Abstract: Methods and apparatuses for adjusting and controlling conditions within the environment of a workpiece handling module include sensors for detecting humidity and concentration of harmful gasses. Signals generated by these sensors are utilized to generate signals that control the flow of air into the environment and/or the flow of air and gases out of the environment. By controlling the humidity, negative impacts on processes carried out in the environment are avoided. By controlling the gas concentration, exposure of workers to harmful gases is avoided.

Abstract: An external power supply system for spindles is revealed. The external power supply system includes a tool holder, a rectifier circuit, an overvoltage protection circuit. and a buck/boost converter. The tool holder receives an external power source of a spindle while the rectifier circuit converts the external power source into a rectified output signal with a power factor through step-down transformation. The overvoltage protection circuit is used to check whether the rectified output signal is larger than an overvoltage signal for outputting an operating potential or a non-operating potential. The buck/boost converter is used for receiving the rectified output signal with the power factor and converting the rectified output signal to an output voltage according to the power factor. Then the output voltage is provided to a load of a low voltage power supply, a high voltage power supply, or a constant voltage power supply.

Abstract: A method and a system for establishing a light source information prediction model are provided. A plurality of training images are captured for a target object. A white object is attached on the target object. True light source information of the training images is obtained according to a color of the white object in each of the training images. A neural network model is trained according to the training images and the true light source information, and a plurality of pieces of predicted light source information is generated according to the neural network model during the training. A learning rate for training the neural network model is adaptively adjusted based on the predicted light source information.

Abstract: A method for estimating a ventricular volume is provided and includes: obtaining a left ventricular mask image corresponding to a heart ultrasound image, where the left ventricular mask image is a binary image; finding 3 reference point pixels in the left ventricular mask image, where each of the reference point pixels has a first value, each of the reference point pixels is surrounded by N surrounding pixels, and the surrounding pixels of each of the reference point pixels include N1 first surrounding pixels with the first value and N2 second surrounding pixels with a second value; estimating a left ventricular volume corresponding to the heart ultrasound image based on the reference point pixels.

Abstract: A method for evaluating a movement state of a heart is provided and includes: obtaining a plurality of consecutive heart ultrasound images corresponding to a heart and accordingly estimating a plurality of left ventricular volumes corresponding to the heart ultrasound images; finding a plurality of specific extremums in the left ventricular volumes and accordingly estimating a plurality of time differences among the specific extremums; estimating a statistical characteristic value of the time differences based on the time differences; and determining that an abnormal movement state of the heart occurs in response to determining that at least one of the time differences deviates from the statistical characteristic value up to a predetermined threshold.

Abstract: An image processing apparatus for evaluating cardiac images and a ventricular status identification method are provided. In the method, a region of interest (ROI) is determined from multiple target images, a variation in grayscale values of multiple pixels in the ROIs of each target image is determined, and one or more representative images are obtained according to the variation in the grayscale values. The target image is related to the pixels within an endocardial contour of a left ventricle. A boundary of the ROI is approximately located at two sides of a bottom of the endocardial contour. The ROI corresponds to a mitral valve. The variation in the grayscale values is related to a motion of the mitral valve. The representative image is for evaluating a status of the left ventricle.

Abstract: An image processing apparatus for evaluating cardiac images and a ventricular status identification method are provided. In the method, a region of interest (ROI) is determined from multiple target images, a variation in grayscale values of multiple pixels in the ROIs of each target image is determined, and one or more representative images are obtained according to the variation in the grayscale values. The target image is related to the pixels within an endocardial contour of a left ventricle. A boundary of the ROI is approximately located at two sides of a bottom of the endocardial contour. The ROI corresponds to a mitral valve. The variation in the grayscale values is related to a motion of the mitral valve. The representative image is for evaluating a status of the left ventricle.

Abstract: The disclosure provides an eye state assessment method and an electronic device. The method includes: obtaining an optic disc image area from a first fundus photography and generating multiple optic cup-to-disc ratio assessment results by multiple first models based on the optic disc image area; obtaining a first assessment result of an eye based on the optic cup-to-disc ratio assessment results; performing multiple data augmentation operations on the first fundus photography to generate multiple second fundus photographies; generating multiple retinal nerve fiber layer (RNFL) defect assessment results by multiple second models based on the second fundus photographies; obtaining a second assessment result of the eye based on the RNFL defect assessment results; and obtaining an optic nerve assessment result of the eye based on the first assessment result and the second assessment result.

Abstract: A method and a system for establishing a light source information prediction model are provided. A plurality of training images are captured for a target object. A white object is attached on the target object. True light source information of the training images is obtained according to a color of the white object in each of the training images. A neural network model is trained according to the training images and the true light source information, and a plurality of pieces of predicted light source information is generated according to the neural network model during the training. A learning rate for training the neural network model is adaptively adjusted based on the predicted light source information.

Abstract: A method and a system for establishing a light source information prediction model are provided. A plurality of training images are captured for a target object. A white object is attached on the target object. True light source information of the training images is obtained according to a color of the white object in each of the training images. A neural network model is trained according to the training images and the true light source information, and a plurality of pieces of predicted light source information is generated according to the neural network model during the training. A learning rate for training the neural network model is adaptively adjusted based on the predicted light source information.

