In this paper,an integrated guidance and control approach is presented to improve the performance of the missile interception.The approach includes damping augmented system with attitude rate feedback to decrease the ...In this paper,an integrated guidance and control approach is presented to improve the performance of the missile interception.The approach includes damping augmented system with attitude rate feedback to decrease the oscillation during the homing phase for missiles with low damping.In addition,physical constraints,which can affect the performance of the missile interception,such as acceleration limit,seeker’s look angle,and look angle rate constraints are considered.The integrated guidance and control problem is formulated as a convex quadratic optimization problem with equality and inequality constraints,and the solution is obtained by a primal–dual interior point method.The performance of the proposed method is verified through several numerical examples.展开更多
The successive overrelaxation-like （SOR-like） method with the real param- eters ω is considered for solving the augmented system. The new method is called the modified SOR-like （MSOR-like） method. The functional ...The successive overrelaxation-like （SOR-like） method with the real param- eters ω is considered for solving the augmented system. The new method is called the modified SOR-like （MSOR-like） method. The functional equation between the parameters and the eigenvalues of the iteration matrix of the MSOR-like method is given. Therefore, the necessary and sufficient condition for the convergence of the MSOR-like method is derived. The optimal iteration parameter ω of the MSOR-like method is derived. Finally, the proof of theorem and numerical computation based on a particular linear system are given, which clearly show that the MSOR-like method outperforms the SOR-like （Li, C. J., Li, B. J., and Evans, D. J. Optimum accelerated parameter for the GSOR method. Neural, Parallel ＆ Scientific Computations, 7（4）, 453-462 （1999）） and the modified sym- metric SOR-like （MSSOR-like） methods （Wu, S. L., Huang, T. Z., and Zhao, X. L. A modified SSOR iterative method for augmented systems. Journal of Computational and Applied Mathematics, 228（4）, 424-433 （2009））.展开更多
Endoscopic transnasal optic nerve decompression surgery plays a crucial role in minimal invasive treatment of complex traumatic optic neuropathy.However,a major challenge faced during the procedure is the inability to...Endoscopic transnasal optic nerve decompression surgery plays a crucial role in minimal invasive treatment of complex traumatic optic neuropathy.However,a major challenge faced during the procedure is the inability to visualize the optic nerve intraoperatively.To address this issue,an endoscopic image-based augmented reality surgical navigation system is developed in this study.The system aims to virtually fuse the optic nerve onto the endoscopic images,assisting surgeons in determining the optic nerve’s position and reducing surgical risks.First,a calibration algorithm based on a checkerboard grid of immobile points is proposed,building upon existing calibration methods.Additionally,to tackle accuracy issues associated with augmented reality technology,an optical navigation and visual fusion compensation algorithm is proposed to improve the intraoperative tracking accuracy.To evaluate the system’s performance,model experiments were meticulously designed and conducted.The results confirm the accuracy and stability of the proposed system,with an average tracking error of(0.99±0.46)mm.This outcome demonstrates the effectiveness of the proposed algorithm in improving the augmented reality surgical navigation system’s accuracy.Furthermore,the system successfully displays hidden optic nerves and other deep tissues,thus showcasing the promising potential for future applications in orbital and maxillofacial surgery.展开更多
Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of s...Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of surgical failure.Therefore,it is necessary to design a visualization system for closed thoracic drainage.Augmented reality(AR)technology can assist in visualizing the internal anatomical structure and determining the insertion point on the body surface.The structure of the currently used steel-needle-guided chest tube was modified by integrating it with an ultrafine diameter camera to provide real-time visualization of the puncture process.After simulation experiments,the overall registration error of the AR method was measured to be within(3.59±0.53)mm,indicating its potential for clinical application.The ultrafine diameter camera module and improved steel-needle-guided chest tube can timely reflect the position of the needle tip in the human body.A comparative experiment showed that video guidance could improve the safety of the puncture process compared to the traditional method.Finally,a qualitative evaluation of the usability of the system was conducted through a questionnaire.This system facilitates the visualization of closed thoracic drainage puncture procedure and pro-vides an implementation scheme to enhance the accuracy and safety of the operative step,which is conducive to reducing the learning curve and improving the proficiency of the doctors.展开更多
BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolvi...BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.展开更多
Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal i...Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal interventions for cancer treatment.Methods:A specialized software transforms 2D medical images into precise 3D digital models, facilitating improved anatomical understanding and surgical planning. Patient-specific 3D printed anatomical models are utilized for preoperative planning, intraoperative guidance, and surgical education. AR technology enables the overlay of digital perceptions onto real-world surgical environments.Results:Patient-specific 3D printed anatomical models have multiple applications, such as preoperative planning, intraoperative guidance, trainee education, and patient counseling. Virtual reality involves substituting the real world with a computer-generated 3D environment, while AR overlays digitally created perceptions onto the existing reality. The advances in 3D modeling technology have sparked considerable interest in their application to partial nephrectomy in the realm of renal cancer. 3D printing, also known as additive manufacturing, constructs 3D objects based on computer-aided design or digital 3D models. Utilizing 3D-printed preoperative renal models provides benefits for surgical planning, offering a more reliable assessment of the tumor's relationship with vital anatomical structures and enabling better preparation for procedures. AR technology allows surgeons to visualize patient-specific renal anatomical structures and their spatial relationships with surrounding organs by projecting CT/MRI images onto a live laparoscopic video. Incorporating patient-specific 3D digital models into healthcare enhances best practice, resulting in improved patient care, increased patient satisfaction, and cost saving for the healthcare system.展开更多
Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics.Furthermore,i...Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics.Furthermore,it remains a challenge when there are additional constraints on control torques of robotic systems.In this article,we propose a novel high-order control barrier function(HoCBF)-based safety control method for robotic systems subject to input-output constraints,which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque.In our design,augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system,so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics.This proposed scheme leads to the quadratic program(QP),which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law.The proposed closed-loop control system not only achieves the requirements of real-time capability,stability,safety and compliance,but also reduces undesired chattering of control inputs.Finally,the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
Augmented reality(AR)is a technology that superimposes digital information onto real-world objects via head-mounted display devices to improve surgical finesse through visually enhanced medical information.With the ra...Augmented reality(AR)is a technology that superimposes digital information onto real-world objects via head-mounted display devices to improve surgical finesse through visually enhanced medical information.With the rapid development of digital technology,AR has been increasingly adopted in orthopedic surgeries across the globe,especially in total knee arthroplasty procedures which demand high precision.By overlaying digital information onto the surgeon's field of view,AR systems enhance precision,improve alignment accuracy,and reduce the risk of complications associated with malalignment.Some concerns have been raised despite accuracy,including the learning curve,long-term outcomes,and technical limitations.Furthermore,it is essential for health practitioners to gain trust in the utilisation of AR.展开更多
1.Introduction With the increasing demand for petroleum and natural gas resources,along with technological advancements in exploration and production,the primary frontier of oil and gas resources has shifted from conv...1.Introduction With the increasing demand for petroleum and natural gas resources,along with technological advancements in exploration and production,the primary frontier of oil and gas resources has shifted from conventional oil and gas development to the domains of“Two Deeps,One Unconventional,One Mature,”which include deep onshore,deepwater,unconventional resources,and mature oilfields[1].展开更多
The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed ...The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed description of the architecture and workflow of a platform for UAS traffic management(UTM),designed to pave the way for increased,improved and safer UAS operations in the civil airspace.In particular,access to low-altitude airspace for UAS operations is managed,while facilitating the implementation of beyond visual line-of-sight(BVLOS)operations,and ensuring a safe and efficient integration of UAS into both controlled and uncontrolled airspace.Detection and management of unidentified or uncooperative UAS’s is also taken care of.To this end,an architecture based on three interacting layers is proposed,with the air traffic control at the highest level,the UAS operator(s)at the bottom,and a UAS service supplier acting as an interface.The platform,with its physical and digital elements,guarantees the effective and efficient interaction among these three layers,including management of contingency scenarios,which require a variation of admissible flight volumes for UAS operations and/or fast trajectory re-planning.The platform,developed within a research project which involved several partners,was tested in a relevant operational scenario at the Grottaglie-Taranto airport in Italy.The operators involved in the tests provided positive feedback on the services provided by the platform and the usability of the interfaces,while also making suggestions for adding new features in future developments.展开更多
