In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di...In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.展开更多
Based on the principle of vehicle-track coupling dynamics, SIMPACK multi-body dynamics software is used to establish a C80 wagon line-coupled multi-body dynamics model with 73 degrees of freedom. And the reasonablenes...Based on the principle of vehicle-track coupling dynamics, SIMPACK multi-body dynamics software is used to establish a C80 wagon line-coupled multi-body dynamics model with 73 degrees of freedom. And the reasonableness of the line-coupled dynamics model is verified by using the maximum residual acceleration, the nonlinear critical speed of the wagon. The experimental results show that the established vehicle line coupling dynamics model meets the requirements of vehicle line coupling dynamics modeling.展开更多
In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain...In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.展开更多
With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,a...With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.展开更多
Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote m...Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote machine tool intelligence and has attracted considerable research interest.However,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence.Herein,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin.The review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling.Additionally,it highlights three bottleneck issues facing the field.Considering these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel parameters.Finally,future research directions for the DTMT are discussed.This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.展开更多
When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturin...When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.展开更多
Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between...Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this paper.Firstly,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration signals.Secondly,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal data.Then,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information.Finally,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University.The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods.展开更多
Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP syste...Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP system,typically contains a large number of invalid points caused by the background,ambient light,shadows,and object edge regions.Research on noisy point detection and elimination has been conducted over the past two decades.However,existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark.In this paper,we propose a novel invalid point removal framework that consists of two aspects:(1)A convolutional neural network(CNN)is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions.(2)A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects.Experimental results verify that the proposed framework(1)can remove background points intelligently and accurately in different types of complex circumstances,and(2)performs excellently in discrete point detection from object regions.展开更多
The expressway is necessary for the development of the modern transportation industry, and the level of expressway construction reflects the overall grade of national or regional economic development. In order to proc...The expressway is necessary for the development of the modern transportation industry, and the level of expressway construction reflects the overall grade of national or regional economic development. In order to process the expressway road property data information, based on the current mainstream Windows operating system, this study utilizes Geographic Information System (GIS) development technology, road video processing technology, and spatial data mining method to design and develop an expressway video and road infostructure GIS data production system. The system designs a multi-layer distributed application model in accordance with the ideas and methods of GIS engineering and the characteristics of road production data. In addition, according to the characteristics and specification requirements of basic geographic data, the road production database of spatial data and attribute data integrated storage is constructed by combining database and spatial data engine. Through the development of the GIS data production system for expressway video and road infostructure, various functions such as generation of road property data, dynamic management of road infostructure, and visualization of spatial information have been realized. The system focuses on improving the production efficiency and automation level of expressway production data and meet</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the construction requirements for modernization, informatization, and intelligence of expressways.展开更多
Improper flow control devices in a multi-strand tundish can cause some problems, for example, liquid steel cannot reach every nozzle at the same time and the liquid steel in nozzles far away from the entry zone has a ...Improper flow control devices in a multi-strand tundish can cause some problems, for example, liquid steel cannot reach every nozzle at the same time and the liquid steel in nozzles far away from the entry zone has a lower temperature. The water model experiment of a six-strand tundish of Tianjin Iron ＆ Steel Co. Ltd. was performed, a new ＂U＂ type baffle was obtained, and its parameters were defined by perpendicular analysis. The ＂U＂ baffle can not only improve those imperfections, but also prolong the residence time of nonmetallic inclusions, which is good for their flotation and separation.展开更多
In spite of the tumor microenvironments responsive cancer therapy based on Fenton reaction(i.e.,chemodynamic therapy,CDT)has been attracted more attentions in recent years,the limited Fenton reaction efficiency is the...In spite of the tumor microenvironments responsive cancer therapy based on Fenton reaction(i.e.,chemodynamic therapy,CDT)has been attracted more attentions in recent years,the limited Fenton reaction efficiency is the important obstacle to further application in clinic.Herein,we synthesized novel FeO/MoS2 nanocomposites modified by bovine serum albumin(FeO/MoS2-BSA)with boosted Fenton reaction efficiency by the synergistic effect of co-catalyze and photothermal effect of MoS2 nanosheets triggered by the second near-infrared(NIR II)light.In the tumor microenvironments,the MoS2 nanosheets not only can accelerate the conversion of Fe3+ions to Fe2+ions by Mo4+ions on their surface to improve Fenton reaction efficiency,but also endow FeO/MoS2-BSA with good photothermal performances for photothermal-enhanced CDT and photothermal therapy(PTT).Consequently,benefiting from the synergetic-enhanced CDT/PTT,the tumors are eradicated completely in vivo.This work provides innovative synergistic strategy for constructing nanocomposites for highly efficient CDT.展开更多
Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Cu...Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.展开更多
The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict...The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.展开更多
A kinetic model was proposed to predict the seawater fouling process in the seawater heat exchangers.The new model adopted an expression combining depositional and removal behaviors for seawater fouling based on the K...A kinetic model was proposed to predict the seawater fouling process in the seawater heat exchangers.The new model adopted an expression combining depositional and removal behaviors for seawater fouling based on the Kern–Seaton model.The present model parameters include the integrated kinetic rate of deposition(k d)and the integrated kinetic rate of removal(k r),which have clear physical signi ficance.A seawater-fouling monitoring device was established to validate the model.The experimental data were well fitted to the model,and the parameters were obtained in different conditions.SEM and EDX analyses were performed after the experiments,and the results show that the main components of seawater fouling are magnesium hydroxide and aluminum hydroxide.The effects of surface temperature,flow velocity and surface free energy were assessed by the model and the experimental data.The results indicate that the seawater fouling becomes aggravated as the surface temperature increased in a certain range,and the seawater fouling resistance reduced as the flow velocity of seawater increased.Furthermore,the effect of the surface free energy of metals was analyzed,showing that the lower surface free energy mitigates the seawater fouling accumulation.展开更多
Purpose: To develop and test a mission-oriented and multi-dimensional benchmarking method for a small scale university aiming for internationally first-class basic research.Design/methodology/approach: An individualiz...Purpose: To develop and test a mission-oriented and multi-dimensional benchmarking method for a small scale university aiming for internationally first-class basic research.Design/methodology/approach: An individualized evidence-based assessment scheme was employed to benchmark ShanghaiTech University against selected top research institutions,focusing on research impact and competitiveness at the institutional and disciplinary levels.Topic maps opposing ShanghaiTech and corresponding top institutions were produced for the main research disciplines of ShanghaiTech. This provides opportunities for further exploration of strengths and weakness. Findings: This study establishes a preliminary framework for assessing the mission of the university. It further provides assessment principles, assessment questions, and indicators.Analytical methods and data sources were tested and proved to be applicable and efficient.Research limitations: To better fit the selective research focuses of this university, its schema of research disciplines needs to be re-organized and benchmarking targets should include disciplinary top institutions and not necessarily those universities leading overall rankings.Current reliance on research articles and certain databases may neglect important research output types.Practical implications: This study provides a working framework and practical methods for mission-oriented, individual, and multi-dimensional benchmarking that ShanghaiTech decided to use for periodical assessments. It also offers a working reference for other institutions to adapt. Further needs are identified so that ShanghaiTech can tackle them for future benchmarking.Originality/value: This is an effort to develop a mission-oriented, individually designed,systematically structured, and multi-dimensional assessment methodology which differs from often used composite indices.展开更多
Current studies on cable harness layouts have mainly focused on cable harness route planning.However,the topological structure of a cable harness is also extremely complex,and the branch structure of the cable harness...Current studies on cable harness layouts have mainly focused on cable harness route planning.However,the topological structure of a cable harness is also extremely complex,and the branch structure of the cable harness can affect the route of the cable harness layout.The topological structure design of the cable harness is a key to such a layout.In this paper,a novel multi-branch cable harness layout design method is presented,which unites the probabilistic roadmap method(PRM)and the genetic algorithm.First,the engineering constraints of the cable harness layout are presented.An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described.In addition,a new genetic algorithm is proposed,and the algorithm structure of which is redesigned.In addition,the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness.A prototype system of a cable harness layout design was developed based on the method described in this study,and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method.In summary,the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.展开更多
An experimental model of schizophrenia was established using dizocilpine （MK-801）. Rats were intragastrically administered with Wendan decoction or clozapine for 21 days prior to establishing the model. The results ...An experimental model of schizophrenia was established using dizocilpine （MK-801）. Rats were intragastrically administered with Wendan decoction or clozapine for 21 days prior to establishing the model. The results revealed that the latency of schizophrenia model rats to escape from the hidden platform in the Morris water maze was significantly shortened after administration of Wendan decoction or clozapine. In addition, the treated rats crossed the platform significantly more times than the untreated model rats. Moreover, the rate of successful long-term potentiation induction in the Wendan decoction group and clozapine group were also obviously increased compared with the model group, and the population spike peak latency was significantly shortened. These experimental findings suggest that Wendan decoction can improve the learning and memory ability of schizophrenic rats to the same extent as clozapine treatment.展开更多
文摘In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.
文摘Based on the principle of vehicle-track coupling dynamics, SIMPACK multi-body dynamics software is used to establish a C80 wagon line-coupled multi-body dynamics model with 73 degrees of freedom. And the reasonableness of the line-coupled dynamics model is verified by using the maximum residual acceleration, the nonlinear critical speed of the wagon. The experimental results show that the established vehicle line coupling dynamics model meets the requirements of vehicle line coupling dynamics modeling.
