Abstract: A display device includes a plurality of pixel electrodes on a substrate, light-emitting elements on the plurality of pixel electrodes and extending in a thickness direction of the substrate and connecting electrodes between the plurality of pixel electrodes and the light-emitting elements, wherein a width of the connecting electrodes is greater than a width of the light-emitting elements, and upper corners of each of the light-emitting elements and upper corners of each of the connecting electrodes are rounded.

Abstract: A power conversion system of a battery for a vehicle includes a bidirectional charger configured to output a first active line voltage between a first active line and a neutral line and a second active line voltage between a second active line and the neutral line at different levels by converting a voltage of a battery into an AC voltage in a battery discharge mode, and a wiring swap device configured to electrically connect the first active line to a load-side first active line and to cross and electrically connect the second active line and the neutral line to a load-side neutral line and a load-side second active line, respectively.

Abstract: A protective film is provided. The protective film includes a release film, a base film which is disposed on the release film and comprises a protective part and a first pull tab part protruding from a first side surface of the protective part, and a first dummy film which is disposed on the release film, does not overlap the base film in a plan view, and comprises a part partially surrounding the first pull tab part, where edges of the base film and edges of the first dummy film are disposed inside edges of the release film in the plan view.

Abstract: A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.

Abstract: A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.

Abstract: The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.

Abstract: The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.

Abstract: The present invention relates to a cloud-based pathological analysis system and method. The present invention provides a cloud-based pathological analysis system, including: a client device coupled to a microscope, and configured to acquire an image for a tissue sample via the microscope and generate a sample image; and a cloud server coupled to the client device over a network, and configured to receive sample image data from the client device over the network and store the sample image data; wherein the cloud server analyzes the received sample image data, and transmits analysis information to the client device.

Abstract: A visual product searching apparatus includes a product area determining part, a visual word generating part and a product searching part. The product area determining part extracts a product area in an input image. The visual word generating part generates a visual word reflecting human visual cognitive characteristics based on the product area. The product searching part searches a product using the visual word.