AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions.

Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

We have developed a protein array system,named"Phospho-Totum",which reproduces the phosphorylation state of a sample on the array.The protein array contains 1471 proteins from 273 known signaling pathways.According to the activation degrees of tyrosine kinases in the sample,the corresponding groups of substrate proteins on the array are phosphorylated under the same conditions.In addition to measuring the phosphorylation levels of the 1471 substrates,we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation,tyrosine kinase activation,and a list of kinase inhibitors that produce phosphorylation states similar to that of the sample.The Phospho-Totum system,which seamlessly links and interrogates the measurements and analyses,has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples,but also be useful for drug discovery,particularly for screening targeted kinases for potential drug kinase inhibitors.

Competitive Intelligence： Bibliometric Research on Early Signals

Considering the importance of the anticipated intelligence issue on the current scenario, this article aims at understanding the scientific production about the subject, contributing to future researchers with greater academic knowledge about the productivity in the area. Therefore, a bibliometric research was carried out on weak signals and correlated matters between 1980 and 2010 and was supported by the literature review about the subject. The suggested analysis contributes to greater academic knowledge on productivity in the field, providing a broad perspective for future researchers who catch sight of this subject as a potential for further studies.

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.

Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

INTELLIGENT CONTROL SYSTEM OF PULSED MAG WELDING INVERTER BASED ON DIGITAL SIGNAL PROCESSOR

A fuzzy logic intelligent control system of pulsed MAG welding inverter based on digital signal processor （DSP） is proposed to obtain the consistency of arc length in pulsed MAG welding. The proposed control system combines the merits of intelligent control with DSP digital control. The fuzzy logic intelligent control system designed is a typical two-input-single-output structure, and regards the error and the change in error of peak arc voltage as two inputs and the background time as single output. The fuzzy logic intelligent control system is realized in a look-up table （LUT） method by using MATLAB based fuzzy logic toolbox, and the implement of LUT method based on DSP is also discussed. The pulsed MAG welding experimental results demonstrate that the developed fuzzy logic intelligent control system based on DSP has strong arc length controlling ability to accomplish the stable pulsed MAG welding process and controls pulsed MAG welding inverter digitally and intelligently.

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

Artificial intelligence for modeling uveal melanoma

Understanding of the cellular signaling pathways involved in cancer disease is of great importance.These complex biological mechanisms can be thoroughly revealed by their structure,dynamics,and control methods.Artificial intelligence offers rule-based models that favor the research of human signaling processes.In this paper,we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma.A language is described that allows us:(1)To define the system states and elements with their alterations;(2)To model the dynamics of the cellular system;and(3)To perform inference-based analysis with the logical tools of the language.

Adaptive decentralized AI scheme for signal recognition of distributed sensor systems

Artificial intelligence(AI)plays a critical role in signal recognition of distributed sensor systems(DSS),boosting its applications in multiple monitoring fields.Due to the domain differences between massive sensors in signal acquisition conditions,such as manufacturing process,deployment,and environments,current AI schemes for signal recognition of DSS frequently encounter poor generalization performance.In this paper,an adaptive decentralized artificial intelligence(ADAI)method for signal recognition of DSS is proposed,to improve the entire generalization performance.By fine-tuning pre-trained model with the unlabeled data in each domain,the ADAI scheme can train a series of adaptive AI models for all target domains,significantly reducing the false alarm rate(FAR)and missing alarm rate(MAR)induced by domain differences.The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme,showcasing a FAR of merely 4.3%and 0%,along with a MAR of only 1.4%and 2.7%within two specific target domains.The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.

智能传感技术在水肥一体系统中的应用研究

以进一步提升水肥一体机系统的作业效率为目标,选取智能传感的监测技术,针对整机的监测控制与信号处理展开应用设计研究。考虑水肥一体机过程作业肥液融合的均匀性及系统各模块之间的协同性功能实现,结合微分补偿的传感数据算法处理方法,进行智能传感的水肥一体机架构布局,并匹配可执行的软件控制程序及硬件实施结构,进行实地传感应用监测与灌施控制作业试验。结果表明:水肥一体机系统的数据监测准确率可达95.25%,系统故障率相对降低3.79%,监测数据准确及时,能够确保系统各环节指令得到有效的调整与反馈,进而保证灌施土壤的含水稳定率相对提升7.87%,对于作物的稳定生长与产量提升有重要的参考价值。

An ATMS data-driven method for signalized arterial coordination

In order to minimize the delays and stops caused by the early started coordinated green phase of the vehicle- actuated signal systems, a stochastic offsets calculation method based on the new types of advanced traffic management system （ATMS） data is proposed. As the mainline green starts randomly in vehicle-actuated signal systems, the random theory is applied to obtain the distribution of the unused green time at side streets based on the green gap-out mechanism. Then, the green start time of the mainline can be selected at the point with maximum probability to minimize the delays or stops caused by the randomly started mainline green. A case study in Maine, USA, whose traffic conditions are similar to those of the middle-size Chinese cities, proves that the proposed method can significantly reduce the travel time and delays.

