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公开(公告)号：US20220012589A1
公开(公告)日：2022-01-13
申请号：US17370585
申请日：2021-07-08
Inventor： Jung Jae YU , Jong Gook KO , Won Young YOO , Keun Dong LEE , Su Woong LEE , Seung Jae LEE , Yong Sik LEE , Da Un JUNG
Abstract: A data learning device in a deep learning network characterized by a high image resolution and a thin channel at an input stage and an output stage and a low image resolution and a thick channel in an intermediate deep layer includes a feature information extraction unit configured to extract global feature information considering an association between all elements of data when generating an initial estimate in the deep layer; a direct channel-to-image conversion unit configured to generate expanded data having the same resolution as a final output from the generated initial estimate of the global feature information or intermediate outputs sequentially generated in subsequent layers; and a comparison and learning unit configured to calculate a difference between the expanded data generated by the direct channel-to-image conversion unit and a prepared ground truth value and update network parameters such that the difference is decreased.
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公开(公告)号：US20180151083A1
公开(公告)日：2018-05-31
申请号：US15413647
申请日：2017-01-24
Inventor： Ho Young YOO , Su Woong LEE , Hyung Keun JEE
Abstract: The present invention relates to a learning diagnosis apparatus, a method of diagnosing a learner's learning ability, and an adaptive learning system for providing adaptive learning using the diagnosis result. The learning diagnosis apparatus includes a database (DB) configured to store and manage adaptive learning data and store a program for estimating a learner's proficiency to each attribute and recommending learning content and a processor configured to execute the program, wherein the processor calculates a attribute proficiency vector estimation result of a specific learner using information about a solution to a personalized item together with group test solution information.
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公开(公告)号：US20230334321A1
公开(公告)日：2023-10-19
申请号：US17976655
申请日：2022-10-28
Inventor： Su Woong LEE , Jong-Gook KO , Wonyoung YOO , Seungjae LEE , Yongsik LEE , Juwon LEE , Da-Un JUNG
IPC: G06N3/08
Abstract: Disclosed are a deep neural network lightweight device based on batch normalization, and a method thereof. The deep neural network lightweight device based on batch normalization includes a memory that stores at least one data and at least one processor that executes a network lightweight module. When executing the network lightweight module, the processor performs learning on an input neural network based on sparsity regularization to adaptively determine at least one parameter of the sparsity regularization, performs pruning on the learning result, and performs fine tuning on the pruning result.
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4.发明申请公开(公告)号：US20230122553A1
公开(公告)日：2023-04-20
申请号：US17591445
申请日：2022-02-02
Inventor： Seung Jae LEE , Su Woong LEE , Yong Sik LEE , Ju Won LEE , Da Un JUNG , Jong Gook KO , Won Young YOO
Abstract: The present invention relates to an apparatus and method for drawing, the method comprising: inputting a drawing image; recognizing a component in the input drawing image; inferring a structure of an object based on the recognized component; and drawing the inferred structure of the object.
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公开(公告)号：US20200272863A1
公开(公告)日：2020-08-27
申请号：US16702721
申请日：2019-12-04
Inventor： Seung Jae LEE , Jong Gook KO , Keun Dong LEE , Su Woong LEE , Won Young YOO
Abstract: A fast object detection method and a fast object detection apparatus using an artificial neural network. The fast object detection method includes obtaining an input image; inputting the obtained input image into an object detection neural network using a plurality of preset bounding boxes; and detecting an object included in the input image by acquiring output data of the object detection neural network.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20190318650A1
公开(公告)日：2019-10-17
申请号：US16377624
申请日：2019-04-08
Inventor： Ho Young YOO , Su Woong LEE , Hyung Keun JEE
Abstract: Provided are an apparatus and method for learner diagnosis using reliability of a cognitive diagnostic model, which estimates the reliability of the cognitive diagnostic model that estimates a concept vector (α) of a learner through a Q-matrix regarding a question and an R-matrix regarding a response to a question, the method including assuming a probability (P(X|α)) of a learner response (X) when a concept vector (α) of a learner is given; obtaining a concept pattern-specific probability (P(α|X)) of the learner from the assumed concept vector and learner response of the learner; obtaining an information entropy (H) value of the learner from the concept pattern-specific probability (P(α|X)) of the learner; and obtaining reliability (γ) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.
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公开(公告)号：US20180151084A1
公开(公告)日：2018-05-31
申请号：US15413780
申请日：2017-01-24
Inventor： Su Woong LEE , Ho Young YOO , Hyung Keun JEE
Abstract: The present invention relates to an apparatus and method for providing personalized adaptive e-learning, and more specifically, to an apparatus and method for estimating a learner's proficiency to each attribute and providing adaptive e-learning by taking into consideration an item answering result of an individual learner in a personalized learning environment.
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公开(公告)号：US20220222525A1
公开(公告)日：2022-07-14
申请号：US17554870
申请日：2021-12-17
Inventor： Su Woong LEE , Seungjae LEE , Jong-Gook KO , Wonyoung YOO , Jung Jae YU , Keun Dong LEE , Yongsik LEE , Da-Un JUNG
Abstract: Provided are a method and system for training a dynamic deep neural network. The method for training a dynamic deep neural network includes receiving an output of a last layer of the deep neural network and outputting a first loss, receiving an output of a routing module according to an input class of the deep neural network and outputting a second loss, calculating a third loss based on the first loss and the second loss, and updating a weight of the deep neural network by using the third loss.
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公开(公告)号：US20210350241A1
公开(公告)日：2021-11-11
申请号：US17242604
申请日：2021-04-28
Inventor： Seungjae LEE , Jong-Gook KO , Keun Dong LEE , Su Woong LEE , Yongsik LEE , Da-Un JUNG , Wonyoung YOO
Abstract: An apparatus and method for searching a neural network architecture may be disclosed. The apparatus may include an architecture searcher and an architecture evaluator. The architecture searcher may search for a topology between nodes included in a basic cell of a network, search for an operation to be applied between the nodes after searching for the topology, and determine the basic cell. The architecture evaluator may evaluate performance of the determined basic cell.
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公开(公告)号：US20170193665A1
公开(公告)日：2017-07-06
申请号：US15007942
申请日：2016-01-27
Inventor： Su Woong LEE , Jun Suk LEE , Hyung Keun JEE
CPC classification number: G06T7/11 , G06K9/00208 , G06K9/00335 , G06T7/194 , G06T7/73 , G06T2207/10028 , G06T2207/30196
Abstract: Provided is a system for detecting an object from a depth image. The system includes a communication module, a memory, and a processor. By executing the object detection program, the processor extracts a first object area and a second object area from the depth image, based on a predetermined floor plane and an outer plane which is set with respect to the predetermined floor plane. The processor extracts a target area including pixels of the second object area which are spaced apart from the first object area by a predetermined interval. The processor samples a pixel, which is not included in the target area, to extract a floor area from the second object area, calculates a boundary value of an object and a floor, based on the floor area and the target area, and extracts a foreground pixel from the target area, based on the calculated boundary value.
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