Abstract: The present invention relates to a secondary battery capable of improving the coupling force between a safety vent and a cap-up. For example, disclosed is a secondary battery comprising: an electrode assembly; a case for accommodating the electrode assembly; a cap assembly coupled to the upper part of the case; and a gasket interposed between the cap assembly and the case, wherein the cap assembly includes a cap-up and a safety vent, which is provided at the lower part of the cap-up and has a vent extension part extending to the upper part of the cap-up so as to encompass the edge of the cap-up, a welding region, in which the safety vent and the cap-up are welded and coupled by laser welding, is formed in the vent extension part, and the welding region is formed in a line shape.

Abstract: The present disclosure relates to an apparatus for estimating a battery free capacity, and more particularly, an apparatus for estimating a free capacity of a half cell of a battery. According to the present disclosure, it is possible to accurately estimate a free capacity of a half cell without inserting a reference electrode by revising an entire SOC region of a half cell by using an inflection point detected based on SOC-voltage data of a full cell and a half cell of the battery, respectively, and then estimating SOC-voltage data based on a SOC difference between the entire SOC regions before and after the revision.

Abstract: A display device includes a first display area including a plurality of first pixel areas and a second display area including a plurality of second pixel areas and a plurality of light transmitting areas. Each of the plurality of first pixel areas includes at least one first pixel to display an image. Each of the plurality of second pixel areas includes at least one second pixel to display the image. The light transmitting area does not include a pixel capable of displaying the image and has higher light transmittance than the second pixel area. In the second display area, at least one of the plurality of light transmitting areas is between two second pixel areas adjacent in a first direction, and at least one of the plurality of light transmitting areas is between two second pixel areas adjacent in a second direction different from the first direction.

Abstract: A method, performed by an electronic device, of generating a virtual defect image includes training a virtual defect image generation model based at least on a first normal image and a defect image of a first product, and a user input, and generating a virtual defect image from a second normal image of a second product by using the trained virtual defect image generation model.

Abstract: Provided is an apparatus for treating a substrate including: a processing vessel having a processing space; a support unit for supporting the substrate in the processing space and rotating the substrate; a liquid supply unit for supplying a processing liquid to the substrate; and a heating unit for heating the substrate, wherein the support unit includes: a spin chuck; a driver for rotating the spin chuck; a chuck pin installed on the spin chuck to be rotated together with the spin chuck; and a chuck pin moving unit for moving the chuck pin between a contact position wherein the chuck pin is in contact with a side portion of the substrate and an open position wherein the chuck pin is spaced apart from the side portion of the substrate.

Abstract: A multilayer ceramic electronic component includes: a ceramic body including an active portion having dielectric layers and first and second internal electrodes and first and second cover portions disposed on opposite surfaces of the active portion in a stacking direction, respectively; wherein when a region of the cover portion in contact with the first or second internal electrode is an inner region of the cover portion and a region of the active portion in contact with the inner region of the cover portion is an outer region of the active portion, 1.00<XA/XB?1.04 in which XA/XB is a ratio of a molar ratio (XA) of barium (Ba) to titanium (Ti) in the inner region of the cover portion to a molar ratio (XB) of barium (Ba) to titanium (Ti) in the outer region of the active portion.

Abstract: The neuromorphic arithmetic device comprises an input monitoring circuit that outputs a monitoring result by monitoring that first bits of at least one first digit of a plurality of feature data and a plurality of weight data are all zeros, a partial sum data generator that skips an arithmetic operation that generates a first partial sum data corresponding to the first bits of a plurality of partial sum data in response to the monitoring result while performing the arithmetic operation of generating the plurality of partial sum data, based on the plurality of feature data and the plurality of weight data, and a shift adder that generates the first partial sum data with a zero value and result data, based on second partial sum data except for the first partial sum data among the plurality of partial sum data and the first partial sum data generated with the zero value.

Abstract: An artificial neural network apparatus and an operating method including a plurality of layer processors for performing operations on input data are disclosed. The artificial neural network apparatus may include: a flag layer processor for outputting a flag according to a comparison result between a pooling output value of a current frame and a pooling output value of a previous frame; and a controller for stopping operation of a layer processor which performs operations after the flag layer processor among the plurality of layer processors when the flag is outputted from the flag layer processor, wherein the flag layer processor is a layer processor that performs a pooling operation first among the plurality of layer processors.