Abstract: A method for evaluating a movement state of a heart is provided and includes: obtaining a plurality of consecutive heart ultrasound images corresponding to a heart and accordingly estimating a plurality of left ventricular volumes corresponding to the heart ultrasound images; finding a plurality of specific extremums in the left ventricular volumes and accordingly estimating a plurality of time differences among the specific extremums; estimating a statistical characteristic value of the time differences based on the time differences; and determining that an abnormal movement state of the heart occurs in response to determining that at least one of the time differences deviates from the statistical characteristic value up to a predetermined threshold.

Abstract: An electronic device and a method for determining the intensity of a low-frequency current are provided. The method includes: individually applying a corresponding first current to a body part of a user in N consecutive time intervals, wherein the time intervals include an i-th time interval to an (i+N)-th time interval; obtaining electromyography values of the body part in each time interval; determining a second current corresponding to an (i+N+1)-th time interval based on the first current corresponding to each time interval, the body part, personal information of the user, and the electromyography values of each time interval; and applying a second current to the body part of the user in the (i+N+1)-th time interval.

Abstract: A method for estimating a ventricular volume is provided and includes: obtaining a left ventricular mask image corresponding to a heart ultrasound image, where the left ventricular mask image is a binary image; finding 3 reference point pixels in the left ventricular mask image, where each of the reference point pixels has a first value, each of the reference point pixels is surrounded by N surrounding pixels, and the surrounding pixels of each of the reference point pixels include N1 first surrounding pixels with the first value and N2 second surrounding pixels with a second value; estimating a left ventricular volume corresponding to the heart ultrasound image based on the reference point pixels.

Abstract: An image processing apparatus for evaluating cardiac images and a ventricular status identification method are provided. In the method, a region of interest (ROI) is determined from multiple target images, a variation in grayscale values of multiple pixels in the ROIs of each target image is determined, and one or more representative images are obtained according to the variation in the grayscale values. The target image is related to the pixels within an endocardial contour of a left ventricle. A boundary of the ROI is approximately located at two sides of a bottom of the endocardial contour. The ROI corresponds to a mitral valve. The variation in the grayscale values is related to a motion of the mitral valve. The representative image is for evaluating a status of the left ventricle.

Abstract: A system of training behavior labeling model is provided. Specifically, a processing unit inputs each data of a training data set into a plurality of learning modules to establish a plurality of labeling models. The processing unit obtains a plurality of second labeling information corresponding to each data of a verification data set and generates a behavior labeling result according to the second labeling information corresponding to each data of the verification data set. The processing unit obtains a labeling change value according to the behavior labeling result and first labeling information corresponding to each data of the verification data set. The processing unit, if determining that the labeling change value is greater than a change threshold, updates the first labeling information according to the behavior labeling results, exchanges the training data set and the verification data set and reestablishes the labeling models.

Abstract: The present disclosure relates to a strategy to synthesize antimony- and zinc-doped tin oxide particles with tunable band gap characteristics. The methods yield stable and monodispersed particles with great control on uniformity of shape and size. The methods produce undoped and antimony and zinc-doped tin oxide stand-alone and core-shell particles, both nanoparticles and microparticles, as well as antimony and zinc-doped tin oxide shells for coating particles, including plasmonic core particles.

Abstract: A method for detecting abnormality adapted to detect abnormal operations of an operating system is provided. The method includes: calculating a safe range of usage of the operating system during one or more time periods according to a historical data stream; calculating abnormal ratios corresponding to the one or more time periods according to a current data stream and the safe range of usage; selecting one or more abnormal time periods from the one or more time periods according to a threshold and the abnormal ratios; calculating an abnormal indicator for each of the one or more abnormal time periods according to the historical data stream and the current data stream; and ranking the one or more abnormal time periods according to the abnormal indicator(s).

Abstract: A method and a system for establishing a light source information prediction model are provided. A plurality of training images are captured for a target object. A white object is attached on the target object. True light source information of the training images is obtained according to a color of the white object in each of the training images. A neural network model is trained according to the training images and the true light source information, and a plurality of pieces of predicted light source information is generated according to the neural network model during the training. A learning rate for training the neural network model is adaptively adjusted based on the predicted light source information.

Abstract: A system of training behavior labeling model is provided. Specifically, a processing unit inputs each data of a training data set into a plurality of learning modules to establish a plurality of labeling models. The processing unit obtains a plurality of second labeling information corresponding to each data of a verification data set and generates a behavior labeling result according to the second labeling information corresponding to each data of the verification data set. The processing unit obtains a labeling change value according to the behavior labeling result and first labeling information corresponding to each data of the verification data set. The processing unit, if determining that the labeling change value is greater than a change threshold, updates the first labeling information according to the behavior labeling results, exchanges the training data set and the verification data set and reestablishes the labeling models.

Abstract: A method for detecting abnormality adapted to detect abnormal operations of an operating system is provided. The method includes: calculating a safe range of usage of the operating system during one or more time periods according to a historical data stream; calculating abnormal ratios corresponding to the one or more time periods according to a current data stream and the safe range of usage; selecting one or more abnormal time periods from the one or more time periods according to a threshold and the abnormal ratios; calculating an abnormal indicator for each of the one or more abnormal time periods according to the historical data stream and the current data stream; and ranking the one or more abnormal time periods according to the abnormal indicator(s).