Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract ...Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.展开更多
A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickl...A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.展开更多
Large-scale new energy pressures on the grids bring challenges to power system's security and stability.In order to optimize the user's electricity consumption behavior and ease pressure,which is caused by new...Large-scale new energy pressures on the grids bring challenges to power system's security and stability.In order to optimize the user's electricity consumption behavior and ease pressure,which is caused by new energy on the grid,this paper proposes a time-of-use price model that takes wind power uncertainty into account.First,the interval prediction method is used to predict wind power.Then typical wind power scenes are selected by random sampling and bisecting the K-means algorithm.On this basis,integer programming is used to divide the peak-valley period of the multi-scenes load.Finally,under the condition of many factors such as user response based on consumer psychology,user electricity charge and power consumption,this paper takes the peak-valley difference of equivalent net load and the user dissatisfaction degree as the goal,and using the NSGA-II multi-objective optimization algorithm,evaluates the Pareto solution set to obtain the optimal solution.In order to test the validity of the model proposed in this paper,we apply it to an industrial user and wind farms in Yan'an city,China.The results show that the model can effectively ensure the user's electrical comfort while achieving the role of peak shaving and valley flling.展开更多
基金The authors would like to thank Research Supporting Project Number(RSP2024R444)King Saud University,Riyadh,Saudi Arabia.
文摘Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.
基金National Natural Science Foundation of China (No. 60975083)Key Grant Project,Ministry of Education,China(No. 104145)
文摘A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.
基金supported by the Research Fund of the State Key Laboratory of Eco-hydraulics in Northwest Arid Region,Xi'an University of Technology(Grant No.2019KJCXTD-5)the Natural Science Basic Research Program of Shaanxi(Grant No.2019JLZ-15)the Key Research and Development Plan of Shaanxi Province(Grant No.2018-ZDCXL-GY-10-04).
文摘Large-scale new energy pressures on the grids bring challenges to power system's security and stability.In order to optimize the user's electricity consumption behavior and ease pressure,which is caused by new energy on the grid,this paper proposes a time-of-use price model that takes wind power uncertainty into account.First,the interval prediction method is used to predict wind power.Then typical wind power scenes are selected by random sampling and bisecting the K-means algorithm.On this basis,integer programming is used to divide the peak-valley period of the multi-scenes load.Finally,under the condition of many factors such as user response based on consumer psychology,user electricity charge and power consumption,this paper takes the peak-valley difference of equivalent net load and the user dissatisfaction degree as the goal,and using the NSGA-II multi-objective optimization algorithm,evaluates the Pareto solution set to obtain the optimal solution.In order to test the validity of the model proposed in this paper,we apply it to an industrial user and wind farms in Yan'an city,China.The results show that the model can effectively ensure the user's electrical comfort while achieving the role of peak shaving and valley flling.
