The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isome...The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isomers,such as boiling point,of formation are found to be better measured by the ABS index than by the Randi,atom-bond connectivity(ABC),and sum-connectivity(SC)indices.One important source of information for researching the molecular structure is the bounds for its topological indices.The extrema of the ABS index of the line,total,and Mycielski graphs are calculated in this work.Moreover,the pertinent extremal graphs were illustrated.展开更多
Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general contr...Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.展开更多
With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has f...With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has found widespread application in the field of lane line detection.However,the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions,occlusions,and wear and tear on the lane lines.Additionally,DeepLabv3+suffers from high memory consumption and challenges in deployment on embedded platforms.To address these issues,this paper proposes a lane line detection method for complex road scenes based on DeepLabv3+and MobileNetV4(MNv4).First,the lightweight MNv4 is adopted as the backbone network,and the standard convolutions in ASPP are replaced with depthwise separable convolutions.Second,a polarization attention mechanism is introduced after the ASPP module to enhance the model’s generalization capability.Finally,the Simple Linear Iterative Clustering(SLIC)superpixel segmentation algorithmis employed to preserve lane line edge information.MNv4-DeepLabv3+was tested on the TuSimple and CULane datasets.On the TuSimple dataset,theMean Intersection over Union(MIoU)and Mean Pixel Accuracy(mPA)improved by 1.01%and 7.49%,respectively.On the CULane dataset,MIoU andmPA increased by 3.33%and 7.74%,respectively.Thenumber of parameters decreased from 54.84 to 3.19 M.Experimental results demonstrate that MNv4-DeepLabv3+significantly optimizes model parameter count and enhances segmentation accuracy.展开更多
Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
Thinopyrum ponticum(2n=10×=70),a wild relative of common wheat(Triticum aestivum L.),is considered an invaluable genetic resource for wheat improvement due to its abundance of genes conferring resistance to bioti...Thinopyrum ponticum(2n=10×=70),a wild relative of common wheat(Triticum aestivum L.),is considered an invaluable genetic resource for wheat improvement due to its abundance of genes conferring resistance to biotic and abiotic stresses.This study focused on the CH97 line,derived from the BC1F7 progeny of a cross between wheat cv.7182 and Th.ponticum.Cytological evidence showed that CH97 has 42 chromosomes,forming 21 bivalents at meiotic metaphase I,with the bivalents subsequently separating and moving to opposite poles during meiotic anaphase I.Through a combination of fluorescence in situ hybridization(FISH),genomic in situ hybridization(GISH),multicolor GISH(mc-GISH),and liquid array analysis,it was determined that CH97 comprises 40 wheat chromosomes and two alien chromosomes from the Ee genome of Th.ponticum,featuring the absence of a pair of 5D chromosomes and variations in 1B,6B,and 7B chromosomes.These findings confirm that CH97 is a stable wheat-Th.ponticum 5E(5D)alien disomic substitution line.Inoculation experiments revealed that CH97 exhibits high resistance to wheat powdery mildew and stripe rust throughout the growth period,in contrast to the highly susceptible common wheat parent 7182.Compared to 7182,CH97 displayed improvements in thousand-kernel weight and kernel length.Additionally,utilizing specific-locus amplified fragment sequencing(SLAF-seq)technology,chromosome 5E-specific molecular markers were developed and validated,achieving a 33.3% success rate,facilitating marker-assisted selection for disease resistance in wheat.Overall,the CH97 substitution line,with its resistance to diseases and improved agronomic traits,represents valuable new germplasm for wheat chromosome engineering and breeding.展开更多
On November 26th,Zhengzhou Textile Machinery Co.,Ltd.(hereinafter referred to as"ZFJ")signed an order for a high-speed intelligent wide-width wetmethod spunlace production line with Hubei Lijie New Material ...On November 26th,Zhengzhou Textile Machinery Co.,Ltd.(hereinafter referred to as"ZFJ")signed an order for a high-speed intelligent wide-width wetmethod spunlace production line with Hubei Lijie New Material Technology Co.,Ltd.(hereinafter referred to as"Hubei Lijie").This cooperation marks a further consolidation of ZFJ's leading position in the nonwoven fabric equipment market in Hubei Province and lays a solid foundation for deeper cooperation between the two companies in the future.展开更多
Dear Editor,Linear and whorled nevoid hypermelanosis(LWNH)is a rare,sporadic pigmentary disorder characterized by hyperpigmented macules arranged in linear streaks and whorls along Blaschko's lines,typically appea...Dear Editor,Linear and whorled nevoid hypermelanosis(LWNH)is a rare,sporadic pigmentary disorder characterized by hyperpigmented macules arranged in linear streaks and whorls along Blaschko's lines,typically appearing within the first few weeks of life[1],and remains a challenge to treat.Here,we report a case of LWNH and review the relevant literature to help clinicians better understand this disease.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-cap...To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.展开更多
In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify ...In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.展开更多
The published article titled“Puerarin inhibits proliferation and induces apoptosis by upregulation of miR-16 in bladder cancer cell line T24”has been retracted from Oncology Research,Vol.26,No.8,2018,pp.1227–1234.