The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmente...The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.展开更多
With the iteration and upgrading of medical technology and the continuous growth of public health demands,the quality of nursing services has become a core indicator for measuring the effectiveness of the medical syst...With the iteration and upgrading of medical technology and the continuous growth of public health demands,the quality of nursing services has become a core indicator for measuring the effectiveness of the medical system.The clinical practice ability of nursing staff is directly related to the safety of patient diagnosis and treatment and the rehabilitation process.However,the current clinical nursing talent training model is facing bottlenecks such as limited practical scenarios and fragmented case cognition.This study focuses on the teaching application of augmented reality(AR)technology in hospital Settings and systematically reviews the research progress on the improvement of clinical practice ability of trainee nurses based on the AR immersive teaching model.By constructing a clinical teaching scenario that integrates virtual and real,AR technology can dynamically simulate complex case handling processes and enhance nursing students’three-dimensional cognition of condition assessment,operation norms,and emergency plans.Hospitals,as the core base for practical teaching,can effectively shorten the connection cycle between theoretical teaching and clinical practice by integrating AR technology,improve the clinical practice level of trainee nurses,and provide an innovative model for optimizing the path of clinical nursing talent cultivation.展开更多
The aim of this study was to assess the potential of surgical guides as a complementary tool to augmented reality(AR)in enhancing the safety and precision of pedicle screw placement in spinal surgery.Four trainers wer...The aim of this study was to assess the potential of surgical guides as a complementary tool to augmented reality(AR)in enhancing the safety and precision of pedicle screw placement in spinal surgery.Four trainers were divided into the AR navigation group using surgical guides and the free-hand group.Each group consisted of a novice and an experienced spine surgeon.A total of 80 pedicle screws were implanted.First,the AR group reconstructed the 3D model and planned the screw insertion route according to the computed tomography data of L2 lumbar vertebrae.Then,the Microsoft HoloLens™2 was used to identify the vertebral model,and the planned virtual path was superimposed on the real cone model.Next,the screw was placed according to the projected trajectory.Finally,Micron Tracker was used to measure the deviation of screws from the preoperatively planned trajectory,and pedicle screws were evaluated using the Gertzbein-Robbins scale.In the AR group,the linear deviations of the experienced doctor and the novice were(1.59±0.39)mm and(1.73±0.52)mm respectively,and the angle deviations were 2.72°±0.61°and 2.87°±0.63°respectively.In the free-hand group,the linear deviations of the experienced doctor and the novice were(2.88±0.58)mm and(5.25±0.62)mm respectively,and the angle deviations were 4.41°±1.18°and 7.15°±1.45°respectively.Both kinds of deviations between the two groups were significantly different(P<0.05).The screw accuracy rate was 95%in the AR navigation group and 77.5%in the free-hand group.The results of this study indicate that the integration of surgical guides and AR is an innovative technique that can substantially enhance the safety and precision of spinal surgery and assist inexperienced doctors in completing the surgery.展开更多
Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of ...Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.展开更多
Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when impl...Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.展开更多
With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown ma...With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.展开更多
In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the ...In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.展开更多
文摘In this paper,an integrated guidance and control approach is presented to improve the performance of the missile interception.The approach includes damping augmented system with attitude rate feedback to decrease the oscillation during the homing phase for missiles with low damping.In addition,physical constraints,which can affect the performance of the missile interception,such as acceleration limit,seeker’s look angle,and look angle rate constraints are considered.The integrated guidance and control problem is formulated as a convex quadratic optimization problem with equality and inequality constraints,and the solution is obtained by a primal–dual interior point method.The performance of the proposed method is verified through several numerical examples.
