Steel production causes a third of all industrial CO_(2) emissions due to the use of carbon-based substances as reductants for iron ores,making it a key driver of global warming.Therefore,research efforts aim to repla...Steel production causes a third of all industrial CO_(2) emissions due to the use of carbon-based substances as reductants for iron ores,making it a key driver of global warming.Therefore,research efforts aim to replace these reductants with sustainably produced hydrogen.Hydrogen-based direct reduction(HyDR)is an attractive processing technology,given that direct reduction(DR)furnaces are routinely operated in the steel industry but with CH_(4) or CO as reductants.Hydrogen diffuses considerably faster through shaft-furnace pellet agglomerates than carbon-based reductants.However,the net reduction kinetics in HyDR remains extremely sluggish for high-quantity steel production,and the hydrogen consumption exceeds the stoichiometrically required amount substantially.Thus,the present study focused on the improved understanding of the influence of spatial gradients,morphology,and internal microstructures of ore pellets on reduction efficiency and metallization during HyDR.For this purpose,commercial DR pellets were investigated using synchrotron high-energy X-ray diffraction and electron microscopy in conjunction with electron backscatter diffraction and chemical probing.Revealing the interplay of different phases with internal interfaces,free surfaces,and associated nucleation and growth mechanisms provides a basis for developing tailored ore pellets that are highly suited for a fast and efficient HyDR.展开更多
Edmund Husserl's first important move about phenomenology is the"phenomenological reduction"which means that we should reduce the external world to the contents of our consciousness alone. However, Hans-...Edmund Husserl's first important move about phenomenology is the"phenomenological reduction"which means that we should reduce the external world to the contents of our consciousness alone. However, Hans-Georg Gadamer holds the opinion that all interpretation of a past work consists in a dialogue between past and present(Eagleton, T. 2009:62). Gadamer's famous theory is fusion of horizons which means that the event of understanding comes about when our own"horizon"of historical meanings and assumptions"fuses"with the"horizon"within which the work itself is placed. The present thesis takes Hawthorne's YoungGoodmanBrown as an example to illustrate different understandings when readers apply the two different theories.展开更多
For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce i...For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.展开更多
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
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
基金financial support from the Walter Benjamin Programme of the Deutsche Forschungsgemeinschaft(No.468209039)the financial support from Capes-Humboldt(No.88881.512949/2020-01)the financial support from the Heisenberg Programme of the Deutsche Forschungsgemeinschaft(SP16662/1)。
文摘Steel production causes a third of all industrial CO_(2) emissions due to the use of carbon-based substances as reductants for iron ores,making it a key driver of global warming.Therefore,research efforts aim to replace these reductants with sustainably produced hydrogen.Hydrogen-based direct reduction(HyDR)is an attractive processing technology,given that direct reduction(DR)furnaces are routinely operated in the steel industry but with CH_(4) or CO as reductants.Hydrogen diffuses considerably faster through shaft-furnace pellet agglomerates than carbon-based reductants.However,the net reduction kinetics in HyDR remains extremely sluggish for high-quantity steel production,and the hydrogen consumption exceeds the stoichiometrically required amount substantially.Thus,the present study focused on the improved understanding of the influence of spatial gradients,morphology,and internal microstructures of ore pellets on reduction efficiency and metallization during HyDR.For this purpose,commercial DR pellets were investigated using synchrotron high-energy X-ray diffraction and electron microscopy in conjunction with electron backscatter diffraction and chemical probing.Revealing the interplay of different phases with internal interfaces,free surfaces,and associated nucleation and growth mechanisms provides a basis for developing tailored ore pellets that are highly suited for a fast and efficient HyDR.
文摘Edmund Husserl's first important move about phenomenology is the"phenomenological reduction"which means that we should reduce the external world to the contents of our consciousness alone. However, Hans-Georg Gadamer holds the opinion that all interpretation of a past work consists in a dialogue between past and present(Eagleton, T. 2009:62). Gadamer's famous theory is fusion of horizons which means that the event of understanding comes about when our own"horizon"of historical meanings and assumptions"fuses"with the"horizon"within which the work itself is placed. The present thesis takes Hawthorne's YoungGoodmanBrown as an example to illustrate different understandings when readers apply the two different theories.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61173052)the China Postdoctoral Scinece Foundation(Grant No.2014M561363)
文摘For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.
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supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
