Abstract: A method for performing on-device learning of embedded machine learning network of autonomous vehicle by using multi-stage learning with adaptive hyper-parameter sets is provided. The processes include: (a) dividing the current learning into a 1-st stage learning to an n-th stage learning, assigning 1-st stage training data to n-th stage training data, generating a 1_1-st hyper-parameter set candidate to a 1_h-th hyper-parameter set candidate, training the embedded machine learning network in the 1-st stage learning, and determining a 1-st adaptive hyper-parameter set; (b) generating a k_1-st hyper-parameter set candidate to a k_h-th hyper-parameter set candidate, training the (k?1)-th stage-completed machine learning network in the k-th stage learning, and determining a k-th adaptive hyper-parameter set; and (c) generating an n-th adaptive hyper-parameter set, and executing the n-th stage learning, to thereby complete the current learning.

Abstract: A method for performing on-device learning of embedded machine learning network of autonomous vehicle by using multi-stage learning with adaptive hyper-parameter sets is provided. The processes include: (a) dividing the current learning into a 1-st stage learning to an n-th stage learning, assigning 1-st stage training data to n-th stage training data, generating a 1_1-st hyper-parameter set candidate to a 1_h-th hyper-parameter set candidate, training the embedded machine learning network in the 1-st stage learning, and determining a 1-st adaptive hyper-parameter set; (b) generating a k_1-st hyper-parameter set candidate to a k_h-th hyper-parameter set candidate, training the (k?1)-th stage-completed machine learning network in the k-th stage learning, and determining a k-th adaptive hyper-parameter set; and (c) generating an n-th adaptive hyper-parameter set, and executing the n-th stage learning, to thereby complete the current learning.

Abstract: A method of on-vehicle active learning for training a perception network of an autonomous vehicle is provided. The method includes steps of: an on-vehicle active learning device, (a) if a driving video and sensing information are acquired from a camera and sensors on an autonomous vehicle, inputting frames of the driving video and the sensing information into a scene code assigning module to generate scene codes including information on scenes in the frames and on driving events; and (b) at least one of selecting a part of the frames, whose object detection information satisfies a condition, as specific frames by using the scene codes and the object detection information and selecting a part of the frames, matching a training policy, as the specific frames by using the scene codes and the object detection information, and storing the specific frames and specific scene codes in a frame storing part.

Abstract: A method of on-vehicle active learning for training a perception network of an autonomous vehicle is provided. The method includes steps of: an on-vehicle active learning device, (a) if a driving video and sensing information are acquired from a camera and sensors on an autonomous vehicle, inputting frames of the driving video and the sensing information into a scene code assigning module to generate scene codes including information on scenes in the frames and on driving events; and (b) at least one of selecting a part of the frames, whose object detection information satisfies a condition, as specific frames by using the scene codes and the object detection information and selecting a part of the frames, matching a training policy, as the specific frames by using the scene codes and the object detection information, and storing the specific frames and specific scene codes in a frame storing part.

Abstract: A method for calibrating a pitch of a camera on a vehicle is provided. The method includes steps of: a calibration device (a) inputting driving images from the camera into an object detection network to detect objects and generate object detection information and into a lane detection network to detect lanes and generate lane detection information; (b) profiling the object and the lane detection information to generate object profiling information and lane profiling information, inputting the object profiling information into an object-based pitch estimation module to select a first target object and a second target object to generate a first pitch and a second pitch, and (iii) inputting vanishing point detection information and the lane profiling information into a lane-based pitch estimation module to generate a third pitch and a fourth pitch; and (c) inputting the first to the fourth pitches into a pitch-deciding module to generate a decided pitch.

Abstract: A method of adjustable continual learning of a deep neural network model by using a selective deep generative replay module is provided. The method includes steps of: a learning device (a) (i) inputting training data from a total database and a sub-database into the selective deep generative replay module to generate first and second low-dimensional distribution features, (ii) inputting binary values, random parameters, and the second low-dimensional distribution features into a data generator to generate a third training data, and (iii) inputting a first training data into a solver to generate labeled training data; (b) inputting the training data, the low-dimensional distribution features, and the binary values into a discriminator to generate a first and a second training data scores, a first and a second feature distribution scores, and a third training data score; and (c) training the discriminator, the data generator, the distribution analyzer and the solver.

