Abstract: The inventive concept relates to an apparatus for phototherapy treatment of a pet. The apparatus includes a grippable main body having a pair of heads for respectively emitting light, a plurality of first LED modules respectively disposed in the pair of heads, wherein the plurality of first LED modules irradiate light of a specific wavelength toward a skin and an external auditory meatus of the pet, respectively, at least one second LED module disposed in one of the pair of heads, wherein the at least one second LED module irradiates light of a wavelength different from the wavelength of the light of the plurality of first LED modules toward the external auditory meatus of the pet, and a controller disposed in the main body to control the first LED module and the second LED module to respectively irradiate the light.

Abstract: A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.

Abstract: A machine learning apparatus according to an embodiment includes a feature extractor configured to extract features from an object region of an image, a label processor configured to create sentence label embeddings from a sentence label corresponding to the object region, a first training data creator to extract first sub-features from a plurality of first sub-regions created by partitioning the object region, add the sentence label embeddings to the extracted first sub-features, and add the first sub-features added with the sentence label embeddings to the features of the object region, a second training data creator to extract a plurality of second sub-regions along a bounding surface of the object region, create an attention matrix from the second sub-regions, and create a training data by applying the attention matrix to the features of the object region, and a trainer to train an object detection model using the training data.

Abstract: A method of training a model for object classification and detection includes training a first classification model including a shared feature extractor shared by classification models and a first classifier for outputting a result of an object in a first input image based on feature values of the first input image, training a second classification model including the shared feature extractor and a second classifier for outputting a result about authenticity of a second input image based on feature values of the second input image, and training a third classification model including the shared feature extractor and a third classifier for outputting a classification result about a rotation angle of a third input image on the basis of feature values of the third input image extracted by the shared feature extractor, using a third training image set including images rotated at one or more angles.

Abstract: A method of training a model for object classification and detection includes training a first classification model including a shared feature extractor shared by classification models and a first classifier for outputting a result of an object in a first input image based on feature values of the first input image, training a second classification model including the shared feature extractor and a second classifier for outputting a result about authenticity of a second input image based on feature values of the second input image, and training a third classification model including the shared feature extractor and a third classifier for outputting a classification result about a rotation angle of a third input image on the basis of feature values of the third input image extracted by the shared feature extractor, using a third training image set including images rotated at one or more angles.

Abstract: A battery mounting structure and an electronic device having the same is provided. The electronic device includes a housing including a first plate that faces a first direction, a second plate that faces a second direction opposite to the first direction, and a side member that surrounds a space between the first and second plates, a printed circuit board (PCB) disposed in the space of the housing, a battery that at least partially faces the PCB, a bracket that surrounds at least one face of the battery, and an elastic structure disposed between the battery and the bracket and located on an edge region of the battery. The elastic structure may include an elastic member, a first adhesive member disposed on a first face of the elastic member that faces the first direction, and a second adhesive member disposed on a second face that faces the second direction.

Abstract: Disclosed is a data storing method performed by a controller. The method includes storing an attribute value of first data to be written to a nonvolatile memory device in a command queue, determining whether the first data is garbage collection data on the basis of the attribute value when a power interruption occurs, and writing the first data to the nonvolatile memory device according to a result of the determination of whether the first data is garbage collection data or not.

Abstract: An image processing apparatus includes a superpixel extractor configured to extract a plurality of superpixels from an input original image, a backbone network including N feature extracting layers (here, N is a natural number of two or more) which divide the input original image into grids including a plurality of regions and generate an output value including a feature value for each of the divided regions, and a superpixel pooling layer configured to generate a superpixel feature value corresponding to each of the plurality of superpixels using a first output value to an Nth output value output from each of the N feature extracting layers.

Abstract: A semi-supervised learning apparatus includes a backbone network configured to extract one or more feature values from input data, and a plurality of autoencoders as many of which are provided as the number of classes to be classified of the input data, wherein each of the plurality of autoencoders is assigned any one class of the classes to be classified as a target class and learns the one or more feature values according to whether the class, with which the input data is labeled, is identical to the target class.

