A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state ...A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state of the medium and low speed maglev coupled system by the additional deformation of the maglev track.This study investigated the dynamic properties of the coupled vibration system affected by the subgrade settlement.First,a theoretical coupled vibration model of a maglev train-track-ground girder system with uneven subgrade settlement was proposed and verified.Then,the effect mechanism of the coupled system caused by the uneven subgrade settlement was explored.Finally,settlement types and subgrade support voiding were examined.The analysis showed that the uneven subgrade settlement considerably increased the dynamic responses of the levitation control system and maglev vehicle while having a minor influence on those of the track-ground girder.The influence of a single ground girder settling was the strongest,and adjacent sides’settling of two ground girders was the weakest for the vibration of a maglev train.An extremely large uneven settlement exceeding 6 mm led to active levitation control system instability.The subgrade support voiding enlarged the vehicle-induced vibration of the track ground girder.展开更多
In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion s...In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion source.Through the finite element analysis method,the electrostatic analyses of insulators and grid plates were carried out,the material and structure parameters of insulators were determined.The maximum electric field around each insulator is about 4 kV/mm,and the maximum electric field between grids is about 14 kV/mm,which can meet the 120 keV withstand voltage holding.The insulation system for the positive ion source accelerator with 120 keV is designed,and the connection and basic parameters of insulators and support flanges are analyzed and determined.展开更多
The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A...The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.展开更多
Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of w...Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versat...BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam suppor...Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.展开更多
Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic...Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.展开更多
BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and op...BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and optimize patient outcomes across the entire surgical continuum.AIM To comprehensively evaluate current AI applications in GI surgery,highlighting its role in preoperative planning,intraoperative guidance,postoperative monitoring,endoscopic diagnosis,and surgical education.METHODS This systematic review was conducted in accordance with PRISMA guidelines.We searched the Web of Science Core Collection through March 31,2025 using the terms“artificial intelligence”AND“gastrointestinal surgery”.Inclusion criteria:Original,English-language,full-text articles indexed under the“Surgery”category reporting quantitative AI performance metrics in GI surgery.Exclusion criteria:Reviews,editorials,letters,conference abstracts,non-English publications,ESCI/SSCI/Index Chemicus-only papers,studies without full text,and articles outside the surgical domain.Full texts of potentially eligible studies were assessed,yielding 45 studies from an initial 955 records for qualitative and quantitative synthesis.RESULTS The included studies demonstrated that AI has superior performance compared to traditional clinical tools in areas such as risk prediction,lesion detection,nerve identification,and complication forecasting.Notably,convolutional neural networks,random forests,support vector machines,and reinforcement learning models were commonly used.AI-enhanced systems improved diagnostic accuracy,procedural safety,documentation quality,and educational feedback.However,there are several limitations,such as lack of external validation,dataset standardization,and explainability.CONCLUSION AI is transforming GI surgery from preoperative risk assessment to postoperative care and training.While many tools now match or exceed expert-level performance,successful clinical adoption requires transparent,validated models that seamlessly integrate into surgical workflows.With continued multidisciplinary collaboration,AI is positioned to become a trusted companion in surgical practice.展开更多
Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to pr...Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to protect players’mental health remain inadequate.Comprehensive analysis of structural risk factors,including performance pressure,public evaluation,and career instability,remains insufficient.This study,aimed to explore stressors encountered by esports athletes,coping strategies,and the role of social support systems in safeguarding mental health.Using the transactional model of stress and coping,the job demands–resources model,and social support theory,the study adopts an integrated perspective to examine challenges faced by athletes in the competitive esports environment.Methods:A qualitative case study was conducted involving in-depth interviews and nonparticipant observations with 11 esports athletes who competed at national or international levels,as well as two team managers.Thematic analysis identified recurring patterns in the data,and credibility was ensured through triangulation and cross-review among researchers.Results:Esports athletes experience multiple interacting stressors,including performance demands,emotional strain duringmatches,and continuous evaluation on socialmedia.In response,they employed coping strategies—problem-focused,emotion-focused,and avoidance-based,which provided temporary relief but often led to burnout and self-regulation failure owing to absence of support systems.Social support networks had ambivalent effects:while offering comfort,they also intensified pressure through negative feedback and high expectations from fans and online communities.Conclusion:The findings show that mental health issues among esports athletes are not only related to individual factors but are closely linked to performance-driven structures,competitive environments,and social relationships.This study integrates the transactional model of stress and coping,the JobDemands–Resourcesmodel,and social support theoryto provide comprehensive analysis.It also offers practical recommendations,including psychological counseling,emotional labor programs,and improved communication with families and fan communities.展开更多
Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of tra...Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.展开更多
BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a ne...BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a new idea.AIM To analyze the post-surgical effect of the artificial liver support system with TCM in patients with liver cancer.METHODS Ninety-eight patients with liver cancer who underwent surgical treatment at the Fifth People’s Hospital of Huai’an from January 2023-2024 were selected and divided into two groups(49 patients each)via random lottery method.Both groups underwent surgery.The control group received artificial liver support,and the observation group was additionally treated with TCM for strengthening the body and removing blood stasis.Gastrointestinal recovery,liver function,tumor marker levels,immune function,and safety were compared between both groups.RESULTS There were significant differences in the levels of indicators related to gastrointestinal recovery between the groups(P<0.05).After treatment,the levels of alanine aminotransferase,aspartate aminotransferase,total bilirubin,and gamma-glutamyl transpeptidase in the observation group were lower,whereas the albumin level was higher(P<0.05).After treatment,tumor marker levels in the observation group were relatively lower(P<0.05).After treatment,compared to the control group,the CD4+level in the observation group was higher and the CD8+level was lower(P<0.05).There was no significant difference in the incidence of adverse reactions between both groups(P>0.05).CONCLUSION Combining the artificial liver support system with TCM significantly improves liver and gastrointestinal functions,enhances immune responses,and reduces tumor marker levels with high safety,suggesting that it could be a promising approach for optimizing postoperative care and improving patient outcomes,potentially reducing complications and enhancing quality of life.展开更多
Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Design...Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.展开更多
To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile...To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.展开更多
In underground engineering with complex conditions,the bolt(cable)anchorage support system is in an environment where static and dynamic stresses coexist,under the action of geological conditions such as high stresses...In underground engineering with complex conditions,the bolt(cable)anchorage support system is in an environment where static and dynamic stresses coexist,under the action of geological conditions such as high stresses and strong disturbances and construction conditions such as the application of high prestress.It is essential to study the support components performance under dynamic-static coupling conditions.Based on this,a multi-functional anchorage support dynamic-static coupling performance test system(MAC system)is developed,which can achieve 7 types of testing functions,including single component performance,anchored net performance,anchored rock performance and so on.The bolt and cable mechanical tests are conducted by MAC system under different prestress levels.The results showed that compared to the non-prestress condition,the impact resistance performance of prestressed bolts(cables)is significantly reduced.In the prestress range of 50–160 k N,the maximum reduction rate of impact energy resisted by different types of bolts is 53.9%–61.5%compared to non-prestress condition.In the prestress range of 150–300 k N,the impact energy resisted by high-strength cable is reduced by76.8%–84.6%compared to non-prestress condition.The MAC system achieves dynamic-static coupling performance test,which provide an effective means for the design of anchorage support system.展开更多
BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modi...BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.展开更多
Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant no...Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.展开更多
Guided by SAMR, China's standardization has achieved remarkable results. The State Grid Corporation of China steadily operates the power grid with the highest voltage level, the strongest energy resource allocatio...Guided by SAMR, China's standardization has achieved remarkable results. The State Grid Corporation of China steadily operates the power grid with the highest voltage level, the strongest energy resource allocation capacity, and the largest scale of new energy grid connection. The State Grid thoroughly implements the National Standardization Development Outline, and strengthens the top-level design to build a new pattern of the coordinated development of domestic and international standardization work, which demonstrates the role of standards in supporting the high-quality development of power grid.展开更多
Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for...Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.展开更多
基金National Natural Science Foundation of China under Grant Nos.52478467and 52108417Guangdong Basic and Applied Basic Research Foundation under Grant No.2024A1515012569the Natural Science Basic Research Program of Shaanxi under Grant No.2021JQ-101。
文摘A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state of the medium and low speed maglev coupled system by the additional deformation of the maglev track.This study investigated the dynamic properties of the coupled vibration system affected by the subgrade settlement.First,a theoretical coupled vibration model of a maglev train-track-ground girder system with uneven subgrade settlement was proposed and verified.Then,the effect mechanism of the coupled system caused by the uneven subgrade settlement was explored.Finally,settlement types and subgrade support voiding were examined.The analysis showed that the uneven subgrade settlement considerably increased the dynamic responses of the levitation control system and maglev vehicle while having a minor influence on those of the track-ground girder.The influence of a single ground girder settling was the strongest,and adjacent sides’settling of two ground girders was the weakest for the vibration of a maglev train.An extremely large uneven settlement exceeding 6 mm led to active levitation control system instability.The subgrade support voiding enlarged the vehicle-induced vibration of the track ground girder.
