Abstract: A display assembly includes a display panel, a support plate disposed at a non-display surface of the display panel, and an adhesive layer for bonding the display panel and the support plate together. The display panel includes at least a bending area and non-bending areas disposed at two sides of the bending area. The support plate is disposed at the non-bending areas. The adhesive layer includes a first adhesive layer disposed at the non-bending areas and between the display panel and the support plate and a second adhesive layer disposed at the bending area and connected to the first adhesive layer. At least a portion of the bending area is not covered by the adhesive layer.

Abstract: A driving circuit, a display panel, and a display device are disclosed. In the driving circuit, the display panel, and the display device disclosed in the present disclosure, the gamma module outputs the first initialization voltage and the second initialization voltage when the display panel is in the internal compensation mode, and the gamma module outputs the third initialization voltage and the reference voltage when the display panel is in the detection mode.

Abstract: A sensing circuit and a detection method of a sensing signal are provided. The detection method of the sensing signal includes in a display cycle of a first frame, reading a signal of a display panel to obtain a first sensing signal; in a display cycle of a second frame, reading the signal of the display panel to obtain a second sensing signal, wherein a polarity of a display voltage corresponding to the display cycle of the second frame and a polarity of a display voltage corresponding to the display cycle of the first frame are opposite to each other; and producing a target sensing signal according to the first sensing signal and the second sensing signal.

Abstract: There is provided methods, systems and techniques for optimized anomaly prediction using machine learning. A data set is obtained which corresponds to a query for anomaly detection. Feature classification is performed along with anomaly labelling using an unsupervised clustering technique based on determining similar groups of data and behaviours and determining a distribution for a particular feature of interest in each cluster such as to apply a threshold to each cluster to extract the anomaly data and label same. Once the labelled dataset is generated, a tree classification model is trained based on the labelled data set for detecting anomaly. Once trained, a set of computing model rules may be extracted from the tree classification model to generate a rules executable for anomaly spotting to define combinations of feature characteristics resulting in outlier data so that the rules executable may be applied to new data.