Broadband wavelength designable achromatic grating based on a cholesteric liquid crystal template.

Liquid crystal (LC) gratings have played important roles in light field control due to the advantages of being lightweight, low cost, having no moving parts, and low power consumption. However, the chromatic aberration limits the bandwidth of the LC device and affects the efficiency of the grating. To solve the chromatic aberration issue, a broadband wavelength designable achromatic grating is proposed. Different grating structures are integrated into a single-layer templated cholesteric liquid crystal (CLC) device, and the achromatic diffraction wavelength of the grating can be freely designed from the visible spectral region to the infrared range within the Bragg reflection band of the CLCs. The diffraction intensity of different orders can be changed with the electric field applied to meet the need for dynamic modulation. This grating shows suitable potential applications in optical communication and displays.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Machine learning phase modulation of liquid crystal devices for three-dimensional display.

A machine learning phase modulation scheme based on convolutional neural networks (CNN) and recurrent neural network (RNN) is proposed to carry out the regression task of liquid crystal (LC) device electric field prediction for the 2D/3D switchable display. The hybrid neural network is built and trained based on the illuminance distribution under three-dimensional (3D) display. Compared with manual phase modulation, the modulation method using a hybrid neural network can achieve higher optical efficiency and lower crosstalk in the 3D display. The validity of the proposed method is confirmed through simulations and optical experiments.

Zero-liquid discharge technologies for desulfurization wastewater: A review.

Zero-Liquid Discharge (ZLD) has received a lot of attention due to water scarcity and pollution. This article reviews the treatment of desulfurization wastewater in pretreatment, concentration and solidification with the trend of zero-liquid discharge in China. We summarize the advantages, disadvantages, performance, benefits and other characteristics of different ZLD technologies. Membrane-based technology is an effective means of recycling wastewater in ZLD systems. Therefore, we focus on the application of bipolar membrane electrodialysis (BMED) in desulfurization wastewater and high-salt wastewater treatment, discussing its limitations and solutions. In addition, several Chinese ZLD cases and economic analysis are introduced. It is believed that ZLD will become a new trend in desulfurization wastewater treatment in the future. Therefore, exploring new materials and technologies with low cost and high efficiency is the focus of future work.

A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case.

Multivariable time series forecasting is an important topic of machine learning, and it frequently involves a complex mix of inputs, including static covariates and exogenous time series input. A targeted investigation of this input data is critical for improving prediction performance. In this paper, we propose the fusion transformer (FusFormer), a transformer-based model for forecasting time series data, whose framework fuses various computation modules for time series input and static covariates. To be more precise, the model calculation consists of two parallel stages. First, it employs a temporal encoder-decoder framework for extracting dynamic temporal features from time series data input, which analyzes and integrates the relative position information of sequence elements into the attention mechanism. Simultaneously, the static covariates are fed to the static enrichment module, which is inspired by gated linear units, to suppress irrelevant information and control the extent of nonlinear processing. Finally, the prediction results are calculated by fusing the outputs of the above two stages. Using Mooney viscosity forecasting as a case study, we demonstrate considerable forecasting performance improvements over existing methodologies and verify the effectiveness of each component of FusFormer via ablation analysis, and an interpretability use case is conducted to visualize temporal patterns of time series. The experimental results prove that FusFormer can achieve accurate Mooney viscosity prediction and improve the efficiency of the tire production process.

An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems.

This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the RMSE by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems.

A Tension/Pressure Integrated Resistive Sensor Comprising of a PDMS-LC-MWCNT Composite.

A flexible strain sensor which integrates both pressure sensing and tension sensing functions is demonstrated with an active layer comprising of polydimethy-lsiloxane (PDMS) elastomer, liquid crystal (LC), and multi-walled carbon nanotubes (MWCNTs). The introduction of LC improves the agglomeration of MWCNTs in PDMS and decreases Young's modulus of flexible resistive sensors. The tension/pressure integrated resistive sensor not only shows a broad tensile sensing range of 140% strain but also shows a good sensitivity of the gauge factor, 40, with tensile force. Besides, the tension/pressure integrated resistive sensor also shows good linearity and sensitivity under pressure. The resistance of the pressure sensor increases as the applied pressure increases because of the decrease in the cross-sectional area of the path. The sensor also shows good hydrophobic properties which may help it to work under complex environment. The tension/pressure integrated sensor shows great promising applications in electronic skins and wearable devices.

