In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by s RNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability cr...
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In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by s RNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect.
The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burd...
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The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burden operation. To solve these problems, this paper presents a model to adjust the burden distribution using fuzzy C-means(FCM) and wavelet analysis. First,multiple condition states of blast furnaces are analysed through data processing, the state parameters are clustered based on the similarity, and then this paper searches for the corresponding burden parameters from the history data. Finally, for different state clusters, best burden parameters are selected to adjust the conditions. Simulation results show that the burden distribution adjustments based on the state parameters clustering are efficient.
The classification of hyperspectral images(HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network(CNN) has achieved great success for HSI classification. However,...
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The classification of hyperspectral images(HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network(CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN,nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis(PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-ofthe-art methods.
A new measuring system for magnetic properties of the ferromagnetic thin film has developed based on magneto-optical Kerr effect (MOKE). This system can realize both polar MOKE and longitudinal MOKE measurements throu...
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A new measuring system for magnetic properties of the ferromagnetic thin film has developed based on magneto-optical Kerr effect (MOKE). This system can realize both polar MOKE and longitudinal MOKE measurements through the optimization of optical path and electromagnet's poles. The signal processing software on Lab View has been also designed to acquire the Kerr signal and plot the hysteresis loop. The MOKE measurements have been performed to investigate the magnetic properties of ferromagnetic films such as CoFeSiB, permalloy and NiFe/Ag/NiFe multilayer. The experimental results proved that the system has a high angular accuracy of 0.0008°.
Affective computing plays a key role in music artificial intelligence, in which a music emotion classification model is indispensable. Both discrete classification model and continuous dimensional model are commonly u...
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Affective computing plays a key role in music artificial intelligence, in which a music emotion classification model is indispensable. Both discrete classification model and continuous dimensional model are commonly used for music emotion classification. However, these models are not designed in views of composers, which is insufficient for the perception of music emotion. In this paper, a fuzzy music emotion classification model is proposed by extracting the music expression marks considering the composers emotion. Experiments on subjective evaluation in the feeling of pleasure and arousal according to the change of three selected features(i.e., tempo, register, and dynamic) are conducted by listeners from different subjects. The experimental results show that the correlation between the proposal fuzzy model and the the results from questionnaires reaches 80% on average, which demonstrate the validity of the proposed fuzzy music emotion classification model. The proposal could be applied to music emotion generation conveyed from either composers or players, and to emotion recognition of music as for audiences.
Piezoelectric geophones are vibration detectors that convert vibration acceleration signals into electrical signals. High performance piezoelectric materials can improve the sensitivity of piezoelectric geophone and m...
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Piezoelectric geophones are vibration detectors that convert vibration acceleration signals into electrical signals. High performance piezoelectric materials can improve the sensitivity of piezoelectric geophone and meet the need of high-resolution seismic data acquisition. The comprehensive performance of relaxor piezoelectric single crystal PMN-PT is more superior than PZT, and it is potential to be applied to high sensitivity and small volume geophones. In this paper, the central compressed geophone core model based on PMN-PT was established and theoretically analyzed. Then, a multiphysics simulation model was set up in COMSOL for simulation calculation. Finally, experimental verification was carried out. The results show that using PMN-PT in geophone core design can improve the sensitivity of the model by more than 120% compared with the traditional PZT material. The PMN-PT has the potential to be applied to high sensitivity and small volume geophones.
Operational drilling parameters optimization is necessary for increase drilling efficiency in complex geological drilling process. There are four key objectives to evaluate the drilling efficiency, including drilling ...
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Operational drilling parameters optimization is necessary for increase drilling efficiency in complex geological drilling process. There are four key objectives to evaluate the drilling efficiency, including drilling cost, rate of penetration(ROP), drill bit life, and drill bit’s specific energy. In this paper, we proposed a multi-objective optimization method to improve drilling efficiency in the complex geological drilling process considering all the four key objectives. Firstly, the characteristics of complex geological drilling process and optimization problems are analyzed to find the vital process parameters and problems for drilling efficiency optimization. Then, a multi-objective optimization model which combines the four key objectives is ***, an improved nondominated sorting genetic algorithm(improved NSGA-II) is used to optimize the operational drilling parameters include weight on bit(WOB) and rotational speed(RS) to make drilling efficiency improved. The real case results demonstrate that our method increases the drilling efficiency in four key objectives and saves the simulation time. The proposed method provides the foundation for intelligent optimization control in complex geological drilling process.
Magnetic field generator is an essential part of the automatic measurement of geomagnetic elements. Based on the Biot-Savart law, mathematical models of magnetic field distribution of different types of magnetic field...
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Magnetic field generator is an essential part of the automatic measurement of geomagnetic elements. Based on the Biot-Savart law, mathematical models of magnetic field distribution of different types of magnetic field generators are established. The magnetic intensity and the homogenous area of them are discussed. The analysis results show that increasing the number of coils can increase magnetic intensity. Meanwhile, the designed sphere coil can generate a larger uniform magnetic field with a smaller geometric size, and overcomes the deficiencies in the space utilization of the traditional magnetic field generator. In addition, it has great potential to miniature the size of the geomagnetic measurement instruments.
In practical applications of electromagnetic measurement while drilling(EM-MWD),the signal-to-noise ratio(SNR)of receiver cannot always meet the requirements of reliable communication conditions due to the earth-atten...
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In practical applications of electromagnetic measurement while drilling(EM-MWD),the signal-to-noise ratio(SNR)of receiver cannot always meet the requirements of reliable communication conditions due to the earth-attenuation,interfering signal from well site,*** to solve these problems,this paper presents a BCH encoder and decoder technology based on BPSK for ***,the paper studies the relationship between BCH encoding error performance and decoding method,source length and other factors through simulation;then,we obtain an optimal length of BCH code for EM-MWD through analyzing the bit error performance of hard-decision decoding and soft-decision decoding with different lengths of BCH *** results show that the proposed algorithm can reduce the bit error rate at a lower SNR,achieving a reliable communication condition when the SNR of a received signal is *** is demonstrated the effectiveness of the proposed BCH encoder and decoder algorithm based on BPSK for EM-MWD.
Non-dominated sorting is widely used in multi-objective evolutionary algorithms. There are many approaches to non-dominated sorting, but they are computationally expensive when the number of objectives or the number o...
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Non-dominated sorting is widely used in multi-objective evolutionary algorithms. There are many approaches to non-dominated sorting, but they are computationally expensive when the number of objectives or the number of fronts becomes relatively large. This paper proposes an efficient non-dominated sorting approach based on a dominance ranking graph created in this paper. The approach takes advantages of both the dominated and non-dominated relationships, it is able to efficiently handle the case of large number of objectives/fronts. Experimental results show that the proposed algorithm outperforms its several popular variants in most of the tests.
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