Research progress of cholesteric liquid crystals with broadband reflection characteristics in application of intelligent optical modulation materials

Cholesteric liquid crystals（CLCs） have recently sparked an enormous amount of interest in the development of soft matter materials due to their unique ability to self-organize into a helical supra-molecular architecture and their excellent selective reflection of light based on the Bragg relationship.Nowadays,by the virtue of building the self-organized nanostructures with pitch gradient or non-uniform pitch distribution,extensive work has already been performed to obtain CLC films with a broad reflection band.Based on authors＇ many years＇ research experience,this critical review systematically summarizes the physical and optical background of the CLCs with broadband reflection characteristics,methods to obtain broadband reflection of CLCs,as well as the application in the field of intelligent optical modulation materials.Combined with the research status and the advantages in the field,the important basic and applied scientific problems in the research direction are also introduced.

Reconfigurable Intelligent Surface-Based Hybrid Phase and Code Modulation for Symbiotic Radio

Reconfigurable intelligent surface(RIS)-assisted symbiotic radio is a spectrum-and energy-efficient communication paradigm,in which an RIS performs passive beamforming to enhance active transmission,while using the electromagnetic waves from the active transmission for additional information transfer(i.e.,passive transmission).In this paper,a hybrid RIS-based modulation,termed hybrid phase and code modulation(HPCM),is proposed to improve the reliability of RIS-assisted symbiotic radio.In RIS-HPCM,the RIS simultaneously performs direct sequence spread spectrum and passive beamforming on incident signals.Moreover,both the spreading code and phase offset are exploited to carry the RIS’s own information.A low-complexity detector is designed,in which the receiver first detects the spreading codes and then demodulates the constellation symbols.We analyze the bit error rate(BER)performance of RIS-HPCM over Rician fading channels.BER upper bounds and approximate BER expressions are derived in closed-form for maximum-likelihood and low-complexity detectors,respectively.Simulation results in terms of BER verify the analysis and show the superiority of RIS-HPCM over the existing RIS-based modulation.

Differential Scheme for Reconfigurable Intelligent Surface-Based Spatial Modulation System

In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the receiver.The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems,as it avoids the high-order matrix calculation and improves the spectral efficiency.A mathematical framework is developed to determine the theoretical average bit error probability(ABEP)of the system using RIS-DSM.The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification.Finally,simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.

Intelligent Automation Module-Based Gear Edge Grinding System

As industry progresses toward intelligent production and development,on-site workers that perform repetitive tasks will be replaced by intelligent machines.Currently,automation applications still have the following problems:(1)on-site personnel are required to line up the workpieces before a robot arm can pick it up;(2)the trajectory generated by offline programming software must be adjusted by on-site personnel in accordance with the processing results;and(3)because of workpiece positioning errors and tool wear,achieving acceptable processing results is difficult.This study developed intelligent application modules that solve the aforementioned automation application problems.These modules predict processing quality,generate trajectory,enable robot arms to load and unload randomly positioned workpieces,and automatically calibrate the system.An automatic gear edge grinding system was developed by integrating each module;the system increases the processing efficiency and solves the current problem of manual grinding being required after gear processing.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

Controller Design of Intelligent Lighting Network

Studied are the controller design and basic principles of intelligent lighting network. TI’s MSP430F123 is used as a main controller. By using the ZigBee modules(Xbee/Xbee-PRO) and the GSM module(SIM300C) for wireless communications, the lighting control is enabled to access wireless network. This system uses a mobile phone to achieve light on-off directly, which can accomplish wireless control of intelligent lighting in families.

Research on Intelligent Control and protection of flexible HVDC

This paper introduces the working principle of HVDC control and protection system. And the paper summarizes the flexible HVDC technology features and application fields and describes the composition and software intelligent flexible HVDC control and protection system configuration. Finally, the simulation results are also given, experimental results show： HVDC flexible intelligent control and protection has a good control effect in the steady state and transient process, suitable for practical engineering application.

