To address the challenges of low accuracy and insufficient real-time performance in dynamic object detection for UAV surveillance,this paper proposes a novel tracking framework that integrates a lightweight improved Y...To address the challenges of low accuracy and insufficient real-time performance in dynamic object detection for UAV surveillance,this paper proposes a novel tracking framework that integrates a lightweight improved YOLOv5s model with adaptive motion compensation.A UAV-view dynamic feature enhancement strategy is innovatively introduced,and a lightweight detection network combining attention mechanisms and multi-scale fusion is constructed.The robustness of tracking under motion blur scenarios is also optimized.Experimental results demonstrate that the proposed method achieves a mAP@0.5 of 68.2%on the VisDrone dataset and reaches an inference speed of 32 FPS on the NVIDIA Jetson TX2 platform.This significantly improves the balance between accuracy and efficiency in complex scenes,offering reliable technical support for real-time applications such as emergency response.展开更多
A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decod...A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.展开更多
High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution ...High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution in the main urban area of Chongqing, based on realtime traffic data from 820 RFID detectors covering 454 roads, and the differences in spatiotemporal emission characteristics between inner and outer districts were analysed. The result showed that the daily vehicular emission intensities of CO, hydrocarbons, PM2.5, PM10,and NO_(x) were 30.24, 3.83, 0.18, 0.20, and 8.65 kg/km per day, respectively, in the study area during 2018. The pollutants emission intensities in inner district were higher than those in outer district. Light passenger cars(LPCs) were the main contributors of all-day CO emissions in the inner and outer districts, from which the contributors of NO_(x) emissions were different. Diesel and natural gas buses were major contributors of daytime NO_(x) emissions in inner districts, accounting for 40.40%, but buses and heavy duty trucks(HDTs) were major contributors in outer districts. At nighttime, due to the lifting of truck restrictions and suspension of buses, HDTs become the main NO_(x) contributor in both inner and outer districts,and its three NO_(x) emission peak hours were found, which are different to the peak hours of total NO_(x) emission by all vehicles. Unlike most other cities, bridges and connecting channels are always emission hotspots due to long-time traffic congestion. This knowledge will help fully understand vehicular emissions characteristics and is useful for policymakers to design precise prevention and control measures.展开更多
The new reality of smart distribution systems with use of generation sources of small and medium sizes brings new challenges for the operation of these systems. The complexity and the large number of nodes requires us...The new reality of smart distribution systems with use of generation sources of small and medium sizes brings new challenges for the operation of these systems. The complexity and the large number of nodes requires use of methods which can reduce the processing time of algorithms such as power flow, allowing its use in real time. This paper presents a known methodology for calculating the power flow in three phases using backward/forward sweep method, and also considering other network elements such as voltage regulators, shunt capacitors and sources of dispersed generation of types PV （active power and voltage） and PQ （active and reactive power）. After that, new elements are introduced that allow the parallelization of this algorithm and an adequate distribution of work between the available processors. The algorithm was implemented using a multi-tiered architecture; the processing times were measured in many network configurations and compared with the same algorithm in the serial version.展开更多
A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find ou...A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Mach numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Henne shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is constructed with those points to reduce computing costs. Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.展开更多
Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method wa...Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.展开更多
With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the o...With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.展开更多
Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving o...Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.展开更多
In automatic visual inspection, the object image subspace should be segmented and matched quickly so that the affine relationship can be built between the template image and the sample image. When the interference is ...In automatic visual inspection, the object image subspace should be segmented and matched quickly so that the affine relationship can be built between the template image and the sample image. When the interference is strong and the illumination is uneven, for example in an industrial application, this can make it difficult to obtain an objects subspace quickly and accurately in real-time. In this paper, a novel strategy is proposed to adopt discrete radial search paths instead of searching all points in an image. Therefore, the searching time can be substantially reduced. In order to reduce the influence coming from the industrial environment, the paper proposes another method that is local energy level set segmentation, which can locate the object subspace more efficiently and accurately. The detection of ＂crown caps＂ is presented as an example in this paper. Detection effects and computing time are compared between several detection methods, and the mechanisms of inspection have also been analyzed.展开更多
A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environment...A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.展开更多
Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the wat...In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.展开更多
Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their ...Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.展开更多
