Abstract: A method for manufacturing a semiconductor device and a semiconductor device produced thereby. For example and without limitation, various aspects of this disclosure provide a method for manufacturing a semiconductor device, and a semiconductor device produced thereby, that comprises an interposer without through silicon vias.

Abstract: Disclosed is a an apparatus for performing smart logistics monitoring including a display unit, a memory, a sensor unit sensing at least one of a temperature, acceleration, humidity, illuminance, inclination, impact, and location of an inside of a logistics vehicle, and a control unit that generates respective status information of a logistics according to a sensed result at a predetermined period, generates a respective QR code indicating the respective status information, and displays the respective QR code on a screen of the display unit. The respective QR code is converted and displayed on the screen depending on an order in which the respective QR code is generated.

Abstract: Disclosed is a display device including a display panel having a plurality of pixels, the display panel comprising a first display area having first resolution and a second display area having second resolution, the second resolution being lower than the first resolution, and a controller configured to generate display area information of each of the plurality of pixels, to blur an image that is displayed in the second display area based on the display area information, and to perform control such that the blurred image is displayed on the display panel.

Abstract: The present invention relates to a breast image analysis method with four mammogram images which are input to a convolutional neural network as one input and a system therefor and the system includes an image receiving unit which receives four mammogram images; an image size adjusting unit which adjusts a size of a mammogram image received from the image receiving unit; a preprocessing unit which performs preprocessing on the mammogram image adjusted by the image size adjusting unit; a convolutional neural network (CNN)-based CNN learning unit which generates learning information by learning the mammogram image preprocessed by the preprocessing unit; and a CNN inference unit which receives the learning information learned from the CNN learning unit and a mammogram image to be classified from the image receiving unit to diagnose a breast abnormality.

Abstract: A method for promoting growth and bioactive substances of Crepidiastrum denticulatum including performing a stress treatment on Crepidiastrum denticulatum during cultivation thereof, in which the stress treatment includes at least of applying visible light, drying, exposing to low temperature, irradiating ultraviolet rays, and applying a chemical elicitor.

Abstract: The present invention relates to a breast image analysis method with four mammogram images which are input to a convolutional neural network as one input and a system therefor and the system includes an image receiving unit which receives four mammogram images; an image size adjusting unit which adjusts a size of a mammogram image received from the image receiving unit; a preprocessing unit which performs preprocessing on the mammogram image adjusted by the image size adjusting unit; a convolutional neural network (CNN)-based CNN learning unit which generates learning information by learning the mammogram image preprocessed by the preprocessing unit; and a CNN inference unit which receives the learning information learned from the CNN learning unit and a mammogram image to be classified from the image receiving unit to diagnose a breast abnormality.

Abstract: Provided are a data processing apparatus and method for merging and processing deterministic knowledge and non-deterministic knowledge. The data processing apparatus and method may efficiently process various real-time and large-scale data to convert the data into knowledge by merging and processing non-deterministic knowledge and also deterministic knowledge perceived by an expert. Thus, it is possible to adaptively operate in accordance with a dynamically changing application service environment by converting a conversion rule for converting collected data generated from an application service system into semantic data, a context awareness rule for perceiving context information from given information, and a user query for searching for knowledge information into knowledge and gradually augmenting the knowledge information in accordance with an application service environment.

Abstract: An apparatus/method for extracting multiple features from time series data collected from a plurality of sensors and for performing transfer learning on them. There is provided an apparatus including: a multi-feature extraction unit for extracting multiple features from a data stream for each sensor inputted from the plurality of sensors; a transfer-learning model generation unit for extracting useful multi-feature information from a learning model which has finished pre-learning, for the multiple features for forwarding the extracted multi-feature information to a multi-feature learning unit to generate a learning model that performs transfer learning on the multiple features; and the multi-feature learning unit for receiving learning variables from the learning model for each of the multiple features and for performing parallel learning for the multiple features, to calculate and output a loss. In addition, there is provided an apparatus for detecting leaks in plant pipelines.

Abstract: Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.

Abstract: A method for promoting growth and bioactive substances of Crepidiastrum denticulatum including performing a stress treatment on Crepidiastrum denticulatum during cultivation thereof, in which the stress treatment includes at least of applying visible light, drying, exposing to low temperature, irradiating ultraviolet rays, and applying a chemical elicitor.

Abstract: Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.

Abstract: Provided is a data meta-scaling method. The data meta-scaling method optimizes an abbreviation criterion for abbreviating data through continuous knowledge augmentation in various dimensions which enable expression of data in a process of performing machine learning.

Abstract: Provided are a self-learning system and method for automatically performing machine learning (ML). The self-learning system includes a memory configured to store an ML knowledge database (DB) in which ML knowledge is stored and a program for automatically performing ML based on request information of a user, and a processor configured to execute the program stored in the memory. Here, when executing the program, the processor creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge stored in the ML knowledge DB and generates an execution code for performing the created or recommended workflow.

Abstract: Provided are a data processing apparatus and method for merging and processing deterministic knowledge and non-deterministic knowledge. The data processing apparatus and method may efficiently process various real-time and large-scale data to convert the data into knowledge by merging and processing non-deterministic knowledge and also deterministic knowledge perceived by an expert. Thus, it is possible to adaptively operate in accordance with a dynamically changing application service environment by converting a conversion rule for converting collected data generated from an application service system into semantic data, a context awareness rule for perceiving context information from given information, and a user query for searching for knowledge information into knowledge and gradually augmenting the knowledge information in accordance with an application service environment.

Abstract: A cup-shaped container capable of being converted into a Dutch coffee machine includes a main body of cup-shape, a drainer protrusion protruding outward from the bottom surface of the main body, and a drain hole to connect to an inside of the drainer protrusion and an inside of the main body. The cup-shaped container can be used as a cup in normal times and can be converted into a Dutch coffee machine by simple operation when necessary.

Abstract: Disclosed herein are a tag transmission apparatus and a signal transmitting method thereof. The tag transmission apparatus converts a plurality of data bits into a symbol, which is one of a plurality of symbols, and multiplies the converted symbol by a square-wave having a predetermined frequency to thereby generate a subcarrier signal. Here, in the case in which the plurality of data bits are n (n is a natural number larger than 2) bits, the number of the plurality of symbols is 2n.

Abstract: A tag of an apparatus for simultaneously identifying massive tags according to the present invention may include an analog circuit unit to communicate with a reader through an analog signal and to receive energy via magnetic coupling with the reader. Further, the tag may include a digital circuit unit to be supplied with power from the analog circuit unit. The digital circuit unit may support a sleep mode for the tag to stand by in a low power state after transmitting an identifier (ID) to the reader and a wait mode for controlling random access to the reader.

Abstract: A reader antenna includes dipole antennas, the dipole antennas including: a horizontal dipole antenna having the characteristic of horizontal polarization; and a dipole antenna perpendicular to the horizontal dipole antenna and including a vertical dipole antenna having the characteristic of vertical polarization, wherein the horizontal dipole antenna and the vertical dipole antenna may both have the characteristic of circular polarization. The reader antenna can be easily stored in an electronic shelf because it is realized in an ultrathin structure using a single-layered dielectric substrate.

Abstract: Disclosed herein are a tag transmission apparatus and a signal transmitting method thereof. The tag transmission apparatus converts a plurality of data bits into a symbol, which is one of a plurality of symbols, and multiplies the converted symbol by a square-wave having a predetermined frequency to thereby generate a subcarrier signal. Here, in the case in which the plurality of data bits are n (n is a natural number larger than 2) bits, the number of the plurality of symbols is 2n.