Owing to the dramatically enhanced charge-mass transport and abundant electrochemically active sites,transition metal compound electrodes are increasingly attractive for achieving high-performance supercapacitors(SCs)...Owing to the dramatically enhanced charge-mass transport and abundant electrochemically active sites,transition metal compound electrodes are increasingly attractive for achieving high-performance supercapacitors(SCs).Here,we report the fabrication of nickel foam supported three-dimensional(3 D)branched nickel-cobalt phosphides@tri-metal cobalt-nickel-molybdenum phosphides core/shell nanowire heterostructures(denoted as NiCo-P@CoNiMo-P)as high-performance electrode materials for hybrid supercapacitors.The presence of multiple valences of the cations in such NiCo-P@CoNiMo-P enables rich redox reactions and promoted synergy effects.Benefiting from their collective effects,the resulting electrode demonstrates high specific capacity of 1366 C g^(-1) at 2 A g^(-1)(2.03 C cm^(-2) at2 mA cm^(-2))and 922 C g^(-1) at 10 A g^(-1),as well as good cycling stability(retaining~94%of the initial capacity after 6000 cycles at 15 A g^(-1)).A hybrid SC using the NiCo-P@CoNiMo-P as the positive electrode and N-doped rGOs as the negative electrode exhibits a high energy density of 81.4 Wh kg^(-1) at a power density of 1213 W kg^(-1) and a capacity retention of 132%even after 6000 cycles at 10 A g^(-1).Our findings can facilitate the material design for boosting the performance of transition metal compounds based materials for fast energy storage.展开更多
<span style="font-family:Verdana;">Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Af...<span style="font-family:Verdana;">Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Africa. Recently, with the advent and development of high-speed trains, continuous monitoring of the railway vehicle suspension is of significant importance. For this reason, railway vehicles should be monitored continuously to avoid catastrophic events, ensure comfort, safety, and also improved performance while reducing life cycle costs. The suspension system is a very important part of the railway vehicle which supports the car-body and the bogie, isolates the forces generated by the track unevenness at the wheels and also controls the attitude of the car-body with respect to the track surface for ride comfort. Its reliability is directly related to the vehicle safety. The railway vehicle suspension often develops faults;worn springs and dampers in the primary and secondary suspension. To avoid a complete system failure, early detection of fault in the suspension of trains is of high importance. The main contribution of the research work is the prediction of faulty regimes of a</span> <span style="font-family:Verdana;">railway vehicle suspension based on a hybrid model. The hybrid model</span><span style="font-family:Verdana;"> framework is in four folds;first, modeling of vehicle suspension system to generate vertical acceleration of the railway vehicle, parameter estimation or identification was performed to obtain the nominal parameter values of the vehicle suspension system based on the measured data in the second fold, furthermore, a supervised machine learning model was built to predict faulty and healthy state of the suspension system components (damage scenarios) based on support vector machine (SVM) and lastly, the development of a new SVM model with the damage scenarios to predict faults on the test data. The level of degradation at which the spring and damper becomes faulty for both pri</span><span style="font-family:Verdana;">mary and secondary suspension system was determined. The spring and</span><span style="font-family:Verdana;"> damper becomes faulty when the nominal values degrade by 50% and 40% and 30% and 40% for the secondary and primary suspension system respectively. The proposed model was able to predict faulty components with an accuracy of 0.844 for the primary and secondary suspension system.</span>展开更多
Enterprise diagnosis is a complex process in which various enterprise knowledge are involved. It is proved that hybrid inference mechanism is an effective solution to deal with such problems. Aimed at the issue that c...Enterprise diagnosis is a complex process in which various enterprise knowledge are involved. It is proved that hybrid inference mechanism is an effective solution to deal with such problems. Aimed at the issue that current representation of diagnosis knowledge depends excessively on the inference mechanism, the strategy that separates the enterprise model knowledge and the inference knowledge is adopted in this paper. By systematically analyzing the diagnosis knowledge of enterprise model, a general representation of diagnosis model knowledge is put forward based upon XML schema and its validity and feasibility is tested through a practical application.展开更多