基于LoRa通信的无线发射台信号智能监测技术研究

为实现大面积无线发射台信号覆盖监测,有效地抵御监测过程中多径干扰和频率选择性衰减等干扰因素的影响,研究基于LoRa通信的无线发射台信号智能监测技术。在无线发射台发出信号后,转换成数字信号进行对比,识别出有效发射台信号后,通过级联型LoRa组网方式以及LoRa通信技术,将数据发送到服务器中存储,实现无线发射台信号的传输。利用循环位移因子对基带产生的Chirp信号完成调频扩频调制,扩展信号频谱,增强信号抗干扰能力以及传输可靠性,增强信号监测的效果。实验结果表明:该方法能够在距离LoRa终端设备2.5 km处依旧接收到发射台信号,丢包率为12%,能够准确地监测到无线发射台是否存在发射信号。

虚拟I/O模块在DCS以太网通信接口模块上的实现

为减少硬件模块的重复开发,在保留原DCS软硬件系统架构不变的基础上,在以太网通信接口模块硬件上虚拟不同的I/O模块。虚拟I/O模块与硬点I/O模块具有一致的输入、输出及参数属性,且组态方式和产物格式一致。同一以太网通信模块上可在不同的虚拟I/O槽位上配置不同的虚拟I/O模块,如Modbus TCP虚拟I/O模块和GOOSE虚拟I/O模块,进而在同一硬件实现不同的通信功能。虚拟I/O模块的使用一方面减少了DCS系统硬件模块的种类,简化备品备件,另一方面也提高了通信扩展的灵活性和便利性。

Thickness-modulated in-plane Bi2O2Se homojunctions for ultrafast high-performance photodetectors

Bi2O2Se thin film could be one of the promising material candidates for the next-generation electronic and optoelectronic applications. However, the performance of Bi2O2Se thin film-based device is not fully explored in the photodetecting area. Considering the fact that the electrical properties such as carrier mobility, work function, and energy band structure of Bi2O2Se are thickness-dependent, the in-plane Bi2O2Se homojunctions consisting of layers with different thicknesses are successfully synthesized by the chemical vapor deposition(CVD) method across the terraces on the mica substrates,where terraces are created in the mica surface layer peeling off process. In this way, effective internal electrical fields are built up along the Bi2O2Se homojunctions, exhibiting diode-like rectification behavior with an on/off ratio of 102, what is more, thus obtained photodetectors possess highly sensitive and ultrafast features, with a maximum photoresponsivity of 2.5 A/W and a lifetime of 4.8 μs. Comparing with the Bi2O2Se uniform thin films, the photo-electric conversion efficiency is greatly improved for the in-plane homojunctions.

RIS Assisted Dual-Function Radar and Secure Communications Based on Frequency-Shifted Chirp Spread Spectrum Index Modulation

Reconfigurable intelligent surface(RIS)assisted dual-function radar communications(DFRC)system is a promising integrated sensing and communication(ISAC)technology for future 6G.In this paper,we propose a scheme of RIS-assisted DFRC system based on frequency shifted chirp spread spectrum index modulation(RDFI)for secure communications.The proposed RDFI achieves the sensing and transmission of target location information in its radar and communication modes,respectively.In both modes,the frequency-shifted chirp spread spectrum index modulation(FSCSS-IM)signal is used as the baseband signal for radar and communications,so that the signal sent by the radar also carries information.This scheme implements the RIS-assisted beamforming in the communication mode through the azimuth information of the target acquired in the radar mode,so that the signal received from the eavesdropper is distorted in amplitude and phase.In addition,this paper analyzes the radar measurement accuracy and communication security of the FSCSS-IM signal using ambiguity function and secrecy rate(SR)analysis,respectively.Simulation results show that RDFI achieves both excellent bit error rate(BER)performance and physical layer security of communications.