Abstract: Processes of explainable active learning, for an object detector, by using a Bayesian dual encoder is provided. The processes include: (a) inputting test images into the object detector to generate cropped images, resizing the test images and the cropped images, and inputting the resized images into a data encoder to output data codes; (b) (b1) one of (i) inputting the test images into the object detector, applying Bayesian output embedding and resizing the activation entropy maps and the cropped activation entropy maps, and (ii) inputting resized object images and applying the Bayesian output embedding and (b2) inputting the resized activation entropy maps into a model encoder to output model codes; and (c) (i) confirming reference data codes, selecting specific test images as rare samples, and updating the data codebook, and (ii) confirming reference model codes and selecting specific test images as hard samples.

Abstract: A method for optimizing an on-device neural network model by using a Sub-kernel Searching Module is provided. The method includes steps of a learning device (a) if a Big Neural Network Model having a capacity capable of performing a targeted task by using a maximal computing power of an edge device has been trained to generate a first inference result on an input data, allowing the Sub-kernel Searching Module to identify constraint and a state vector corresponding to the training data, to generate architecture information on a specific sub-kernel suitable for performing the targeted task on the training data, (b) optimizing the Big Neural Network Model according to the architecture information to generate a specific Small Neural Network Model for generating a second inference result on the training data, and (c) training the Sub-kernel Searching Module by using the first and the second inference result.

Abstract: A fluid analysis apparatus including: a fluid analysis cartridge comprising a fluid supplying part configured to supply a fluid sample; and a pressing member configured to press and seal the fluid supplying part, wherein the pressing member is configured to couple with the fluid analysis cartridge so that the fluid supplying part is inserted into the pressing member.

Abstract: A drum washing machine including a cabinet, a tub disposed at an inside of the cabinet, a drum rotatively disposed at an inside of the tub, a condensing duct configured to condense moisture in air that is introduced from an inside of the drum, and a drying duct configured to heat air being introduced from the condensing duct to supply the heated air to an inside of the drum, wherein the condensing duct is coupled to a rear of the tub, while the drying duct is positioned at an upper portion of the condensing duct and is coupled to the cabinet, and in between the condensing duct and the drying duct, a buffer coupling member configured to mediate the condensing duct and the drying duct is provided.

Abstract: A washing machine having a holder, which is configured to fix a hose in a simplified manner, thus achieving enhanced assembly efficiency and productivity. The washing machine includes a cabinet defining an outer appearance of the washing machine, a tub placed in the cabinet, a circulation pump placed beneath the tub, a circulation hose connected to the circulation pump and serving to supply wash water pumped by the circulation pump into the tub, and a holder provided at a position of the tub to fix the circulation hose. The holder includes at least one fixing protrusion protruding from a front surface of the tub.

Abstract: A washing machine including a tub which is configured to accommodate water; a rotating tub which is disposed at the inside of the tub; at least one boss which is protruded from the tub and equipped with a coupling hole; a weight balance which is equipped with at least one insertion hole at which at least one boss is inserted; a pin body which is inserted at the coupling hole of the boss, and a pin which is equipped with a pin head which is extended from the pin body for preventing the weight balance from separating away, and the pin body includes at least one boss weld metal zone which is metal-welded at the inner surface of the coupling hole for the pin to be fixed to the boss.

Abstract: A washing machine having a holder, which is configured to fix a hose in a simplified manner, thus achieving enhanced assembly efficiency and productivity. The washing machine includes a cabinet defining an outer appearance of the washing machine, a tub placed in the cabinet, a circulation pump placed beneath the tub, a circulation hose connected to the circulation pump and serving to supply wash water pumped by the circulation pump into the tub, and a holder provided at a position of the tub to fix the circulation hose. The holder includes at least one fixing protrusion protruding from a front surface of the tub.

Abstract: A tub of a washing machine capable of being commonly used for motors mounted on different positions and a washing machine including the tub are disclosed. The washing machine includes a tub which includes a first motor mounting portion to mount a direct connection-type motor thereon and a second motor mounting portion to mount a direct connection-type motor thereon, a rotary drum rotatably installed in the tub, and a motor mounted on one of the first motor mounting portion and the second motor mounting portion to drive the rotary drum. The first motor mounting portion and the second motor mounting portion are integrally formed as one body with the tub when the tub is manufactured.

Abstract: A tub for a drum washing machine, wherein the tub is assembled by connecting parts, separately prepared, to avoid retaining water therein when the tub is drained, and a drum washing machine having the same. The drum washing machine includes a housing; a tub assembled by connecting a front tub part and a rear tub part, and located in the housing to contain washing water; and a drainage exit formed through a connection portion juxtaposed to the front tub part and the rear tub part, for example, a lowermost position of the tub due to a draft angle, thereby causing the washing water in the tub to be completely discharged to the outside when the tub is drained, and thus, preventing the generation of a disagreeable smell and the contamination of laundry.