基金supported by National Natural Science Foundation of China(No.11975261)。
文摘In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion source.Through the finite element analysis method,the electrostatic analyses of insulators and grid plates were carried out,the material and structure parameters of insulators were determined.The maximum electric field around each insulator is about 4 kV/mm,and the maximum electric field between grids is about 14 kV/mm,which can meet the 120 keV withstand voltage holding.The insulation system for the positive ion source accelerator with 120 keV is designed,and the connection and basic parameters of insulators and support flanges are analyzed and determined.
文摘The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N).
文摘Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
基金Supported by Natural Science Foundation of Hunan Province,China,No.2022JJ30842 and No.2024JJ6560Clinical Medical Research Center for Viral Hepatitis of Hunan Province,No.2023SK4009Beijing iGandan Foundation,No.RGGJJ-2021-017 and No.iGandanF-1082022-RGG023.
文摘BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.
基金supported by the National Key Research and Development Program(No.2023YFC3502604)the National Natural Science Foundation of China(Nos.U23B2062,82274352,82174533,82374302,82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Co〓〓operation Project(No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture(No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region(No.2022BEG02036)。
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
基金Project(2023YFC2907600)supported by the National Key Research and Development Program of ChinaProjects(42277174,42477166)supported by the National Natural Science Foundation of China+1 种基金Project(2024JCCXSB01)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(KFJJ24-01M)supported by the State Key Laboratory of Explosion Science and Safety Protection,Beijing Institute of Technology,China。
文摘Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.
文摘Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.
文摘BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and optimize patient outcomes across the entire surgical continuum.AIM To comprehensively evaluate current AI applications in GI surgery,highlighting its role in preoperative planning,intraoperative guidance,postoperative monitoring,endoscopic diagnosis,and surgical education.METHODS This systematic review was conducted in accordance with PRISMA guidelines.We searched the Web of Science Core Collection through March 31,2025 using the terms“artificial intelligence”AND“gastrointestinal surgery”.Inclusion criteria:Original,English-language,full-text articles indexed under the“Surgery”category reporting quantitative AI performance metrics in GI surgery.Exclusion criteria:Reviews,editorials,letters,conference abstracts,non-English publications,ESCI/SSCI/Index Chemicus-only papers,studies without full text,and articles outside the surgical domain.Full texts of potentially eligible studies were assessed,yielding 45 studies from an initial 955 records for qualitative and quantitative synthesis.RESULTS The included studies demonstrated that AI has superior performance compared to traditional clinical tools in areas such as risk prediction,lesion detection,nerve identification,and complication forecasting.Notably,convolutional neural networks,random forests,support vector machines,and reinforcement learning models were commonly used.AI-enhanced systems improved diagnostic accuracy,procedural safety,documentation quality,and educational feedback.However,there are several limitations,such as lack of external validation,dataset standardization,and explainability.CONCLUSION AI is transforming GI surgery from preoperative risk assessment to postoperative care and training.While many tools now match or exceed expert-level performance,successful clinical adoption requires transparent,validated models that seamlessly integrate into surgical workflows.With continued multidisciplinary collaboration,AI is positioned to become a trusted companion in surgical practice.
文摘Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to protect players’mental health remain inadequate.Comprehensive analysis of structural risk factors,including performance pressure,public evaluation,and career instability,remains insufficient.This study,aimed to explore stressors encountered by esports athletes,coping strategies,and the role of social support systems in safeguarding mental health.Using the transactional model of stress and coping,the job demands–resources model,and social support theory,the study adopts an integrated perspective to examine challenges faced by athletes in the competitive esports environment.Methods:A qualitative case study was conducted involving in-depth interviews and nonparticipant observations with 11 esports athletes who competed at national or international levels,as well as two team managers.Thematic analysis identified recurring patterns in the data,and credibility was ensured through triangulation and cross-review among researchers.Results:Esports athletes experience multiple interacting stressors,including performance demands,emotional strain duringmatches,and continuous evaluation on socialmedia.In response,they employed coping strategies—problem-focused,emotion-focused,and avoidance-based,which provided temporary relief but often led to burnout and self-regulation failure owing to absence of support systems.Social support networks had ambivalent effects:while offering comfort,they also intensified pressure through negative feedback and high expectations from fans and online communities.Conclusion:The findings show that mental health issues among esports athletes are not only related to individual factors but are closely linked to performance-driven structures,competitive environments,and social relationships.This study integrates the transactional model of stress and coping,the JobDemands–Resourcesmodel,and social support theoryto provide comprehensive analysis.It also offers practical recommendations,including psychological counseling,emotional labor programs,and improved communication with families and fan communities.