In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an ...In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.展开更多
Exploring advanced techniques capable of probing nanometric acoustic waves in nanostructures is critically important for the development of miniaturized acoustic devices.In this study,we probe the optically-excited ac...Exploring advanced techniques capable of probing nanometric acoustic waves in nanostructures is critically important for the development of miniaturized acoustic devices.In this study,we probe the optically-excited acoustic waves in a single silicon nanowire(NW)using the time-resolved(tr-)high-order Laue-zone(HOLZ)lines under convergent-beam electron diffraction(CBED)conditions in an ultrafast transmission electron microscope(UTEM).We devise an experimental scheme to obtain tr-HOLZ lines under off-zone-axis CBED conditions.We also propose a geometric description of HOLZ line formation and use this alternative description to quantitatively evaluate the dynamics of optically-excited silicon NW.Using part of the deformation gradient tensor,our simulations of the dynamics of Si NW reproduce the experimental results.We further discuss the feasibility of a full retrieval of the deformation gradient tensor by using a set of HOLZ lines from three zone axes.Our findings illustrate a strategy for the quantitative access to dynamical acoustic waves optically excited in micro-and nano-structures using UTEM.展开更多
Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the...Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the authors.DOI:10.31209/2019.100000097 URL:https://www.techscience.com/iasc/v25n2/39663 The article duplicates significant parts of a paper published in Journal of Intelligent&Fuzzy Systems[2].展开更多
Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice d...Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice detection on power transmission lines require a substantial amount of sample data to support their training,and their drawback is that detection accuracy is significantly affected by the inaccurate annotation among training dataset.Therefore,we propose a transformer-based detection model,structured into two stages to collectively address the impact of inaccurate datasets on model training.In the first stage,a spatial similarity enhancement(SSE)module is designed to leverage spatial information to enhance the construction of the detection framework,thereby improving the accuracy of the detector.In the second stage,a target similarity enhancement(TSE)module is introduced to enhance object-related features,reducing the impact of inaccurate data on model training,thereby expanding global correlation.Additionally,by incorporating a multi-head adaptive attention window(MAAW),spatial information is combined with category information to achieve information interaction.Simultaneously,a quasi-wavelet structure,compatible with deep learning,is employed to highlight subtle features at different scales.Experimental results indicate that the proposed model in this paper outperforms existing mainstream detection models,demonstrating superior performance and stability.展开更多
Incorporation of the Monte Carlo(MC)algorithm in optimizing CyberKnife(CK)plans is cumbersome,and early models unconfigured MC calculations,therefore,this study investigated algorithm-based dose calculation discrepanc...Incorporation of the Monte Carlo(MC)algorithm in optimizing CyberKnife(CK)plans is cumbersome,and early models unconfigured MC calculations,therefore,this study investigated algorithm-based dose calculation discrepancies by selecting different prescription isodose lines(PIDLs)in head and lung CK plans.CK plans were based on anthropomorphic phantoms.Four shells were set at 2-60 mm from the target,and the constraint doses were adjusted according to the design stratcgy.After optimization,30%-90%PIDL plans were generated by ray tracing(RT).In the evaluation module,CK plans were recalculated using the MC algorithm.Therefore,the