2results about How to "Flexible compensation" patented technology

The application discloses a linear voltage stabilizing circuit based on NMOS regulating tube, comprising a bias voltage generating circuit, a voltage division feedback circuit, an oscillator circuit, a charge pump circuit and a pre-voltage stabilizing circuit, wherein the bias voltage is generated through the bias voltage generating circuit; the output voltage is divided to obtain a feedback voltage through the voltage division feedback circuit; the feedback voltage is amplified and then input to the oscillator circuit to generate an oscillation signal; the generated oscillation signal is input to the charge pump circuit; the gate voltage of the NMOS regulating tube is adjusted through the charge pump circuit, so as to obtain the output voltage; the pre-voltage stabilizing circuit widens the input voltage range, so that the linear voltage stabilizing circuit can work in a high input voltage environment; the linear voltage stabilizing circuit based on the NMOS regulating tube has the characteristics of wide input range and low voltage difference, fast transient response speed, and is fully compatible with the thin gate oxide process.

The application discloses a kind of based on retrieval enhancement cross-modal condition diffusion model emotion recognition method and system, the method receives text, speech, image multimodal data, detects modality integrity and extracts existing modal feature;If there is missing mode, based on existing modal feature in external sample library retrieval similar sample, weighted fusion generates retrieval enhancement representation;Cross-modal condition diffusion model is constructed, and missing modal feature is recovered by combining forward diffusion into information, reverse diffusion and iteration unified cross-modal attention mechanism;Splice complete modal feature, pass through Transform fusion network and joint loss function optimization, output emotion category and intensity.The application breaks through the limitation that existing generation model only relies on local information, fully utilizes the combination advantage of external retrieval and diffusion generation, can maintain stable performance under a variety of missing patterns, and has wide application prospect.