Abstract: Disclosed is a method of operating a storage device including a NAND flash memory including memory cells grouped into blocks, each block being divided into pages. According to the method, a controller in the storage device loads, onto a memory region, a look-up table containing first read reference voltage sets corresponding to respective retention degradation stages of the NAND flash memory and second read reference voltages sets corresponding to respective pages which vary in terms of the threshold voltages. Subsequently, the controller performs a read operation on the memory cells on a per-block basis by using the first read reference voltage set corresponding to a current retention degradation stage, the second read reference voltage set corresponding to a current page, or both, until all of the memory cells in a current block are correctly read.

Abstract: Provided are an optimization system and method of a deep learning model. According to example embodiments, by optimizing the structure of the deep learning model appropriately for a target dataset without fixing the structure of the deep learning model, it is possible to generate a model structure capable of having high performance on the target dataset and also saving resources.

Abstract: An apparatus and a method for training a deep learning model are disclosed. According to the disclosed embodiments, performance of deep learning can be enhanced by performing bidirectional training of learning information on the target dataset on the basis of the source dataset and learning information on the source dataset on the basis of the target dataset.

Abstract: An apparatus and method for training a deep learning model are provided. According to the disclosed embodiments, a deep learning model may be trained using learning data regarding problems of various fields so that there is an ample amount of data on which the model is trained and the performance of the trained model can be improved.

Abstract: An electronic device is provided. The electronic device includes a housing, a first communication circuit configured to provide first wireless communication using a low power wide area network (LPWAN), a second communication circuit configured to provide second wireless communication using Wi-Fi, a battery configured to supply power to the first communication circuit and the second communication circuit, and a control circuit configured to control the battery to supply power to the first communication circuit as default, determine whether the second communication circuit needs to be used for wireless communication, determine at least one state of the electronic device, and control the battery to supply power to the second communication circuit based on the at least one state.

Abstract: An electronic device is provided. The electronic device includes one or more processors that, while the electronic device is in a locked state, control to detect whether the electronic device is lifted, in response to detecting that the electronic device is lifted, control to activate the display, obtain an image from a camera, detect a face included in the image, and perform authentication of a user based on the face being detected in the image. Based on a result of the authentication, the one or more processors switch a state of the electronic device from the locked state to an unlocked state and display an indicator for indicating that the electronic device is in the unlocked state.

Abstract: A transparent display apparatus is provided. The transparent display apparatus includes a transparent display which displays information, a sensor which senses background information in a first direction, and a controller configured to modify a display state of the displayed information on the transparent display based on the sensed background information.

Abstract: Disclosed is a method of operating a storage device including a NAND flash memory including memory cells grouped into blocks, each block being divided into pages. According to the method, a controller in the storage device loads, onto a memory region, a look-up table containing first read reference voltage sets corresponding to respective retention degradation stages of the NAND flash memory and second read reference voltages sets corresponding to respective pages which vary in terms of the threshold voltages. Subsequently, the controller performs a read operation on the memory cells on a per-block basis by using the first read reference voltage set corresponding to a current retention degradation stage, the second read reference voltage set corresponding to a current page, or both, until all of the memory cells in a current block are correctly read.

Abstract: Disclosed is a data storing method performed by a controller. The method includes storing an attribute value of first data to be written to a nonvolatile memory device in a command queue, determining whether the first data is garbage collection data on the basis of the attribute value when a power interruption occurs, and writing the first data to the nonvolatile memory device according to a result of the determination of whether the first data is garbage collection data or not.

Abstract: A transparent display apparatus is provided. The transparent display apparatus includes a transparent display which displays information, a sensor which senses background information in a first direction, and a controller configured to modify a display state of the displayed information on the transparent display based on the sensed background information.