dosimetric parameters of different PIDL plans based on the RT and MC algorithms were obtained and analyzed.The discrepancies(mean+SD)were 3.72%+0.31%,3.40%+0.11%,3.47%+0.32%,0.17%+0.11%,0.64%+3.60%,7.73%+1.60%,14.62%+3.21%and 10.10%+1.57%for Djs,Dmeam),Dys,and coverage of the PTV,DGI,V,,V;and V,in the head plans and-6.32%+1.15%,-13.46%+0.98%,-20.63%+2.25%,-34.78%+25.03%,12248%+175.60%,-12.92%+5.41%,3.19%+4.67%and 7.13%+1.56%in the lung plans,respectively.The following parameters were significantly correlated with PIDL:dp98%at the 0.05 level and dpal,dys and dv3 at the 0.01 level for the head plans;dp98e%at the 0.05 level and do1e%,dpmeam,Ccoweange,dool,dvs and dv;at the 0.01 level for the lung plans.RT may be used to calculate the dose in CK head plans,but when the dose of organs at risk is close to the limit,it is necessary to refer to the MC results or to further optimize the CK plan to reduce the dose.For lung plans,the MC algorithm is recommended.For early models without the MC algorithm,a lower PIDL plan is recommended;otherwise,a large PIDL plan risks serious underdosage in the target area.展开更多
The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and wi...The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.展开更多
In this paper, the Eigenvalue Complementarity Problem (EiCP) with real symmetric matrices is addressed, which appears in the study of contact problem in mechanics. We discuss a quadratic programming formulation to the...In this paper, the Eigenvalue Complementarity Problem (EiCP) with real symmetric matrices is addressed, which appears in the study of contact problem in mechanics. We discuss a quadratic programming formulation to the problem. The resulting problems are nonlinear programs that can be solved by a line search filter-SQP algorithm.展开更多
基金Supported by Ningbo NSF(No.2021J234)Zhejiang Provincial Philosophy and Social Sciences Planning Project(No.24NDJC057YB)。
文摘The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isomers,such as boiling point,of formation are found to be better measured by the ABS index than by the Randi,atom-bond connectivity(ABC),and sum-connectivity(SC)indices.One important source of information for researching the molecular structure is the bounds for its topological indices.The extrema of the ABS index of the line,total,and Mycielski graphs are calculated in this work.Moreover,the pertinent extremal graphs were illustrated.
基金supported by the National Natural Science Foundation of China(grant numbers 42171085)and the National Key R&D Program of China(Grant No.2024YFF1307801,2024YFF1307804).
文摘Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.
文摘With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has found widespread application in the field of lane line detection.However,the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions,occlusions,and wear and tear on the lane lines.Additionally,DeepLabv3+suffers from high memory consumption and challenges in deployment on embedded platforms.To address these issues,this paper proposes a lane line detection method for complex road scenes based on DeepLabv3+and MobileNetV4(MNv4).First,the lightweight MNv4 is adopted as the backbone network,and the standard convolutions in ASPP are replaced with depthwise separable convolutions.Second,a polarization attention mechanism is introduced after the ASPP module to enhance the model’s generalization capability.Finally,the Simple Linear Iterative Clustering(SLIC)superpixel segmentation algorithmis employed to preserve lane line edge information.MNv4-DeepLabv3+was tested on the TuSimple and CULane datasets.On the TuSimple dataset,theMean Intersection over Union(MIoU)and Mean Pixel Accuracy(mPA)improved by 1.01%and 7.49%,respectively.On the CULane dataset,MIoU andmPA increased by 3.33%and 7.74%,respectively.Thenumber of parameters decreased from 54.84 to 3.19 M.Experimental results demonstrate that MNv4-DeepLabv3+significantly optimizes model parameter count and enhances segmentation accuracy.
