KEYWORDS: Power consumption, Data modeling, Data communications, Data storage, Matrices, Mathematical optimization, Data processing, Data analysis, Statistical modeling, Statistical analysis
In the context of the problem of excessive MAPE values in the method of filling in missing big data of electricity consumption information, a method of filling in missing big data of electricity consumption information based on variational self-encoder is designed. Optimising the electricity information pre-processing model, store and manage the various types of raw and application data collected in a classified manner, define the objective function as the algebraic sum of the squared measurement errors, construct an electricity big data tensor filling model, treat missing values as variables, and design a missing filling method based on a variational self-encoder. Experimental results: The mean value of MAPE of the big data missing fill method for electricity consumption information in the paper is: 38.514%, indicating that the performance of the designed big data missing fill method for electricity consumption information is better after fusing the variational self-encoder.
KEYWORDS: Analytical research, Statistical analysis, Data analysis, Data modeling, Data mining, Power grids, Distributed computing, Data processing, Parallel computing
In view of the increasing data volume and the increasingly difficult data analysis in the power industry, an intelligent and efficient analysis and mining framework for power big data is designed to quickly obtain valuable information. Analyze the overall framework of the power big data center, mainly including the service layer, verification layer, data source layer, and feature analysis layer. In addition, through analyzing the process of data mining, it is found that the business needs to be strengthened And realize expansion. The framework design of power big data intelligent analysis and mining technology mainly includes power market demand, customer analysis, high-performance data analysis, service system, data security governance and other modules. Through the analysis of an example of intelligent power big data mining, the analysis results show that the intelligent power data mining has good analysis effect and high mining accuracy
Some intelligent detection methods for ultra-dense network attacks are likely to generate false alarms in the application process. In order to improve the security in ultra-dense network, an intelligent detection method based on edition learning is designed. Considering the SRP change rate, different thresholds are set, the node switching structural features of ultra-dense networks are extracted, the function sets that can effectively control error detection are selected, the host recognition algorithm is designed, the function field selection model based on joint learning is constructed, the iteration points are created in the feasible domain, real-time network traffic is collected, and the doattack intelligent detection model is optimized. Experimental results: in the paper, the average probability of non-intelligent detection methods for attacks in ultra-dense networks is 24.864%, which shows that when combined with federated learning algorithm, it has more advantages in practical performance.
As to object tracking, the local context surrounding of the target could provide much effective information for getting a robust tracker. The spatial-temporal context (STC) learning algorithm proposed recently considers the information of the dense context around the target and has achieved a better performance. However STC only used image intensity as the object appearance model. But this appearance model not enough to deal with complicated tracking scenarios. In this paper, we propose a novel object appearance model learning algorithm. Our approach formulates the spatial-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between high-level features (Circular-Multi-Block Local Binary Pattern) from the target and its surrounding regions. The tracking problem is posed by computing a visual saliency map, and obtaining the best target location by maximizing an object location likelihood function. Extensive experimental results on public benchmark databases show that our algorithm outperforms the original STC algorithm and other state-of-the-art tracking algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.