Abstract: An anode active material and a high-capacity secondary battery for high speed charging including the same are described. When an anode active material including a composite material in which a high-capacity silicon-based anode active material and a graphite active material capable of enabling high-speed charging by increasing the interlayer distance are mixed is used as an anode, the high-speed charging performance of a secondary battery can be improved and the capacity thereof can be increased, and a secondary battery that can be mounted on small and medium-sized electronic devices such as portable phones and the like, various electric mobilities including commercial electric vehicles, and energy storage systems (ESS) can be provided.

Abstract: In one example, a semiconductor device can comprise a substrate, a device stack, first and second internal interconnects, and an encapsulant. The substrate can comprise a first and second substrate sides opposite each other, a substrate outer sidewall between the first substrate side and the second substrate side, and a substrate inner sidewall defining a cavity between the first substrate side and the second substrate side. The device stack can be in the cavity and can comprise a first electronic device, and a second electronic device stacked on the first electronic device. The first internal interconnect can be coupled to the substrate and the device stack. The encapsulant can cover the substrate inner sidewall and the device stack and can fill the cavity. Other examples and related methods are disclosed herein.

Abstract: The present invention relates to a compound represented by Formula 1, a pharmaceutically acceptable salt thereof, or an isomer thereof, and a pharmaceutical composition for treating or preventing proteasome-mediated diseases, comprising the same as an active ingredient.

Abstract: The present invention relates to an amorphous compound represented by formula 1, a method for preparing the same, and a pharmaceutical composition comprising the same. The amorphous compound represented by formula 1 of the present invention may be characterized by XPRD patterns, DSC profiles, and/or a NMR spectrum.

Abstract: An automatic noise signal interval detection method and device are provided, and the method includes receiving input data and generating initial data in a time-frequency domain, calculating power for each epoch for each channel of the initial data, generating a power graph for a specific frequency region of each channel based on the power for each epoch, generating a baseline based on an average of each channel value on the power graph, and determining, as a noise signal interval, an interval exceeding a predetermined threshold based on the baseline on the power graph.

Abstract: The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the update

Abstract: The present disclosure relates to an EEG measuring device which is in close contact with a head of a subject while having an adjustable size to fit the head of the subject.

Abstract: An automatic noise signal interval detection method and device are provided, and the method includes receiving input data and generating initial data in a time-frequency domain, calculating power for each epoch for each channel of the initial data, generating a power graph for a specific frequency region of each channel based on the power for each epoch, generating a baseline based on an average of each channel value on the power graph, and determining, as a noise signal interval, an interval exceeding a predetermined threshold based on the baseline on the power graph.

Abstract: The present disclosure relates to an EEG measuring device which is in close contact with a head of a subject while having an adjustable size to fit the head of the subject.

Abstract: Provided is a cognitive impairment diagnosis method including: receiving an electroencephalogram signal of a user by a cognitive impairment diagnosis device; preprocessing the electroencephalogram signal by the cognitive impairment diagnosis device; extracting features, by the cognitive impairment diagnosis device, from the electroencephalogram signal by using a brain connectivity-based analysis method; and outputting cognitive impairment diagnosis information of the user by the cognitive impairment diagnosis device, on the basis of features having a causal relationship with the cognitive impairment diagnosis information, among the extracted features.

Abstract: Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.

Abstract: The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the update

Abstract: Provided are a method, apparatus, and computer program for creating a standardized brainwave image for training an artificial intelligence model. The method of creating a standardized brainwave image for training an artificial intelligence model executed by a computing device according to various embodiments of the present invention includes collecting a plurality of brainwave signals of a user, processing the plurality of collected brainwave signals, and creating a brainwave image using the plurality of processed brainwave signals.

Abstract: Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.

Abstract: The present invention relates to a compound exhibiting excellent agonist activity against melanocortin receptors. More specifically, the present invention relates to a compound of Formula 1, a pharmaceutical composition comprising the compound as an active ingredient, and a use thereof, and the compound of the present invention exhibits excellent agonist activity against melacortin-4 receptors and can be particularly useful in preventing or treating obesity, diabetes, inflammation and erectile dysfunction.

Abstract: The present disclosure relates to an EEG measuring device which is in close contact with a head of a subject while having an adjustable size to fit the head of the subject.

Abstract: The present invention relates to a biaryl derivative expressed by the chemical formula 1, a method for producing the biaryl derivative, a pharmaceutical composition comprising same, and use of same, the biaryl derivative expressed by the chemical formula 1, as a GPR120 agonist, promoting GLP-1 generation in the gastro-intestinal tract, reducing insulin resistance in the liver, muscles and the like from anti-inflammatory activity in the macrophage, pancreatic cells and the like, and allowing effective use in prevention or treatment of inflammation or metabolic diseases such as diabetes, complications from diabetes, obesity, non-alcoholic fatty liver disease, fatty liver disease, and osteoporosis.

Abstract: Compounds having the chemical formula 1, a method for producing the compounds of chemical formula 1, a pharmaceutical composition comprising same, and use thereof as a GPR120 agonist for prevention or treatment of inflammation or metabolic diseases such as diabetes, complications from diabetes, obesity, non-alcoholic fatty liver disease, fatty liver disease, and osteoporosis.

Abstract: Disclosed is a computer program stored in a computer-readable storage medium for achieving the above-described objects. When the computer program is executed by one or more processors, the computer program causes the one or more processors to perform the following operations of generating video data for diagnosing a health condition and a pathological symptom on the basis of a biosignal. The operations include receiving a biosignal of a user, preprocessing the biosignal to generate a plurality of pieces of video sub-data, and selecting at least two of the plurality of pieces of video sub-data to generate one or more pieces of video data.

Abstract: A bio information measurement device and a bio information measurement method are provided. The bio information measurement device includes: a heart information acquisition unit for acquiring heart information; a brainwave information acquisition unit for acquiring brainwave information; a control unit for acquiring heart-brain synchronization information on the basis of the heart information and the brainwave information; and a display unit for displaying the heart-brain synchronization information. According to the present invention, it is possible to provide a health index reflecting the influence of both the automatic nervous system and the central nervous system.