基于改进的YOLOv7油田井场压力表小目标识别

针对油田井场压力表小目标在不同光照条件下特征提取困难且目标检测率低等问题,提出一种基于改进的YOLOv7小目标井场压力表实时检测算法。首先,利用全局最大池化和全局最小池化的简单线性组合生成一种自适应池化层,并使用轻量级模块生成SPEM注意力模块,后将其加入YOLOv7主干网络中,提高网络对不同光照条件下压力表小目标的特征提取;其次,使用模量激活函数替代SiLU函数,减少死亡节点,解决梯度消失问题,提高模型的泛化能力;最后,将原来的YOLOv7中的CIoU损失函数替换为Wise-IoU,实现对损失函数的优化,并利用梯度增益分配策略来减少低质量示例产生的有害梯度,以聚焦普通质量锚盒的预测和回归。实验结果表明,改进后的算法相较于原YOLOv7算法精准率上升1.07%,召回率提升2.08%,同时也优于Faster RCNN、SSD、YOLOv3算法的检测结果,能有效满足油田井场压力表小目标的检测要求,具有较强的工程实践意义。

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

面向智慧交通的神经网络辅助OFDM-IM信道估计方法

作为新一代通信技术,正交频分复用索引调制(OFDM-IM)具有良好的抗子载波间干扰能力与较高的频谱能量效率,在6G协作智慧交通系统(C-ITS)中应用前景广阔。但OFDM-IM由于其载波排布结构,对高速衰落信道的估计性能较差,影响ITS通信可靠性。针对此问题,提出了一种基于索引导频(IP)和神经网络的OFDM-IM信道估计方法。所提方法通过基于IP的时域可靠测试频域插值算法实现信道的精确初始化,并通过深度神经网络(DNN)弥补初始估计中的非线性缺陷与插值误差。仿真结果显示,在IEEE 802.11p帧结构下,所提算法信道估计精度较现有非深度学习辅助方法在不同交通场景与通信参数下均有较为明显的提高。

Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna

Bigeye tuna Thunnus obesus is an important migratory species that forages deeply,and El Niño events highly influence its distribution in the eastern Pacific Ocean.While sea surface temperature is widely recognized as the main factor affecting bigeye tuna(BET)distribution during El Niño events,the roles of different types of El Niño and subsurface oceanic signals,such as ocean heat content and mixed layer depth,remain unclear.We conducted A spatial-temporal analysis to investigate the relationship among BET distribution,El Niño events,and the underlying oceanic signals to address this knowledge gap.We used monthly purse seine fisheries data of BET in the eastern tropical Pacific Ocean(ETPO)from 1994 to 2012 and extracted the central-Pacific El Niño(CPEN)indices based on Niño 3 and Niño 4indexes.Furthermore,we employed Explainable Artificial Intelligence(XAI)models to identify the main patterns and feature importance of the six environmental variables and used information flow analysis to determine the causality between the selected factors and BET distribution.Finally,we analyzed Argo datasets to calculate the vertical,horizontal,and zonal mean temperature differences during CPEN and normal years to clarify the oceanic thermodynamic structure differences between the two types of years.Our findings reveal that BET distribution during the CPEN years is mainly driven by advection feedback of subsurface warmer thermal signals and vertically warmer habitats in the CPEN domain area,especially in high-yield fishing areas.The high frequency of CPEN events will likely lead to the westward shift of fisheries centers.

ODS层数据对智慧运维应用指标支撑的判断方法探索

通过对智慧运维大数据ODS层中表及数据量、字段数据占比、关键字段规则检查分析手段,为快速判断ODS层数据对智慧运维应用指标的支撑进行了探索。首先分析ODS层的数据表,确定哪些表有数据,有数据表的数据量,然后分析有数据表的每个字段的数据占比,最后通过关键字段的规则设定,分析符合规则字段的数据量、数据占比和不符合规则字段的数据量,利用以上分析结果达到智慧运维应用指标支撑的快速判断和优化提升ODS层数据质量的目的。