文摘Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.
文摘BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a new idea.AIM To analyze the post-surgical effect of the artificial liver support system with TCM in patients with liver cancer.METHODS Ninety-eight patients with liver cancer who underwent surgical treatment at the Fifth People’s Hospital of Huai’an from January 2023-2024 were selected and divided into two groups(49 patients each)via random lottery method.Both groups underwent surgery.The control group received artificial liver support,and the observation group was additionally treated with TCM for strengthening the body and removing blood stasis.Gastrointestinal recovery,liver function,tumor marker levels,immune function,and safety were compared between both groups.RESULTS There were significant differences in the levels of indicators related to gastrointestinal recovery between the groups(P<0.05).After treatment,the levels of alanine aminotransferase,aspartate aminotransferase,total bilirubin,and gamma-glutamyl transpeptidase in the observation group were lower,whereas the albumin level was higher(P<0.05).After treatment,tumor marker levels in the observation group were relatively lower(P<0.05).After treatment,compared to the control group,the CD4+level in the observation group was higher and the CD8+level was lower(P<0.05).There was no significant difference in the incidence of adverse reactions between both groups(P>0.05).CONCLUSION Combining the artificial liver support system with TCM significantly improves liver and gastrointestinal functions,enhances immune responses,and reduces tumor marker levels with high safety,suggesting that it could be a promising approach for optimizing postoperative care and improving patient outcomes,potentially reducing complications and enhancing quality of life.
文摘Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.
基金substantially supported by the National Natural Science Foundation of China(Grant No.42072302)Shuguang Program from Shanghai Education Development Foundation and Shanghai Municipal Education Commission(Grant No.19SG19)Fundamental Research Funds for the Central Universities.
文摘To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.
基金supported by the National Natural Science Foundation of China(Nos.51927807,52074164,42277174,42077267 and 42177130)the Natural Science Foundation of Shandong Province,China(No.ZR2020JQ23)China University of Mining and Technology(Beijing)Top Innovative Talent Cultivation Fund for Doctoral Students(No.BBJ2023048)。
文摘In underground engineering with complex conditions,the bolt(cable)anchorage support system is in an environment where static and dynamic stresses coexist,under the action of geological conditions such as high stresses and strong disturbances and construction conditions such as the application of high prestress.It is essential to study the support components performance under dynamic-static coupling conditions.Based on this,a multi-functional anchorage support dynamic-static coupling performance test system(MAC system)is developed,which can achieve 7 types of testing functions,including single component performance,anchored net performance,anchored rock performance and so on.The bolt and cable mechanical tests are conducted by MAC system under different prestress levels.The results showed that compared to the non-prestress condition,the impact resistance performance of prestressed bolts(cables)is significantly reduced.In the prestress range of 50–160 k N,the maximum reduction rate of impact energy resisted by different types of bolts is 53.9%–61.5%compared to non-prestress condition.In the prestress range of 150–300 k N,the impact energy resisted by high-strength cable is reduced by76.8%–84.6%compared to non-prestress condition.The MAC system achieves dynamic-static coupling performance test,which provide an effective means for the design of anchorage support system.
基金Supported by Research Project of Zhejiang Provincial Department of Education,No.Y202045115.
文摘BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.
基金Supported by National Natural Science Foundation of China (Grant No.51975294)Fundamental Research Funds for the Central Universities of China (Grant No.30922010706)。
文摘Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.
文摘Guided by SAMR, China's standardization has achieved remarkable results. The State Grid Corporation of China steadily operates the power grid with the highest voltage level, the strongest energy resource allocation capacity, and the largest scale of new energy grid connection. The State Grid thoroughly implements the National Standardization Development Outline, and strengthens the top-level design to build a new pattern of the coordinated development of domestic and international standardization work, which demonstrates the role of standards in supporting the high-quality development of power grid.
基金financially supported by the National Council for Scientific and Technological Development(CNPq,Brazil),Swedish-Brazilian Research and Innovation Centre(CISB),and Saab AB under Grant No.CNPq:200053/2022-1the National Council for Scientific and Technological Development(CNPq,Brazil)under Grants No.CNPq:312924/2017-8 and No.CNPq:314660/2020-8.
文摘Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
