The prediction theories for complex systems with a hierarchical structure and their applications to the climate process are a significant and forward-looking field of research. However, up to the present, they are yet...The prediction theories for complex systems with a hierarchical structure and their applications to the climate process are a significant and forward-looking field of research. However, up to the present, they are yet not known and understood very well. This paper presents a preliminary theoretical frame for them. As a normal example, the basic behaviors and the dynamic structure of the climate system are discussed in detail. The conclusions indicate that the climate system may be considered as a cascade of several subsystems located in different hierarchies. Such a dynamic structure is just the cause resulting in the nonstationarity. The conclusions also indicate that the main barrier of the climate prediction in theory derives from the contrary between the stationarity hypothesis for the current prediction theory and the nonstationary behavior of the real climate process. In addition, some work is discussed for detecting the nonstationarity in climate and other geophysical data and predicting the nonstationary process developed in recent years. These works may construct a preliminary base for applying to the climate predictions.展开更多
Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the p...Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.展开更多
The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control...The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.展开更多
Thermostatically controlled appliances(TCAs)have great thermal storage capability and are therefore excellent demand response(DR) resources to solve the problem of power fluctuation caused by renewable energy.Traditio...Thermostatically controlled appliances(TCAs)have great thermal storage capability and are therefore excellent demand response(DR) resources to solve the problem of power fluctuation caused by renewable energy.Traditional centralized management is affected by communication quality severely and thus usually has poor realtime control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation(OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.展开更多
黏结漏钢的准确识别和预报对于连铸全流程的控制至关重要。黏结漏钢是连铸过程中最具危害的重大事故,如果不能及时、准确地对黏结漏钢进行提前预测和处置,由此带来的漏报、误报不仅严重损坏铸机设备,还将极大影响铸坯质量和生产顺行,带...黏结漏钢的准确识别和预报对于连铸全流程的控制至关重要。黏结漏钢是连铸过程中最具危害的重大事故,如果不能及时、准确地对黏结漏钢进行提前预测和处置,由此带来的漏报、误报不仅严重损坏铸机设备,还将极大影响铸坯质量和生产顺行,带来巨大经济损失。为了捕捉和识别黏结漏钢时结晶器铜板的温度时序特征,将层次聚类(Hierarchical clustering)与动态时间弯曲(Dynamic time warping,DTW)计算方法相结合,构建并开发了一种基于机器学习的新型黏结漏钢预报方法。与现场服役的漏钢预报系统进行测试和对比,结果显示,建立的方法在保证真黏结100%报出率的同时,将误报次数降低了近60%,大幅提高了黏结漏钢预报准确率,避免了由错误报警引起的铸机降速或停机,对于促进连铸过程顺行、稳定和改善连铸坯质量具有积极意义。基于聚类的黏结漏钢预报方法展示出良好的应用潜力,为连铸过程异常监控提供了新思路。展开更多
Redundant information and inaccurate model will greatly affect the precision of quality prediction.A multiphase orthogonal signal correction modeling and hierarchical statistical analysis strategy are developed for th...Redundant information and inaccurate model will greatly affect the precision of quality prediction.A multiphase orthogonal signal correction modeling and hierarchical statistical analysis strategy are developed for the improvement of process understanding and quality prediction.Bidirectional orthogonal signal correction is used to remove the structured noise in both X and Y,which does not contribute to prediction model.The corresponding loading vectors provide good interpretation of the covariant part in X and Y.According to background,hierarchical PLS（Hi-PLS）is used to build regression model of process variables and property variables.This blocking leads to two model levels：the lower level shows the relationship of variables in each annealing furnace using hierarchical PLS based on bidirectional orthogonal signal correction,and the upper level reflects the relationship of annealing furnaces.With analysis of continuous annealing line data,the production precisions of hardness and elongation are improved by comparison of previous method.Result demonstrates the efficiency of the proposed algorithm for better process understanding X and property interpretation Y.展开更多
基金This work Was supported by the National Natural Science Foundation of China(Grant No.40035010).
