Abstract: The present embodiment relates to a touch sensing apparatus and a touch sensing method and, more specifically, to a touch sensing apparatus and a touch sensing method, which rapidly and accurately separate a multi-touch in which touch areas are adjacent to each other.

Abstract: Provided is a method for drying a wet-gel blanket which can have an excellent heat insulation performance by minimizing shrinkage of a gel network structure that occurs during drying, and has excellent drying efficiency versus time, and a method for producing an aerogel blanket using same.

Abstract: Provided are a training system and an operating method thereof. The training system includes a job proxy configured to partition a training job corresponding to a neural network model into a plurality of microservices respectively executed by a plurality of logical workers, and a scheduler configured to schedule the plurality of microservices for a plurality of processing units, respectively, wherein the plurality of microservices includes a plurality of first microservices executed by a first logical worker among the plurality of logical workers and a plurality of second microservices executed by a second logical worker among the plurality of logical workers, and the scheduler is configured to schedule the plurality of first microservices and the plurality of second microservices to any one processing unit among the plurality of processing units based on an availability status of the plurality of processing units.

Abstract: A dynamic hashing apparatus includes a processor configured to obtain a target segment based on at least a part of a key value of an insertion request, compute a hash value by applying a remapping function corresponding to the obtained target segment to an input value based on the key value, and update the remapping function by adjusting a slope of the remapping function in response to the key value not being insertable into a target bucket selected based on at least a part of the computed hash value from among buckets included by the target segment.

Abstract: Disclosed is a training system performing training on a plurality of neural network models in parallel. The training system includes a first job proxy that receives a training request for a first neural network model and partitions a first training job corresponding to the first neural network model into first microservices, a second job proxy that receives a training request for a second neural network model and partitions a second training job corresponding to the second neural network model into second microservices, a scheduler that dynamically schedules the first microservices and the second microservices so as to be executed by heterogeneous processing units, a cluster that includes the heterogeneous processing units, sequentially executes the first microservices and sequentially executes the second microservices, and a distributed in-memory database that stores parameters generated in response to the execution of the first microservices and the second micro services.