Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution

The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T₁) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T₁ value was better than the transverse relaxation time constant (T₂) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

MG410采煤机滚筒辅助叶片板的增设与应用

针对MG170/410-WD滚筒式采煤机在大倾角、仰角、“三软”煤层使用中遗漏在机道中的浮煤多、装煤效率低等问题,通过添加螺旋板数量对采煤机滚筒进行改造。改造后,在滚筒转速不变的情况下,提高抛煤效率,缩短了工作面割煤和工作面空顶时间,提高了装煤效率,从源头上降低了顶板管理的安全风险。     

富平银沟遗址青瓷标本的类型学分析

富平银沟遗址位于陕西省渭南市富平县华朱乡东北部银沟村。经过为期两年的考古调查与勘探,在该地区发现和搜集了大量的精美陶瓷产品。其中青瓷产品占比最大,具有很高的研究价值。本文主要通过选取银沟地区青瓷器形较为完整,并具有典型代表性的青瓷标本进行类型学的整理分析。根据器形,青瓷标本可分为碗、盘、盒、钵、香炉、瓶、罐、壶等。通过与耀州窑典型青瓷器的器形与纹饰对比,从器物的类型学解析银沟遗址青瓷器的烧造年代时期。     

Nb和时效时间对热轧中锰TRIP钢中P偏聚的影响

热轧态中锰TRIP钢首先经650 ℃退火2 h,随后在550 ℃进行等温时效热处理,采用场发射扫描电镜(FE-SEM)研究该钢中P的偏聚和时效析出行为的变化情况。结果表明,中锰TRIP钢中P在晶界的偏聚是一种非平衡偏聚现象,临界时间约为50 h,与理论计算结果48 h较为吻合。在局部偏聚区域内,C与P存在共偏聚的关系,即P偏聚量高的地方,C含量也高。而合金元素Nb具有抑制P偏聚的效果,在20~70 h时效时间内,可以相对降低6.57%~19.5%的最大P偏聚量。根据电子背散射衍射(EBSD)菊池线分析,P偏聚量低于2.28at%时,P为固溶状态,高于2.28at.%时,P为析出状态。     

Use of conditioning time to investigate the mechanisms of interactions of selected fatty acids on hematite Part II laboratory investigations

It is the aim of this paper to examine the effects of conditioning time on the flotation of hematite using three technical grade fatty acid reagents as providing additional evidence on their mechanism of interaction with the hematite surface. Various mechanisms have been postulated as occurring as conditioning time is increased. Both physical (e.g. conditioning time and power input) and chemical (nature, dispersion and solubility of the adsorbing species) contribute to the mechanisms of attachment of collector. In this paper, the mechanism of attachment of oleate to hematite can be readily explained by chemisorption, but the mechanism of attachment of lauric acid appears to be physical adsorption at neutral pH. The flotation of hematite with a mixture of tallow-type fatty acids (palmitic, stearic and oleic acids) is very sensitive to conditioning time, and suggests that, even though flotation is very effective at short conditioning times, it is very susceptible to the presence of fines and their associated high surface areas. It is therefore obvious that both the physical and chemical conditions contribute to the mechanisms of adsorption of fatty acids on iron-containing oxide minerals and must be understood in order to optimise the flotation of these minerals in an industrial situation.     

Non-chaotic and chaotic propagation of stationary and dynamic images through MVKS turbulence

Anisoplanatic electromagnetic (EM) propagation across a turbulent atmosphere has been recently examined for an unmodulated carrier propagating over an image-bearing transparency through optical lensing, and for the embedded information inside a carrier recovered using heterodyning and digital demodulation. Carrier modulation yielded better recovery than simple lens-based imaging. A possible mitigation strategy is proposed whereby the image information is encrypted on an RF chaotic carrier, thereafter secondarily embedded onto an optical carrier. Results based on the modified von Karman (MVKS) and the Hufnagel-Valley (H-V) models showed that the signal/image recovery under turbulence is improved compared with non-chaotic propagation. The case of time-varying/dynamic images is also taken up; it is demonstrated via cross-correlation products that turbulence is mitigated by the use of chaotic carrier encryption. Overall, transmission via chaos offers mitigation against distortions due to turbulence along with the security feature inherent via the chaos keys which prevent signal recovery without key-matching.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.