Search

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

The paper proposes an improved algorithm, for the problem that SPHP algorithm is not suitable for the UAV Remote sensing image mosaic algorithm because it is susceptible to geomorphic factors, resulting in deformation or ghosting. The algorithm firstly extracts feature points by combining SURF algorithm and Harris algorithm. The obtained feature points are coarsely matched by KNN algorithm, then matched by PROSAC algorithm. Finally, the weight coefficient is introduced to calculate the spatial transformation model of the image overlap region. This model replaces the original spatial model of SPHP algorithm. The ghosting of the image overlap area is reduced, and the stitched image is caused to have a smaller deformation. The final result shows that this improved SPHP algorithm can effectively remove the ghost image of the stitched image and generate better stitching results.

As the largest public service provider in the world, State Grid Corporation of China (SGCC) generates huge amounts of data every day, much of which are paper work order in offline businesses. Paper work orders greatly affect office efficiency and reduce the data processing capacity. To solve this problem, technicians of SGCC implement an intelligent work order recognition system. This system based on deep neural network such as CNN, CRNN, uses graphical morphology analysis, segmentation, recognizes handwritten numerals, handwritten Chinese characters and printed characters automatically, imports various paper work orders rapidly. In practice, the accuracy of handwritten numeral recognition is 96%, the accuracy of handwritten Chinese character recognition is 94%, the accuracy of printed character recognition is 99%. This system greatly improves the work order import efficiency of grass-roots staff.

The current concept of defibrillation correlates defibrillation threshold to cardiac cell excitation threshold (ET). Previous work has shown a 10-15 ms biphasic waveform preferential window (BWPW), identified in the early diastolic phase of an action potential, where ET of the biphasic waveform (BW) is much lower than that of the monophasic waveform (MW). In this paper we investigated the ionic mechanism behind this phenomenon using computer simulations and found that waveforms of the electrical field which can recruit more h and j gates and can provide enough force to drive the membrane potential over the sodium current threshold lowered the ET. This finding may explain some questions arising from basic research and clinical defibrillation in exciting cardiac tissue using different waveforms.

The authors developed a one-dimensional model of the atrioventricular node and used it to study the importance of resistive intercellular coupling. The authors' preliminary results suggest that unlike model strands of myocardial or atrial cells, large changes in the intercellular coupling resistance, make only modest changes in the conduction velocity in the atrioventricular node. >

Action potential propagation in a strand of cardiac fibers was studied using a model developed from potential theory which incorporates both microscopic structural details and realistic membrane dynamics. The conduction velocity and τ foot of the propagating transmembrane action potential were found to vary directly with the density of fibers within the bundle, whereas V max varied in the opposite direction.