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Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department.

Photoalignment by azo dye nanolayers can provide high alignment quality for large-area liquid crystal devices. Application of this technology to active optical elements for signal processing and communications is a hot topic of photonics research. In this article, we review recent demonstrations and performance of liquid crystal photonic devices, discuss the advantages of the proposed technology, and identify challenges and future prospects in the research field of photoaligned multi-domain liquid crystal structures. We believe that the developments discussed here can provide directions for future research and potential opportunities for applications of liquid crystal devices based on multi-domain photoalignment.

The rapid development of modern industrial technology has led to the increase of machinery precision. Laser tracking measurement systems represent a novel type of coordinate measurement method, which was developed on the basis of metrology. In this paper, we aim to define a single-station 3D coordinate rotating laser tracking measurement system based on the principle of the space coordinate method. In view of the current architecture and optical path of the system, we establish the ideal mathematical model of the system and derive the coordinate expression for arbitrary measured points in the measurement space. The output response of the photoelectric position detector to the rotating laser and the linearity of the position signal in the detection circuit have been detected via a concrete experiment. A laser tracking system was used to track the target mirror mounted on the coordinate measuring machine measuring spindle. It is shown that stable tracking is possible during the 3D movement of a cat's eye retroreflector if its velocity is 0.2 m/s and the distance to the moving object is 1–2 m. The corresponding velocity of the object must be 0.4 m/s. Our system provides a feasible implementation method for the tracking of the moving target space position.

Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we propose fractional Lévy stable motion for degradation modeling. First, we define fractional Lévy stable motion simulation algorithms. Then, we demonstrate the LRD and heavy-tailed property of fLsm to provide support for the model. The proposed method is validated with the C-MAPSS dataset obtained from the turbofan engine. Principle components analysis (PCA) is conducted to extract sources of variance. Experimental data show that the predictive model based on fLsm with exponential drift exhibits superior accuracy relative to the existing methods.

Interactions between liquid crystal molecules and target analytes open up various biosensing applications for quick screening and point-of-care applications. In this review, we categorized biosensors by type, depending on the liquid crystal mesophase, and considered several applications for the detection of biomolecules, point-of-care diagnostics and environmental monitoring. We also discuss interactions between polarized light and target pathogens dispersed in biological fluids, which result in the change of the polarization state. An array of the Stokes parameters can be compared with the pattern, and a proper pathogen can be manifested. We suggest that a combination of a micropolarizer array and a complementary metal oxide semiconductor sensor is an optimal setup for the detection of pathogens. Herein, we discuss the working principles of liquid crystal biosensors and their fabrication principles. In addition, relevant theoretical and practical issues related to liquid crystal biosensors are outlined. In general, this review gives an in-depth survey of the research on liquid crystal-based sensors, making it easier for researchers to locate their niche and make contributions to this subject from multiple viewpoints.

The capacity regeneration phenomenon is often overlooked in terms of prediction of the remaining useful life (RUL) of LIBs for acceptable fitting between real and predicted results. In this study, we suggest a novel method for quantitative estimation of the associated uncertainty with the RUL, which is based on adaptive fractional Lévy stable motion (AfLSM) and integrated with the Mellin–Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long-range dependence, has a non-Gaussian distribution, and accurately describes heavy-tailed properties. Additionally, the nonlinear drift coefficients of the model can be adaptively updated on the basis of the degradation trajectory. The performance of the proposed RUL prediction model was verified by using the University of Maryland CALEC dataset. Our forecasting results demonstrate the high accuracy of the method and its superiority over other state-of-the-art methods.

In this article, we present the procedure of preparation of flexible electronic paper with a photosensitive azo dye layer as the key element for changing the orientation of the polarization plane. The main steps of the technology for the fabrication of flexible e-paper are reported. The possible production of Digital Mirror Devices and the roll-to-roll process is discussed. Images on flexible e-paper are demonstrated, including bank card options. The advantages of optically rewritable e-paper technology in comparison with the e-ink usually used for this purpose are highlighted. Potential applications of flexible optically rewritable e-paper include price tags for supermarkets, indoor and outdoor advertisements, smart card labels, etc.