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
基金funded by the Key R&D Program of Yangling Seed Industry Innovation,China(Ylzy-xm-02)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2021QNRC001)。
文摘Thinopyrum ponticum(2n=10×=70),a wild relative of common wheat(Triticum aestivum L.),is considered an invaluable genetic resource for wheat improvement due to its abundance of genes conferring resistance to biotic and abiotic stresses.This study focused on the CH97 line,derived from the BC1F7 progeny of a cross between wheat cv.7182 and Th.ponticum.Cytological evidence showed that CH97 has 42 chromosomes,forming 21 bivalents at meiotic metaphase I,with the bivalents subsequently separating and moving to opposite poles during meiotic anaphase I.Through a combination of fluorescence in situ hybridization(FISH),genomic in situ hybridization(GISH),multicolor GISH(mc-GISH),and liquid array analysis,it was determined that CH97 comprises 40 wheat chromosomes and two alien chromosomes from the Ee genome of Th.ponticum,featuring the absence of a pair of 5D chromosomes and variations in 1B,6B,and 7B chromosomes.These findings confirm that CH97 is a stable wheat-Th.ponticum 5E(5D)alien disomic substitution line.Inoculation experiments revealed that CH97 exhibits high resistance to wheat powdery mildew and stripe rust throughout the growth period,in contrast to the highly susceptible common wheat parent 7182.Compared to 7182,CH97 displayed improvements in thousand-kernel weight and kernel length.Additionally,utilizing specific-locus amplified fragment sequencing(SLAF-seq)technology,chromosome 5E-specific molecular markers were developed and validated,achieving a 33.3% success rate,facilitating marker-assisted selection for disease resistance in wheat.Overall,the CH97 substitution line,with its resistance to diseases and improved agronomic traits,represents valuable new germplasm for wheat chromosome engineering and breeding.
文摘On November 26th,Zhengzhou Textile Machinery Co.,Ltd.(hereinafter referred to as"ZFJ")signed an order for a high-speed intelligent wide-width wetmethod spunlace production line with Hubei Lijie New Material Technology Co.,Ltd.(hereinafter referred to as"Hubei Lijie").This cooperation marks a further consolidation of ZFJ's leading position in the nonwoven fabric equipment market in Hubei Province and lays a solid foundation for deeper cooperation between the two companies in the future.
文摘Dear Editor,Linear and whorled nevoid hypermelanosis(LWNH)is a rare,sporadic pigmentary disorder characterized by hyperpigmented macules arranged in linear streaks and whorls along Blaschko's lines,typically appearing within the first few weeks of life[1],and remains a challenge to treat.Here,we report a case of LWNH and review the relevant literature to help clinicians better understand this disease.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by the Shanghai Science and Technology Innovation Action Plan High-Tech Field Project(Grant No.22511100601)for the year 2022 and Technology Development Fund for People’s Livelihood Research(Research on Transmission Line Deep Foundation Pit Environmental Situation Awareness System Based on Multi-Source Data).
文摘To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.
基金the Sichuan Science and Technology Program(No.2022YFS0557)the National Natural Science Foundation of China(No.61972271)。
文摘In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.
文摘The published article titled“Puerarin inhibits proliferation and induces apoptosis by upregulation of miR-16 in bladder cancer cell line T24”has been retracted from Oncology Research,Vol.26,No.8,2018,pp.1227–1234.
文摘In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.
基金supported by the Guangxi Natural Science Foundation(Grant No.2024GXNSFDA010014)the National Natural Science Foundation of China(Grant Nos.12364018 and U22A6005)+1 种基金the Guangxi Science and Technology Major Program(Grant No.AA23073019)the Innovation Project of Guangxi Graduate Education(Grant Nos.YCBZ2022049 and YCBZ2023015)。
文摘Exploring advanced techniques capable of probing nanometric acoustic waves in nanostructures is critically important for the development of miniaturized acoustic devices.In this study,we probe the optically-excited acoustic waves in a single silicon nanowire(NW)using the time-resolved(tr-)high-order Laue-zone(HOLZ)lines under convergent-beam electron diffraction(CBED)conditions in an ultrafast transmission electron microscope(UTEM).We devise an experimental scheme to obtain tr-HOLZ lines under off-zone-axis CBED conditions.We also propose a geometric description of HOLZ line formation and use this alternative description to quantitatively evaluate the dynamics of optically-excited silicon NW.Using part of the deformation gradient tensor,our simulations of the dynamics of Si NW reproduce the experimental results.We further discuss the feasibility of a full retrieval of the deformation gradient tensor by using a set of HOLZ lines from three zone axes.Our findings illustrate a strategy for the quantitative access to dynamical acoustic waves optically excited in micro-and nano-structures using UTEM.
