Abstract: A battery management apparatus includes a voltage sensor configured to measure a voltage of one or more battery cells, a control circuit configured to diagnose a defect in the one or more battery cells by determining a voltage change amount of the one or more battery cells using the voltage of the one or more battery cells measured by the voltage sensor, and a storage configured to store data and algorithms driven by the control circuit. The control circuit is configured to obtain a voltage for each battery cell by sequentially measuring the one or more battery cells, and to perform first voltage measurement on the one or more battery cells in a forward chronological order and to perform second voltage measurement on the one or more battery cells in a reverse chronological order.

Abstract: An apparatus for monitoring a battery of a vehicle includes one or more sensors and a processor, wherein the one or more sensors may obtain a first voltage during a stabilization time of the battery, obtain a minimum voltage measured from a time point when the stabilization time ends to a time point when parking of the vehicle ends as a second voltage of the battery, and detect a temperature of the battery from a time point when the first voltage is obtained to a time point when the second voltage is obtained, and the processor may determine a first discharge amount based on the first voltage and the second voltage, determine a second discharge amount consumed in balancing the battery and operating the one or more sensors, determine a first self-discharge amount and a second self-discharge amount based on the first discharge amount, the second discharge amount, and the temperature, and determine whether a short circuit has occurred in the battery based on the first self-discharge amount and the second self-di

Abstract: An apparatus for detecting soft shorts of a battery cell and a method for the same are provided. The apparatus includes a sensor to measure voltages of a plurality of battery cells, and a controller to determine a slope of a voltage of each battery cell, the deviation of the slope of the voltage for each battery cell, and detect a battery cell, which has the soft shorts, of the plurality of battery cells, based on the slope and the deviation.

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, multi-dimensional vectors representing network parameters are constructed from a trained neural network model. The multi-dimensional vectors are quantized to obtain shared quantized vectors as cluster centers, which are fine-tuned. The fine-tuned and shared quantized vectors/cluster centers are then encoded. Decoding reverses the process.

Abstract: A method for training a generator, by a generator training system including a processor and memory, includes: extracting training statistical characteristics from a batch normalization layer of a pre-trained model, the training statistical characteristics including a training mean ? and a training variance ?2; initializing a generator configured with generator parameters; generating a batch of synthetic data using the generator; supplying the batch of synthetic data to the pre-trained model; measuring statistical characteristics of activations at the batch normalization layer and at the output of the pre-trained model in response to the batch of synthetic data, the statistical characteristics including a measured mean {circumflex over (?)}? and a measured variance {circumflex over (?)}?2; computing a training loss in accordance with a loss function L? based on ?, ?2, {circumflex over (?)}?, and {circumflex over (?)}?2; and iteratively updating the generator parameters in accordance with the training loss unti

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

Abstract: A nonvolatile memory device and method for fabricating the same are provided. The nonvolatile memory device comprising: a substrate; a mold structure including a first insulating pattern and a plurality of gate electrodes alternately stacked in a first direction on the substrate; and a word line cut region which extends in a second direction different from the first direction and cuts the mold structure, wherein the word line cut region includes a common source line, and the common source line includes a second insulating pattern extending in the second direction, and a conductive pattern extending in the second direction and being in contact with the second insulating pattern and a cross-section in the second direction.

Abstract: Disclosed is a method and apparatus for dynamic models to identify similar tasks when no task identifier is provided during the training phase in continual learning (CL). The method includes maintaining a memory comprising one or more previously learned tasks, determining, in response to receiving a new task, one of more similarities between at least one previously learned task and the new task, generating, based on the one or more similarities determined and a previously used task-specific encoder corresponding to the at least one previously learned task, a test error value for classifying the new task, and applying the previously used task-specific encoder to the new task based on the generated test error value.

Abstract: A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables; generating a variational posterior of the joint latent variable model; training the variational posterior; performing inference of the first variable from the second variable based on the variational posterior, wherein performing the inference comprises conditionally generating the first variable from the second variable; and extracting common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function.

Abstract: A multilayer capacitor includes a body including dielectric layers and internal electrodes, and external electrodes, wherein the body has first and second surfaces opposing each other in a first direction, third and fourth surfaces opposing each other in a second direction, and fifth and sixth surfaces opposing each other in a third direction perpendicular to the first and second directions. A length of a portion of the plurality of internal electrodes in the third direction in an intermediate region of the body in the first direction is greater than a length of the first surface or the second surface of the body in the third direction. The plurality of internal electrodes have a bottleneck structure between the intermediate region and at least one of the first and second surfaces, and wherein the bottleneck structure has a shape recessed into an inner portion of the body.

Abstract: A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

Abstract: A server and method thereof are provided for use in a federated network. A method includes receiving local updates from client devices; updating a global model based on the received local updates; quantizing the updated global model; reconstructing feature maps based on the received local updates; refining the quantized, updated global model based on the reconstructed feature maps; and transmitting the refined, quantized, updated global model to the client devices.

Abstract: According to the present disclosure, when drip coffee is extracted, it may learn drip coffee recipe information of a barista to be imitated and extract the drip coffee based on the learned reference acidity and reference concentration. The extracted drip coffee may be compared with the learned reference acidity and the reference concentration and evaluated, and when the acidity and concentration of the drip coffee are matched with the reference acidity and the reference concentration, the drip coffee having the same acidity and concentration as the drip coffee may be extracted. Alternatively, when the acidity and concentration of the drip coffee are not matched with the reference acidity and the reference concentration, drip coffee having the same or/and similar acidity and concentration as or/and to the reference acidity and the reference concentration may be extracted by changing extraction conditions of the drip coffee through reinforcement learning.

Abstract: A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables, generating a variational posterior of the joint latent variable model, training the variational posterior, and performing inference of the first variable from the second variable based on the variational posterior.

Abstract: A system and method for operating a neural network. In some embodiments, the neural network includes a variational autoencoder, and the training of the neural network includes training the variational autoencoder with a plurality of samples of a first random variable; and a plurality of samples of a second random variable, the plurality of samples of the first random variable and the plurality of samples of the second random variable being unpaired, the training of the neural network including updating weights in the neural network based on a first loss function, the first loss function being based on a measure of deviation from consistency between: a conditional generation path from the first random variable to the second random variable, and a conditional generation path from the second random variable to the first random variable.

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method includes training a conditional autoencoder using a Lagrange multiplier and training a neural network that includes the conditional autoencoder with mixed quantization bin sizes.

Abstract: A method and system for constructing a convolutional neural network (CNN) model are herein disclosed. The method includes regularizing spatial domain weights, providing quantization of the spatial domain weights, pruning small or zero weights in a spatial domain, fine-tuning a quantization codebook, compressing a quantization output from the quantization codebook, and decompressing the spatial domain weights and using either sparse spatial domain convolution and sparse Winograd convolution after pruning Winograd-domain weights.

Abstract: An electronic device and method for performing class-incremental learning are provided. The method includes designating a pre-trained first model for at least one past data class as a first teacher; training a second model; designating the trained second model as a second teacher; performing dual-teacher information distillation by maximizing mutual information at intermediate layers of the first teacher and second teacher; and transferring the information to a combined student model.