[{"data":1,"prerenderedAt":75},["ShallowReactive",2],{"research-en":3},{"id":4,"title":5,"approach":6,"awards":9,"diagram":18,"extension":23,"lab":24,"lead":25,"limitations":26,"meta":27,"problem":28,"publications":31,"results":59,"role":72,"stem":73,"__hash__":74},"research_en\u002Fen\u002Fresearch.yml","Light-pollution prediction for Urban Air Mobility",[7,8],"I split the problem in two. A geometry-aware neural surrogate (a PyTorch 3D-CNN + MLP) learns how an aircraft reflects sunlight in open space; it is trained once and shared across cities.","A geometric occlusion correction handles each city's buildings. Visibility is precomputed into a cache, so prediction only needs fast lookups. Reference data comes from Unreal Engine 5 (Nanite \u002F Lumen) simulations.",[10],{"title":11,"event":12,"paper":13,"links":14},"Encouragement Presentation Award","IPSJ 105th SIG-ITS Workshop (Intelligent Transport Systems and Smart Community), 2026","Surrogate-Based Dynamic Risk Mapping of Light Pollution for Urban Air Mobility",[15],{"label":16,"href":17},"Award list","itsAwardList",{"inputs":19,"surrogate":20,"occlusion":21,"output":22},"City and flight data|3D buildings, UAM trajectories and attitude, sun direction, observer positions","Neural surrogate|Learns reflected sunlight in open space. Trained once, shared across cities.","Occlusion correction|Per-city visibility cache built from building geometry. Fast lookups at prediction time.","Glare map|Reflected-sunlight exposure per location, for comparing routes and times","yml","Yamaguchi Lab, The University of Osaka","Flying taxis (Urban Air Mobility, UAM) will reflect sunlight into streets and windows. I built a method that predicts this glare across a whole city in seconds instead of hours, so routes and flight times can be compared before anything flies.","A simulation study under clear-sky conditions. The output is a relative exposure metric and has not yet been calibrated against human discomfort or safety thresholds.",{},[29,30],"UAM aircraft keep changing position and attitude, so the direction of reflected sunlight — and which buildings block it — changes every moment.","Accurate ray tracing at city scale takes hours. A neural network alone is fast, but it overestimates glare when a building hides the aircraft.",[32,44,51,55],{"title":33,"authors":34,"venue":35,"status":36,"links":37},"A Simulation-based Framework for Dynamic Light Pollution Prediction in Urban Air Mobility","Ying Chieh Wang (first author), Tatsuya Amano, Hirozumi Yamaguchi","IEEE SmartComp 2026 · Messina, Italy · pp. 136–143","Published",[38,41],{"label":39,"href":40},"IEEE Xplore","ieeeXplore",{"label":42,"href":43},"DOI","doi",{"title":13,"authors":34,"venue":45,"status":46,"links":47},"IPSJ SIG Technical Report, Vol. 2026-ITS-105, No. 38, pp. 1–8 · May 2026","Published · Award",[48],{"label":49,"href":50},"Program","itsProgram",{"title":52,"authors":34,"venue":53,"status":54},"A Geometry-Aware Neural Surrogate for City-Scale Simulation of Light Pollution from Urban Air Mobility","Journal of Information Processing (JIP) · journal extension","Under review",{"title":56,"authors":34,"venue":57,"status":58},"A Real-Time Urban Air Mobility Simulator Using a Ray-Tracing Surrogate Model","DPSWS 2026 (IPSJ DPS Workshop) · demo \u002F poster paper","Accepted",[60,64,68],{"value":61,"label":62,"note":63},"3 h → 1.62 s","Evaluation time","10,000 observation points over a five-second window, after one-time city initialization (Core i5-13600K + RTX 3090).",{"value":65,"label":66,"note":67},"10–54%","Lower RMSE","Versus raw neural prediction, across 18 settings in Rome, Taipei, and New York.",{"value":69,"label":70,"note":71},"+32–91%","Precision in dense high-rise areas","Relative improvement in New York versus raw neural prediction. Occlusion correction trades off some recall.","I led the method design, simulation environment, data processing, model implementation, experiments, evaluation, and paper writing, building on research directions discussed with my advisor.","en\u002Fresearch","fWdFY6NoZSgo629bTRgNu9vTJpEIISEW3umi-39r3V4",1789832903615]