基金supported by the Central University Basic Research Business Fee Fund Project(J2023-027)Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(No.FZ2022KF06)China Postdoctoral Science Foundation(No.2022M722248).
文摘In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.
基金supported by the Central University Basic Research Business Fee Fund Project(J2023-027)China Postdoctoral Science Foundation(No.2022M722248).
文摘With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.
基金Supported by Tianjin Municipal University Science and Technology Development Foundation of China(Grant No.2021KJ176).
文摘Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote machine tool intelligence and has attracted considerable research interest.However,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence.Herein,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin.The review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling.Additionally,it highlights three bottleneck issues facing the field.Considering these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel parameters.Finally,future research directions for the DTMT are discussed.This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.
基金Supported by Jiangsu Provincial Agriculture Science and Technology Innovation Fund(Grant No.CX(23)3036)National Natural Science Foundation of China(Grant No.52375479)+1 种基金Jiangsu Provincal Graduate Research and Practical Innovation Program(Grant No.KYCX24_0825)Changzhou Municipal Sci&Tech Program(Grant No.CM20223014).
文摘When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.
基金supported by the National Key R&D Program of China(2021YFF0501101)the Youth Project of Hunan Provincial Department of Education(22B0586)the Education Reform Project of Hunan Provincial Department of Education(2022JGYB186).
文摘Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this paper.Firstly,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration signals.Secondly,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal data.Then,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information.Finally,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University.The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods.
基金Supported by National Defense Basic Scientific Research Program of China(Grant No.JCKY2021602B032)。
文摘Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP system,typically contains a large number of invalid points caused by the background,ambient light,shadows,and object edge regions.Research on noisy point detection and elimination has been conducted over the past two decades.However,existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark.In this paper,we propose a novel invalid point removal framework that consists of two aspects:(1)A convolutional neural network(CNN)is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions.(2)A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects.Experimental results verify that the proposed framework(1)can remove background points intelligently and accurately in different types of complex circumstances,and(2)performs excellently in discrete point detection from object regions.
文摘The expressway is necessary for the development of the modern transportation industry, and the level of expressway construction reflects the overall grade of national or regional economic development. In order to process the expressway road property data information, based on the current mainstream Windows operating system, this study utilizes Geographic Information System (GIS) development technology, road video processing technology, and spatial data mining method to design and develop an expressway video and road infostructure GIS data production system. The system designs a multi-layer distributed application model in accordance with the ideas and methods of GIS engineering and the characteristics of road production data. In addition, according to the characteristics and specification requirements of basic geographic data, the road production database of spatial data and attribute data integrated storage is constructed by combining database and spatial data engine. Through the development of the GIS data production system for expressway video and road infostructure, various functions such as generation of road property data, dynamic management of road infostructure, and visualization of spatial information have been realized. The system focuses on improving the production efficiency and automation level of expressway production data and meet</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the construction requirements for modernization, informatization, and intelligence of expressways.
文摘Improper flow control devices in a multi-strand tundish can cause some problems, for example, liquid steel cannot reach every nozzle at the same time and the liquid steel in nozzles far away from the entry zone has a lower temperature. The water model experiment of a six-strand tundish of Tianjin Iron ＆ Steel Co. Ltd. was performed, a new ＂U＂ type baffle was obtained, and its parameters were defined by perpendicular analysis. The ＂U＂ baffle can not only improve those imperfections, but also prolong the residence time of nonmetallic inclusions, which is good for their flotation and separation.
基金This work was supported by the financial aid from the National Natural Science Foundation of China(Grant Nos.51502284,21834007,21521092,21590794,and 21673220)the Program of Science and Technology Development Plan of Jilin Province of China(No.20170101186JC)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB20030300)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2019232).
文摘In spite of the tumor microenvironments responsive cancer therapy based on Fenton reaction(i.e.,chemodynamic therapy,CDT)has been attracted more attentions in recent years,the limited Fenton reaction efficiency is the important obstacle to further application in clinic.Herein,we synthesized novel FeO/MoS2 nanocomposites modified by bovine serum albumin(FeO/MoS2-BSA)with boosted Fenton reaction efficiency by the synergistic effect of co-catalyze and photothermal effect of MoS2 nanosheets triggered by the second near-infrared(NIR II)light.In the tumor microenvironments,the MoS2 nanosheets not only can accelerate the conversion of Fe3+ions to Fe2+ions by Mo4+ions on their surface to improve Fenton reaction efficiency,but also endow FeO/MoS2-BSA with good photothermal performances for photothermal-enhanced CDT and photothermal therapy(PTT).Consequently,benefiting from the synergetic-enhanced CDT/PTT,the tumors are eradicated completely in vivo.This work provides innovative synergistic strategy for constructing nanocomposites for highly efficient CDT.
