Abstract: A deep learning-based coloring system includes a memory network configured to provide a color feature in response to a specific query and a coloring network configured to perform coloring, based on the color feature generated by the memory network. The memory network includes: a query generation unit configured to generate a query; a neighbor calculation unit configured to calculate k-nearest neighbors, based on similarities between the query and key memory values; a color feature determination unit configured to generate color features for indicating color information stored in the key memory; a threshold triplet loss calculation unit configured to calculate a threshold triplet loss, based on a comparison between a threshold and a distance between the color features; and a memory update unit configured to update a memory, based on whether a distance between a top value and a value of a newly input query is within the threshold.

Abstract: Disclosed are a system and method for automating the design of a sound source separation deep learning model. A method of automating a design of a sound source separation deep learning model, which is performed by a design automation system, may include automatically searching for a combination of hyper parameters of a separation model constructed in a sound source separation deep learning model by using a neural architecture search (NAS) algorithm and reconstructing the sound source separation deep learning model based on the retrieved combination of the hyper parameters of the separation model.

Abstract: A method of automatically creating an artificial intelligence (AI) diagnostic model for diagnosing an abnormal state of a vehicle includes: acquiring noise and vibration data measured by a sensor of the vehicle as input data, processing the input data, searching and selecting an architecture of the AI diagnostic model based on the processed input data, and providing the AI diagnostic model to diagnose the abnormal state of the vehicle, where an efficient neural architecture search (ENAS) is applied to update the AI diagnostic model and a parameter configuring the AI diagnostic model, the ENAS sharing the parameter with the updated AI diagnostic model.

Abstract: A method for rearranging webtoon content includes obtaining first content including a plurality of image cuts including a plurality of elements, extracting the plurality of elements included in the first content, generating a plurality of image cut layers by reconstructing the extracted plurality of elements, and generating second content by arranging the generated plurality of image cut layers in a designated arrangement.

Abstract: An apparatus for estimating blood pressure includes: a photoplethysmogram (PPG) sensor configured to measure a PPG signal from an object; a force sensor configured to measure a force signal acting between the object and the PPG sensor; and a processor configured to (i) divide a predetermined blood pressure range into a plurality of classes, (ii) input the measured PPG signal and the measured force signal into a blood pressure estimation model to obtain the probability values for each of the classes, and (iii) estimate blood pressure based on the obtained probability values for the respective classes.

Abstract: According to an aspect, a WPE-based dereverberation apparatus using virtual acoustic channel expansion based on a deep neural network includes a signal reception unit for receiving as input a first speech signal through a single channel microphone, a signal generation unit for generating a second speech signal by applying a virtual acoustic channel expansion algorithm based on a deep neural network to the first speech signal and a dereverberation unit for removing reverberation of the first speech signal and generating a dereverberated signal from which the reverberation has been removed by applying a dual-channel weighted prediction error (WPE) algorithm based on a deep neural network to the first speech signal and the second speech signal.

Abstract: Proposed are a speech recognition system and method for automatically calibrating a data label. A speech recognition method for automatically calibrating a data label according to an embodiment may comprise the steps of: performing confidence-based filtering to find the location of occurrence of a wrong label in time-series speech data, in which a correct label and the wrong label are temporally mixed, by using a transformer-based speech recognition model; and after performing filtering, replacing a label at a decoder time step, which has been determined to be a wrong label by the location of occurrence of the wrong label, so as to improve the performance of the transformer-based speech recognition model, wherein the step of performing confidence-based filtering to find the location of occurrence of the wrong label in the time-series speech data comprises finding and calibrating the wrong label using the confidence obtained by using a transition probability between labels at every decoder time step.

Abstract: A vehicle judder diagnostic method using artificial intelligence applied to a mobile-based GDS according to the present disclosure is characterized in that the mobile-based GDS samples a plurality of sensor signals of a sensor mounted in a vehicle in a vehicle during operation in a judder evaluation mode to quickly, separately diagnose whether the judder phenomenon of the vehicle is a geometric judder or a friction judder by mounting a deep neural network (DNN) model, developed by the trial and error process of a DNN by using the plurality of sensor signals of a test vehicle mounted with a double clutch transmission (DCT), as a judder determination artificial intelligence model 30 in the mobile-based GDS.

Abstract: The present invention relates to a display apparatus that allows a compensated data voltage to be supplied to each pixel by compensating for the data voltage so as to prevent burn-in from occurring in a display panel, a method for compensating a data signal thereof, and a method for generating a deep learning-based compensation model. To implement same, the present invention provides the display apparatus comprising a timing controller having mounted therein the compensation model generated by learning, in a deep learning method, the temperature, time, average brightness, and data voltage for each pixel. Accordingly, the present invention has an effect of preventing burn-in from occurring in each pixel by supplying each pixel with the compensated data voltage generated via the compensation model.

Abstract: A device for AI-based vehicle diagnosis using CAN data may include an engine; a vibration sensor mounted in an engine compartment in which the engine is mounted and configured for detecting a vibration signal; and a controller area network (CAN) communicating with one or more of an environmental condition, a vehicle status, an engine status, and an engine control parameter, wherein data preprocessing from the vibration sensor and the CAN is performed to determine features in which correlation between vibration data (dB) exceeding a threshold value of irregular vibrations being generated by the engine and the CAN data is equal to or greater than 90%.

