METHOD OF LOAD FORECASTING VIA KNOWLEDGE DISTILLATION, AND AN APPARATUS FOR THE SAME

    公开(公告)号:US20230102489A1

    公开(公告)日:2023-03-30

    申请号:US17902626

    申请日:2022-09-02

    Abstract: A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

    TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES

    公开(公告)号:US20230117162A1

    公开(公告)日:2023-04-20

    申请号:US17870212

    申请日:2022-07-21

    Abstract: The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.

    ELECTRONIC APPARATUS AND CONTROLLING METHOD THEREOF

    公开(公告)号:US20210101279A1

    公开(公告)日:2021-04-08

    申请号:US17014175

    申请日:2020-09-08

    Abstract: An electronic apparatus is provided. The electronic apparatus includes a communicator comprising communication circuitry, a memory storing information on an artificial intelligence model, and a processor configured to: obtain a map generated based on sensing data obtained by an external electronic apparatus, simulate driving of the external electronic apparatus on the obtained map based on a plurality of parameter values and obtain driving result data for the plurality of parameter values, train the artificial intelligence model based on the plurality of parameter values and the obtained driving result data and obtain a plurality of parameter values related to driving of the external electronic apparatus, and control the communicator to transmit the plurality of obtained parameter values to the external electronic apparatus.

    AUTONOMOUS DRIVING APPARATUS AND METHOD FOR AUTONOMOUS DRIVING OF A VEHICLE

    公开(公告)号:US20190212736A1

    公开(公告)日:2019-07-11

    申请号:US16241083

    申请日:2019-01-07

    CPC classification number: G05D1/0088 G05D1/0212 G05D1/0274 G05D2201/0213

    Abstract: Disclosed are an autonomous driving apparatus for performing autonomous driving of a vehicle and a controlling method thereof. An autonomous driving apparatus according to an example aspect of the present disclosure includes a sensor configured to acquire sensing information to determine a driving state of the vehicle; a storage configured to store a plurality of autonomous driving models; and at least one processor configured to perform autonomous driving of the vehicle using one of the plurality of autonomous driving models stored in the storage based on sensing information sensed by the sensor. Accordingly, the autonomous driving apparatus is capable of performing autonomous driving of a vehicle by rapidly changing a driving mode to an autonomous driving mode suitable for an event occurring during autonomous driving of the vehicle.

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