Thus far, the adoption of hydrogen fuel cell vehicles (HCEVs) has been hampered by the lack of hydrogen fueling infrastructure. This study aimed to determine the optimal location and prioritization of hydrogen fueling stations (HFSs) in Seoul by utilizing a multi-standard decision-making approach and optimization method. HFS candidate sites were evaluated with respect to relevant laws and regulations. Key factors such as safety, economy, convenience, and demand for HCEVs were considered. Data were obtained through a survey of experts in the fields of HCEV and fuel cells, and the Analytic Hierarchy Process method was applied to prioritize candidate sites. The optimal quantity and placement of HFSs was then obtained using optimization software, based on the acceptable travel time from intersections of popular roads in Seoul. Our findings suggest that compliance with legal safety regulations is the most important factor when constructing HFSs. Furthermore, sensitivity analysis revealed that the hydrogen supply cost currently holds the same weight as other elements. The study highlights the importance of utilizing a multi-standard decision-making approach and optimization methods when determining the optimal location and prioritization of HFSs and can help develop a systematic plan for the nationwide construction of HFSs in South Korea.
The exterior location of a user can be accurately determined using a global positioning system (GPS). However, accurately locating objects indoors poses challenges due to signal penetration limitations within buildings. In this study, an MLP with stochastic gradient descent (SGD) among artificial neural networks (ANNs) and signal strength indicator (RSSI) data received from a Zigbee sensor are used to estimate the indoor location of an object. Four fixed nodes (FNs) were placed at the corners of an unobstructed area measuring 3 m in both length and width. Within this designated space, mobile nodes (MNs) captured position data and received RSSI values from the nodes to establish a comprehensive database. To enhance the precision of our results, we used a data augmentation approach which effectively expanded the pool of selected cells. We also divided the area into sectors using an ANN to increase the estimation accuracy, focusing on selecting sectors that had measurements. To enhance both accuracy and computational speed in selecting coordinates, we used B-spline surface equations. This method, which is similar to using a lookup table, brought noticeable benefits: for indoor locations, the error margin decreased below the threshold of sensor hardware tolerance as the number of segmentation steps increased. By comparing our proposed deep learning methodology with the traditional fingerprinting technique that utilizes a progressive segmentation algorithm, we verified the accuracy and cost-effectiveness of our method. It is expected that this research will facilitate the development of practical indoor location-based services that can estimate accurate indoor locations with minimal data.
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