during braking tests were quantitatively analyzed using infrared thermographic images. Results revealed that monitoring disc surface temperature using IR thermographic technique can be a new method for predicting surface temperature changes without installing a thermocouple inside the disc.

Elderly monitoring systems are gaining significant attention in our increasingly aging society. Existing monitoring systems, which utilize RGB and infrared cameras, often encounter errors when recognizing human-like objects, photos, and videos as actual humans. Additionally, privacy concerns arise due to this issue. However, these challenges can potentially be overcome by employing thermal images. Thus, our study aimed to investigate the feasibility of identifying and categorizing human postures depicted in thermal images using deep learning models and algorithms. To conduct our experiment, we developed a system that utilizes a thermal pose algorithm and a convolutional neural network. As a result, we achieved an average accuracy of 88.3%, with the highest accuracy reaching 91.2%.

Damage to the units related to driving and running of the railway vehicle may cause an inevitable accident due to defects and malfunctions in operation. In order to prevent such an accident, a non-destructive diagnostic technology that detects the damage is required. Previous researchers have researched and developed a monitoring system of the infrared thermography method to diagnose the condition of the railway vehicle driving and driving units. A system for monitoring running of the railway vehicle and temperature condition of the drive unit at a vehicle speed of 30 to 100 km/h was constructed, and a study on its applicability was conducted. In this study, a system for diagnosing an abnormal condition of the driving and running units while the vehicle is running with an infrared thermography diagnostic system was installed in the depot and operation route, and evaluation of the abnormal condition of the driving and running units was performed. The results show that the diagnosis system using infrared thermography can be used to identify abnormal conditions in the driving and running units of a railway vehicle. The diagnosis system can effectively inspect the normal and abnormal conditions in operation of a railway vehicle.

Recently, in-depth studies on sensors of autonomous vehicles have been conducted. In particular, the trend to pursue only camera-based autonomous driving is progressing. Studies on object detection using IR (Infrared) cameras is essential in overcoming the limitations of the VIS (Visible) camera environment. Deep learning-based object detection technology requires sufficient data, and data augmentation can make the object detection network more robust and improve performance. In this paper, a method to increase the performance of object detection by generating and learning a high-resolution image of an infrared dataset, based on a data augmentation method based on a Generative Adversarial Network (GAN) was studied. We collected data from VIS and IR cameras under severe conditions such as snowfall, fog, and heavy rain. The infrared data images from KAIST were used for data learning and verification. We confirmed that the proposed data augmentation method improved the object detection performance, by applying generated dataset to various object detection networks. Based on the study results, we plan on developing object detection technology using only cameras, by creating IR datasets from numerous VIS camera data to be secured in the future and fusion with VIS cameras.

The repeated thermal load on the railway wheel for tread brakes has been remarkably tightened due to increase in speed of trains and increase of operation frequency. As overheating and cooling between the wheel and brake block are continuously repeated, the railway wheel is damaged. To understand the process, thermal cracks for wheel tread can be experimentally reproduced under the condition of cyclic frictional heat from brake blocks, through bench experiments using a railway wheel. Thermal cracks generated in the wheel were investigated to observe the cracks’ initiation processes using full-scale brake dynamometer. Results show that as braking energy and braking temperature continued to accumulate, a hot spot appeared on the wheel surface and 2 mm of thermal crack occurred in the wheel rim.

Hot embossing techniques are used to engrave patterns on plastic substrates. Roll based hot embossing uses a heated roll for a continuous process. A heated roll with relief patterns is impressed on a preheated plastic substrate. Then, the substrate is cooled down quickly to prevent thermal shrinkage. The roll speed is normally very slow to ensure substrate temperature increase up to the glass transition temperature. In this paper, we propose a noncontact preheating technique using focused infrared light. The infrared light is focused as a line beam on a plastic substrate using an elliptical mirror just before entering the hot embossing roll. The mid range infrared light efficiently raises the substrate temperature. For preliminary tests, substrate deformation and temperature changes were monitored according to substrate speed. The experiments show that the proposed technique is a good possibility for high speed hot embossing.

In this investigation, we describe a metrological technique for surface and thickness profiles of a silicon (Si) wafer by using a 6 degree of freedom (DOF) stitching method. Low coherence scanning interferometry employing near infrared light, partially transparent to a Si wafer, is adopted to simultaneously measure the surface and thickness profiles of the wafer. For the large field of view, a stitching method of the sub-aperture measurement is added to the measurement system; also, 6 DOF parameters, including the lateral positioning errors and the rotational error, are considered. In the experiment, surface profiles of a double-sided polished wafer with a 100 mm diameter were measured with the sub-aperture of an 18 mm diameter at 10x10 locations and the surface profiles of both sides were stitched with the sub-aperture maps. As a result, the nominal thickness of the wafer was 483.2 μm and the calculated PV values of both surfaces were 16.57 μm and 17.12μm, respectively.