Abstract: Disclosed is an artificial intelligence apparatus, which includes an episode conversion module that receives an electronic medical record (EMR) of a patient and converts the received EMR into an episode including a condition of the patient, a treatment method, and a treatment history, a patient condition predictive intelligence deep learning module that trains a patient condition predictive intelligence for predicting a following condition of the patient after applying the treatment method, a local policy intelligence reinforcement learning module that performs reinforcement learning of a policy intelligence for planning an optimized treatment path for the patient based on the episode, an optimized treatment path exploration module that plans the optimized treatment path for the patient by using the policy intelligence, and a global policy intelligence management module that updates a global policy intelligence for planning and exploring the optimized treatment path based on the policy intelligence.

Abstract: Provided is a multi-port gas flow rate control apparatus. The multi-port gas flow rate control apparatus includes a gas supply chamber configured to supply a measurement gas input through one gas inflow channel while allowing the measurement gas to diverge into a plurality of flows, a plurality of gas divergence flow channels each having one side connected to the gas supply chamber and configured to transfer the measurement gas flowing through the gas supply chamber to a plurality of gas sensors, respectively, and a gas measurement chamber configured to accommodate the plurality of gas sensors, including the plurality of gas divergence flow channels configured to connect to the gas supply chamber to the plurality of gas sensors to transfer a gas outflow diverging through the gas supply chamber to the plurality of accommodated gas sensors, and configured to discharge the gas outflow sensed by the plurality of gas sensors.

Abstract: The image processing device includes a voxel extractor, a learner, and a predictor. The voxel extractor extracts a target voxel and neighboring voxels adjacent to the target voxel from a 3D image. The learner generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates vector weights corresponding to each of the vectors, based on the vectors and a parameter group, and adjusts the parameter group, based on an analysis result of the target voxel generated by applying the vector weights to the vectors. The predictor generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates correlation weights among the vectors by applying a parameter group to the vectors, generates vector weights corresponding to each of the vectors by applying the correlation weights to the vectors, and generates an analysis result of the target voxel by applying the vector weights to the vectors.

Abstract: A multi-measurement apparatus using a search-coil type sensor includes: sensor modules, each of which has one or more sensors including respective housings having respective inner spaces, respective cores formed to be inserted into the inner spaces of the housings, and respective coils which are each wound around a portion of an outer circumferential surface of each of the housings, the portion corresponding to a position of each of the cores; impedance matching units that are connected to a plurality of the sensors, respectively, and perform impedance matching; and amplifiers that are connected to the impedance matching units, respectively, and amplify a fine current and voltage generated during approach of objects to the sensors.

Abstract: A metal detecting system using search-coil type sensor includes sensor modules that detect respective objects which move around the corresponding sensor modules, each sensor module having one or more sensors that include respective housings having respective inner spaces, respective cores formed to be inserted into the inner spaces of the housings, and respective coils which are each wound around a portion of an outer circumferential surface of each of the housings, the portion corresponding to a position of each of the cores; imaging units that image the respective objects that move around the corresponding sensor modules and image respective persons possessing the respective objects; impedance matching units that are connected to a plurality of the sensor modules, respectively, and perform impedance matching; and amplifiers that are connected to the impedance matching units, respectively, and amplify a fine current and voltage generated during approach of the objects to the sensor modules.

Abstract: An apparatus for correcting non-polarity and measuring displacement of an object using a search-coil type sensor includes a plurality of sensors that include respective housings having respective inner spaces, respective cores formed to be inserted into the inner spaces of the housings, and respective coils which are each wound around a portion of an outer circumferential surface of each of the housings, the portion corresponding to a position of each of the cores. The plurality of sensors are arranged in parallel with each other. A position of a core and a coil with respect to one housing differs from a position of a core and a coil with respect to another housing. An induced magnetic field is formed due to a distance change with respect to the object containing iron (Fe).

Abstract: A steel material for a vacuum tube according to an aspect of the present disclosure may include C: 0.1˜0.2%, Si: 0.05?0.5%, Mn: 1.0?1.6%, Ni: 0.5?1.0%, Cr: 1.5?4.0%, and the balance of Fe and unavoidable impurities in percentage by weight, and may have a complex structure of ferrite and pearlite as a microstructure.

Abstract: Disclosed is an operation method of a health state prediction system which includes an ensemble prediction model. The operation method includes sending a prediction result request for health time-series data to a plurality of external medical support systems, receiving a plurality of external prediction results associated with the health time-series data from the plurality of external medical support systems, generating long-term time-series data and short-term time-series data for each of the health time-series data, and the plurality of external prediction results, extracting a plurality of long-term trends based on the long-term time-series data, extracting a plurality of short-term trends based on the short-term time-series data, calculating external prediction goodness-of-fit based on the plurality of long-term trends and the plurality of short-term trends, and generating an ensemble prediction result based on the external prediction goodness-of-fit and the plurality of external prediction results.

Abstract: Disclosed is a time series data processing device which includes a pre-processor that performs pre-processing on time series data to generate pre-processing data, and a learner that creates or updates a feature model through machine learning for the pre-processing data. The learner includes a time series irregularity learning model that learns time series irregularity of the pre-processing data, and a feature irregularity learning model that learns feature irregularity of the pre-processing data.

