Abstract: A sealing structure may include a lid including a first lid face, a second lid face opposite to the first lid face, and a fragile area between the first lid face and the second lid face, a cover including a first cover face facing the second lid face and covering the fragile area and a second cover face opposite to the first cover face, wherein a first distance between the second lid face and the first cover face is substantially equal to or less than a second distance between the first cover face and the second cover face, and a connector configured to connect the lid and the cover.

Abstract: An integrated circuit included in a device for performing a neural network operation includes a buffer configured to store feature map data in units of cells each including at least one feature, wherein the feature map data is for use in the neural network operation; and a multiplexing circuit configured to receive the feature map data from the buffer, and output extracted data by extracting feature data of one of features that are included within a plurality of cells in the received feature map data, the features each corresponding to an identical coordinate value.

Abstract: A redundancy dynamic braking system may include: a main braking force adjusting device configured to control a hydraulic braking device of a vehicle; a receiving unit configured to receive driving information of the vehicle; an electronic control braking device configured to be operated electrically to generate braking force; and an auxiliary braking force adjusting device configured to control the hydraulic braking device and the electronic control braking device, in an event of a failure in the main braking force adjusting device, wherein the auxiliary braking force adjusting device is configured to control the electronic control braking device by adjusting a magnitude and an application time of a current applied to the electronic control braking device based on an estimated target pressure using the driving information.

Abstract: A frequency multiplier is provided. A harmonic generator of the frequency multiplier comprises: a harmonic generating core unit; a first resonant tank which is connected to a first output terminal and a second output terminal of the harmonic generating core unit; and a first feedback circuit which is connected to the first output terminal and the second output terminal of the harmonic generating core unit to change the effective resistance of the first resonant tank.

Abstract: Provided is a hot-dip galvanized steel sheet used for an automobile outer panel and so forth and, more specifically, to a method for producing a hot-dip galvanized steel sheet having excellent press formability and clarity after painting, and to a hot-dip galvanized steel sheet produced by the method.

Abstract: An artificial neural network (ANN) system includes a processor, a virtual overflow detection circuit and a data format controller. The processor performs node operations with respect to a plurality of nodes included in each layer of an ANN to obtain a plurality of result values of the node operations and performs a quantization operation on the obtained plurality of result values based on a k-th fixed-point format for a current quantization of the each layer to obtain a plurality of quantization values. The virtual overflow detection circuit generates a virtual overflow information indicating a distribution of valid bit numbers of the obtained plurality of quantization values. The data format controller determines a (k+1)-th fixed-point format for a next quantization of the each layer based on the generated virtual overflow information. An overflow and/or an underflow are prevented efficiently by controlling the fixed-point format using the virtual overflow.

Abstract: An integrated circuit included in a device for performing a neural network operation includes a buffer configured to store feature map data in units of cells each including at least one feature, wherein the feature map data is for use in the neural network operation; and a multiplexing circuit configured to receive the feature map data from the buffer, and output extracted data by extracting feature data of one of features that are included within a plurality of cells in the received feature map data, the features each corresponding to an identical coordinate value.

Abstract: An integrated circuit included in a device for performing a neural network operation includes a buffer configured to store feature map data in units of cells each including at least one feature, wherein the feature map data is for use in the neural network operation; and a multiplexing circuit configured to receive the feature map data from the buffer, and output extracted data by extracting feature data of one of features that are included within a plurality of cells in the received feature map data, the features each corresponding to an identical coordinate value.

Abstract: An electrostatic chuck of the present invention includes a top block and a bottom block bonded by a bonding layer. The top block has a first plate on which a chucking electrode and a heater are installed, and the bottom block is provided with a cooling member. A second plate made of a material having lower heat transfer rate than the first plate is disposed between the first plate and the bottom block. Accordingly, when the heater is heated at a high temperature, it is possible to prevent the bonding layer from being damaged by thermal impact.

Abstract: Proposed is a substrate support unit, a method of manufacturing the same, and a substrate processing apparatus including the same. The substrate support unit includes a base component, a chucking component mounted on the base component, and an intermediate layer interposed between the base component and the chucking component, wherein the intermediate layer includes a joining part formed in a partial region to couple the base component and the chucking component.

