公开(公告)号：US20250069207A1

公开(公告)日：2025-02-27

申请号：US18781142

申请日：2024-07-23

Applicant: Korea Electronics Technology Institute

Inventor： Ki Woong KWON ,  Seung Hyeon PARK ,  Sang Hun KIM

IPC: G06T5/80 ,  G06T5/20 ,  G06T7/13

Abstract: There are provided a method and a system for CCTV radial distortion estimation with low-complexity. An image distortion estimation method according to an embodiment includes: adding a certain distortion to an inputted image; detecting outlines from the image to which the distortion is added; detecting straight lines from the detected outlines; calculating a sum of the detected straight lines; performing the above operations N times, and determining parameters of an objective function by fitting an ‘objective function resulting from modeling of a sum of detected straight lines caused by an added distortion’ to the N distortions and the N sums; and estimating a distortion on the image by using the objective function the parameters of which are determined. Accordingly, the method does not need a cumbersome process since a separate reference image is not used, and is performed fast due to low-complexity of computation and less resources are required, and furthermore, relatively accurate distortion estimation is possible only with one image.

公开(公告)号：US20250164962A1

公开(公告)日：2025-05-22

申请号：US18951016

申请日：2024-11-18

Applicant: Korea Electronics Technology Institute

Inventor： Seung Hyeon PARK ,  Ki Woong KWON ,  Sang Hun KIM

IPC: G05B19/18

Abstract: Provided are AI-based optimal operation number control system and method for increasing operation efficiency of industrial boilers. A boiler control method according to an embodiment includes: collecting operation data of boilers; deriving operating boiler combinations by inputting the collected operation data to an AI model that is trained to receive operation data and to derive operating boiler combinations; and controlling operation of the boilers according to the derived operating boiler combinations. Accordingly, by analyzing operating conditions changeable according to a schedule of the field and using the operating conditions for deriving a control value, operation efficiency of industrial boilers are increased.

公开(公告)号：US20240191871A1

公开(公告)日：2024-06-13

申请号：US18384202

申请日：2023-10-26

Applicant: Korea Electronics Technology Institute

Inventor： Ki Woong KWON ,  Seung Hyeon PARK ,  Sang Hun KIM ,  Dong Jin YANG

IPC: F22B35/00 ,  F22B35/18

CPC classification number: F22B35/001 ,  F22B35/18

Abstract: There is provided an AI-based air damper control system and method for industrial boilers. An AI-based optimal air damper control method according to an embodiment calculates energy efficiency under a given control condition and an environment by extracting energy efficiency-related data from industrial boiler operational data and analyzing a correlation between corresponding data, trains an AI-based optimal air volume-for-load prediction model by using the extracted data and the calculated energy efficiency as training data, and derives an air volume condition that results in peak energy efficiency under a given load, based on the trained optimal air volume-for-load prediction model, and automatically controls the air damper according to the corresponding air volume condition.

公开(公告)号：US20220373207A1

公开(公告)日：2022-11-24

申请号：US17622480

申请日：2021-05-07

Applicant: Korea Electronics Technology Institute

Inventor： Ki Woong KWON ,  Sang Hun KIM

IPC: F24F11/46 ,  F24F11/64 ,  G05B19/042 ,  G06K9/62 ,  G06N20/00

Abstract: Provided are a system and a method for predicting an occupancy time of a user in a city building based on big data for auto control of heating or cooling for energy saving. The user occupancy time prediction system according to an embodiment includes: a sensor configured to collect data regarding whether a user occupies a predetermined space in a building; a database configured to store the collected data; a data pre-processing unit configured to process the stored data into a format suitable for machine learning; and a prediction unit configured to input the processed data into a machine learning model, and to predict an expected unoccupancy time of the user regarding the predetermined space in the building. Accordingly, a user occupancy/unoccupancy time may be predicted by analyzing big data which uses previous occupancy data of a user in a city building, and energy may be saved by adjusting a temperature of a heating or cooling device before the unoccupancy time. In addition, when the temperature of the heating or cooling device is adjusted before unoccupancy is predicted, the temperature is only changed to the extent that the user does not recognize inconvenience, and comfortability may be maintained or improved.