基金supported by the National Natural Science Foundation of China(No.10771031)the Fundamental Research Funds for Central Universities(No.090405013)
文摘The successive overrelaxation-like （SOR-like） method with the real param- eters ω is considered for solving the augmented system. The new method is called the modified SOR-like （MSOR-like） method. The functional equation between the parameters and the eigenvalues of the iteration matrix of the MSOR-like method is given. Therefore, the necessary and sufficient condition for the convergence of the MSOR-like method is derived. The optimal iteration parameter ω of the MSOR-like method is derived. Finally, the proof of theorem and numerical computation based on a particular linear system are given, which clearly show that the MSOR-like method outperforms the SOR-like （Li, C. J., Li, B. J., and Evans, D. J. Optimum accelerated parameter for the GSOR method. Neural, Parallel ＆ Scientific Computations, 7（4）, 453-462 （1999）） and the modified sym- metric SOR-like （MSSOR-like） methods （Wu, S. L., Huang, T. Z., and Zhao, X. L. A modified SSOR iterative method for augmented systems. Journal of Computational and Applied Mathematics, 228（4）, 424-433 （2009））.
基金the National Natural Science Foundation of China(Nos.82330063 and M-0019)the Interdisciplinary Program of Shanghai Jiao Tong University(Nos.YG2022QN056,YG2023ZD19,and YG2023ZD15)+2 种基金the Cross Disciplinary Research Fund of Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine(No.JYJC202115)the Translation Clinical R&D Project of Medical Robot of Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine(No.IMR-NPH202002)the Shanghai Key Clinical Specialty,Shanghai Eye Disease Research Center(No.2022ZZ01003)。
文摘Endoscopic transnasal optic nerve decompression surgery plays a crucial role in minimal invasive treatment of complex traumatic optic neuropathy.However,a major challenge faced during the procedure is the inability to visualize the optic nerve intraoperatively.To address this issue,an endoscopic image-based augmented reality surgical navigation system is developed in this study.The system aims to virtually fuse the optic nerve onto the endoscopic images,assisting surgeons in determining the optic nerve’s position and reducing surgical risks.First,a calibration algorithm based on a checkerboard grid of immobile points is proposed,building upon existing calibration methods.Additionally,to tackle accuracy issues associated with augmented reality technology,an optical navigation and visual fusion compensation algorithm is proposed to improve the intraoperative tracking accuracy.To evaluate the system’s performance,model experiments were meticulously designed and conducted.The results confirm the accuracy and stability of the proposed system,with an average tracking error of(0.99±0.46)mm.This outcome demonstrates the effectiveness of the proposed algorithm in improving the augmented reality surgical navigation system’s accuracy.Furthermore,the system successfully displays hidden optic nerves and other deep tissues,thus showcasing the promising potential for future applications in orbital and maxillofacial surgery.
基金the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant(No.20172005)。
文摘Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of surgical failure.Therefore,it is necessary to design a visualization system for closed thoracic drainage.Augmented reality(AR)technology can assist in visualizing the internal anatomical structure and determining the insertion point on the body surface.The structure of the currently used steel-needle-guided chest tube was modified by integrating it with an ultrafine diameter camera to provide real-time visualization of the puncture process.After simulation experiments,the overall registration error of the AR method was measured to be within(3.59±0.53)mm,indicating its potential for clinical application.The ultrafine diameter camera module and improved steel-needle-guided chest tube can timely reflect the position of the needle tip in the human body.A comparative experiment showed that video guidance could improve the safety of the puncture process compared to the traditional method.Finally,a qualitative evaluation of the usability of the system was conducted through a questionnaire.This system facilitates the visualization of closed thoracic drainage puncture procedure and pro-vides an implementation scheme to enhance the accuracy and safety of the operative step,which is conducive to reducing the learning curve and improving the proficiency of the doctors.
文摘BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.