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images.

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.

High-Reflective Templated Cholesteric Liquid Crystal Filters.

Cholesteric liquid crystals (CLCs) have been widely applied in optical filters due to Bragg reflection caused by their helical structure. However, the reflectivity of CLC filters is relatively low, commonly less than 50%, as the filters can only reflect light polarized circularly either left- or right-handedly. Therefore, a high-reflective CLC filter with a single-layer template was proposed which may reflect both right- and left-handed polarized light. The CLC filters of the red, green, blue color were fabricated by the templating technology, which show good wavelength consistency. Additionally, a multi-phase liquid crystal filter with high reflectance was demonstrated by the single-layer templating technology. The templated CLC or multi-phase liquid crystal filters show great potential applications in the optical community, reflective display, and lasing.

High SNR fingerprinting structure for LC displays.

An optical fingerprinting solution with a switchable backlight is demonstrated for liquid crystal displays (LCDs). In fingerprinting mode, the collimated infrared light is applied to improve the signal-noise ratio (SNR) between the valley and ridge of the fingerprint. Compared with the conventional backlight, the proposed structure can effectively improve the SNR. Furthermore, the fingerprinting mode and the display mode may work at the same time without interference because of the different wavelengths and light paths. It shows the great potential application of LCD-based optical fingerprints.

Multi-plane augmented reality display based on cholesteric liquid crystal reflective films.

To address the accommodation-convergence conflict problem in conventional augmented reality (AR) head-mounted displays, we propose a compact multi-plane display design based on cholesteric liquid crystal (CLC) reflective films and a polarization switch. Because of the polarization selectivity of CLC films, circularly-polarized light with different handedness is reflected by different CLC films, resulting in different optical path lengths and different image depths by the lens. A flicker-free dual-plane prototype with correct focus cues and relatively low operating voltage has been implemented. Moreover, a multi-plane AR display scheme with more than 2 depth planes is proposed by stacking multiple CLC films and polarization switches together.

A Flexible Temperature Sensor Array with Polyaniline/Graphene-Polyvinyl Butyral Thin Film.

Thermal-resistance temperature sensors generally employ temperature-sensitive materials as active layers, which are always deposited on a flexible substrate to improve flexibility. Such a temperature sensor is usually integrated in wearable devices with other sensors, such as pressure sensors and stretchable sensors. In prior works, the temperature and pressure sensors are usually located in different layers in a multifunction sensor, which results in a complicated fabrication process, as well as a large thickness of devices. Meanwhile, many temperature sensors are based on large areas of non-transparent materials, leading to difficulties in integrating display applications. In this paper, we demonstrate a flexible temperature sensor based on polyaniline/graphene (GPANI)-polyvinyl butyral (PVB) thin film and indium tin oxides (ITO)- polyethylene terephthalate (PET) substrates. The GPANI particles embedded in PVB film not only contribute to temperature detection, but also response to external pressures, due to weak deformations. In addition, the thin composite film (2.7 µm) highly improved the transparency. By optimizing the device structure, the sensor integrates temperature and pressure detection into one single layer, which shows a wide temperature range of 25-80 °C, a pressure range of 0-30 kPa, and a high transparency (>80%). The temperature sensor offers great potential for applications in emerging wearable devices and electronic skins.

Three-dimensional display with directional beam splitter array.

Multi-view three-dimensional (3-D) displays using directional beam splitter array were proposed to achieve a perfect 3-D perception with low cross-talk. The multi-direction collimated light may project different images to different viewing zones to form the multi-view autostereoscopic display. Furthermore, a high resolution 3-D display can be realized with a sequential beam splitter array and a sequential liquid crystal display. By optimization, the cross-talk of the directional beam splitter backlight system was lowered to 5% to improve the perception of the 3-D displays.

Super terahertz transparent electrodes.

The use of DMSO-doped poly (3,4-ethylenedioxythiophene):poly (4-styrenesulfonate) ( PEDOT: PSS) thin films produced by spin coating as transparent electrodes is reported. The transmittance of these electrodes at THz frequencies, as well as their surface morphology and electrical conductivity, are subsequently investigated in relation to the thickness of their DMSO-doped PEDOT: PSS film. A maximum conductivity of 5078 S/cm is obtained for a single-layer film (52 nm) doped with 15 vol% DMSO in PEDOT: PSS solution, providing a transmittance of up to 83.5% at 1.22 THz. The newly proposed blue-phase liquid crystal THz modulator provides continuous tunability and full electrical controllability at THz frequencies.

