Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

Speed selection of traveling waves to an epidemic model

This paper is devoted to investigating the selection mechanism of the minimal wave speed for traveling waves to an epidemic model.The determinacy of linear and nonlinear selections is further discussed by the upper-lower solutions and comparison principle.A threshold is defined by the eigenvalue problem of the linearized system.We show that the nonlinear determinacy is obtained as long as there exists a lower solution with a faster decay and a speed parameter that is larger than the threshold.When the speed parameter equals to the threshold,if there exists an upper solution satisfying proper limit behavior,then the linear selection is realized.For a special function of infection rate,we obtain a threshold parameter that determines the linear and nonlinear selections.

Nonlinear optical selection rule based on valley-exciton locking in monolayer ws2

Optical selection rules fundamentally determine the optical transitions between energy states in a variety of physical systems,from hydrogen atoms to bulk crystals such as gallium arsenide.These rules are important for optoelectronic applications such as lasers,energy-dispersive X-ray spectroscopy,and quantum computation.Recently,single-layer transition metal dichalcogenides have been found to exhibit valleys in momentum space with nontrivial Berry curvature and excitons with large binding energy.However,there has been little study of how the unique valley degree of freedom combined with the strong excitonic effect influences the nonlinear optical excitation.Here,we report the discovery of nonlinear optical selection rules in monolayer WS2,an important candidate for visible 2D optoelectronics because of its high quantum yield and large direct bandgap.We experimentally demonstrated this principle for second-harmonic generation and two-photon luminescence(TPL).Moreover,the circularly polarized TPL and the study of its dynamics evince a sub-ps interexciton relaxation(2p R 1s).The discovery of this new optical selection rule in a valleytronic 2D system not only considerably enhances knowledge in this area but also establishes a foundation for the control of optical transitions that will be crucial for valley optoelectronic device applications such as 2D valley-polarized THz sources with 2p–1s transitions,optical switches,and coherent control for quantum computing.

Step-size selection for split-step based nonlinear compensation with coherent detection in 112-Gb/s 16-QAM transmission

Non-uniform step-size distribution is implemented for split-step based nonlinear compensation in singlechannel 112-Gb/s 16 quadrature amplitude modulation （QAM） transmission. Numerical simulations of the system including a 20 × 80 km uncompensated link are performed using logarithmic step size distribution to compensate signal distortions. 50% of reduction in number of steps with respect to using constant step sizes is observed. The performance is further improved by optimizing nonlinear calculating position （NLCP） in case of using constant step sizes while NLCP optimization becomes unnecessary when using logarithmic step sizes, which reduces the computational effort due to uniformly distributed nonlinear phase for all successive steps.

Active Disturbance Rejection Control for PMLM Servo System in CNC Machining

Uncertain friction is a key factor that influences the accuracy of servo system in CNC machine.In this paper,based on the principle of Active Disturbance Rejection Control(ADRC),a control method is proposed,where both the extended state observer(ESO) and the reduced order extended state observer(RESO) are used to estimate and compensate for the disturbance.The authors prove that both approaches ensure high accuracy in theory,and give the criterion for parameters selection.The authors also prove that ADRC with RESO performs better than that with ESO both in disturbance estimation and tracking error.The simulation results on CNC machine show the effectiveness and feasibility of our control approaches.