Currently, residual useful life (RUL) prediction models for insulated-gate bipolar transistors (IGBT) do not focus on the multi-modal characteristics caused by the pulse-width modulation (PWM). To fill this gap, the Markovian stochastic process is proposed to model the mode transition process, due to the memoryless properties of the grid operation. For the estimation of the mode transition probabilities, transfer learning is utilized between different control signals. With the continuous mode switching, fractional Weibull motion (fWm) of multiple modes is established to model the stochasticity of the multi-modal IGBT degradation. The drift and diffusion coefficients are adaptively updated in the proposed RUL prediction model. In the case study, two sets of the real thermal-accelerated IGBT aging data are used. Different degradation modes are extracted from the meta degradation data, and then fused to be a complex health indicator (CHI) via a multi-sensor fusion algorithm. The RUL prediction model based on the fWm of multiple modes can reach a maximum relative prediction error of 2.96% and a mean relative prediction error of 1.78%. The proposed RUL prediction model with better accuracy can reduce the losses of the power grid caused by the unexpected IGBT failures.

With the advancement of information display technologies, research on liquid crystals is undergoing a tremendous shift to photonic devices. For example, devices and configurations based on liquid crystal materials are being developed for various applications, such as spectroscopy, imaging, and fiber optics. One of the problems behind the development of photonic devices lies in the preparation of patterned surfaces that can provide high resolution. Among all liquid crystal alignment techniques, photoalignment represents a promising non-contact method for the fabrication of patterned surfaces. In this review, we discuss the original research findings on electro-optic effects, which were mainly achieved at the Department of Electronic and Computer Engineering of the Hong Kong University of Science and Technology and the collaborating research laboratories.

In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

Tool wear will reduce workpieces’ quality and accuracy. In this paper, the vibration signals of the milling process were analyzed, and it was found that historical fluctuations still have an impact on the existing state. First of all, the linear fractional alpha-stable motion (LFSM) was investigated, along with a differential iterative model with it as the noise term is constructed according to the fractional-order Ito formula; the general solution of this model is derived by semimartingale approximation. After that, for the chaotic features of the vibration signal, the time-frequency domain characteristics were extracted using principal component analysis (PCA), and the relationship between the variation of the generalized Hurst exponent and tool wear was established using multifractal detrended fluctuation analysis (MDFA). Then, the maximum prediction length was obtained by the maximum Lyapunov exponent (MLE), which allows for analysis of the vibration signal. Finally, tool condition diagnosis was carried out by the evolving connectionist system (ECoS). The results show that the LFSM iterative model with semimartingale approximation combined with PCA and MDFA are effective for the prediction of vibration trends and tool condition. Further, the monitoring of tool condition using ECoS is also effective.

The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of power distribution networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief network (DBN) simulation model for power distribution network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of power distribution network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural network to the reliability analysis of distribution network.

Power from solar energy is not reliable, due to weather-related factors, which diminishes the power system’s reliability. Therefore, this study suggests a way to predict the intensity of solar irradiance using various statistical algorithms and artificial intelligence. In particular, we suggest the use of a hybrid predictive model, combining statistical properties and historical data training. In order to evaluate the maximum prediction steps of solar irradiance, the maximum Lyapunov exponent was applied. Then, we used the cosine similarity algorithm in the hidden Markov model for the initial prediction. The combination of the Hurst exponent and tail parameter revealed the self-similarity and long-range dependence of the fractional generalized Pareto motion, which enabled us to consider the iterative predictive model. The initial prediction was substituted into a stochastic differential equation to achieve the final prediction, which prevents error propagation. The effectiveness of the hybrid model was demonstrated in the case study.

A comprehensive theory of light-reflective characteristics and experimental technique of liquid crystal layer thickness control for flexible optically rewritable electronic paper is presented. Cylindrical pillars were used to control the gap between flexible substrates. The introduced prototype of optically rewritable electronic paper has shown very promising performance. In this regard, we report theoretical results of structural photosensitive alignment of nematic liquid crystals on flexible substrate. The focus of theoretical study is on understanding the self-assembled complex structure, governed by the interplay between surface anchoring and liquid crystal elasticity. Mueller matrix spectroscopic ellipsometry was used to study light-reflecting characteristics and polarization properties of the twisted nematic film.

This paper is concerned with the more general nonlinear stochastic Volterra integral equations with doubly singular kernels, whose singular points include both s=t and s=0. We propose a Galerkin approximate scheme to solve the equation numerically, and we obtain the strong convergence rate for the Galerkin method in the mean square sense. The rate is min{2−2(α1+β1),1−2(α2+β2)} (where α1,α2,β1,β2 are positive numbers satisfying 0<α1+β1<1, 0<α2+β2<12), which improves the results of some numerical schemes for the stochastic Volterra integral equations with regular or weakly singular kernels. Moreover, numerical examples are given to support the theoretical result and explain the priority of the Galerkin method.

Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.

