4 results about "Instance selection" patented technology

This invention discloses a fault diagnosis method and apparatus that integrates alarm and trend event data. The method includes the following steps: preprocessing the raw time-series data of an industrial system to obtain preprocessed data; extracting multi-value alarm events and multi-class trend events, and fusing the two types of feature events to form a hybrid sequence; using the Word2Vec core model to vectorize and encode the events in the hybrid sequence; constructing an LSTM model; and employing an instance transfer learning strategy to update the model trained using source domain data to meet the needs of target domain diagnosis, thus achieving cross-domain fault diagnosis. This invention, by simultaneously extracting alarm and trend events, can more comprehensively capture the evolution of faults; through instance selection and weight adjustment, it can effectively solve the problem of scarce target domain data and improve diagnostic accuracy; the method can be extended to complex systems such as shipbuilding, petroleum, and chemical industries, and is compatible with different industrial scenarios. It can be adapted to new systems by adjusting the event definitions, making it widely applicable.

The application discloses an instance selection algorithm based on local Coulomb force, and relates to the technical field of data preprocessing, which comprises the following steps: calculating the local density of each data point in a given data set; mapping the calculated local density to the amount of electric charge; calculating the local resultant force of each data point on its reverse neighbors based on a Coulomb force model; defining and calculating the change rate difference of the local resultant force; detecting boundary points based on the change rate difference; selecting the detected boundary points and performing random sampling; and using the selected boundary points and the randomly sampled data to jointly construct a compressed training set. The application quantifies the change rate difference of the force of the data points by using the Coulomb force model, accurately identifies the boundary points, and fuses the random sampling to construct a compressed training set with high quality and high robustness, thereby significantly improving the data efficiency.

In a computing-aware traffic steering (CATS) network, such as those conforming to an IETF proposal, a service contact instance for a requested service is selected based on at least cache metrics associated with data cached at one or more egress nodes of the CATS network. In some embodiments, the selection is also based on compute and network metrics. In one implementation, the selection is based only on cache metrics unless that selection is too costly in terms of compute and / or network load. In that case, the selection is based on the compute and network metrics. In this way, cache metrics are prioritized over compute and network metrics as long as the cache-based selection is not too costly.