基金supported by the National Natural Science Foundation of China(Grants Nos.52072323 and 51872098)the Leading Project Foundation of Science Department of Fujian Province(Grants No.2018H0034)+1 种基金the“Double-First Class”Foundation of Materials and Intelligent Manufacturing Discipline of Xiamen Universitythe financial support from the Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials,&Henan Key Laboratory of High-temperature Structural and Functional Materials,Henan University of Science and Technology(Grants No.HKDNM2019013)。
文摘Owing to the dramatically enhanced charge-mass transport and abundant electrochemically active sites,transition metal compound electrodes are increasingly attractive for achieving high-performance supercapacitors(SCs).Here,we report the fabrication of nickel foam supported three-dimensional(3 D)branched nickel-cobalt phosphides@tri-metal cobalt-nickel-molybdenum phosphides core/shell nanowire heterostructures(denoted as NiCo-P@CoNiMo-P)as high-performance electrode materials for hybrid supercapacitors.The presence of multiple valences of the cations in such NiCo-P@CoNiMo-P enables rich redox reactions and promoted synergy effects.Benefiting from their collective effects,the resulting electrode demonstrates high specific capacity of 1366 C g^(-1) at 2 A g^(-1)(2.03 C cm^(-2) at2 mA cm^(-2))and 922 C g^(-1) at 10 A g^(-1),as well as good cycling stability(retaining~94%of the initial capacity after 6000 cycles at 15 A g^(-1)).A hybrid SC using the NiCo-P@CoNiMo-P as the positive electrode and N-doped rGOs as the negative electrode exhibits a high energy density of 81.4 Wh kg^(-1) at a power density of 1213 W kg^(-1) and a capacity retention of 132%even after 6000 cycles at 10 A g^(-1).Our findings can facilitate the material design for boosting the performance of transition metal compounds based materials for fast energy storage.
文摘<span style="font-family:Verdana;">Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Africa. Recently, with the advent and development of high-speed trains, continuous monitoring of the railway vehicle suspension is of significant importance. For this reason, railway vehicles should be monitored continuously to avoid catastrophic events, ensure comfort, safety, and also improved performance while reducing life cycle costs. The suspension system is a very important part of the railway vehicle which supports the car-body and the bogie, isolates the forces generated by the track unevenness at the wheels and also controls the attitude of the car-body with respect to the track surface for ride comfort. Its reliability is directly related to the vehicle safety. The railway vehicle suspension often develops faults;worn springs and dampers in the primary and secondary suspension. To avoid a complete system failure, early detection of fault in the suspension of trains is of high importance. The main contribution of the research work is the prediction of faulty regimes of a</span> <span style="font-family:Verdana;">railway vehicle suspension based on a hybrid model. The hybrid model</span><span style="font-family:Verdana;"> framework is in four folds;first, modeling of vehicle suspension system to generate vertical acceleration of the railway vehicle, parameter estimation or identification was performed to obtain the nominal parameter values of the vehicle suspension system based on the measured data in the second fold, furthermore, a supervised machine learning model was built to predict faulty and healthy state of the suspension system components (damage scenarios) based on support vector machine (SVM) and lastly, the development of a new SVM model with the damage scenarios to predict faults on the test data. The level of degradation at which the spring and damper becomes faulty for both pri</span><span style="font-family:Verdana;">mary and secondary suspension system was determined. The spring and</span><span style="font-family:Verdana;"> damper becomes faulty when the nominal values degrade by 50% and 40% and 30% and 40% for the secondary and primary suspension system respectively. The proposed model was able to predict faulty components with an accuracy of 0.844 for the primary and secondary suspension system.</span>
文摘Enterprise diagnosis is a complex process in which various enterprise knowledge are involved. It is proved that hybrid inference mechanism is an effective solution to deal with such problems. Aimed at the issue that current representation of diagnosis knowledge depends excessively on the inference mechanism, the strategy that separates the enterprise model knowledge and the inference knowledge is adopted in this paper. By systematically analyzing the diagnosis knowledge of enterprise model, a general representation of diagnosis model knowledge is put forward based upon XML schema and its validity and feasibility is tested through a practical application.