轴承故障声发射信号的涡旋聚焦传输方法

针对滚动轴承内部供电及电信号传输难题,研究了基于声发射的轴承智能感知技术。首先,基于故障轴承转动振动特性构建声信号时序相差涡旋传输模型。然后,设计了环形中空式声超表面结构,研究了声涡旋聚焦机制及超表面透射性能,并仿真分析了故障类型与声场涡旋拓扑荷数的关联关系。最后,通过故障轴承的模拟与试验装置验证了轴承故障声发射信号的涡旋聚焦传输方法。

水下通信中的自适应门限信号智能检测方法

在水下通信的自适应门限信号检测过程中,采用一个固定的阈值进行判决,这种方法忽略了信号和噪声之间的动态关系,误码率较高。基于此,研究水下通信中的自适应门限信号智能检测方法。首先识别信号的特性,对信号进行初步分类。其次根据识别的信号特性,动态调整门限值,以确保在不同的信号环境下都能获得最佳的检测效果。最后利用调整后的门限值判决接收到的信号,区分出有效信号、噪声或无效信号,从而智能检测水下通信信号。实验结果证明了该方法在不同信噪比条件下的卓越性能和可靠性,为水下通信系统的性能提升提供了重要依据。

滚珠丝杠副故障振动信号分析及智能诊断方法综述

滚珠丝杠副作为一种旋转运动与直线运动相互转化的高精度部件,被广泛应用在机床、汽车、航空航天等机械设备中,其健康状态对设备的性能和质量具有重大影响;针对滚珠丝杠副振动信号的特点,系统综述了滚珠丝杠副故障振动信号处理及智能诊断方法;介绍了滚珠丝杠副振动信号的特征分析方法,包括时域分析和基展开方法;讨论了滚珠丝杠副智能故障分类方法,包括支持向量机、反向传播神经网络和卷积神经网络等;对当前滚珠丝杠副振动信号处理方法及故障诊断的研究现状进行了总结,并对未来潜在的发展方向进行了展望。

一种提高船闸运行效率的信号装置系统

针对当前船闸信号装置缺乏倒计时显示和人工操作可能导致信号跳转不及时的问题,本文提出了一种提高船闸运行效率的信号装置系统。该系统采用红绿信号灯和倒计时器组成信号装置,通过监控摄像头和激光雷达等感知设备实时采集过闸船舶数据,并利用人工智能和图像处理技术进行处理和分析。系统能够根据闸室内船舶的进出情况,实现红绿信号的自动跳转和倒计时显示功能,从而提高船闸的运行效率,减少过闸时间损耗,提升船舶过闸的安全性和顺畅性。

具有隐含概率条件的磁力泵机组健康状况评估

为了解决磁力泵在智能诊断过程中因缺乏概率条件不能评估磁力泵机组的健康状况,以及调控过程不可知的问题,以磁力泵为研究对象,对它的振动信号进行了研究,提出了一种基于Baum-Welch的磁力泵机组健康诊断机制。首先,利用小波包技术与K均值聚类分析,对采集的振动信号进行了滤波和分类,实现了对磁力泵机组在某一时刻运行状态进行分类的目的;其次,根据识别得到的状态序列,使用Baum-Welch算法得到了磁力泵在一段运行时间内可能存在的隐含概率组合;最终,根据概率序列的计算结果,达到了评估磁力泵机组健康状况的目的,应用某磁力泵机组在不同健康条件下的空化和非空化状态数据,对其健康诊断机制进行了验证。研究结果表明:使用小波包分解算法提取的第9层第12组子频带信号对空化和非空化状态最大的识别率为100%,因而该组信号可用于特征状态的识别;提取的均方根、方差、标准差、绝对均值特征在对空化状态分类时,最小的重复率为0,因而这四个特征可用于工作状态的自动分类;经Baum-Welch算法估测得到的健康机组和衰退机组的非空化状态收敛概率分别为95.43%和90.2%,与历史统计值95.40%和91.11%基本一致,完全可用于磁力泵机组运行健康状况的评估。该研究结果可为具有隐含概率条件的磁力泵机组运行健康状态评估提供技术支撑。