Abstract: An NFC device that receive a data frame with a start pattern including first and second pattern signals, and a data pattern corresponding to the start pattern. The NFC device comprising an analog-to-digital converter which generates first and second input signals based on the first and second pattern signals, respectively, a modem that includes a first sub-matched filter which multiplies a first match signal by the first and second input signals to respectively calculate first and second result values of the first match signal and multiplies a second match signal by the first and second input signals to respectively calculate first and second result values of the second match signal. The first sub-matched filter determines reception of the start pattern when the first and second result values of the first match signal or the first and second result values of the second match signal exceed a predetermined start pattern threshold.

Abstract: Disclosed is a data processing device that processes unbalanced data, which includes a preprocessor that calculates a reference value based on a plurality of training data and target data, and a learner that applies the plurality of training data to a first weight model to generate first prediction data, calculates a loss value based on a first distance between the target data and the reference value and a second distance between the target data and the first prediction data, and updates the first weight model based on the calculated loss value, and the plurality of training data and the target data have an unbalanced distribution.

Abstract: Disclosed are a device and a method for anomaly detection of a gas sensor. The device includes a measuring unit that extracts a characteristic of a gas supplied from the outside, generates data based on the extracted characteristic, and outputs the data, and a data processing unit that receives the data, determines whether an error occurs in the data, and outputs an anomaly detection result based on a result of determining whether the error occurs in the data. The measuring unit performs a calibration operation or an environment adjusting operation before extracting the characteristic, and the data processing unit determines whether the error occurs in the data, based on machine learning.

Abstract: Disclosed are a gas detection intelligence training system and an operating method thereof. The gas detection intelligence training system includes a mixing gas measuring device that collects an environmental gas from a surrounding environment, generates a mixing gas based on the collected environmental gas and a target gas, senses the mixing gas by using a first sensor array and a second sensor array under a first sensing condition and a second sensing condition, respectively, and generates measurement data based on the sensed results of the first sensor array and the second sensor array, and a detection intelligence training device including a processor that generates an ensemble prediction model based on the measurement data.

Abstract: Disclosed is a time-series data processing device that includes a preprocessor, a learner, and a predictor. The preprocessor generates time-series interval data based on a time interval of time-series data, generates feature interval data based on a time interval of each of features of the time-series data, and preprocesses the time-series data. The learner generates a weight group of a prediction model for generating a prediction result based on the time-series interval data, the feature interval data, and the preprocessed time-series data. The predictor generates a time-series weight, which depends on a feature weight of each of the features and a time flow of the time-series data, based on the time-series interval data, the feature interval data, and the preprocessed time-series data and generates a prediction result based on the feature weight and the time-series weight.

Abstract: Provided is a dehumidifier capable of preventing condensation from flowing to the outside of a water container when the dehumidifier is turned over or while the dehumidifier is moved, since a suction structure for suctioning the condensation and a discharge structure for discharging the condensation are improved.

Abstract: Disclosed are a time series data processing device and an operating method thereof. The time series data processing device includes a preprocessor, a learner, and a predictor. The preprocessor generates preprocessed data and interval data. The learner may adjust a feature weight, a time series weight, and a weight group of a feature distribution model for generating a prediction distribution, based on the interval data and the preprocessed data. The predictor may generate a feature weight, based on the interval data and the preprocessed data, may generate a time series weight, based on the feature weight and the interval data, and may calculate a prediction result and a reliability of the prediction result, based on the time series weight.

Abstract: The image processing device includes a voxel extractor, a learner, and a predictor. The voxel extractor extracts a target voxel and neighboring voxels adjacent to the target voxel from a 3D image. The learner generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates vector weights corresponding to each of the vectors, based on the vectors and a parameter group, and adjusts the parameter group, based on an analysis result of the target voxel generated by applying the vector weights to the vectors. The predictor generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates correlation weights among the vectors by applying a parameter group to the vectors, generates vector weights corresponding to each of the vectors by applying the correlation weights to the vectors, and generates an analysis result of the target voxel by applying the vector weights to the vectors.

Abstract: Disclosed is a device which includes a data manager, a learner, and a predictor. The data manager generates output data based on time-series data, receives device prediction results corresponding to the output data from the prediction devices, and calculates device errors based on the difference between device prediction results and time-series data. The learner may adjust a parameter group of a prediction model for generating device weights, based on device prediction results and device errors. The predictor generates the ensemble result of first and second device prediction results based on device weights.

Abstract: An electromagnetic wave absorber includes a first layer and a second layer disposed on the first layer. The first layer may include a first reinforcing fiber impregnated in a first matrix. The second layer may include a second reinforcing fiber impregnated in a second matrix. The second reinforcing fiber may be plated with a metal magnetic coating layer. The electromagnetic wave absorber may provide electromagnetic wave absorbing ability with a low uncertainty without reducing mechanical properties due to the metal magnetic coating layer.

Abstract: The time series data processing device according to an embodiment of the inventive concept includes a preprocessor, a learner, and a predictor. The preprocessor preprocesses time series data to generate interval data, interpolation data, and masking data. The learner generates a weight value group of a prediction model that generates a feature weight value and a time series weight value, based on the interval data, the interpolation data, and the masking data. The feature weight value depends on a time and a feature of the time series data and the time series weight value depends on a time flow of the time series data. The predictor generates a feature weight value and a time series weight value, based on the weight value group, and generates a prediction result, based on the feature weight value and time series weight value.