Abstract: An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

Abstract: Provided are an improved culture process for reducing unwanted culture byproducts when a target protein is produced in a glutamine-free cell culture medium, an improved culture process for producing a target protein in a glutamine-free cell culture medium, or an improved culture process for producing a target protein in a glutamine-free cell culture medium.

Abstract: Provided is a video playback system based on approval of a playback approver. A decryption key that can decrypt an encrypted and stored recorded video is generated only when video playback is approved by a playback approver using an appointed playback approval method and transmitted to the video recording device so that it is possible to prevent leakage of the recorded video with sensitive personal information.

Abstract: Disclosed herein method and apparatus for simultaneous video retrieval and alignment. According to an embodiment of the present disclosure, there is provided a method for retrieving a video. The method comprising: detecting a section of interest in a query video that is a retrieval request video; producing one or more frame-level descriptor and a video-level descriptors for the query video by using key frames within the detected section of interest; and retrieving a reference video corresponding to the query video based on the frame-level descriptor and the video-level descriptor for the query video and one or more frame-level descriptor and a video-level descriptor for each of reference videos stored in a database.

Abstract: An electronic device and method are disclosed herein. The electronic device includes a network interface and processor. The processor implements the method, including receiving a voice input through a network interface as transmitted from a first external device, including a request to execute a function using at least one application which is not indicated in the voice input, extracting a first text from the voice input by executing automatic speech recognition (ASR), when the at least one application is identified based on the first text, transmitting, through the network interface to the first external device, second data associated with the identified at least one application for display by the first external device, and when the at least one application is not identified based at least in part on the first text, reattempting identification of the at least one application by executing natural language understanding (NLU) on the first text.

Abstract: A method and apparatus for inspecting an aeronautical light using an aerial vehicle are disclosed. The method includes defining a first reference point based on a plurality of aeronautical lights installed on a runway surface, and defining a second reference point based on the first reference point, defining a flight path of the aerial vehicle taking off from the second reference point and heading to the first reference point, transmitting, to the ground, an image of the aeronautical lights photographed by the aerial vehicle, and inspecting and adjusting elevation angles of the aeronautical lights by analyzing the image of the aeronautical lights in real time.

Abstract: Methods of encoding and decoding a speech signal using a neural network model that recognizes sound sources, and encoding and decoding apparatuses for performing the methods are provided. A method of encoding a speech signal includes identifying an input signal for a plurality of sound sources; generating a latent signal by encoding the input signal; obtaining a plurality of sound source signals by separating the latent signal for each of the plurality of sound sources; determining a number of bits used for quantization of each of the plurality of sound source signals according to a type of each of the plurality of sound sources; quantizing each of the plurality of sound source signals based on the determined number of bits; and generating a bitstream by combining the plurality of quantized sound source signals.

Abstract: Disclosed is an apparatus and method for augmenting training data using a notch filter. The method may include obtaining original data, and obtaining training data having a modified frequency component from the original data by filtering the original data using a filter configured to remove a component of a predetermined frequency band.

Abstract: Disclosed are methods of encoding and decoding an audio signal, and an encoder and a decoder for performing the methods. The method of encoding an audio signal includes identifying an input signal corresponding to a low frequency band of the audio signal, windowing the input signal, generating a first latent vector by inputting the windowed input signal to a first encoding model, transforming the windowed input signal into a frequency domain, generating a second latent vector by inputting the transformed input signal to a second encoding model, generating a final latent vector by combining the first latent vector and the second latent vector, and generating a bitstream corresponding to the final latent vector.

Abstract: Provided is a method of optimizing parameter intervals of manufacturing processes based on prediction intervals. The method includes: collecting process data by applying an experiment design method to a target process; training a second-order polynomial regression model based on the collected process data; estimating importance values of each input variable with respect to each output variable using the second-order polynomial regression model; defining an objective function for process optimization based on the second-order polynomial regression model; optimizing each parameter value by applying an optimization algorithm to the defined objective function; and optimizing each parameter interval including the optimized parameter value in an input space using the prediction interval of the second-order polynomial regression model.