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,...Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.展开更多
Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their excep...Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their exceptional ability to decompose organic matter.However,accurately identifying larval instars is critical for optimizing feeding efficiency and downstreamapplications,as different stages exhibit only subtle visual differences.This study proposes a real-timemobile application for automatic classification of BSFL larval stages.The systemdistinguishes between early instars(Stages 1–4),suitable for food waste processing and animal feed,and late instars(Stages 5–6),optimal for pupation and industrial use.A baseline YOLO11 model was employed,achieving a mAP50-95 of 0.811.To further improve performance and efficiency,we introduce YOLO11-DSConv,a novel adaptation incorporating Depthwise Separable Convolutions specifically optimized for the unique challenges of BSFL classification.Unlike existing YOLO+DSConv implementations,our approach is tailored for the subtle visual differences between larval stages and integrated into a complete end-to-end system.The enhanced model achieved a mAP50-95 of 0.813 while reducing computational complexity by 15.5%.The proposed system demonstrates high accuracy and lightweight performance,making it suitable for deployment on resource-constrained agricultural devices,while directly supporting circular economy initiatives through precise larval stage identification.By integrating BSFL classification with realtime AI,this work contributes to sustainable food wastemanagement and advances intelligent applications in precision agriculture and circular economy initiatives.Additional supplementary materials and the implementation code are available at the following link:YOLO11-DSConv,Server Side,Mobile Application.展开更多
Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this stud...Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.展开更多
Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed ...Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed real-time databases, which adopts a main memory database as its ground support. In our scheme, each site maintains a real-time log for local transactions and the subtransactions, which execute at the site, and execte local checkpointing independently. Log records are stored in non-volatile high- speed store, which is divided into four different partitions based on transaction classes. During restart recovery after a site crash, partitioned crash recovery strategy is adopted to ensure that the site can be brought up before the entire local secondary database is reloaded in main memory. The partitioned crash recovery strategy not only guarantees the internal consistency to be recovered, but also guarantee the temporal consistency and recovery of the sates of physical world influenced by uncommitted transactions. Combined with two- phase commit protocol, TCLCRS can guarantee failure atomicity of distributed real-time transactions.展开更多
文摘To address the challenges of low accuracy and insufficient real-time performance in dynamic object detection for UAV surveillance,this paper proposes a novel tracking framework that integrates a lightweight improved YOLOv5s model with adaptive motion compensation.A UAV-view dynamic feature enhancement strategy is innovatively introduced,and a lightweight detection network combining attention mechanisms and multi-scale fusion is constructed.The robustness of tracking under motion blur scenarios is also optimized.Experimental results demonstrate that the proposed method achieves a mAP@0.5 of 68.2%on the VisDrone dataset and reaches an inference speed of 32 FPS on the NVIDIA Jetson TX2 platform.This significantly improves the balance between accuracy and efficiency in complex scenes,offering reliable technical support for real-time applications such as emergency response.
基金supported in part by the National Key R&D Program of China(Grant No.2023YFB3307604)the Shanxi Province Basic Research Program Youth Science Research Project(Grant Nos.202303021212054 and 202303021212046)+3 种基金the Key Projects Supported by Hebei Natural Science Foundation(Grant No.E2024203125)the National Science Foundation of China(Grant No.52105391)the Hebei Provincial Science and Technology Major Project(Grant No.23280101Z)the National Key Laboratory of Metal Forming Technology and Heavy Equipment Open Fund(Grant No.S2308100.W17).
文摘A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.
基金supported by the National Key Research Program(No.2018YFB1601105,No.2018YFB1601102)the Natural Science Foundation of China(No.41975165,No.U1811463)Chongqing Science and Technology Project(No.cstc2019jscxfxydX0035)。
文摘High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution in the main urban area of Chongqing, based on realtime traffic data from 820 RFID detectors covering 454 roads, and the differences in spatiotemporal emission characteristics between inner and outer districts were analysed. The result showed that the daily vehicular emission intensities of CO, hydrocarbons, PM2.5, PM10,and NO_(x) were 30.24, 3.83, 0.18, 0.20, and 8.65 kg/km per day, respectively, in the study area during 2018. The pollutants emission intensities in inner district were higher than those in outer district. Light passenger cars(LPCs) were the main contributors of all-day CO emissions in the inner and outer districts, from which the contributors of NO_(x) emissions were different. Diesel and natural gas buses were major contributors of daytime NO_(x) emissions in inner districts, accounting for 40.40%, but buses and heavy duty trucks(HDTs) were major contributors in outer districts. At nighttime, due to the lifting of truck restrictions and suspension of buses, HDTs become the main NO_(x) contributor in both inner and outer districts,and its three NO_(x) emission peak hours were found, which are different to the peak hours of total NO_(x) emission by all vehicles. Unlike most other cities, bridges and connecting channels are always emission hotspots due to long-time traffic congestion. This knowledge will help fully understand vehicular emissions characteristics and is useful for policymakers to design precise prevention and control measures.
