Research on digital twin technology and its application in intelligent operation and maintenance of highspeed railway infrastructure

Purpose–This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approach–This paper provides a comprehensive overview of the definition,connotations,characteristics and key technologies of digital twin technology.It also conducts a thorough analysis of the current state of digital twin applications,with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure.Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study,the paper details the construction process of the twin system from the perspectives of system architecture,theoretical definition,model construction and platform design.Findings–Digital twin technology can play an important role in the whole life cycle management,fault prediction and condition monitoring in the field of high-speed rail operation and maintenance.Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/value–This paper systematically summarizes the main components of digital twin railway.The general framework of the digital twin bridge is given,and its application in the field of intelligent operation and maintenance is prospected.

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.

ET-Net:A Novel Framework for Fine-Grained Traffic Classification in Intelligent Vehicle Applications

Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.

An Intelligent MCGDM Model in Green Suppliers Selection Using Interactional Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Sets

Green supplier selection is an important debate in green supply chain management(GSCM),attracting global attention from scholars,especially companies and policymakers.Companies frequently search for new ideas and strategies to assist them in realizing sustainable development.Because of the speculative character of human opinions,supplier selection frequently includes unreliable data,and the interval-valued Pythagorean fuzzy soft set(IVPFSS)provides an exceptional capacity to cope with excessive fuzziness,inconsistency,and inexactness through the decision-making procedure.The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers(IVPFSNs)and create two interaction operators-the intervalvalued Pythagorean fuzzy soft interaction weighted average(IVPFSIWA)and the interval-valued Pythagorean fuzzy soft interaction weighted geometric(IVPFSIWG)operators,and analyze their properties.These operators are highly advantageous in addressing uncertain problems by considering membership and non-membership values within intervals,providing a superior solution to other methods.Moreover,specialist judgments were calculated by the MCGDM technique,supporting the use of interaction AOs to regulate the interdependence and fundamental partiality of green supplier assessment aspects.Lastly,a statistical clarification of the planned method for green supplier selection is presented.

How Do Deep Learning Forecasting Models Perform for Surface Variables in the South China Sea Compared to Operational Oceanography Forecasting Systems?

It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.

Clinical observation on prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation

BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different treatment options.AIM To analyze the clinical effect and prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation(RPH).METHODS A total of 100 patients with mixed hemorrhoids who visited our hospital from October 2022 to September 2023 were selected and randomly divided into the control group(n=50)and the treatment group(n=50)by rolling the dice.The procedure for prolapse and hemorrhoids(PPH)was adopted in the control group,while polidocanol foam injection+RPH was adopted in the treatment group.The therapeutic effects,operation time,wound healing time,hospital stay,pain situation(24 hours post-operative pain score,first defecation pain score),quality of life(QOL),incidence of complications(post-operative hemorrhage,edema,infection),incidence of anal stenosis 3 months post-operatively and recurrence rate 1 year post-operatively of the two groups were compared.RESULTS Compared with the control group,the total effective rate of treatment group was higher,and the difference was significant(P<0.05).The operation time/wound healing time/hospital stay in the treatment group were shorter than those in the control group(P<0.05).The pain scores at 24 hours after operation/first defecation pain score of the treatment group was significantly lower than those in the control group(P<0.05).After surgery,the QOL scores of the two groups decreased,with the treatment group having higher scores than that of the control group(P<0.05).Compared with the control group,the incidence of postoperative complications in the treatment group was lower,and the difference was significant(P<0.05);However,there was no significant difference in the incidence of postoperative bleeding between the two groups(P>0.05);There was no significant difference in the incidence of anal stenosis 3 months after operation and the recurrence rate 1 year after operation between the two groups(P>0.05).CONCLUSION For patients with mixed hemorrhoids,the therapeutic effect achieved by using polidocanol injection combined with RPH was better.The wounds of the patients healed faster,the postoperative pain was milder,QOL improved,and the incidence of complications was lower,and the short-term and long-term prognosis was good.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning

BACKGROUND Prolonged postoperative ileus(PPOI)delays the postoperative recovery of gastrointestinal function in patients with gastric cancer(GC),leading to longer hospitalization and higher healthcare expenditure.However,effective monitoring of gastrointestinal recovery in patients with GC remains challenging because of AIM To explore the risk factors for delayed postoperative bowel function recovery and evaluate bowel sound indicators collected via an intelligent auscultation system to guide clinical practice.METHODS This study included data from 120 patients diagnosed with GC who had undergone surgical treatment and postoperative bowel sound monitoring in the Department of General Surgery II at Shaanxi Provincial People's Hospital between January 2019 and January 2021.Among them,PPOI was reported in 33 cases.The patients were randomly divided into the training and validation cohorts.Significant variables from the training cohort were identified using univariate and multivariable analyses and were included in the model.RESULTS The analysis identified six potential variables associated with PPOI among the included participants.The incidence rate of PPOI was 27.5%.Age≥70 years,cTNM stage(Ⅰ and Ⅳ),preoperative hypoproteinemia,recovery time of bowel sounds(RTBS),number of bowel sounds(NBS),and frequency of bowel sounds(FBS)were independent risk factors for PPOI.The Bayesian model demonstrated good performance with internal validation:Training cohort[area under the curve(AUC)=0.880,accuracy=0.823,Brier score=0.139]and validation cohort(AUC=0.747,accuracy=0.690,Brier score=0.215).The model showed a good fit and calibration in the decision curve analysis,indicating a significant net benefit.CONCLUSION PPOI is a common complication following gastrectomy in patients with GC and is associated with age,cTNM stage,preoperative hypoproteinemia,and specific bowel sound-related indices(RTBS,NBS,and FBS).To facilitate early intervention and improve patient outcomes,clinicians should consider these factors,optimize preoperative nutritional status,and implement routine postoperative bowel sound monitoring.This study introduces an accessible machine learning model for predicting PPOI in patients with GC.

An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.

Intelligent decision support system of operation-optimization in copper smelting converter

An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times.

AN INTELLIGENT DECISION SUPPORT SYSTEM （IDSS） IN THE OPERATION PROCESS OF ELECTRIC FURNACE FOR CLEANING SLAG

In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.

The Development of Intelligent Operation Method of Urban Public Infrastructure Driven by Accurate Spatio-temporal Information

Urban public infrastructure is an important basis for urban development.It is of great significance to deepen the research on intelligent management and control of urban public infrastructure.Spatio-temporal information contains the law of state evolution of urban public infrastructure,which is the information base of intelligent control of infrastructure.Due to the needs of operation management and emergency response,efficient sharing and visualization of spatio-temporal information are important research contents of comprehensive management and control of urban public infrastructure.On the basis of summarizing the theoretical research and application in recent years,the basic methods and current situation of the acquisition and analysis of spatio-temporal information,the forecast and early warning,and the intelligent control of urban public infrastructure are reviewed in this paper.

Design of Multi-Sensors Cooperation Control System for Intelligent Building

This paper presents a design of intelligent building control system based on multi-sensors. In order to achieve comfort, low carbon and energy conservation for buildings. The design uses STC89C52 chip, combined with monitoring data of Pyroelectric infrared microwave dual sensor, temperature sensors and light sensors to control the optical system, the temperature control system and the security system. It helps to achieve low carbon and energy conservation. In addition, the energy-saving effect of a medium-sized multimedia classroom was analyzed. We analyze the feasibility of the system. It is proved that the system is low cost, high yield and easy to operate, with strong engineering application value.

Research on the Application of Intelligent Evaluation and Teacher-student Cooperation Assessment System in Teaching English Writing

Low efficiency in teaching and time-consuming in writing evaluation are two big problems for college English teachers.Therefore,it is necessary to create a new teaching model to solve these problems existing in traditional classroom-based teaching.This research adopts the research methods of test comparison before and after the students’composition experiment,questionnaire and semi-open interviews.Empirical research on a new teaching model that integrates the intelligent composition review and reform system represented by Piangai.com and the collaborative evaluation of teachers and students is conducted.The research results show that the new writing teaching model improves the quality of students’writing,promotes students’learning initiative,and enhances students’writing self-efficacy.This writing teaching model provides ideas for solving the problem of time-consuming and inefficient English writing teaching in large classes.

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.

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.

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.

Effects of spot size on the operation mode of Ga As photoconductive semiconductor switch employing extrinsic photoconductivity

To guide the illuminating design to improve the on-state performances of gallium arsenide(GaAs)photoconductive semiconductor switch(PCSS),the effect of spot size on the operation mode of GaAsPCSS based on a semi-insulating wafer with a thickness of 1 mm,triggered by a 1064-nm extrinsic laser beam with the rectangular spot,has been investigated experimentally.It is found that the variation of the spot size in length and width can act on the different parts of the output waveform integrating the characteristics of the linear and nonlinear modes,and then significantly boosts the PCSS toward different operation modes.On this basis,a two-channel model containing the active and passive parts is introduced to interpret the relevant influencing mechanisms.Results indicate that the increased spot length can peak the amplitude of static domains in the active part to enhance the development of the nonlinear switching,while the extended spot width can change the distribution of photogenerated carriers on both parts to facilitate the linear switching and weaken the nonlinear switching,which have been proved by comparing the domain evolutions under different spot sizes.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.