Abstract: A neural processing system includes a first frontend module, a second frontend module, a first backend module, and a second backend module. The first frontend module executes a feature extraction operation using a first feature map and a first weight, and outputs a first operation result and a second operation result. The second frontend module executes the feature extraction operation using a second feature map and a second weight, and outputs a third operation result and a fourth operation result. The first backend module receives an input of the first operation result provided from the first frontend module and the fourth operation result provided from the second frontend module via a second bridge to sum up the first operation result and the fourth operation result.

Abstract: A neural processing system includes a first frontend module, a second frontend module, a first backend module, and a second backend module. The first frontend module executes a feature extraction operation using a first feature map and a first weight, and outputs a first operation result and a second operation result. The second frontend module executes the feature extraction operation using a second feature map and a second weight, and outputs a third operation result and a fourth operation result. The first backend module receives an input of the first operation result provided from the first frontend module and the fourth operation result provided from the second frontend module via a second bridge to sum up the first operation result and the fourth operation result.

Abstract: A neural processing system includes a first frontend module, a second frontend module, a first backend module, and a second backend module. The first frontend module executes a feature extraction operation using a first feature map and a first weight, and outputs a first operation result and a second operation result. The second frontend module executes the feature extraction operation using a second feature map and a second weight, and outputs a third operation result and a fourth operation result. The first backend module receives an input of the first operation result provided from the first frontend module and the fourth operation result provided from the second frontend module via a second bridge to sum up the first operation result and the fourth operation result.

Abstract: A neural network device may generate an input feature list based on an input feature map, where the input feature list includes an input feature index and an input feature value, generating an output feature index based on the input feature index corresponding to an input feature included in the input feature list and a weight index corresponding to a weight included in a weight list, and generating an output feature value corresponding to the output feature index based on the input feature value corresponding to the input feature and a weight value corresponding to the weight.

Abstract: A neural network device may generate an input feature list based on an input feature map, where the input feature list includes an input feature index and an input feature value, generating an output feature index based on the input feature index corresponding to an input feature included in the input feature list and a weight index corresponding to a weight included in a weight list, and generating an output feature value corresponding to the output feature index based on the input feature value corresponding to the input feature and a weight value corresponding to the weight.

Abstract: A scent visualization system may comprise a display apparatus for generating a target image including a target object; an olfactory sensor for detecting a scent; and a scent visualization apparatus for generating target associative visualization information that reminds the scent from the target image received from the display apparatus and sensing information received from the olfactory sensor, and generating an associative image by combining the target associative visualization information and the target image. Therefore, a low-cost, high-efficiency, high-utilization, and high-convenience scent visualization system can be provided.

Abstract: Provided are a composite precursor of a cathode active material, the composite precursor including a cobalt hydroxide and a cobalt oxyhydroxide, where an X-ray diffraction spectrum of the composite precursor has a first peak observed at a diffraction angle (2?) of 19.5°±0.5° and a second peak observed at a diffraction angle (2?) of 38.5°±0.5°; a cathode active material prepared from the composite precursor; a cathode and a lithium battery including the composite precursor; and a method of preparing the composite precursor.

Abstract: A method of operating a neural network device configured to perform a neural network operation on successively input image frames includes generating, by a processing circuit, a second delta feature map by performing a linear operation on a first delta feature map generated based on a difference between a current image frame and a previous image frame; loading feature values as a second previous feature map onto the processing circuit from at least one memory, the loaded feature values being feature values corresponding to a first partial region to be updated in a first feature map stored in the at least one memory; generating, at the processing circuit, a second current feature map based on the second delta feature map and the second previous feature map; and updating the first feature map by storing the second current feature map in the at least one memory.

Abstract: A sensory substitution method includes obtaining a color image by using a sensor device, converting color information about the color image, received from the sensor device, into tactile information by using a user terminal, and generating a tactile stimulus corresponding to the tactile information received from the user terminal by using a tactile output device.

Abstract: In a method of operating a neural network device, a plurality of consecutive input data is received by an input layer. A delta data is generated by the input layer based on a difference between a current input data and a previous input data. A first current feature is generated by a first linear layer based on a first delta feature generated by performing a first linear operation on the delta data and a first previous feature. A second delta feature is generated by a first nonlinear layer based on a second current feature generated by performing a first nonlinear operation on the first current feature and a second previous feature. A third current feature is generated by a second linear layer based on a third delta feature generated by performing a second linear operation on the second delta feature and a third previous feature.