187 results about "Enhancement Technologies" patented technology

The invention discloses a service data real-time monitoring system and method based on a Java byte code enhancement technology. The system comprises the components: a monitored application side, Agentbeing deployed in a JVM, when the application program is loaded into the JVM, the Agent modifying and loading the class of the JVM by using ASM, codes of various events and measurement values being dynamically inserted and collected when the application program runs, the newly-added codes executing and obtaining various data and then sending the data to the monitoring platform side, and the Agentperiodically obtaining and executing instructions such as starting and stopping of the Agent and thread analysis issued by the monitoring platform side; a monitoring platform side, used for acquiringthe data of the monitored application side, processing the acquired data and putting the processed data into message queues with different themes of the message middleware according to data types, aconsumer acquiring the data processing from the message queues according to the different themes and performing pre-computation on the data of the themes according to time windows and dimensions by utilizing stream computation; and a user side obtaining the calculation result and outputting the calculation result.

The invention discloses a combined type environment-friendly device using the MVR-bipolar electrolysis-biological enhancement technology. The combined type environment-friendly device comprises a preprocessing unit, an evaporating and concentrating unit, a biochemistry unit and a bipolar electrolysis processing unit, wherein the preprocessing unit, the evaporating and concentrating unit, the biochemistry unit and the bipolar electrolysis processing unit are connected with one another through a pipeline. An MVR unit conducts proper concentration on industrial waste water through the steam recompression technology, distilled water can be recycled or discharged according to the standard, and a concentrated solution enters the bipolar electrolysis processing unit. The mesh-shaped unit type structural design is adopted for the units, the water flow short circuit can be avoided, the mass transfer effect is improved, the oxidoreduction efficiency is ensured, and therefore pollutants are thoroughly removed. An on-line bacterium throwing type membrane biological fixing system is adopted for a biological unit. PVA floating fillers where special efficient bacterium strains are attached are fixed in a biochemical pool, and the organic matter removing effect is enhanced through a timed on-line bacterium throwing system. The combined type environment-friendly device is an integrated device, and the wastewater treatment efficiency and the standard reaching effect are greatly improved when the MVR-bipolar electrolysis-biological enhancement technology is combined with a traditional wastewater treatment technology.

The invention discloses a scalp electroencephalogram feature extraction and classification method based on an end-to-end convolutional neural network, and the method comprises the steps: carrying out the data enhancement of training data, and enabling the enhanced training data to train the convolutional neural network; inputting the to-be-detected data into the convolutional neural network for feature extraction and classification. The feature extraction and classification method comprises the following steps: S1, filtering an original scalp electroencephalogram signal by using a band-pass filter to obtain signals xtheta, xmu and xbeta; S2, performing multi-scale time convolution and spatial convolution on the signals xtheta, xmu and xbeta respectively to extract features; s3, performing pooling operation on the feature map output by the convolutional layer; s4, after pooling, carrying out feature fusion, and then sending the feature fusion to a full connection layer to integrate the input abstract features; and S5, sending the output of the full connection layer to a softmax layer for classification. According to the method, a brand new data enhancement technology is applied in a training stage, data is input into a plurality of convolutional neural network branches for multi-scale convolution operation after passing through a filter bank in a test stage, the overfitting phenomenon is reduced, and the classification accuracy is improved.