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Patent Agency Ranking
1.发明公开公开(公告)号：US20240249614A1
公开(公告)日：2024-07-25
申请号：US18417769
申请日：2024-01-19
Applicant: NEC Laboratories America, Inc.
Inventor： Shaobo HAN , Yuheng CHEN , Ming-Fang HUANG , Tingfeng LI , Ting WANG
IPC: G08G1/01
CPC classification number: G08G1/0116 , G08G1/0112
Abstract: Disclosed are vehicle-infrastructure interaction systems and methods employing a distributed fiber optic sensing (DFOS) system operating with pre-deployed fiber-optic telecommunication cables buried alongside/proximate to highways/roadways which provide 24/7 continuous information stream of vehicle traffic at multiple sites; only require a single optical sensor cable that senses/monitors multiple locations of interest and multiple lanes of traffic; the single optical sensor cable measures multiple related information (multi-parameters) about a vehicle, including driving speed, wheelbase, number of axles, tire pressure, and others, that can be used to derive secondary information such as weight-in-motion; and overall information about a fleet of vehicles, such as traffic congestion or traffic-cargo volume. Different from merely traffic counts, our approach can provide the count grouped by vehicle-types and cargo weights. Precise measurements are facilitated by high temporal sampling rates of the distributed acoustic sensing and a dedicated peak finding algorithm for extracting the timing information reliably.
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公开(公告)号：US20250124716A1
公开(公告)日：2025-04-17
申请号：US18901822
申请日：2024-09-30
Applicant: NEC Laboratories America, Inc.
Inventor： Shaobo HAN , Ming-Fang HUANG , Tingfeng LI , Yuheng CHEN , Zhuowei LI
Abstract: Disclosed is a stroke-based differentiable rendering for both representation of spatiotemporal sensor data and unsupervised spatiotemporal events detection wherein we encode DAS waterfall data into a structured latent space based on parameterized brushstrokes. The structured brushstroke representation can (1) suppress background noise and distracting clutters from the original waterfall data, (2) allow easy leverage of geometrical prior knowledge for physics-informed pattern recognition. Guided by multiple specially designed targets that emphasize different aspects of the original data, the optimized strokes not only preserve the salient information, but also align well with the original data in terms of spatial and temporal coordinates. As a results, it also provides pixel-level annotation as a byproduct. Based on long term DFOS data and cumulative statistics, we can further localize landmarks (such as traffic lights, manholes, etc.) from the waterfall data. These landmarks can be used for cable mapping.
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3.发明公开公开(公告)号：US20240241275A1
公开(公告)日：2024-07-18
申请号：US18414419
申请日：2024-01-16
Applicant: NEC Laboratories America, Inc.
Inventor： Shaobo HAN , Yuheng CHEN , Ming-Fang HUANG , Tingfeng LI
CPC classification number: G01V1/288 , G01V1/001 , G01V1/226 , G08G1/0104
Abstract: Disclosed are machine learning (ML) based Distributed Fiber Optic Sensing (DFOS) systems, methods, and structures for Sonic Alert Pattern (SNAP) event detection performed in real time including an intelligent SNAP informatic system in conjunction with DFOS/Distributed Acoustic Sensing (DAS) and machine learning technologies that utilize SNAP vibration signals as an indicator. Without installation of additional sensors, vibration signals indicative of SNAP events are detected along a length of an existing optical fiber through DAS. Raw DFOS data is utilized—and not DFOS waterfall data—resulting in faster and more accurate information derivation as rich, time-frequency information in the raw DFOS/DAS waveform data is preserved. A deep learning module Temporal Relation Network (TRN) that accurately detects SNAP events from among chaotic signals of normal traffic is employed, making it reliable when applied to busy roads with dense traffic and vehicles of different speed.
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4.发明申请公开(公告)号：US20220327814A1
公开(公告)日：2022-10-13
申请号：US17715901
申请日：2022-04-07
Applicant: NEC Laboratories America, Inc.
Inventor： Shaobo HAN , Renqiang MIN , Tingfeng LI
IPC: G06V10/778 , G06V10/82
Abstract: A reinforcement learning based approach to the problem of query object localization, where an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. It enables test-time policy adaptation to new environments where the reward signals are not readily available, and thus outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing of the trained agent for new tasks, such as annotation refinement, or selective localization from multiple common objects across a set of images. Experiments on corrupted MNIST dataset and CU-Birds dataset demonstrate the effectiveness of our approach
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公开(公告)号：US20220196463A1
公开(公告)日：2022-06-23
申请号：US17556928
申请日：2021-12-20
Applicant: NEC Laboratories America, Inc.
Inventor： Shaobo HAN , Yuheng CHEN , Ming-Fang HUANG , Tingfeng LI
Abstract: A fiber optic sensing technology for vehicle run-off-road incident automatic detection by an indicator of sonic alert pattern (SNAP) vibration patterns. A machine learning method is employed and trained and evaluated against a variety of heterogeneous factors using controlled experiments, demonstrating applicability for future field deployment. Extracted events resulting from operation of our system may be advantageously incorporated into existing management systems for intelligent transportation and smart city applications, facilitating real-time alleviation of traffic congestion and/or providing a quick response rescue and clearance operation.
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