文摘Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal interventions for cancer treatment.Methods:A specialized software transforms 2D medical images into precise 3D digital models, facilitating improved anatomical understanding and surgical planning. Patient-specific 3D printed anatomical models are utilized for preoperative planning, intraoperative guidance, and surgical education. AR technology enables the overlay of digital perceptions onto real-world surgical environments.Results:Patient-specific 3D printed anatomical models have multiple applications, such as preoperative planning, intraoperative guidance, trainee education, and patient counseling. Virtual reality involves substituting the real world with a computer-generated 3D environment, while AR overlays digitally created perceptions onto the existing reality. The advances in 3D modeling technology have sparked considerable interest in their application to partial nephrectomy in the realm of renal cancer. 3D printing, also known as additive manufacturing, constructs 3D objects based on computer-aided design or digital 3D models. Utilizing 3D-printed preoperative renal models provides benefits for surgical planning, offering a more reliable assessment of the tumor's relationship with vital anatomical structures and enabling better preparation for procedures. AR technology allows surgeons to visualize patient-specific renal anatomical structures and their spatial relationships with surrounding organs by projecting CT/MRI images onto a live laparoscopic video. Incorporating patient-specific 3D digital models into healthcare enhances best practice, resulting in improved patient care, increased patient satisfaction, and cost saving for the healthcare system.
基金supported in part by the National Natural Science Foundation of China(62273311,61773351)Henan Provincial Science Foundation for Distinguished Young Scholars(242300421051)。
文摘Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics.Furthermore,it remains a challenge when there are additional constraints on control torques of robotic systems.In this article,we propose a novel high-order control barrier function(HoCBF)-based safety control method for robotic systems subject to input-output constraints,which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque.In our design,augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system,so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics.This proposed scheme leads to the quadratic program(QP),which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law.The proposed closed-loop control system not only achieves the requirements of real-time capability,stability,safety and compliance,but also reduces undesired chattering of control inputs.Finally,the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
基金Supported by The Hunan Provincial Natural Science Foundation of China,No.2023JJ30773,No.2025JJ60480,and No.2025JJ60552The Scientific Research Program of The Hunan Provincial Health Commission,No.202204072544+4 种基金The Science and Technology Innovation Program of Hunan Province,No.2024RC3053The CBT ECR/MCR Scheme,No.324910-0028/07National Natural Science Foundation of China,No.32300652The Scientific Research Program of Hunan Provincial Health Commission,No.W20243023The Scientific Research Launch Project for New Employees of The Second Xiangya Hospital of Central South University.
文摘Augmented reality(AR)is a technology that superimposes digital information onto real-world objects via head-mounted display devices to improve surgical finesse through visually enhanced medical information.With the rapid development of digital technology,AR has been increasingly adopted in orthopedic surgeries across the globe,especially in total knee arthroplasty procedures which demand high precision.By overlaying digital information onto the surgeon's field of view,AR systems enhance precision,improve alignment accuracy,and reduce the risk of complications associated with malalignment.Some concerns have been raised despite accuracy,including the learning curve,long-term outcomes,and technical limitations.Furthermore,it is essential for health practitioners to gain trust in the utilisation of AR.
基金the Science Foundation of China University of Petroleum,Beijing(Grant No.2462024YJRC021)the National Natural Science Foundation of China(Grant No.U24B2031 and 52104013).
文摘1.Introduction With the increasing demand for petroleum and natural gas resources,along with technological advancements in exploration and production,the primary frontier of oil and gas resources has shifted from conventional oil and gas development to the domains of“Two Deeps,One Unconventional,One Mature,”which include deep onshore,deepwater,unconventional resources,and mature oilfields[1].
基金supported by the European Union and Italian Ministry of University and Research through the call PON Research and Innovation 2014-2020,Axis Ⅱ,Action 2,project AcrOSS(Environment for Safe Operations of Remotely Piloted Aircraft),project number ARS01_00702-CUP:F36C18000210005.
文摘The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed description of the architecture and workflow of a platform for UAS traffic management(UTM),designed to pave the way for increased,improved and safer UAS operations in the civil airspace.In particular,access to low-altitude airspace for UAS operations is managed,while facilitating the implementation of beyond visual line-of-sight(BVLOS)operations,and ensuring a safe and efficient integration of UAS into both controlled and uncontrolled airspace.Detection and management of unidentified or uncooperative UAS’s is also taken care of.To this end,an architecture based on three interacting layers is proposed,with the air traffic control at the highest level,the UAS operator(s)at the bottom,and a UAS service supplier acting as an interface.The platform,with its physical and digital elements,guarantees the effective and efficient interaction among these three layers,including management of contingency scenarios,which require a variation of admissible flight volumes for UAS operations and/or fast trajectory re-planning.The platform,developed within a research project which involved several partners,was tested in a relevant operational scenario at the Grottaglie-Taranto airport in Italy.The operators involved in the tests provided positive feedback on the services provided by the platform and the usability of the interfaces,while also making suggestions for adding new features in future developments.