城轨信号智能运维系统研究

针对当前城市轨道交通信号传统维保存在的设备数据感知差、故障判断和维修指导不够准确、故障发生到恢复运维时间较长等诸多痛点,本文通过分析传统运维模式及技术现状和探索城市轨道交通智能运维网络化、智能化运维管理需求,提出一种城轨信号智能运维系统,利用多元数据感知、物联网、人工智能等技术实现信号关键系统及设备状态可视化、故障智能分析诊断、设备健康度表现打分等功能。城轨信号智能运维系统实现示范应用线路信号设备全生命周期管理,信号运维从高速向高质量发展。

基于视觉感知技术的绘画色彩智能调控系统设计

为了提升绘画色彩呈现效果,设计基于视觉感知技术的绘画色彩智能调控系统。设计系统模块结构和模块的硬件结构;系统软件上设计了RGB信号传输算法流程,利用基于视觉感知的改进信号映射算法调整接收的RGB信号,得到新的绘画色彩RGBW信号;依据RGBW信号设置绘画色彩调整参数,依据色彩调整参数智能调控4种色彩信号;通过RGB信号合成单元,合成调控后的4种色彩信号,得到新的RGB信号,即智能调控后的绘画色彩。实验证明:该系统可有效采集RGB信号,并精准智能调控绘画色彩,提升绘画色彩呈现效果;经过该系统调控后的绘画色彩RGB信号分布更为集中,即绘画色彩智能调控效果较优。

基于混合模型的非侵入式负荷监测数据的生成

非侵入式负荷监测(non-intrusive load monitoring,NILM)是一种无需进入每个用电器内部系统,仅在用户总线入口处安装监测设备的技术.在开展NILM技术研究时,往往需要收集大规模的用户负荷数据来证明所提出方法的普适性,此需求不可避免地带来了繁重的数据收集与整理负担.为克服该挑战,设计了一种结合周期信号频率不变变换(frequency invariant transformation for periodic signals,FIT-PS)原理与时间序列生成对抗网络(time series generative adversarial networks,TimeGAN)的混合模型,记为FIT-PSTimeGAN.针对全球家庭与工业瞬态能量数据集(worldwide household and industry transient energy dataset,WHITED)中的空调、微波炉、吸尘器、冰箱和热水壶5种电器,运用FIT-PS对负荷数据集进行切割和拼接,构建TimeGAN不同状态下的训练集和测试集.评估测试集的效果发现,生成的波形数据与真实数据表现出高度一致性.进一步采用FIT-PS对训练得到的生成数据进行截取和拼接,生成满足测试需求的完整的单负荷波形和多负荷波形.对这些生成的波形与相同状态下的真实数据进行对比,结果显示两者吻合度很高.与自回归模型和生成对抗网络(generative adversarial network,GAN)模型相比,FIT-PS-TimeGAN模型在生成数据的性能方面表现更优.研究结果表明,FIT-PS-TimeGAN混合模型能够有效生成符合标准电器运行规律的波形和场景数据.

Development of an Intelligent Vehicle Experiment System

Existing vehicle experiment systems tend to focus on the research of vehicle dynamics by conducting performance tests on every system or some parts of the vehicle so as to improve the entire performance of the vehicle. Virtual technology is widely utilized in various vehicle test-beds. These test-beds are mainly used to simulate the driving training, conduct the research on drivers＇ behaviors, or give virtual demonstrations of the transportation environment. However, the study on the active safety of the running vehicle in the virtual environment is still insufficient. A virtual scene including roads and vehicles is developed by using the software Creator and Vega, and radars and cameras are also simulated in the scene. Based on dSPACE＇s rapid prototyping simulation and its single board DS1103, a simulation model including vehicle control signals is set up in MATLAB/Simulink, the model is then built into C code, and the system defined file（SDF） is downloaded to the DS1103 board through the experiment debug software ControlDesk and is kept running. Programming is made by mixing Visual C＋＋ 6.0, MATLAB API and Vega API. Control signals are read out by invoking library function MLIB/MTRACE of dSPACE. All the input, output, and system state values are acquired by arithmetic and are dynamically associated with the running status of the virtual vehicle. An intelligent vehicle experiment system is thus developed by virtue of program and integration. The system has not only the demonstration function, such as general driving, cruise control, active avoiding collision, but also the function of virtual experiment. Parameters of the system can be set according to needs, and the virtual test results can be analyzed and studied and used for the comparison with the existing models. The system reflects the running of the intelligent vehicle in the virtual traffic environment, at the same time, the system is a new attempt performed on the intelligent vehicle travel research and provides also a new research method for the development of intelligent vehicles.