A method for presenting visual feedback includes receiving a steady-state visual evoked potential (SSVEP) signal extracted through an electroencephalogram (EEG) analysis of a user gazing at a visual stimulus of a specific frequency. The method also includes classifying the visual stimulus and generate a classification result based on the SSVEP signal. The method additionally includes disposing, on the visual stimulus, a visual feedback having a same frequency as the visual stimulus. The method further includes reflecting the classification result in the visual feedback in real time.

A method for presenting visual feedback includes receiving a steady-state visual evoked potential (SSVEP) signal extracted through an electroencephalogram (EEG) analysis of a user gazing at a visual stimulus of a specific frequency. The method also includes classifying the visual stimulus and generate a classification result based on the SSVEP signal. The method additionally includes disposing, on the visual stimulus, a visual feedback having a same frequency as the visual stimulus. The method further includes reflecting the classification result in the visual feedback in real time.

A fault detection apparatus and computer-implemented method for providing physically explainable fault information of a bearing built in a machine by a fault detection model is provided, including: obtaining sensor data measured at the bearing as input data relating to an input data domain and the fault detection model, mapping the measured sensor data from the input data domain to a selected data domain resulting in an augmented fault detection model which outputs augmented predicted failure value related to the selected data domain, wherein the selected data domain has a physical meaning to the fault of the bearing, performing a feature attribution on the augmented fault detection model quantifying an importance of at least one individual feature to the augmented failure value related to the selected data domain, and displaying the individual feature and the respective quantified importance in the selected data domain at a user interface.

A fault detection apparatus and computer-implemented method for providing physically explainable fault information of a bearing built in a machine by a fault detection model is provided, including: obtaining sensor data measured at the bearing as input data relating to an input data domain and the fault detection model, mapping the measured sensor data from the input data domain to a selected data domain resulting in an augmented fault detection model which outputs augmented predicted failure value related to the selected data domain, wherein the selected data domain has a physical meaning to the fault of the bearing, performing a feature attribution on the augmented fault detection model quantifying an importance of at least one individual feature to the augmented failure value related to the selected data domain, and displaying the individual feature and the respective quantified importance in the selected data domain at a user interface.

Method for contactless diagnosing power facility using artificial intelligence and signal processing technology and device using the same is disclosed. According to an embodiment of the present disclosure, a method of diagnosing a power facility using artificial intelligence comprises acquiring an ultrasonic signal of the power facility, generating a mel spectrogram based on the ultrasonic signal, wherein the mel spectrogram including time information, frequency information, and magnitude information corresponding to the ultrasonic signal, and being in a form of an image, inputting the mel spectrogram into a pre-trained diagnostic model and acquiring diagnostic data for the power facility based on the diagnostic model, wherein the diagnostic model is based on a convolution neural network algorithm.

Method for contactless diagnosing power facility using artificial intelligence and signal processing technology and device using the same is disclosed. According to an embodiment of the present disclosure, a method of diagnosing a power facility using artificial intelligence comprises acquiring an ultrasonic signal of the power facility, generating a mel spectrogram based on the ultrasonic signal, wherein the mel spectrogram including time information, frequency information, and magnitude information corresponding to the ultrasonic signal, and being in a form of an image, inputting the mel spectrogram into a pre-trained diagnostic model and acquiring diagnostic data for the power facility based on the diagnostic model, wherein the diagnostic model is based on a convolution neural network algorithm.

A computer-implemented system and method for iterative compound value creation management is disclosed. The method includes receiving organizational input data, normalizing and embedding the data, and aggregating the embedded features using a neural model to generate a composite value index. Recommended actions are generated based on the index, and feedback is simulated or collected in response to these actions. The system updates the state of the input data based on feedback and repeats the process for a predetermined number of iterations or until a convergence criterion is satisfied. The invention enables adaptive, data-driven decision-making and continuous improvement in organizational value creation through a modular, scalable software architecture.

A computer-implemented system and method for iterative compound value creation management is disclosed. The method includes receiving organizational input data, normalizing and embedding the data, and aggregating the embedded features using a neural model to generate a composite value index. Recommended actions are generated based on the index, and feedback is simulated or collected in response to these actions. The system updates the state of the input data based on feedback and repeats the process for a predetermined number of iterations or until a convergence criterion is satisfied. The invention enables adaptive, data-driven decision-making and continuous improvement in organizational value creation through a modular, scalable software architecture.