Abstract: An electronic device includes a communication circuit, a speaker, and a processor. The processor is configured to identify a bitrate of a first audio bitstream received via the communication circuit from an external electronic device. The processor is configured to obtain, in response to the bitrate lower than a reference value, an audio signal by executing a bandwidth extension (BWE) for the first audio bitstream based on at least one coding parameter obtained from a second audio bitstream previously received via the communication circuit from the external electronic device before the first audio bitstream. The processor is configured to obtain, in response to the bitrate higher than or equal to the reference value, the audio signal for the first audio bitstream without executing the BWE. The processor is configured to output, based on the audio signal, audio via the speaker.

Abstract: A wireless communication method and apparatus in a wireless local area network (WLAN) system are disclosed. A wireless communication method according to one embodiment may include generating a high-efficiency Wi-Fi (HEW) frame including at least one of an HEW-SIG-A field and an HEW-SIG-B field which include channel information for communications according to an Orthogonal Frequency-Division Multiple Access (OFDMA) mode, and transmitting the generated HEW frame to a reception apparatus.

Abstract: The technology and implementations disclosed in this patent document generally relate to a lithium secondary battery including: a first unit cell including a first anode including a 1-1 anode mixture layer and a 1-2 anode mixture layer on the 1-1 anode mixture layer, and a second unit cell including a second anode including a 2-1 anode mixture layer and a 2-2 anode mixture layer on the 2-1 anode mixture layer, wherein a weight ratio of the silicon-based active material in the 1-2 anode mixture layer is greater than a weight ratio of the silicon-based active material in the 1-1 anode mixture layer, and a weight ratio of the silicon-based active material in the 2-2 anode mixture layer is less than or equal to a weight ratio of the silicon-based active material in the 2-1 anode mixture layer.

Abstract: A computing method for privacy-preserving data analysis is provided, in which the computing method is performed by at least one processor and includes receiving a data analysis request including data-to-be-analyzed from a client, homomorphically encrypting the data-to-be-analyzed using an encryption engine, and uploading the homomorphically encrypted data-to-be-analyzed to a target path of a blockchain, and then, the method includes, based on the request to check analysis result, decrypting an analysis result of the homomorphically encrypted data stored in a designated path of the blockchain by using the encryption engine, and providing the decrypted analysis result to the client, in which the analysis result of the homomorphically encrypted data is data analyzed by a worker linked to the blockchain, and stored in a designated path in association with the target path of the blockchain.

Abstract: Provided is a ceiling storage system capable of correcting a working position and constantly checking stability of a structure by detecting an amount of change in a facility. According to the ceiling storage system, a transport vehicle moves to an upper portion of a first storage area of a plurality of storage areas in a state of gripping an article, and the transport vehicle measures a first distance value between the transport vehicle and the first storage area using a distance sensor and measures a relative position value between the transport vehicle and the first storage area using a vision sensor, before unloading the article from the first storage area.

Abstract: Disclosed herein is a learning method of a neural network model for flame determination. The learning method of a neural network includes generating a learning image including a fake image generated by combining a real fire image and an arbitrary flame image with a background image; inputting the learning image to a first neural network model and outputting a determination result for whether a flame is present; and updating a weight in a layer extracting features of the learning image from the first neural network model using the determination result. According to the present invention, data of various fire situations may be secured, a performance of the neural network model that determines an occurrence of the fire through the secured data may be increased, and a quality of data for learning may be increased to allow the neural network model itself to predict various situations of fires.

Abstract: Disclosed herein is a learning method of a neural network model for flame determination. The learning method of a neural network includes generating a learning image including a fake image generated by combining a real fire image and an arbitrary flame image with a background image; inputting the learning image to a first neural network model and outputting a determination result for whether a flame is present; and updating a weight in a layer extracting features of the learning image from the first neural network model using the determination result. According to the present invention, data of various fire situations may be secured, a performance of the neural network model that determines an occurrence of the fire through the secured data may be increased, and a quality of data for learning may be increased to allow the neural network model itself to predict various situations of fires.

Abstract: A backlight device includes: a light source to emit coherent light; an optical path difference generator on the light source, the optical path difference generator including an incident surface and a plurality of light emitting surfaces, the light emitting surfaces being parallel to the incident surface and having different separation distances from the incident surface; a light condenser on the optical path difference generator; a diffuser on the light condenser; and a collimator on the diffuser.

Abstract: The present invention relates to a neural network for object segmentation. A method for object segmentation using a trained neural network according to an embodiment of the present invention includes receiving a segmentation target image, splitting the target image into unit images having a predetermined size, outputting a unit activation map for the split unit image as a first segmentation result by using the neural network, merging the unit activation map, and outputting a second segmentation result according to the merged unit activation map, in which the neural network is trained by mutually using the entire activation map for the target image and the merged activation map. According to the present invention, it is possible to generate a more accurate object segmentation result, and generate a label using the segmentation result and use the generated label for training of a neural network.