Abstract: An embodiment of the present invention provides a quantization method for weights of a plurality of batch normalization layers, including: receiving a plurality of previously learned first weights of the plurality of batch normalization layers; obtaining first distribution information of the plurality of first weights; performing a first quantization on the plurality of first weights using the first distribution information to obtain a plurality of second weights; obtaining second distribution information of the plurality of second weights; and performing a second quantization on the plurality of second weights using the second distribution information to obtain a plurality of final weights, and thereby reducing an error that may occur when quantizing the weight of the batch normalization layer.

Abstract: Provided is an image recognition device. The image recognition device includes a frame data change detector that sequentially receives a plurality of frame data and detects a difference between two consecutive frame data, an ensemble section controller that sets an ensemble section in the plurality of frame data, based on the detected difference, an image recognizer that sequentially identifies classes respectively corresponding to a plurality of section frame data by applying different neural network classifiers to the plurality of section frame data in the ensemble section, and a recognition result classifier that sequentially identifies ensemble classes respectively corresponding to the plurality of section frame data by combining the classes in the ensemble section.

Abstract: A multilayer ceramic electronic component includes: a ceramic body including an active portion having dielectric layers and first and second internal electrodes and first and second cover portions disposed on opposite surfaces of the active portion in a stacking direction, respectively; wherein when a region of the cover portion in contact with the first or second internal electrode is an inner region of the cover portion and a region of the active portion in contact with the inner region of the cover portion is an outer region of the active portion, 1.00<XA/XB<1.04 in which XA/XB is a ratio of a molar ratio (XA) of barium (Ba) to titanium (Ti) in the inner region of the cover portion to a molar ratio (XB) of barium (Ba) to titanium (Ti) in the outer region of the active portion.

Abstract: Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.

Abstract: A method for controlling a memory from which data is transferred to a neural network processor and an apparatus thereof are provided, the method including: generating prefetch information of data by using a blob descriptor and a reference prediction table after history information is input; reading the data in the memory based on the pre-fetch information and temporarily archiving read data in a prefetch buffer; and accessing next data in the memory based on the prefetch information and temporarily archiving the next data in the prefetch buffer after the data is transferred to the neural network from the prefetch buffer.

Abstract: A method and apparatus for multi-level stepwise quantization for neural network are provided. The apparatus sets a reference level by selecting a value from among values of parameters of the neural network in a direction from a high value equal to or greater than a predetermined value to a lower value, and performs learning based on the reference level. The setting of a reference level and the performing of learning are iteratively performed until the result of the reference level learning satisfies a predetermined value and there is no variable parameter that is updated during learning among the parameters.

Abstract: Disclosed is a neural network computing device. The neural network computing device includes a neural network accelerator including an analog MAC, a controller controlling the neural network accelerator in one of a first mode and a second mode, and a calibrator that calibrating a gain and a DC offset of the analog MAC. The calibrator includes a memory storing weight data, calibration weight data, and calibration input data, a gain and offset calculator reading the calibration weight data and the calibration input data from the memory, inputting the calibration weight data and the calibration input data to the analog MAC, receiving calibration output data from the analog MAC, and calculating the gain and the DC offset of the analog MAC, and an on-device quantizer reading the weight data, receiving the gain and the DC offset, generating quantized weight data, based on the gain and the DC offset.

Abstract: A display device includes a flexible substrate including a display area and a non-display area. The substrate has a first surface and a second surface opposite to the first surface. A first protection film is disposed on the first surface of the substrate. The first protection film is disposed over the display area of the substrate. A second protection film is disposed on the first surface of the substrate. The second protection film is disposed over the non-display area of the substrate. A light transmittance of the first protection film is higher than that of the second protection film.

Abstract: A display device includes a flexible substrate including a display area and a non-display area. The substrate has a first surface and a second surface opposite to the first surface. A first protection film is disposed on the first surface of the substrate. The first protection film is disposed over the display area of the substrate. A second protection film is disposed on the first surface of the substrate. The second protection film is disposed over the non-display area of the substrate. A light transmittance of the first protection film is higher than that of the second protection film.

Abstract: A method for controlling a memory from which data is transferred to a neural network processor and an apparatus thereof are provided, the method including: generating prefetch information of data by using a blob descriptor and a reference prediction table after history information is input; reading the data in the memory based on the pre-fetch information and temporarily archiving read data in a prefetch buffer; and accessing next data in the memory based on the prefetch information and temporarily archiving the next data in the prefetch buffer after the data is transferred to the neural network from the prefetch buffer.

Abstract: Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.

Abstract: Provided is a convolutional neural network system including a data selector configured to output an input value corresponding to a position of a sparse weight from among input values of input data on a basis of a sparse index indicating the position of a nonzero value in a sparse weight kernel, and a multiply-accumulate (MAC) computator configured to perform a convolution computation on the input value output from the data selector by using the sparse weight kernel.