BEGIN:VCALENDAR VERSION:2.0 PRODID:-//N0MES: Net Zero Maritime Energy Solutions - ECPv6.17.3.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:N0MES: Net Zero Maritime Energy Solutions X-ORIGINAL-URL:https://n0mes.org X-WR-CALDESC:Events for N0MES: Net Zero Maritime Energy Solutions REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/London:20260902T140000 DTEND;TZID=Europe/London:20260902T150000 DTSTAMP:20260819T103829Z CREATED:20260818T112516Z LAST-MODIFIED:20260819T103829Z UID:1609-1788357600-1788361200@n0mes.org SUMMARY:Net-Zero Hour | September DESCRIPTION:N0MES SPEAKER SERIES \nNet-Zero Hour | Maritime Transport Special\n \nSep 2\, 2026  ·  2:00–3:00PM BST  ·  Online via Teams \n \n Register Now → \n\n\n \nOverview\nJoin us for the September instalment of Net-Zero Hour\, where three N0MES researchers share work on autonomy\, route structure\, and collision risk in maritime traffic. \nJiale will present an LLM-based patent analytics framework for mapping Remote Operation Centre technologies\, the shore-side infrastructure behind autonomous ships\, and how much of the work stays human. \nBoyuan will present a two-stage method for extracting ship routes in structured waterways\, separating traffic that shares a passage but arrives and leaves by different paths. \nXinyuan will present a framework for measuring traffic complexity in narrow channels\, separating genuinely hazardous vessel interactions from traffic that is merely busy. \n \nWednesday\, September 2\, 2026\, 2:00–3:00PM BST\, online via Teams\, click Register Now above to save your seat. \n \nSpeakers\n\n \n\n\n \n \n\n \nJiale Xiang\nMapping Remote Operation Centre technologies with LLM-based patent analytics \n\n\nRemote Operation Centres (ROCs) are safety-critical shore infrastructures enabling Maritime Autonomous Surface Ships (MASS) through remote supervision\, decision-making\, and human-machine cooperation. However\, fragmented ROC technologies hinder a system-level understanding of their interactions\, constraining reliability and safety assurance for fully autonomous maritime operations. A Large Language Model (LLM)-based patent analytics framework is developed to systematically identify and map ROC-related technologies from patent data. By integrating LLM-assisted topic modelling\, complex network analysis\, and temporal trend evaluation\, the framework provides a data-driven perspective on both the evolutionary dynamics and structural interrelations among ROC technologies. In addition\, a human-machine collaboration evaluation framework is constructed to evaluate the levels of human involvement and technological involvement for each ROC technology. This can help improve the understanding of HMC in future ROCs and provide insights into future remote operators and regulatory development. \n \n \n\n \n \n\n\n \n \n\n \nBoyuan Zhang\nA two-stage ship route extraction method for structured waterways \n\n\nShipping route information provides important spatial references for understanding ship behaviour and maritime traffic organisation. Accurate route extraction can support route and speed optimisation to reduce fuel consumption and emissions\, while also providing finer-grained spatial information for traffic monitoring and the identification of potentially conflict-prone areas. In structured waterways\, such as Traffic Separation Schemes (TSSs) and navigational channels\, ship trajectories share a common passage while following different approach and departure paths due to their different origins and destinations. This study proposes a two-stage route extraction method based on ordered turning point sequences: Change Point Detection is first used to identify turning points from course variations\, and hierarchical clustering is then performed in two stages\, first according to spatial similarity and then according to course differences. A case study in Øresund shows that the method can distinguish trajectories with similar turning point locations but different approach and departure patterns\, providing more detailed route representations for traffic analysis and maritime traffic supervision. \n \n \n\n \n \n\n\n \n \n\n \nXinyuan Li\nDynamic multi-scale assessment of maritime traffic complexity in narrow channels \n\n\nMaritime traffic in narrow channels is characterised by limited navigable space\, dense vessel movements and rapidly evolving multi-vessel interactions\, making real-time risk identification particularly challenging. This study proposes a data-driven\, dynamic multi-scale framework for assessing maritime traffic complexity using Automatic Identification System (AIS) data. Historical vessel trajectories are first used to construct category-specific empirical ship domains and identify near-miss events\, allowing historical conflict patterns to be incorporated as spatial risk priors. Three physically interpretable indicators are then developed to capture traffic complexity at different interaction scales: the Conflict Factor (CF) identifies abnormal course deviations at the individual-vessel level\, the Approach Factor (AF) measures dynamic interaction risk between vessel pairs\, and the Group Clustering Factor (GCF) evaluates aggregation and directional inconsistency within vessel groups. The framework is validated using AIS data from the Nanjing section of the Yangtze River and a collision case in the Malacca Strait. Results show that the proposed method can better distinguish hazardous vessel interactions from normal high-density traffic and demonstrates strong sensitivity to interaction-driven risks. With an average processing time of 0.28 seconds\, the framework also shows potential for real-time Vessel Traffic Service supervision and proactive maritime risk identification. \n \n \n\n \n\n \n\n \n Register Now URL:https://n0mes.org/events/net-zero-hour-september/ END:VEVENT END:VCALENDAR