Electronics and Telecommunications Research Institute

Donghun Lee

Hyunseok Kim

Young Sung Son

Jin Chul Choi

Myung Eun Kim

Soonyong Song

Seonghyun Kim

Jongkwon Son

Ingook Jang

Jung Mo Moon

Tae Jung Kim

Jeehyeon Na

A method for load balancing in a base station is provided. The base station updates a traffic amount of a current time by reflecting a predicted traffic amount of a future time in the traffic amount of the current time, and determines parameters necessary for load balancing of the base station by comparing the updated traffic amount of the current time with a predetermined threshold.

Base station load balancing method and apparatus

A method and an apparatus for exclusive reinforcement learning are provided, comprising: collecting information of states of an environment through the communication interface and performing a statistical analysis on the states using the collected information; determining a first state value of a first state among the states in a training phase and a second state value of a second state among the states in an inference phase based on analysis results of the statistical analysis; performing reinforcement learning by using one reinforcement learning unit of a plurality of reinforcement learning unit which performs reinforcement learnings from different perspectives according to the first state value; and selecting one of actions determined by the plurality of reinforcement learning unit based on the second state value and applying selected action to the environment.

Method and apparatus for reinforcement machine learning

An apparatus and method for abnormal situation detection are disclosed. An abnormal situation detection apparatus can map first sensor data among sensor data transmitted from a plurality of sensors into a vector value, convert it into first situation information in the form of an image pattern, and generate a learning model using the first situation information and an abnormal situation reference range. In addition, the abnormal situation detection apparatus can convert second sensor data among sensor data transmitted from a plurality of sensors into a form that can be input to the learning model, and determine whether an abnormal situation occurs by applying the converted second data to the learning model.

Apparatus and method for abnormal situation detection

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Sejong-si, South Korea

Donghun Lee