MoS_(2)核壳球上氧掺杂的动力学调制及其电化学分解水效应

在氩气气氛中900℃煅烧前驱物MoS_(2)核壳超级球得到MoS_(2)核壳球。在升温速率为20、10、5、2℃·min^(-1)时,MoS_(2)核壳球上氧掺杂量分别从前驱物的23.1%降低到17.6%、10.8%、5.5%、6.2%。结果表明较低的升温速率可以导致更低的氧掺杂量。基于前驱物特殊的准分子超晶格结构,提出了原位阴离子交换反应机理,以深入理解MoS_(2)核壳球上氧掺杂的动力学调制机理。电化学性能研究表明通过调制氧掺杂量可以有效改善MoS_(2)核壳球电化学分解水的性能。

面向智能信号测试系统的MIMO通信调制分类方法

电力系统继电保护日趋智能化、数字化,继电保护测试性能要求也日趋复杂。保护设备型号复杂,对测试系统的信号调制兼容提出了更高要求。因此,提出了一种新的调制分类方法,可以对二进制相移键控、正交相移键控、16正交幅度调制等多种调制信号进行分类。所提出的分类方法可在存在未知频率、时间和相位偏移并且没有信道状态信息的情况下工作。首先,对接收信号进行预处理,消除时间偏移的影响。其次,通过计算频域信号的高阶累积量来提取关键特征。然后,使用似然比检验确定阈值并进行正确分类概率的封闭式理论推导。最后,在仿真中将该方法与其他方法进行了对比,结果表明,所提出的方法在准确度与计算速度方面均优于其他方法,验证了该方法的有效性。

基于改进YOLOv4的公路路面信息识别算法研究

公路路面信息的获取和识别是构建智慧公路系统的关键技术之一,针对现有的公路路面信息检测算法存在的耗时费力、识别精度差与端对端应用难以实现等问题,提出了一种改进的YOLOv4算法。为增强模型泛化能力,以IoU值度量边界框与先验框的距离,形成改进的k-means聚类算法对路面遗撒与病害数据集进行锚框聚类;为提升网络特征描述能力,在特征增强网络PANNet的最后3个分支上分别添加兼具通道与空间注意力机制的轻量化CBAM模块,保证了模块在现有网络架构中做到即插即用;为节约参数与计算力,对稀疏训练后的模型进行通道剪枝,针对道路检测任务对于小目标物体的高识别精度要求进行模型剪枝率的迭代优化,进一步实现公路路面信息识别算法的端对端应用。实验结果表明,改进的YOLOv4网络模型的mAP0.5较原网络模型提升了0.78%,mAP@0.75较原网络模型提升了1.06%,FPS达到了34.85帧/s,检测效果满足自动识别的性能要求;0.4的剪枝率得到的剪枝模型综合性能较好,在保证模型的mAP0.5达到98.3%的条件下,储存空间较原模型降低了47.6%,GFLOPs较原模型降低了34.0%,参数总数较原模型降低了51.4%,FPS较原模型提升了6.3%,计算复杂度和占用内存都显著降低。研究成果可应用于智慧公路的路网感知能力体系建设,实现对公路路面信息的高效精准采集。

基于改进DINO的铁路接触网异物检测方法

针对铁路接触网异物类别多样、场景多变的开放目标检测问题,提出基于改进DINO目标检测算法的铁路接触网异物检测方法。首先,基于铁路接触网异物图像特征,利用EfficientNet网络替换原始模型中的Resnet主干网络,并结合卷积注意力模块(Convolutional Block Attention Module,CBAM)对EfficientNet网络进行改进,在颈部结构采用改进后的加权双向特征金字塔网络(Bidirectional Feature Pyramid Network,BiFPN),以增强模型对重要特征的关注度,提升检测性能;其次,通过马赛克数据增强和环境干扰等多种数据增强技术对输入图片数据进行处理,丰富样本数据的特征;最后,利用铁路人工智能平台实现铁路接触网异物检测的应用。结果表明:所提方法在性能方面表现优异,其平均精度均值可达89.87%,与YOLOv5,DETR和原始DINO这3种典型的目标检测算法相比,分别提高了6.40%,7.31%和5.75%。该方法能够满足行车线路上接触网异物的准确、快速及智能化识别要求,为接触网异物检测提供重要的技术支撑。