基金the University of Shanghai for Science and Technology’s Medical Engineering Interdisciplinary Project(No.10-22-308-520)the Ministry of Education’s First Batch of Industry-Education Cooperation Collaborative Education Projects(No.202101042008)+1 种基金the Fundamental Research Funds for the Central Universities(No.YG2019QNA34)the Shanghai Municipal Health Commission for Youth Clinical Research Project(No.20194Y0134)。
文摘The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.
文摘With the iteration and upgrading of medical technology and the continuous growth of public health demands,the quality of nursing services has become a core indicator for measuring the effectiveness of the medical system.The clinical practice ability of nursing staff is directly related to the safety of patient diagnosis and treatment and the rehabilitation process.However,the current clinical nursing talent training model is facing bottlenecks such as limited practical scenarios and fragmented case cognition.This study focuses on the teaching application of augmented reality(AR)technology in hospital Settings and systematically reviews the research progress on the improvement of clinical practice ability of trainee nurses based on the AR immersive teaching model.By constructing a clinical teaching scenario that integrates virtual and real,AR technology can dynamically simulate complex case handling processes and enhance nursing students’three-dimensional cognition of condition assessment,operation norms,and emergency plans.Hospitals,as the core base for practical teaching,can effectively shorten the connection cycle between theoretical teaching and clinical practice by integrating AR technology,improve the clinical practice level of trainee nurses,and provide an innovative model for optimizing the path of clinical nursing talent cultivation.
基金the National Natural Science Foundation of China(No.11502146)the 1 Batch of 2021 MOE of PRC Industry University Collaborative Education Program(No.202101042008)。
文摘The aim of this study was to assess the potential of surgical guides as a complementary tool to augmented reality(AR)in enhancing the safety and precision of pedicle screw placement in spinal surgery.Four trainers were divided into the AR navigation group using surgical guides and the free-hand group.Each group consisted of a novice and an experienced spine surgeon.A total of 80 pedicle screws were implanted.First,the AR group reconstructed the 3D model and planned the screw insertion route according to the computed tomography data of L2 lumbar vertebrae.Then,the Microsoft HoloLens™2 was used to identify the vertebral model,and the planned virtual path was superimposed on the real cone model.Next,the screw was placed according to the projected trajectory.Finally,Micron Tracker was used to measure the deviation of screws from the preoperatively planned trajectory,and pedicle screws were evaluated using the Gertzbein-Robbins scale.In the AR group,the linear deviations of the experienced doctor and the novice were(1.59±0.39)mm and(1.73±0.52)mm respectively,and the angle deviations were 2.72°±0.61°and 2.87°±0.63°respectively.In the free-hand group,the linear deviations of the experienced doctor and the novice were(2.88±0.58)mm and(5.25±0.62)mm respectively,and the angle deviations were 4.41°±1.18°and 7.15°±1.45°respectively.Both kinds of deviations between the two groups were significantly different(P<0.05).The screw accuracy rate was 95%in the AR navigation group and 77.5%in the free-hand group.The results of this study indicate that the integration of surgical guides and AR is an innovative technique that can substantially enhance the safety and precision of spinal surgery and assist inexperienced doctors in completing the surgery.
文摘Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.
基金funded by the McGill University Graduate Excellence Fellowship Award(00157)the Mitacs Accelerate Program(IT13369)the McGill Engineering Doctoral Award(MEDA).
文摘Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.
基金supported by the National Science Foundation of China(62263020)the Key Project of Natural Science Foundation of Gansu Province(25JRRA061)+1 种基金the Key R&D Program of Gansu Province(23YFGA0061)the Scientific Research Initiation Fund of Lanzhou University of Technology(061602).
文摘In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.
