ADVERSARIAL IMITATION LEARNING ENGINE FOR KPI OPTIMIZATION

    公开(公告)号:US20250149133A1

    公开(公告)日:2025-05-08

    申请号:US18922837

    申请日:2024-10-22

    Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

    AGENT-BASED CARBON EMISSION REDUCTION SYSTEM

    公开(公告)号:US20250148431A1

    公开(公告)日:2025-05-08

    申请号:US18938823

    申请日:2024-11-06

    Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

    INDOOR MIMO ACOUSTIC DETECTION AND LOCALIZATION USING TONE SIGNALS

    公开(公告)号:US20250130307A1

    公开(公告)日:2025-04-24

    申请号:US18901760

    申请日:2024-09-30

    Abstract: Disclosed are systems and methods directed to a MIMO method that detects and localizes an object without requiring the object to emit a sound. Operationally, multiple speakers generate tone signals at different frequencies, while multiple acoustic sensors demodulate the complex amplitude of each frequency. By monitoring the feature variation of the relative phase (or intensity) vector from the complex amplitude, our method detects or localizes movement of the object. For large-scale applications, a fiber-optic system and method that employs distributed acoustic sensing (DAS) in which an optical fiber is used as one or more acoustic sensors located at points along the length of the optical fiber. A single DAS system provides numerous sensors using only a single optical fiber thereby enabling perfect synchronization and centralized signal processing. Notably, with such a DAS arrangement, cost and complexity is significantly reduced, while privacy is preserved.

    CASCADED DFOS TO REDUCE SYSTEM COST AND INCREASE SENSING REACH

    公开(公告)号:US20250123127A1

    公开(公告)日:2025-04-17

    申请号:US18890304

    申请日:2024-09-19

    Abstract: Disclosed are systems, methods, and structures that increase overall sensing reach of a DFOS system and reduces system cost without sacrificing sensed signal quality by employing a cascaded arrangement of DFOS interrogators and operating method providing backscattering DFOS, maintaining each span within a desired length which can advantageously be determined by signal quality, pulse rate, or other factors such as physical layout. The cascaded DFOS interrogators work independently while sharing the pulse light produced by the first interrogator in a cascaded series of interrogators. The light is amplified in successive fiber spans and a circulator may be employed to cut-off any backscattered signal.

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