With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve suffi...With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.展开更多
Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and propert...Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.展开更多
文摘With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.
基金the National Natural Science Foundation of China[grant number 41631177]the Chinese Academy of Sciences Key Project[grant number ZDRW-ZS-2016-6-3].
文摘Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.