基金This research was funded by the National Key Research and Development Program of China(No.2017YFC1501204)the National Natural Science Foundation of China(No.51678536)+4 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(2016ZT06N340)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(18IRTSTHN007)the Research on NonDestructive Testing(NDT)and Rapid Evaluation Technology for Grouting Quality of Prestressed Ducts(Contract No.HG-GCKY-01-002).The authors would like to thank for these financial supports.
文摘Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.
基金National Key Research and Development Program of China(Grant No.2020YFB1710300)National Natural Science Foundation of China(Grant No.52005042)+2 种基金National Defense Fundamental Research Foundation of China(Grant No.JCKY2020203B039)Equipment Pre-research Foundation of China(Grant No.80923010101)Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.
基金Supported by the Leading Academic Discipline Project of Shanghai Municipal Education Commission(J50502)the Construction of Shanghai Science and Technology Commission(13DZ2260900)
文摘A kinetic model was proposed to predict the seawater fouling process in the seawater heat exchangers.The new model adopted an expression combining depositional and removal behaviors for seawater fouling based on the Kern–Seaton model.The present model parameters include the integrated kinetic rate of deposition(k d)and the integrated kinetic rate of removal(k r),which have clear physical signi ficance.A seawater-fouling monitoring device was established to validate the model.The experimental data were well fitted to the model,and the parameters were obtained in different conditions.SEM and EDX analyses were performed after the experiments,and the results show that the main components of seawater fouling are magnesium hydroxide and aluminum hydroxide.The effects of surface temperature,flow velocity and surface free energy were assessed by the model and the experimental data.The results indicate that the seawater fouling becomes aggravated as the surface temperature increased in a certain range,and the seawater fouling resistance reduced as the flow velocity of seawater increased.Furthermore,the effect of the surface free energy of metals was analyzed,showing that the lower surface free energy mitigates the seawater fouling accumulation.
文摘Purpose: To develop and test a mission-oriented and multi-dimensional benchmarking method for a small scale university aiming for internationally first-class basic research.Design/methodology/approach: An individualized evidence-based assessment scheme was employed to benchmark ShanghaiTech University against selected top research institutions,focusing on research impact and competitiveness at the institutional and disciplinary levels.Topic maps opposing ShanghaiTech and corresponding top institutions were produced for the main research disciplines of ShanghaiTech. This provides opportunities for further exploration of strengths and weakness. Findings: This study establishes a preliminary framework for assessing the mission of the university. It further provides assessment principles, assessment questions, and indicators.Analytical methods and data sources were tested and proved to be applicable and efficient.Research limitations: To better fit the selective research focuses of this university, its schema of research disciplines needs to be re-organized and benchmarking targets should include disciplinary top institutions and not necessarily those universities leading overall rankings.Current reliance on research articles and certain databases may neglect important research output types.Practical implications: This study provides a working framework and practical methods for mission-oriented, individual, and multi-dimensional benchmarking that ShanghaiTech decided to use for periodical assessments. It also offers a working reference for other institutions to adapt. Further needs are identified so that ShanghaiTech can tackle them for future benchmarking.Originality/value: This is an effort to develop a mission-oriented, individually designed,systematically structured, and multi-dimensional assessment methodology which differs from often used composite indices.
基金Supported by National Natural Science Foundation of China(Grant No.51675050).
文摘Current studies on cable harness layouts have mainly focused on cable harness route planning.However,the topological structure of a cable harness is also extremely complex,and the branch structure of the cable harness can affect the route of the cable harness layout.The topological structure design of the cable harness is a key to such a layout.In this paper,a novel multi-branch cable harness layout design method is presented,which unites the probabilistic roadmap method(PRM)and the genetic algorithm.First,the engineering constraints of the cable harness layout are presented.An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described.In addition,a new genetic algorithm is proposed,and the algorithm structure of which is redesigned.In addition,the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness.A prototype system of a cable harness layout design was developed based on the method described in this study,and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method.In summary,the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.
基金sponsored by the Natural Science Foundation of China (No. 81160423)Research Plan of Traditional Chinese Medicine of Jiangxi Province Department of Public Health (No. 2009A054)Jiangxi Provincial Youth Science Fund Project (No. GJJ11190)
文摘An experimental model of schizophrenia was established using dizocilpine （MK-801）. Rats were intragastrically administered with Wendan decoction or clozapine for 21 days prior to establishing the model. The results revealed that the latency of schizophrenia model rats to escape from the hidden platform in the Morris water maze was significantly shortened after administration of Wendan decoction or clozapine. In addition, the treated rats crossed the platform significantly more times than the untreated model rats. Moreover, the rate of successful long-term potentiation induction in the Wendan decoction group and clozapine group were also obviously increased compared with the model group, and the population spike peak latency was significantly shortened. These experimental findings suggest that Wendan decoction can improve the learning and memory ability of schizophrenic rats to the same extent as clozapine treatment.