Abstract: A connector assembly comprises a first connector including a first connector body, and a shaft connected to the first connector body. A lever is rotatably connected to the shaft and is selectively fixable to the first connector body in a caught state. A second connector of the assembly includes a second connector body insertable into the first connector body, a releasing element defined on the second connector body for releasing the lever from the caught state, and a biasing element defined on the second connector body for rotating the lever after being released from the caught state.

Abstract: A supercharger for a vehicle includes: a first compression part to compress outside air; a second compression part to compress the outside air independently of the first compression part; a first valve module to selectively switch a path of the air compressed by the first compression part to a power source of the vehicle or the second compression part and to selectively guide or shut off the air compressed by the second compression part to the power source; and a second valve module to selectively shut off a flow of the air compressed by the first compression part into the second compression part or a flow of the outside air into the second compression part.

Abstract: A compressor including a shaft, a flange to support shaft, and a muffler to reduce noise generated in a compression chamber of the compressor. The flange including a plurality of noise reductions units each of which is formed to have a different volume and at least a portion of the noise reduction unit is covered by the muffler.

Abstract: Proposed is a non-electric water purifier in which a flow path provided between a water-purifying filter and a water outlet cock is replaced in order to minimize contamination of purified drinking water. The non-electric water purifier includes a main body having a door formed on one side thereof so as to open and close the main body and having and having a receiving space part defined therein, a connector which is coupled replaceably and rotatably to one side of the main body and incudes a raw water passage connected to a raw water inlet and a purified water passage through which purified water flows, at least one filter housing being received in the receiving space part, with a discharge outlet of the filter housing being coupled detachably to the connector, and the water outlet cock which is connected to a discharge opening of the connector and discharges purified water.

Abstract: An electric vehicle structure includes a drive system, a front lateral member and a rear upper lateral member. The front and rear lateral members are elongated in a lateral direction of a vehicle and spaced apart from each other in a forward-rearward direction of the vehicle with the drive system interposed therebetween. The vehicle structure further includes a rear lower lateral member elongated in the lateral direction of the vehicle and disposed beneath a motor of the drive system.

Abstract: An audio signal encoding method performed by an encoder includes identifying an audio signal of a time domain in units of a block, generating a combined block by combining i) a current original block of the audio signal and ii) a previous original block chronologically adjacent to the current original block, extracting a first residual signal of a frequency domain from the combined block using linear predictive coding of a time domain, overlapping chronologically adjacent first residual signals among first residual signals converted into a time domain, and quantizing a second residual signal of a time domain extracted from the overlapped first residual signal by converting the second residual signal of the time domain into a frequency domain using linear predictive coding of a frequency domain.

Abstract: Disclosed is an apparatus and method for encoding/decoding an audio signal using information of a previous frame. An audio signal encoding method includes: generating a current latent vector by reducing dimension of a current frame of an audio signal; generating a concatenation vector by concatenating a previous latent vector generated by reducing dimension of a previous frame of the audio signal with the current latent vector; and encoding and quantizing the concatenation vector.

Abstract: Disclosed are a method of encoding and decoding an audio signal and an encoder and a decoder performing the method. The method of encoding an audio signal includes identifying an input signal, and generating a bitstring of each encoding layer by applying, to the input signal, an encoding model including a plurality of successive encoding layers that encodes the input signal, in which a current encoding layer among the encoding layers is trained to generate a bitstring of the current encoding layer by encoding an encoded signal which is a signal encoded in a previous encoding layer and quantizing an encoded signal which is a signal encoded in the current encoding layer.

Abstract: An audio encoding/decoding apparatus and method using vector quantized residual error features are disclosed. An audio signal encoding method includes outputting a bitstream of a main codec by encoding an original signal, decoding the bitstream of the main codec, determining a residual error feature vector from a feature vector of a decoded signal and a feature vector of the original signal, and outputting a bitstream of additional information by encoding the residual error feature vector.