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: A semiconductor device may include a substrate, a plurality of cell strings perpendicular to an upper surface of the substrate, and a bit line connected to at least six of the cell strings. Each of the cell strings may include a plurality of memory cells connected in series to each other in a direction perpendicular to the upper surface of the substrate, first to fourth ground selection transistors connected in series to each other between the plurality of memory cells and the substrate, and a string selection transistor between the plurality of memory cells and the bit line. A first one of the first to fourth selection ground selection transistors may have a first threshold voltage distribution, and a second one of the first to fourth ground selection transistors may have a second threshold voltage distribution. The second threshold voltage distribution may be different from the first threshold voltage distribution.

Abstract: An anode for a secondary battery is provided, the anode for a secondary battery including: an anode current collector; a first anode mixture layer formed on at least one surface of the anode current collector, and including a first silicon-based active material and a first conductive material; and a second anode mixture layer formed on the first anode mixture layer, and including a second silicon-based active material and a second conductive material, wherein a content of the first silicon-based active material in the first anode mixture layer is lower than a content of the second silicon-based active material in the second anode mixture layer, and a Radial Breathing Mode (RBM) peak is observed in a Raman spectrum obtained from a surface of the second anode mixture layer.

Abstract: In one example, an electronic device can comprise a substrate, a substrate sidewall over a side of the substrate. An electronic device can be disposed over the side of substrate and adjacent the substrate sidewall. An internal interconnect can be disposed between the electronic device and the substrate sidewall. An encapsulant can cover the internal interconnect between the electronic device and the substrate sidewall. A lid attach material can be disposed over the substrate sidewall and the encapsulant. A lid can be coupled to the encapsulant and the substrate sidewall by the lid attach material. A cavity can be defined between a bottom side of the lid and a top side of the electronic device. Other examples and related methods are also disclosed herein.

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 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: 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: 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.