Abstract: A speech recognition model generating device for generating an E2E speech recognition model using calibration correction comprising an acoustic model including a first artificial neural network module using a speech information as input information and using a first text information corresponding to the speech information as output information, a language model comprising a second artificial neural network module using the first text information as input information and outputting a second text information corresponding to the first text information as output information based on the characteristics of the language model, and a E2E speech model generating unit generating a coupling probability distribution based on a first probability distribution information of the acoustic model output by the acoustic model and a second probability distribution information of the language model output by the language model, and generating a E2E speech model based on the coupling probability distribution, wherein the E2E spe

Abstract: A swash plate compressor including: a housing; a rotating shaft rotatably mounted to the housing; a swash plate accommodated in a crank chamber of the housing and rotating with the rotating shaft; a piston forming a compression chamber with the housing and interlocking with the swash plate to reciprocate; a discharge flow path guiding a refrigerant of the crank chamber to a suction chamber of the housing wherein an inclination angle of the swash plate is adjusted; and a discharge flow path control valve having a valve chamber provided in the discharge flow path and a valve core reciprocating inside the valve chamber, and the valve core includes: a first communication path constantly communicating the discharge flow path; and a second communication path communicating the discharge flow path when differential pressure between pressure of the crank chamber and pressure of the suction chamber is within a certain pressure range.

Abstract: Various embodiments relate to a computing device for attention-based joint training with a noise suppression model for a sound event detection (SED) technology that is robust against a noise environment and a method thereof, and are intended to improve SED performance that is robust against a noise environment by using a joint model in which a noise suppression model and an SED model have been jointed. According to various embodiments, an attention scheme and a weight freezing scheme are added to the joint model.

Abstract: According to an aspect, method for synthesizing multi-speaker speech using an artificial neural network comprises generating and storing a speech learning model for a plurality of users by subjecting a synthetic artificial neural network of a speech synthesis model to learning, based on speech data of the plurality of users, generating speaker vectors for a new user who has not been learned and the plurality of users who have already been learned by using a speaker recognition model, determining a speaker vector having the most similar relationship with the speaker vector of the new user according to preset criteria out of the speaker vectors of the plurality of users who have already been learned, and generating and learning a speaker embedding of the new user by subjecting the synthetic artificial neural network of the speech synthesis model to learning, by using a value of a speaker embedding of a user for the determined speaker vector as an initial value and based on speaker data of the new user.

Abstract: According to an aspect, a WPE-based dereverberation apparatus using virtual acoustic channel expansion based on a deep neural network includes a signal reception unit for receiving as input a first speech signal through a single channel microphone, a signal generation unit for generating a second speech signal by applying a virtual acoustic channel expansion algorithm based on a deep neural network to the first speech signal and a dereverberation unit for removing reverberation of the first speech signal and generating a dereverberated signal from which the reverberation has been removed by applying a dual-channel weighted prediction error (WPE) algorithm based on a deep neural network to the first speech signal and the second speech signal.

Abstract: An article transfer system includes a plurality of stage modules respectively provided at a plurality of layers, an interlayer transfer apparatus configured to transfer an article to each of the plurality of stage modules, and a plurality of loading and unloading apparatus respectively provided on each stage module of the plurality of stage modules, the plurality of loading and unloading apparatus configured to load an article onto respective stage modules of the plurality of stage modules. The interlayer transfer apparatus includes a mast frame extending so as to intersect each stage module of the plurality of stage modules, and a plurality of carriage units configured to move along a length direction of the mast frame, transfer articles, and move in a parallel manner with each other.

Abstract: According to an aspect, a method of synthesizing a multi-speaker speech using an artificial neural network, the method comprises generating a speech learning model for a plurality of users based on speech data of the plurality of users, generating a first speaker vector for speech data of a new speaker and a plurality of second speaker vectors for speech data of the plurality of users using a speaker recognition model, determining a third speaker vector having a highest correlation with the first speaker vector among the plurality of second speaker vectors based on a present criterion and predicting a new speaker vector of the new user based on the third speaker vector and the first speaker vector using an adversarial training method.

Abstract: An apparatus for estimating bio-information is provided. According to an embodiment of the present disclosure, the apparatus for estimating bio-information includes: a pulse wave sensor having a plurality of channels to measure a plurality of pulse wave signals from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the pulse wave sensor; and a processor configured to: obtain a first feature for each channel by inputting the plurality of pulse wave signals for each channel and the force signal, into a first neural network model; obtain a weight for each channel by inputting the first feature to a second neural network model; obtain a second feature by applying the weight to the first feature for each channel by using the second neural network model; and obtain bio-information by inputting the second feature to a third neural network model.

Abstract: A noise data artificial intelligence learning method for identifying the source of problematic noise may include a noise data pre-conditioning method for identifying the source of problematic noise including: selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, wherein an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

Abstract: An apparatus for estimating bio-information is disclosed. The apparatus may include: a pulse wave sensor configured to measure a pulse wave signal from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the force sensor; and a processor configured to obtain a first input value, a second input value, and a third input value based on the pulse wave signal and the force signal, to extract a feature vector by inputting the first input value, the second input value, and the third input value into a first neural network model, and to obtain the bio-information by inputting the feature vector into a second neural network model.