In this review we describe the reversible photoalignment effect imposed on the director in nematic liquid crystals that provides an approach for fabrication of advanced optically addressed devices. Several new concepts have been developed to render photosensitive materials during the past decade. Functional soft azo dye compounds exhibiting distinct functionalities in response to polarized light are highly desirable for fabrication of optically rewritable electronic paper. An optically rewritable element base using simple and inexpensive materials can potentially enable the development of novel environmentally friendly, paper-like gadgets with improved functionality over regular electronic paper. We argue that an optically rewritable technique is relevant for some applications, where conventional paper might be irrelevant. In particular, we have tested and discussed several techniques of color and 3D image formation. This strategy for fabrication of novel devices offers versatile methods for visualization. We also show that the intensity modulation of the irradiation light has a potential to generate improved grayscale visualization. This principle is based on the statistical distribution control of photosensitive azo dye molecules, driven by the incident polarized light. Additionally, we discuss the functional characteristics of the developed prototypes.

This paper deals with the tire dynamic characteristics, which enable to forecast the trend of vibration signals, obtained from the tire-road system. Theory of the long-range dependence and the finite-element method were employed in this paper. To forecast the resonance effects, we use different frequency modes to avoid the appearance of undesirable inherent frequencies. The vibration intensity of the tire is related to the attrition rate with long-range dependence, which is distinguished by the Hurst effect. The model data can be obtained by the tire vibration sequence from a common pavement. Consequently, we used the fractional auto-regressive integrated moving average approach to forecast the vibration trend and the tire spectrum characteristics simultaneously. The proposed method can be useful for the monitoring of driving performance, tire design, and driving comfort improvement.

Photoalignment of liquid crystals by using azo dye molecules is a commonly proposed alternative to traditional rubbing alignment methods. Photoalignment mechanism can be well described in terms of rotational diffusion of azo dye molecules exposed by ultraviolet polarized light. A specific feature of the irradiated light is the intensity dependent change of azimuthal anchoring of liquid crystals. While there are various mechanisms of azo dye photoalignment, photo-reorientation occurs when dye molecules orient themselves perpendicular to the polarization of incident light. In this review, we describe both recent achievements in applications of photoaligned liquid crystal cells and its simulation. A variety of display and photonic devices with azo dye aligned nematic and ferroelectric liquid crystals are presented: q-plates, optically rewritable flexible e-paper (monochromatic and color), and Dammann gratings. Some theoretical aspects of the alignment process and display simulation are also considered.

This paper examines different applications of ferroelectric liquid crystal devices based on photo-alignment. Successful application of the photo-alignment technique is considered to be a critical breakthrough. A variety of display and photonic devices with azo dye aligned ferroelectric liquid crystals is presented: smart glasses, liquid crystal Pancharatnam–Berry phase optical elements, 2D/3D switchable lenses, and laser therapy devices. Comparison of electro-optical behavior of ferroelectric liquid crystals is described considering the performance of devices. This paper facilitates the optimization of device design, and broadens the possible applications in the display and photonic area.

Surface Plasma resonance (SPR) sensors combined with biological receptors are widely used in biosensors. Due to limitations of measurement techniques, small-scale, low accuracy, and sensitivity to the refractive index of solution in traditional SPR prism sensor arise. As a consequence, it is difficult to launch commercial production of SPR sensors. The theory of localized surface plasmon resonance (LSPR) developed based on SPR theory has stronger coupling ability to near-field photons. Based on the LSPR sensing theory, we propose a submicron-sized golden-disk and graphene composite structure. By varying the thickness and diameter of the array disk, the performance of the LSPR sensor can be optimized. A graphene layer sandwiched between the golden-disk and the silver film can prevent the latter from oxidizing. Symmetrical design enables high-low concentration of dual-channel distributed sensing. As the fixed light source, we use a 632.8-nm laser. A golden nano-disk with 45 nm thickness and 70 nm radius is designed, using a finite difference time domain (FDTD) simulation system. When the incident angle is 42°, the figure of merit (FOM) reaches 8826, and the measurable refractive index range reaches 0.2317.

In the affiliation name of the article “Fractional autoregressive integrated moving average and finite-element modal: the forecast of tire vibration trend” [1], the title: “Ufa State Petroleum University” should have read as follows: “Ufa State Petroleum Technological University”.

The present research considers and explains the application of the Haar wavelets as basis functions in solution of the propagation of perturbations in low-dimensional anisotropic media. The computations of the relaxation problem in the form of traveling waves have shown that the present approach possesses several advantages over regular methods.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value. [ABSTRACT FROM AUTHOR]