Highly photorefractive hybrid liquid crystal device for a video-rate holographic display.

In this paper, we demonstrate a dynamic holographic display in a quantum dot (ZnS/InP) doped liquid crystal device, where one of the interior cell surfaces is covered by a ZnSe layer. Such a hybrid device shows substantially improved photorefractive sensitivity of 2.2 cm3/J, which is almost 300 times larger than that in ZnS/InP doped liquid crystal device without the ZnSe layer. The holographic grating can form at intensities as low as ~0.8 mW/cm2, and exhibit a fast optical response of several to tens of milliseconds. Exploiting the superior performances of photosensitivity and fast response of this device, we obtain dynamic holographic videos of red, green, and blue colors, as well as a reconstructed image of high gray-scale fidelity.

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

[An Improved ELM Algorithm for Near Infrared Spectral Quantitative Analysis].

Extreme learning machine (ELM) has been applied in near infrared spectral analysis as a novel chemometric method which attracted the attentions of various researchers. However, the dimension of spectral data is usually very high while more hidden nodes should be incorporated in original ELM model for spectral data. Thus the problems of high dimension and high colinearity in the output matrix of hidden layer of ELM model are inevitable. The solutions obtained with the existing Moore-Penrose generalized inverse can be ill-conditional due to the high dimension and high colinearity in the hidden layer output matrix. This study aims to propose an improved ELM to build spectral regression model. The proposed method firstly uses extreme learning machine (ELM) to relate spectral variables to response variable; then the output of each hidden node are treated as new variables; VIP-SPLS ( improved stacked PLS based on variable importance in the projection) proposed by our group recently is used to build the regression model between those new variables and the response variable. In this paper, this method is called as improved ELM (iELM). VIP-SPLS model can fully utilize the output information of each hidden node and can effectively solve the problems of high dimension and high colineariy. At the same time, VIP-SPLS also has the advantage of model ensemble. Therefore, the performance of ELM model used for spectral data can be improved if the VIP-SPLS is incorporated to relate the hidden layer output matrix and response variable. The proposed method is applied to a commonly used benchmark NIR spectral data for evaluation. The results demonstrate that the precision improvement of iELM model is 29.06% to PLS model and 27.47% to original ELM model, respectively.

Light extraction from electroluminescent devices using micro-rod array embedded within glass substrate.

The total internal reflection (TIR) effect in conventional electroluminescent devices causes a large amount of light energy trapped in the devices and result in heat energy that adversely affects the performance of the device. In order to enhance the light out-coupling efficiency without sacrificing the electrical properties, a micro-rod array (MRA) structure fabricated by a femtosecond laser was demonstrated. Green, blue, and red organic light-emitting diodes were employed to verify the effect of the proposed method, which increases out-coupling efficiencies by a factor of 1.9, 1.7, and 1.82, respectively, compared with conventional devices. This highly effective method is compatible with current device fabrication processes and is applicable to full-color electroluminescent devices.

Switchable viewing angle display with a compact directional backlight and striped diffuser.

A compact high-directionality backlight module combined with a striped diffuser is proposed to achieve an adjustable viewing angle for eco-display. The micro-prisms on the compact light guide plate guide the emitting rays to the normal viewing angle, whereas a set of striped diffusers scatter the rays to a wide viewing angle. View cones of ± 10° / ± 55° were obtained for narrow/wide viewing modes with 88% / 85% uniformity of spatial luminance, respectively. Compared with the conventional backlight, the optical efficiencies were increased by factors of 1.47 and 1.38 in narrow and wide viewing modes, respectively. In addition, only 5% of power consumption was needed when the backlight worked in private narrow viewing mode to maintain the same luminance as that of a conventional backlight.

High directional backlight using an integrated light guide plate.

A high directional backlight system that combined a composite microstructure light guide plate (LGP) with a collimated light source was proposed for eco-displays. The collimated planar light was expanded from a point light source and guided towards the normal direction by utilizing the micro-prism array on LGP. High uniformity of spatial luminous, 91%, with a narrow viewing cone of ± 4° can be achieved without additional optical films. Moreover, compared to the conventional backlight, only 5% of power consumption was needed to keep the same luminance, hence, the optical efficiency increased by a factor of 1.47.