Abstract: A neural-network-based-implemented ophthalmologic intelligent consultation method includes: performing correction filtering on a consultation voice of a patient, framing the voice into a consultation voice frame sequence, generating a consultation text corresponding to the consultation voice frame sequence based on phoneme recognition and phoneme transcoding, and extracting an ophthalmologically-described disease; performing gray-level filtering, primary picture segmentation, and size equalization operation on an eye picture set of the to-be-diagnosed patient to acquire a standard eyeball picture group; extracting eye white features, pupil features and blood vessel features from the standard eyeball picture group, performing lesion feature analysis on the eye white features, the pupil features and the blood vessel features to acquire an ophthalmologically-observed disease, and based on the ophthalmologically-observed disease and the ophthalmologically-described disease, generating a consultation result.

Abstract: A battery cell may include an electrode assembly and a buffer pad, where the electrode assembly may include a main body portion and a tab extending from the main body portion, the main body portion may include a first main body region and a second main body region, and the thickness of the first main body region along a first direction (T) may be smaller than the thickness of the second main body region along the first direction (T); and where the buffer pad may include a compressible first side wall, the first side wall may include a first buffer portion and a second buffer portion, the thickness of the first buffer portion along the first direction (T) may be greater than the thickness of the second buffer portion along the first direction, and the buffer pad may be provided on a side face of the main body portion.

Abstract: A method and a system for thermal infrared facial recognition are provided. The method includes: determining a thermal infrared facial image to be recognized; inputting the thermal infrared facial image to be recognized into a trained three-sense component extractor to position eyes, a nose, and a mouth of a face in the image, and to perform regional positioning of a left eye, a right eye, the nose, and the mouth, so as to constitute a corresponding key region group; performing affine transformation on the thermal infrared facial image to be recognized using a difference between a key region group of a standard face and the key region group of the face to be recognized; inputting a thermal infrared image after face alignment calibration into a trained facial feature extraction to extract corresponding facial features; and inputting the extracted facial features into a trained classifier to recognize information of an owner.

Abstract: A data analyzing device, a data analyzing method, and a quality improvement system are provided. The data analyzing device includes a combination generating module, a ratio calculation module, and a weighting calculation module. The data analyzing device performs relevance analysis of to-be-analyzed data associated with a production operation procedure to generate relevance information. The ratio calculation module calculates a ratio parameter according to a total outlier product quantity corresponding to risky combinations in the relevance information and a total product quantity in the to-be-analyzed data. The weighting calculation module calculates a weighting parameter according to a number of the risky combinations in the relevance information and a total number of combinations of production control factors in the to-be-analyzed data. A production equipment selectively adjusts settings of the production operation procedure according to the ratio parameter and the weighting parameter.

Abstract: An apparatus and method for detecting impact factors for an operating environment. The apparatus generates a detection result for each of the first factors of a plurality of first historical records by analyzing a dissimilarity degree of the plurality of first data corresponding to each first factor. Each detection result is a continuous data type or a discrete data type. The apparatus trains a data type recognition model according to the first historical records and the detection results. The apparatus establishes a basic prediction model by a training set of a plurality of second historical records, generates a comparison set by rearranging the second data corresponding to a specific factor in the training set, establishes a comparison prediction model by the comparison set, and determines a degree of importance of the specific factor by comparing the accuracies of the basic prediction model and the comparison prediction model.

Abstract: An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.

Abstract: An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.