The data on the coal production and consumption in Jilin Province for the last ten years were collected,and the Grey System GM( 1,1) model and unary linear regression model were applied to predict the coal consumption...The data on the coal production and consumption in Jilin Province for the last ten years were collected,and the Grey System GM( 1,1) model and unary linear regression model were applied to predict the coal consumption of Jilin Production in 2014 and 2015. Through calculation,the predictive value on the coal consumption of Jilin Province was attained,namely consumption of 2014 is 114. 84 × 106 t and of 2015 is 117. 98 ×106t,respectively. Analysis of error data indicated that the predicted accuracy of Grey System GM( 1,1) model on the coal consumption in Jilin Province improved 0. 21% in comparison to unary linear regression model.展开更多
Most nonliner programming problems consist of functions which are sums of unary,convex functions of linear fuctions.In this paper.we derive the duality forms of the unary oonvex optimization, and these technuqucs are ...Most nonliner programming problems consist of functions which are sums of unary,convex functions of linear fuctions.In this paper.we derive the duality forms of the unary oonvex optimization, and these technuqucs are applied to the geometric programming and minimum discrimination information problems.展开更多
Intelligent tpower systems scanimprove operational efficiency by installing a large number of sensors.Data-based methods of supervised learning have gained popularity because of available Big Data and computing resour...Intelligent tpower systems scanimprove operational efficiency by installing a large number of sensors.Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources.However,the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data.The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms.Moreover,sensor data faults in power systems are dynamically changing and pose another challenge.Therefore,a fault detection method based on self-supervised feature learning was proposed to address the above two challenges.First,self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data.The self-supervised representation learning uses a sequence-based Triplet Loss.The extracted features of large amounts of normal data are then fed into a unary classifier.The proposed method is validated on exhaust gas temperatures(EGTs)of a real-world 9F gas turbine with sudden,progressive,and hybrid faults.A comprehensive comparison study was also conducted with various feature extractors and unary classifiers.The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults.The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms ofF1 score.展开更多
In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em&...In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em>M</em>, the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension of <em>H</em> through <em>M</em> is given, and the necessary and sufficient conditions for the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension algebra of <em>H</em> through <em>M</em> being 3-Lie algebra are obtained, and the structural characteristics and properties of these two kinds of extended 3-Lie algebras are given.展开更多
We study the computational complexities of three problems on multi-agent scheduling on a single machine.Among the three problems,the computational complexities of the first two problems were still open and the last pr...We study the computational complexities of three problems on multi-agent scheduling on a single machine.Among the three problems,the computational complexities of the first two problems were still open and the last problem was shown to be unary NP-hard in the literature.We show in this paper that the first two problems are unary NP-hard.We also show that the unary NP-hardness proof for the last problem in the literature is invalid,and so,the exact complexity of the problem is still open.展开更多
基金Supported by project of National Natural Science Foundation of China(No.41272360)
文摘The data on the coal production and consumption in Jilin Province for the last ten years were collected,and the Grey System GM( 1,1) model and unary linear regression model were applied to predict the coal consumption of Jilin Production in 2014 and 2015. Through calculation,the predictive value on the coal consumption of Jilin Province was attained,namely consumption of 2014 is 114. 84 × 106 t and of 2015 is 117. 98 ×106t,respectively. Analysis of error data indicated that the predicted accuracy of Grey System GM( 1,1) model on the coal consumption in Jilin Province improved 0. 21% in comparison to unary linear regression model.
文摘Most nonliner programming problems consist of functions which are sums of unary,convex functions of linear fuctions.In this paper.we derive the duality forms of the unary oonvex optimization, and these technuqucs are applied to the geometric programming and minimum discrimination information problems.
基金supported by the National Science and Technology Major Project of China(Grant No.2017-V-0011-0063).
文摘Intelligent tpower systems scanimprove operational efficiency by installing a large number of sensors.Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources.However,the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data.The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms.Moreover,sensor data faults in power systems are dynamically changing and pose another challenge.Therefore,a fault detection method based on self-supervised feature learning was proposed to address the above two challenges.First,self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data.The self-supervised representation learning uses a sequence-based Triplet Loss.The extracted features of large amounts of normal data are then fed into a unary classifier.The proposed method is validated on exhaust gas temperatures(EGTs)of a real-world 9F gas turbine with sudden,progressive,and hybrid faults.A comprehensive comparison study was also conducted with various feature extractors and unary classifiers.The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults.The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms ofF1 score.
文摘In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em>M</em>, the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension of <em>H</em> through <em>M</em> is given, and the necessary and sufficient conditions for the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension algebra of <em>H</em> through <em>M</em> being 3-Lie algebra are obtained, and the structural characteristics and properties of these two kinds of extended 3-Lie algebras are given.
基金the National Natural Science Foundation of China(Nos.11271338 and 71301038)the National Natural Science Foundation of Henan Province(No.15IRTSTHN006).
文摘We study the computational complexities of three problems on multi-agent scheduling on a single machine.Among the three problems,the computational complexities of the first two problems were still open and the last problem was shown to be unary NP-hard in the literature.We show in this paper that the first two problems are unary NP-hard.We also show that the unary NP-hardness proof for the last problem in the literature is invalid,and so,the exact complexity of the problem is still open.
