Five different ionomer dispersions using water–isopropanol (IPA) and N-methylpyrrolidone (NMP) were investigated as ionomer binders for catalyst layers in proton exchange membrane fuel cells. The distribution of ionomer plays an important role in the design of high-performance porous electrode catalyst layers since the transport of species, such as oxygen and protons, is controlled by the thickness of the ionomer on the catalyst surface and the continuity of the ionomer and gas networks in the catalyst layer, with the transport of electrons being related to the continuity of the carbon particle network. In this study, the effect of solvents in ionomer dispersions on the performance and durability of catalyst layers (CLs) is investigated. Five different types of catalyst inks were used: (i) ionomer dispersed in NMP; (ii) ionomer dispersed in water–IPA; (iii) ionomer dispersed in NMP, followed by adding water–IPA; (iv) ionomer dispersed in water–IPA, followed by adding NMP; and (v) a mixture of ionomer dispersed in NMP and ionomer dispersed in water–IPA. Dynamic light scattering of the five dispersions showed different average particles sizes: ~0.40 μm for NMP, 0.91–1.75 μm for the mixture, and ~2.02 μm for water–IPA. The membrane-electrode assembly prepared from an ionomer dispersion with a larger particle size (i.e., water–IPA) showed better performance, while that prepared from a dispersion with a smaller particle size (i.e., NMP) showed better durability.

Conventional methods for positioning electroencephalography electrodes according to the international 10/20 system are based on the manual identification of the principal 10/20 landmarks via visual inspection and palpation, inducing intersession variations in their determined locations due to structural ambiguity or poor visibility. To address the variation issue, we propose an image guidance system for precision electrode placement. Following the electrode placement according to the 10/20 system, affixed electrodes are laser-scanned together with the facial surface. For subsequent procedures, the laser scan is conducted likewise after positioning the electrodes in an arbitrary manner, and following the measurement of fiducial electrode locations, frame matching is performed to determine a transformation from the coordinate frame of the position tracker to that of the laser-scanned image. Finally, by registering the intra-procedural scan of the facial surface to the reference scan, the current tracking data of the electrodes can be visualized relative to the reference goal positions without manually measuring the four principal landmarks for each trial. The experimental results confirmed that use of the electrode navigation system significantly improved the electrode placement precision compared to the conventional 10/20 system (p < 0.005). The proposed system showed the possibility of precise image-guided electrode placement as an alternative to the conventional manual 10/20 system.

Recently, the number of tunnels is increasing due to urbanization, and fire accidents in tunnels are likewise increasing. In particular, in a long tunnel of more than 1 km it is very difficult to track the exact location of a fire, accident vehicles, and the fire brigade, as well as whether a fire occurred. In this paper, we analyze various types of accidents that may occur in tunnel fires and propose detection, search, and rescue techniques to cope with them. For early detection of accidents, we propose various sensors using Internet of Things (IoT) technology and sensor networks to connect them. These sensors can detect not only a fire but also the position of the vehicle in which the fire is occurring in real time. We also propose a robotic system and operation technique that can be controlled by a fire fighter for more precise search operation. For rescue procedures, localization and tracking technology for fire fighters and robots is proposed. Finally, the efficiency of the proposed system was verified through actual performance tests, including simulations of actual placement and operation in tunnels. Through the construction of the equipment in an actual tunnel 1.9 km long, we show that the proposed system is good enough to cope with fire accidents, in terms of the delivery ratio of the collected data, fire recognition ratio, localization accuracy, and response delay.

BackgroundIn longitudinal electroencephalography (EEG) studies, repeatable electrode positioning is essential for reliable EEG assessment. Conventional methods use anatomical landmarks as fiducial locations for the electrode placement. Since the landmarks are manually identified, the EEG assessment is inevitably unreliable because of individual variations among the subjects and the examiners. To overcome this unreliability, an augmented reality (AR) visualization-based electrode guidance system was proposed.MethodsThe proposed electrode guidance system is based on AR visualization to replace the manual electrode positioning. After scanning and registration of the facial surface of a subject by an RGB-D camera, the AR of the initial electrode positions as reference positions is overlapped with the current electrode positions in real time. Thus, it can guide the position of the subsequently placed electrodes with high repeatability.ResultsThe experimental results with the phantom show that the repeatability of the electrode positioning was improved compared to that of the conventional 10–20 positioning system.ConclusionThe proposed AR guidance system improves the electrode positioning performance with a cost-effective system, which uses only RGB-D camera. This system can be used as an alternative to the international 10–20 system.

Recently, radar technology has attracted attention for the realization of an intelligent transportation system (ITS) to monitor, track, and manage vehicle traffic on the roads as well as adaptive cruise control (ACC) and automatic emergency braking (AEB) for driving assistance of vehicles. However, when radar is installed on roads or in tunnels, the detection performance is significantly dependent on the deployment conditions and environment around the radar. In particular, in the case of tunnels, the detection accuracy for a moving vehicle drops sharply owing to the diffuse reflection of radio frequency (RF) signals. In this paper, we propose an optimal deployment condition based on height and tilt angle as well as a noise-filtering scheme for RF signals so that the performance of vehicle detection can be robust against external conditions on roads and in tunnels. To this end, first, we gather and analyze the misrecognition patterns of the radar by tracking a number of randomly selected vehicles on real roads. In order to overcome the limitations, we implement a novel road watch module (RWM) that is easily integrated into a conventional radar system such as Delphi ESR. The proposed system is able to perform real-time distributed data processing of the target vehicles by providing independent queues for each object of information that is incoming from the radar RF. Based on experiments with real roads and tunnels, the proposed scheme shows better performance than the conventional method with respect to the detection accuracy and delay time. The implemented system also provides a user-friendly interface to monitor and manage all traffic on roads and in tunnels. This will accelerate the popularization of future ITS services.