Abstract: A washing machine comprises a housing, a drum rotatably disposed inside the housing, a driving shaft configured to transfer a rotational force from a driving motor of the washing machine to the drum, and a driving shaft flange configured to connect the driving shaft to the drum. The driving shaft flange comprises a first surface, a second surface substantially parallel to the first surface, wherein the first surface and the second surface are spaced apart from each other by a predetermined distance in the vertical direction, and an extension portion formed perpendicular to the first surface and the second surface and connecting the first surface to the second surface. An edge of the first surface and an edge of the second surface and an edge of the extension portion are substantially aligned in vertical direction of the drum.

Abstract: An electrolyte composition with improved high temperature safety and a lithium secondary battery including the same is described herein. The electrolyte composition containing a primary additive comprising a compound represented by Formula 1, and specific amount of a secondary additive that contains one or more of cyclic carbonates, can not only reduce the generation of gas during secondary battery charge-discharge, but also improve storage characteristics and the lifespan characteristics under a high temperature condition by strengthening the SEI coating film on the surface of an electrode. wherein all the variables are described herein.

Abstract: A method of reinforcement learning of a neural network device for generating a verification vector for verifying a circuit design comprising a circuit block includes inputting a test vector to the circuit block, generating one or more rewards based on a coverage corresponding to the test vector, the coverage being determined based on a state transition of the circuit block based on the test vector, and applying the one or more rewards to a reinforcement learning.

Abstract: An image adjustment apparatus includes a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: An image adjustment apparatus includes a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: A method of reinforcement learning of a neural network device for generating a verification vector for verifying a circuit design comprising a circuit block includes inputting a test vector to the circuit block, generating one or more rewards based on a coverage corresponding to the test vector, the coverage being determined based on a state transition of the circuit block based on the test vector, and applying the one or more rewards to a reinforcement learning.

Abstract: An image adjustment apparatus includes a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: The present application relates to a method for producing a substrate which includes a step of exposing and developing a photosensitive resin composition layer formed on a surface of a substrate base layer to produce spacers. The method for producing a substrate of the present application can uniformly form spacers having a height according to a desired cell gap and can also freely control the height of the spacers.

Abstract: An image adjustment apparatus includes a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: A recognition apparatus and a training method are provided. The recognition apparatus includes a memory configured to store a neural network including a previous layer of neurons, and a current layer of neurons that are activated based on first synaptic signals and second synaptic signals, the first synaptic signals being input from the previous layer, and the second synaptic signals being input from the current layer. The recognition apparatus further includes a processor configured to generate a recognition result based on the neural network. An activation neuron among the neurons of the current layer generates a first synaptic signal to excite or inhibit neurons of a next layer, and generates a second synaptic signal to inhibit neurons other than the activation neuron in the current layer.

Abstract: Disclosed is an image adjustment apparatus including a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: A processor-implemented method with data exploration includes: setting first input data and a first target condition; predicting first output data corresponding to the first input data using a first function that models an objective function; and determining second input data using a second function that provides a result of comparison between the first output data and the first target condition.

Abstract: A method and device for predicting a defect. The method includes determining a sequence between a plurality of sub-models by modeling a production process into the plurality of sub-models, mapping production process data into each of the plurality of sub-models, determining, by a corresponding sub-model, output data comprising defect information on a potential defect occurring in a corresponding step, for each of the plurality of sub-models, predicting information associated with a defect in the production process based on the output data corresponding to each of the plurality of sub-models, and inputting the output data of each of the sub-models to a subsequent sub-model of the corresponding sub-model, based on the sequence.

Abstract: A method and device for predicting an anomaly in a manufacturing process. The method includes receiving time-series equipment data including one or both of sensor data and specification data, converting the time-series equipment data into an image, dividing the image into a plurality of patch images, outputting a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN), and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard.

Abstract: A neural network training method and apparatus are disclosed, where the neural network training method using trend includes receiving original training data and trend information, obtaining a first prediction value and a first loss from a neural network based on the original training data, obtaining a second prediction value from the neural network based on the original training data using the trend information, determining a target loss by modifying the first loss based on the first prediction value and the second prediction value, and training the neural network based on the target loss.

Abstract: A deep learning device and system including the same is provided. The deep learning device comprising processing circuitry configured to determine whether a received image is abnormal using an anomaly detection model; merge at least some vectors extracted from the anomaly detection model; input, to a probability approximation model, principal components generated by a principal component analysis (PCA) to detect whether out of distribution (OOD) occurs in data of the received image; store a result of the determinations; and extract at least some the data in which the OOD occurs, as target labeling, using a target labeling extraction model when a rate of the data in which the OOD occurs is greater than or equal to a threshold value, wherein the anomaly detection model determines whether the received image is abnormal using the target labeling.

Abstract: A method of reinforcement learning of a neural network device for generating a verification vector for verifying a circuit design comprising a circuit block includes inputting a test vector to the circuit block, generating one or more rewards based on a coverage corresponding to the test vector, the coverage being determined based on a state transition of the circuit block based on the test vector, and applying the one or more rewards to a reinforcement learning.

Abstract: Disclosed is an image adjustment apparatus including a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.

Abstract: A device for verifying a circuit design including a first circuit block and a second circuit block includes a verification vector generator and a design verifier. The verification vector generator determines a first verification vector by performing reinforcement learning through neural network computation based on a coverage corresponding to a first test vector, the coverage being determined based on a state transition of the first circuit block generated by inputting the first test vector to the first circuit block. The design verifier performs design verification for the first circuit block by using the first verification vector.

Abstract: Disclosed is an image adjustment apparatus including a receiver which is configured to receive a first input image of an object which is time-synchronously captured and a second input image in which a motion event of the object is sensed time-asynchronously, and an adjuster which is configured to adjust the first input image and the second input image.