An electric railway vehicle is supplied with electricity by an OCL (Overhead Contact Line) through the contact strip of its pantograph. This transmitted electricity is then used to power the electrical equipment of the railway vehicle. This contact strip wears out due to contact with the OCL. In particular, deformations due to chipping and material loss occur because of friction with the fittings on the OCL. These deformations on the contact strip affect its power transmission quality because of contact loss with the OCL. However, it is difficult to monitor the contact strip during operation and judge its condition in order to implement accident prevention measures. Thus, in this study, we developed a contact strip monitoring method based on image processing for inspection. The proposed method measures the deformation in the contact strip based on an algorithm that determines the wear on the deformed contact strip using deep learning and image processing. The image of the contact strip is acquired by installing a camera and laser to capture the pantograph as it passes the setup. The proposed algorithm is able to determine the wear size by extracting the edges of the laser line using deep learning and estimating the fitted line of the deformations based on the least squares method.

Recently, as air pollution and particulate matter worsen, the importance of a platform that can monitor the air environment is emerging. Especially, among air pollutants, nitrogen dioxide (NO₂) is a toxic gas that can not only generate secondary particulate matter, but can also derive numerous toxic gases. To detect such NO₂ gas at low concentration, we fabricated a GNWs/NiO-WO₃/GNWs heterostructure-based gas sensor using microwave plasma-enhanced chemical vapor deposition (MPECVD) and sputter, and we confirmed the NO₂ detection characteristics between 10 and 50 ppm at room temperature. The morphology and carbon lattice characteristics of the sensing layer were investigated using field emission scanning electron microscopy (FESEM) and Raman spectroscopy. In the gas detection measurement, the resistance negative change according to the NO₂ gas concentration was recorded. Moreover, it reacted even at low concentrations such as 5–7 ppm, and showed excellent recovery characteristics of more than 98%. Furthermore, it also showed a change in which the reactivity decreased with respect to humidity of 33% and 66%.

The use an inverter is one of the representative ways to utilize regenerative braking energy in railway systems. Due to the nature of urban railways that generate a large amount of regenerative energy, the economic advantages are clear. However, in the case of the existing inverter operation method, a method of operating the inverter using the threshold voltage is used, which has a disadvantage in that power cannot be utilized between the no-load voltage and the threshold voltage. Therefore, in this paper, we propose an optimal location selection method and capacity calculation method for installing a regenerative inverter in an urban rail system, and a control method according to the train brake position to increase the regenerative energy utilization rate. First, the inverter capacity and location were selected by selecting the maximum regenerative energy generation for each substation section through the train performance simulation (TPS) based DC power simulation (DCPS). An inverter control method based on train brake position (BP method) is introduced. Finally, PSCAD/EMTDC, a power analysis program, was used to verify the proposed method. As a result, the use of regenerative energy by an inverter increased by about 62.6%, and more energy was saved at nearby substations through the BP method.