文摘Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the authors.DOI:10.31209/2019.100000097 URL:https://www.techscience.com/iasc/v25n2/39663 The article duplicates significant parts of a paper published in Journal of Intelligent&Fuzzy Systems[2].
文摘Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice detection on power transmission lines require a substantial amount of sample data to support their training,and their drawback is that detection accuracy is significantly affected by the inaccurate annotation among training dataset.Therefore,we propose a transformer-based detection model,structured into two stages to collectively address the impact of inaccurate datasets on model training.In the first stage,a spatial similarity enhancement(SSE)module is designed to leverage spatial information to enhance the construction of the detection framework,thereby improving the accuracy of the detector.In the second stage,a target similarity enhancement(TSE)module is introduced to enhance object-related features,reducing the impact of inaccurate data on model training,thereby expanding global correlation.Additionally,by incorporating a multi-head adaptive attention window(MAAW),spatial information is combined with category information to achieve information interaction.Simultaneously,a quasi-wavelet structure,compatible with deep learning,is employed to highlight subtle features at different scales.Experimental results indicate that the proposed model in this paper outperforms existing mainstream detection models,demonstrating superior performance and stability.
基金This study was supported by grants from National Key Research and Development Plan for Digital Diagnostic Equipment Research and Development(No.2016YFC0106700)the Natural Science Foundation of Union Hospital,Tongji Medical College,Huazhong University of Science and Technology(No.02.03.2018-131).
文摘Incorporation of the Monte Carlo(MC)algorithm in optimizing CyberKnife(CK)plans is cumbersome,and early models unconfigured MC calculations,therefore,this study investigated algorithm-based dose calculation discrepancies by selecting different prescription isodose lines(PIDLs)in head and lung CK plans.CK plans were based on anthropomorphic phantoms.Four shells were set at 2-60 mm from the target,and the constraint doses were adjusted according to the design stratcgy.After optimization,30%-90%PIDL plans were generated by ray tracing(RT).In the evaluation module,CK plans were recalculated using the MC algorithm.Therefore,the dosimetric parameters of different PIDL plans based on the RT and MC algorithms were obtained and analyzed.The discrepancies(mean+SD)were 3.72%+0.31%,3.40%+0.11%,3.47%+0.32%,0.17%+0.11%,0.64%+3.60%,7.73%+1.60%,14.62%+3.21%and 10.10%+1.57%for Djs,Dmeam),Dys,and coverage of the PTV,DGI,V,,V;and V,in the head plans and-6.32%+1.15%,-13.46%+0.98%,-20.63%+2.25%,-34.78%+25.03%,12248%+175.60%,-12.92%+5.41%,3.19%+4.67%and 7.13%+1.56%in the lung plans,respectively.The following parameters were significantly correlated with PIDL:dp98%at the 0.05 level and dpal,dys and dv3 at the 0.01 level for the head plans;dp98e%at the 0.05 level and do1e%,dpmeam,Ccoweange,dool,dvs and dv;at the 0.01 level for the lung plans.RT may be used to calculate the dose in CK head plans,but when the dose of organs at risk is close to the limit,it is necessary to refer to the MC results or to further optimize the CK plan to reduce the dose.For lung plans,the MC algorithm is recommended.For early models without the MC algorithm,a lower PIDL plan is recommended;otherwise,a large PIDL plan risks serious underdosage in the target area.
基金Project(51090385) supported by the Major Program of National Natural Science Foundation of ChinaProject(2011IB001) supported by Yunnan Provincial Science and Technology Program,China+1 种基金Project(2012DFA70570) supported by the International Science & Technology Cooperation Program of ChinaProject(2011IA004) supported by the Yunnan Provincial International Cooperative Program,China
文摘The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.
文摘In this paper, the Eigenvalue Complementarity Problem (EiCP) with real symmetric matrices is addressed, which appears in the study of contact problem in mechanics. We discuss a quadratic programming formulation to the problem. The resulting problems are nonlinear programs that can be solved by a line search filter-SQP algorithm.
