Digitalization and intelligentization of manufacturing industry

1 Relying on innovation to realize the historical leapChina has entered a new historical period in her development. In order to achieve scientific development and to accelerate transformation of economic development pattern, the most fundamental issue is to rely on the power of science and technology, and the most crucial element is to improve the capability of independent innovation. The key for promoting China＇s economic and social development is to embark on the innovation-driven track as soon as possible.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

机器学习在智能反射面辅助的通信系统中的应用综述

智能反射面(intelligent reflecting surfaces,IRS)可以通过大量低成本的无源反射元件巧妙地调整信号反射,从而动态改变无线信道,提高通信性能,目前已成为无线通信研究的焦点。然而,由于IRS的加入,整个通信系统变得更加复杂,系统的动态性也更高,使通信系统面临着许多新的挑战。机器学习(machine learning,ML)具有很强的数据处理与自适应能力,能够不断适应变化的环境和需求,可以很好地应对通信系统中的许多挑战。因此,使用ML解决IRS辅助的通信系统中的问题,已经成为当前研究的重点。基于此,对ML在IRS系统中的应用进行了系统性的概述,从IRS辅助的通信系统中存在的问题入手,分别从反射因子的设计与优化、信道处理与建模、资源分配和管理、安全性增强4个方面对ML在IRS系统中的应用进行阐述和分析,并讨论了将ML应用在IRS系统中的优势及未来的发展趋势与挑战。

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

堆石混凝土坝概述及下一代混凝土坝施工技术展望

Over the past few decades,one of the most significant advances in dam construction has been the inven-tion of the rock-filled concrete(RFC)dam,which is constructed by pouring high-performance self-compacting concrete(HSCC)to fill the voids in preplaced large rocks.The innovative use of large rocks in dam construction provides engineers with a material that requires less cement consumption and hydration heat while enhancing construction efficiency and environmental friendliness.However,two fundamental scientific issues related to RFC need to be addressed:namely,the pouring compactness and the effect of large rocks on the mechanical and physical properties of RFC.This article provides a timely review of fundamental research and innovations in the design,construction,and quality control of RFCdams.Prospects for next-generation concrete dams are discussed from the perspectives of envi-ronmental friendliness,intrinsic safety,and labor savings.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Characteristic Study of Self-Powered Sensors Based on Native Protein Composite Film

Flexible electronic sensors composed of flexible film and conductive materials play an increasingly important role in wearable and internet information transmission.It has received more and more attention and made some progress over the decades.However,it is still a great challenge to prepare biocompatible and highly transparent conductive films.Egg white is a pure natural protein-rich material.Hydroxypropylmethyl cellulose has a good compatibility and high transparency,which is an ideal material for flexible sensors.Here,we overcome the problem of poor mechanical flexibility and electrical conductivity of protein,and develop a high transparency and good flexibility hydroxypropylmethyl cellulose/egg white protein composite membrane-based triboelectric nanogenerator('X'-TENG).The experimental results show that the flexible pressure sensor based on'X'-TENG has a high sensitivity,fast response speed,and low detection limit.It can even be used as a touch/pressure sensing artificial electronic skin.It can also be made into an intelligent waffle keyboard for recording and tracking users of the keyboard.Our strategy may provide a new way to easily build flexible electronic sensors and move toward practical applications.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Short-term effectiveness of intelligent navigated laser photocoagulation versus subthreshold micropulse laser in patients with chronic central serous chorioretinopathy

AIM:To compare the short-term effectiveness of intelligent navigated laser photocoagulation and 577-nm subthreshold micropulse laser(SML)treatment in patients with chronic central serous chorioretinopathy(cCSC).METHODS:This observational retrospective cohort study included 60 consecutive patients who underwent intelligent navigated laser photocoagulation(n=30)or 577-nm SML treatment(n=30)for cCSC between Jan.2021 and Oct.2022.During 3mo follow-up,all patients underwent assessments of best correct visual acuity(BCVA)and optical coherence tomography(OCT).RESULTS:The operation of laser treatment was successful in all cases.At 1mo,BCVA improved significantly more in the intelligent navigated laser photocoagulation group compared to the SML group(P<0.05).The change was not significantly different at 3mo(P>0.05).Central macular thickness(CMT)in the intelligent navigated laser photocoagulation group was lower than in the SML group at 1mo(P<0.05).The subfoveal choroidal thickness(SFCT)in two groups were all significantly improved at 3mo(all P<0.05).The change between two groups was not significantly different at 1mo or at 3mo(P>0.05).CONCLUSION:Intelligent navigated laser photocoagulation is superior to SML for treating cCSC,leading to better improvements in vision and CMT for short term.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Current status and construction scheme of smart geothermal field technology

To address the key problems in the application of intelligent technology in geothermal development,smart application scenarios for geothermal development are constructed.The research status and existing challenges of intelligent technology in each scenario are analyzed,and the construction scheme of smart geothermal field system is proposed.The smart geothermal field is an organic integration of geothermal development engineering and advanced technologies such as the artificial intelligence.At present,the technology of smart geothermal field is still in the exploratory stage.It has been tested for application in scenarios such as intelligent characterization of geothermal reservoirs,dynamic intelligent simulation of geothermal reservoirs,intelligent optimization of development schemes and smart management of geothermal development.However,it still faces many problems,including the high computational cost,difficult real-time response,multiple solutions and strong model dependence,difficult real-time optimization of dynamic multi-constraints,and deep integration of multi-source data.The construction scheme of smart geothermal field system is proposed,which consists of modules including the full database,intelligent characterization,intelligent simulation and intelligent optimization control.The connection between modules is established through the data transmission and the model interaction.In the next stage,it is necessary to focus on the basic theories and key technologies in each module of the smart geothermal field system,to accelerate the lifecycle intelligent transformation of the geothermal development and utilization,and to promote the intelligent,stable,long-term,optimal and safe production of geothermal resources.

Multi-level intelligence empowering lithium-ion batteries

With the significant and widespread application of lithium-ion batteries,there is a growing demand for improved performances of lithium-ion batteries.The intricate degradation throughout the whole lifecycle profoundly impacts the safety,durability,and reliability of lithium-ion batteries.To ensure the long-term,safe,and efficient operation of lithium-ion batteries in various fields,there is a pressing need for enhanced battery intelligence that can withstand extreme events.This work reviews the current status of intelligent battery technology from three perspectives:intelligent response,intelligent sensing,and intelligent management.The intelligent response of battery materials forms the foundation for battery stability,the intelligent sensing of multi-dimensional signals is essential for battery management,and the intelligent management ensures the long-term stable operation of lithium-ion batteries.The critical challenges encountered in the development of intelligent battery technology from each perspective are thoroughly analyzed,and potential solutions are proposed,aiming to facilitate the rapid development of intelligent battery technologies.

Intelligent casting:Empowering the future foundry industry

Emerging technological advances are reshaping the casting sector in latest decades.Casting technology is evolving towards intelligent casting paradigm that involves automation,greenization and intelligentization,which attracts more and more attention from the academic and industry communities.In this paper,the main features of casting technology were briefly summarized and forecasted,and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed.Moreover,the technical visions of intelligent casting process were also put forward.The key technologies for intelligent casting process comprise 3D printing technologies,intelligent mold technologies and intelligent process control technologies.In future,the intelligent mold that derived from mold with sensors,control devices and actuators will probably incorporate the Internet of Things,online inspection,embedded simulation,decision-making and control system,and other technologies to form intelligent cyber-physical casting system,which may pave the way to realize intelligent casting.It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control,with the defects,microstructure,performance,and service life of the fabricated castings can be accurately predicted and tailored.