文摘The prediction theories for complex systems with a hierarchical structure and their applications to the climate process are a significant and forward-looking field of research. However, up to the present, they are yet not known and understood very well. This paper presents a preliminary theoretical frame for them. As a normal example, the basic behaviors and the dynamic structure of the climate system are discussed in detail. The conclusions indicate that the climate system may be considered as a cascade of several subsystems located in different hierarchies. Such a dynamic structure is just the cause resulting in the nonstationarity. The conclusions also indicate that the main barrier of the climate prediction in theory derives from the contrary between the stationarity hypothesis for the current prediction theory and the nonstationary behavior of the real climate process. In addition, some work is discussed for detecting the nonstationarity in climate and other geophysical data and predicting the nonstationary process developed in recent years. These works may construct a preliminary base for applying to the climate predictions.
基金financially supported by the National Natural Science Foundation of China (Nos. 61170136, 61373101, 61472270, and 61402318)the Natural Science Foundation of Shanxi (No. 2014021022-5)+1 种基金the Special/Youth Foundation of Taiyuan University of Technology (No. 2012L014)Youth Team Fund of Taiyuan University of Technology (Nos. 2013T047 and 2013T048)
文摘Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.
基金supported in part by the Joint Funds of the National Natural Science Foundation of China(No.U1966205)Fundamental Research Funds for the Central Universities(No.B210202067).
文摘The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.
基金supported by National High Technology Research and Development Program of China (863 Program) (No. 2015AA050403)National Natural Science Foundation of China (Nos. 51377117, 51407125, 51361135704)+3 种基金China-UK NSFC/EPSRC EV Grant (Nos. 5136113015, EP/L001039/1)‘‘131’’ Talent and Innovative Team of Tianjin City, State Grid Corporation of China (No. KJ16-1-42)Innovation Leading Talent Project of Qingdao, Shandong Province (No. 15-10-3-15-(43)-zch)Innovation and Entrepreneurship Development Funds Projects of Qingdao Blue Valley Core Area (No. 201503004)
文摘Thermostatically controlled appliances(TCAs)have great thermal storage capability and are therefore excellent demand response(DR) resources to solve the problem of power fluctuation caused by renewable energy.Traditional centralized management is affected by communication quality severely and thus usually has poor realtime control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation(OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.
文摘黏结漏钢的准确识别和预报对于连铸全流程的控制至关重要。黏结漏钢是连铸过程中最具危害的重大事故,如果不能及时、准确地对黏结漏钢进行提前预测和处置,由此带来的漏报、误报不仅严重损坏铸机设备,还将极大影响铸坯质量和生产顺行,带来巨大经济损失。为了捕捉和识别黏结漏钢时结晶器铜板的温度时序特征,将层次聚类(Hierarchical clustering)与动态时间弯曲(Dynamic time warping,DTW)计算方法相结合,构建并开发了一种基于机器学习的新型黏结漏钢预报方法。与现场服役的漏钢预报系统进行测试和对比,结果显示,建立的方法在保证真黏结100%报出率的同时,将误报次数降低了近60%,大幅提高了黏结漏钢预报准确率,避免了由错误报警引起的铸机降速或停机,对于促进连铸过程顺行、稳定和改善连铸坯质量具有积极意义。基于聚类的黏结漏钢预报方法展示出良好的应用潜力,为连铸过程异常监控提供了新思路。
基金Item Sponsored by National Natural Science Foundation of China(60774068)National Basic Research Program of China(2009CB320601)
文摘Redundant information and inaccurate model will greatly affect the precision of quality prediction.A multiphase orthogonal signal correction modeling and hierarchical statistical analysis strategy are developed for the improvement of process understanding and quality prediction.Bidirectional orthogonal signal correction is used to remove the structured noise in both X and Y,which does not contribute to prediction model.The corresponding loading vectors provide good interpretation of the covariant part in X and Y.According to background,hierarchical PLS（Hi-PLS）is used to build regression model of process variables and property variables.This blocking leads to two model levels：the lower level shows the relationship of variables in each annealing furnace using hierarchical PLS based on bidirectional orthogonal signal correction,and the upper level reflects the relationship of annealing furnaces.With analysis of continuous annealing line data,the production precisions of hardness and elongation are improved by comparison of previous method.Result demonstrates the efficiency of the proposed algorithm for better process understanding X and property interpretation Y.