文摘The new reality of smart distribution systems with use of generation sources of small and medium sizes brings new challenges for the operation of these systems. The complexity and the large number of nodes requires use of methods which can reduce the processing time of algorithms such as power flow, allowing its use in real time. This paper presents a known methodology for calculating the power flow in three phases using backward/forward sweep method, and also considering other network elements such as voltage regulators, shunt capacitors and sources of dispersed generation of types PV （active power and voltage） and PQ （active and reactive power）. After that, new elements are introduced that allow the parallelization of this algorithm and an adequate distribution of work between the available processors. The algorithm was implemented using a multi-tiered architecture; the processing times were measured in many network configurations and compared with the same algorithm in the serial version.
基金National Natural Science Foundation of China (10377015)
文摘A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Mach numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Henne shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is constructed with those points to reduce computing costs. Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.
文摘Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.
基金Project(61102039)supported by the National Natural Science Foundation of ChinaProject(2014AA052600)supported by National Hi-tech Research and Development Plan,China
文摘With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.
基金supported by Soongsil University Research Fund and BK 21 of Korea
文摘Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.
文摘In automatic visual inspection, the object image subspace should be segmented and matched quickly so that the affine relationship can be built between the template image and the sample image. When the interference is strong and the illumination is uneven, for example in an industrial application, this can make it difficult to obtain an objects subspace quickly and accurately in real-time. In this paper, a novel strategy is proposed to adopt discrete radial search paths instead of searching all points in an image. Therefore, the searching time can be substantially reduced. In order to reduce the influence coming from the industrial environment, the paper proposes another method that is local energy level set segmentation, which can locate the object subspace more efficiently and accurately. The detection of ＂crown caps＂ is presented as an example in this paper. Detection effects and computing time are compared between several detection methods, and the mechanisms of inspection have also been analyzed.
基金Project(20030533011)supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
基金Sponsored by the Project of Application Technology Research and Development Plan in Heilongjiang Province(Grant No.GA13C302)
文摘In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.
文摘Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.
基金supported by National Key Research and Development Program of China(2024YFE0214000)National Natural Science Foundation of China(62173308)+3 种基金Natural Science Foundation of Zhejiang Province of China(LRG25F030002)Zhejiang Province Leading Geese Plan(2025C01056)Jinhua Science and Technology Project(2022-1-042)Natural Science Foundation of Jiangsu Province(BK20240009).
文摘Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.
文摘Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their exceptional ability to decompose organic matter.However,accurately identifying larval instars is critical for optimizing feeding efficiency and downstreamapplications,as different stages exhibit only subtle visual differences.This study proposes a real-timemobile application for automatic classification of BSFL larval stages.The systemdistinguishes between early instars(Stages 1–4),suitable for food waste processing and animal feed,and late instars(Stages 5–6),optimal for pupation and industrial use.A baseline YOLO11 model was employed,achieving a mAP50-95 of 0.811.To further improve performance and efficiency,we introduce YOLO11-DSConv,a novel adaptation incorporating Depthwise Separable Convolutions specifically optimized for the unique challenges of BSFL classification.Unlike existing YOLO+DSConv implementations,our approach is tailored for the subtle visual differences between larval stages and integrated into a complete end-to-end system.The enhanced model achieved a mAP50-95 of 0.813 while reducing computational complexity by 15.5%.The proposed system demonstrates high accuracy and lightweight performance,making it suitable for deployment on resource-constrained agricultural devices,while directly supporting circular economy initiatives through precise larval stage identification.By integrating BSFL classification with realtime AI,this work contributes to sustainable food wastemanagement and advances intelligent applications in precision agriculture and circular economy initiatives.Additional supplementary materials and the implementation code are available at the following link:YOLO11-DSConv,Server Side,Mobile Application.
基金funded by National Key R&D Program of China((Nos.2022YFC3003403 and 2018YFC1505203)Key Research and Development Program of Tibet Autonomous Region(XZ202301ZY0039G)+1 种基金Natural Science Foundation of Hebei Province(No.F2021201031)Geological Survey Project of China Geological Survey(No.DD20221747)。
文摘Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.
基金Project supported by National Natural Science Foundation ofChina (Grant No .60203017) Defense Pre-research Projectof the"Tenth Five-Year-Plan"of China (Grant No .413150403)
文摘Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed real-time databases, which adopts a main memory database as its ground support. In our scheme, each site maintains a real-time log for local transactions and the subtransactions, which execute at the site, and execte local checkpointing independently. Log records are stored in non-volatile high- speed store, which is divided into four different partitions based on transaction classes. During restart recovery after a site crash, partitioned crash recovery strategy is adopted to ensure that the site can be brought up before the entire local secondary database is reloaded in main memory. The partitioned crash recovery strategy not only guarantees the internal consistency to be recovered, but also guarantee the temporal consistency and recovery of the sates of physical world influenced by uncommitted transactions. Combined with two- phase commit protocol, TCLCRS can guarantee failure atomicity of distributed real-time transactions.