Abstract: The present invention relates to a method for training a neural network for object detection. The method includes receiving a detection target image, splitting the detection target image into unit images having a predetermined size, defining an output of the neural network for the split unit images as a first label value, generating a first deformed image by deforming the unit image according to a first rule, and training the neural network by using an output of the neural network for the first deformed image and a loss of the first label value. According to the present invention, it is possible to efficiently train a neural network for detecting an object in a large screen.

Abstract: Disclosed herein is a learning method of a neural network model for flame determination. The learning method of a neural network includes generating a learning image including a fake image generated by combining a real fire image and an arbitrary flame image with a background image; inputting the learning image to a first neural network model and outputting a determination result for whether a flame is present; and updating a weight in a layer extracting features of the learning image from the first neural network model using the determination result. According to the present invention, data of various fire situations may be secured, a performance of the neural network model that determines an occurrence of the fire through the secured data may be increased, and a quality of data for learning may be increased to allow the neural network model itself to predict various situations of fires.

Abstract: Disclosed herein is a learning method of a neural network model for flame determination. The learning method of a neural network includes generating a learning image including a fake image generated by combining a real fire image and an arbitrary flame image with a background image; inputting the learning image to a first neural network model and outputting a determination result for whether a flame is present; and updating a weight in a layer extracting features of the learning image from the first neural network model using the determination result. According to the present invention, data of various fire situations may be secured, a performance of the neural network model that determines an occurrence of the fire through the secured data may be increased, and a quality of data for learning may be increased to allow the neural network model itself to predict various situations of fires.

Abstract: A backlight device includes: a light source to emit coherent light; an optical path difference generator on the light source, the optical path difference generator including an incident surface and a plurality of light emitting surfaces, the light emitting surfaces being parallel to the incident surface and having different separation distances from the incident surface; a light condenser on the optical path difference generator; a diffuser on the light condenser; and a collimator on the diffuser.

Abstract: Disclosed herein is a learning method of a neural network model for flame determination. The learning method of a neural network includes generating a learning image including a fake image generated by combining a real fire image and an arbitrary flame image with a background image; inputting the learning image to a first neural network model and outputting a determination result for whether a flame is present; and updating a weight in a layer extracting features of the learning image from the first neural network model using the determination result. According to the present invention, data of various fire situations may be secured, a performance of the neural network model that determines an occurrence of the fire through the secured data may be increased, and a quality of data for learning may be increased to allow the neural network model itself to predict various situations of fires.

Abstract: A device and method for providing an augmented reality (AR) image are provided. The device includes a display configured to alternately display a first image including an object at an object position and a second image including a mask for blocking transmission of light incident on the display at the object position; a processor configured to determine a time period and control the display to alternately display the first image and the second image during the time period; and a polarization switch configured alternately polarize the light incident on the display at a switching period determined based on the time period.

Abstract: A device and method for providing an augmented reality (AR) image are provided. The device includes a display configured to alternately display a first image including an object at an object position and a second image including a mask for blocking transmission of light incident on the display at the object position; a processor configured to determine a time period and control the display to alternately display the first image and the second image during the time period; and a polarization switch configured alternately polarize the light incident on the display at a switching period determined based on the time period.

Abstract: A chip to package interaction (CPI) test vehicle includes a chip including metal patterns contained in first and second sub-regions, respectively, and in which any one of the following comparable properties (a)˜(e) is the same among the first and second sub-regions, and another one of the properties (a)˜(e) is different among the first and second sub-regions: (a) size and shape of the sub-region, (b) metal density of the metal pattern, (c) type of the metal pattern, (d) distance between the sub-region and a center of the chip, (e) structure of the metal pattern.

Abstract: A repair fuse device is provided. The repair fuse device remarkably reduces the number of the enable fuse cuttings by making initial states of all repair fuse sets to a repair state, cutting an address fuse corresponding to a defective cell, and cutting an enable fuse corresponding to a defective redundancy cell.

Abstract: An array variable capacitor apparatus includes a line unit including a ground line and a signal line which operates as a lower electrode; and a plurality of plates which are engaged with the line unit to generate capacitance and which operate as upper electrodes, the plurality of plates being arranged in an array pattern and having different degrees of stiffness.

Abstract: An array variable capacitor apparatus includes a line unit including a ground line and a signal line which operates as a lower electrode; and a plurality of plates which are engaged with the line unit to generate capacitance and which operate as upper electrodes, the plurality of plates being arranged in an array pattern and having different degrees of stiffness.