Objectives define the boundaries of complex engineering system.It is a hard work to identify the specific objectives of a complex engineering system.The objectives system development needs a complicated process,from n...Objectives define the boundaries of complex engineering system.It is a hard work to identify the specific objectives of a complex engineering system.The objectives system development needs a complicated process,from nix to prototype,and to final definition.The total process will cover the following course:from chaos to well-ordered;from qualitativeness to combination of quantitativeness and qualitativenss,then from qualitativeness to quantitativeness(a recurrent process),expert experience and theoretical science,rationality and sensibility,synthesis analysis and meta-synthesis,routinization and non-routinization.Such process is explicit in phase development yet overlapped;mutually confined yet mutually independent;permeated conflicts yet pregnant in harmony.This article explores the complexity of Sutong Bridge's objectives development and the process of meta-synthesis in the Sutong Bridge engineering.展开更多
Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low effi...Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low efficiency,poor identification accuracy,severe environmental pollution,and other drawbacks.This paper proposes a machine vision-based intelligent coal gangue sorting robot system.Firstly,the OpenMV captures images of coal gangue and utilizes the MobileNetV20.35 lightweight convolutional neural network to train the FOMO(Faster Objects,More Objects)target detection model in the cloud.This enables prediction and recognition of gangue,along with the acquisition of its center point pixel coordinates.Secondly,the position information of the gangue is sent to the STM32 microcontroller using the serial communication protocol for coordinate system conversion,pose algorithm,and path planning.Finally,the STM32 microcontroller controls the start and stop of the conveyor belt through the working status of the relay.When the relay is absorbed,the conveyor belt stops,and at the same time,the robotic arm grasps the gangue for transfer action,thus realizing the sorting of coal and gangue.The experimental results demonstrate that the cloud-trained FOMO neural network model achieves an F1 score of 95.5%and a recall of 91.3%,with a test accuracy of 97.56%.The quantified model deployed on OpenMV can accurately identify multiple gangues and output their position information.The success rate of the robotic arm in tracking and sorting gangue reaches 90.13%,and the positioning error of the robotic arm is[9,12.5]mm.This system realizes high-precision identification,positioning,and intelligent sorting of coal and gangue,meeting the basic requirements for gangue sorting in coal mines.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically co...The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.展开更多
The purpose of this paper is to propose an innovation system of managerial accounting reports, which is actually on the basis of accounting objectives. On the one hand, as managerial accounting is one important branch...The purpose of this paper is to propose an innovation system of managerial accounting reports, which is actually on the basis of accounting objectives. On the one hand, as managerial accounting is one important branch of accounting(the other important branch is financial accounting), some of its characters should be closely connected with accounting. On the other hand, managers need managerial accounting information for enterprise operation(especially for internal management control) decisions, so, managerial accounting should also be in accordance with the enterprise's operation and its management control. Therefore, combined with the existed research of accounting(especially financial accounting research) and for the development requirement of Chinese enterprises, this paper will mainly discuss the relation between accounting objectives and managerial accounting's system and put forward an idea of constructing an applicable reporting system of managerial accounting based on the operation mode in Chinese modern enterprises. This study will develop the accounting reports research(including external reports and internal reports) both in the field of theory and that of practice.展开更多
Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by ...Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.展开更多
The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the infl...The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the influences of indicators,contexts,and scales.Yet,another potential factor,which has been overlooked,may be the mixed use of spatial and temporal approaches.Using twelve ES and seven well-being indicators and multiple statistical methods,we quantified and compared the spatial and temporal ES–HWB relationships for Inner Mongolia,China.The spatial and temporal relationships differed in both correlation direction and strength.Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative.Some relationships of economic and employmentrelated indicators with water retention,sandstorm prevention,and wind erosion were temporally negative but spatially complex.However,the spatial and temporal ES–HWB relationships could also be similar in some cases.We conclude that although both the spatial and temporal approaches have merits,space generally cannot substitute for time in the study of ES–HWB relationship.Our study helps reconcile the seemingly conflicting findings in the literature,and suggests that future studies should explicitly distinguish between the spatial and temporal ES–HWB relationships.展开更多
This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations....This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.展开更多
Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system ...Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system for these patients. Methods: Ninety patients with type 2 diabetes admitted to the Department of Endocrinology of the hospital from January 2024 to June 2024 were selected. The control group (n = 45) received routine nursing care, while the observation group (n = 45) received whole-course nursing. Indicators such as glucose metabolism and compliance behavior were measured before and after care, and the health and quality of life of patients in both groups were evaluated. Results: A comparison of blood glucose levels and compliance behavior showed that the observation group had lower blood glucose levels than the control group (P < 0.05). Additionally, the compliance behavior score of the observation group was higher than that of the control group (P < 0.05). Conclusion: The holistic nursing model demonstrates significant nursing effects for patients with type 2 diabetes. This approach not only assists in blood sugar control, prevents disease progression, and reduces complications, but also enhances patients’ knowledge of health management, aiding in their recovery.展开更多
The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations...The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.展开更多
In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying proh...In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying prohibited items that are not visible in one view due to rotation or overlap.However,existing work still focuses mainly on single-view,and the limited dual-viewbasedwork only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view.To this end,this paper proposes an end-to-end dual-view prohibited item detection method,the core of which is an adaptive material-aware coordinate-aligned attention module(MACA)and an adaptive adjustment strategy(AAS).Specifically,we observe that in X-ray images,the material information of an object can be represented by color and texture features,and remains consistent across views,even under complex backgrounds.Therefore,our MACA first integrates the material information of the prohibited items in each view and then smoothly transfers these clearmaterial clues along the shared axis to the corresponding locations in the other view to enhance the feature representation of the blurred prohibited items in the other view.In addition,AAS can autonomously adjust the importance of the two views during feature learning to make joint optimizationmore stable and effective.Experiments on the DvXray dataset demonstrate that the proposed MACA and AAS can be plug-and-played into various detectors,such as Faster Region-based Convolutional Neural Network(Faster R-CNN)and Fully Convolutional One-Stage Object Detector(FCOS),and bring consistent performance gains.The entire framework performs favorably against state-of-the-art methods,especially on small-sized prohibited items,highlighting its potential application in reality.展开更多
The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integratio...The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation 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
The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance ...The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.展开更多
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.展开更多
Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while ob...Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.展开更多
In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, an...In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.展开更多
It has become a trend of the city development in the world to build up the ecological city with an ecological system that is balanced in structure,high-efficient in service function.Taking Suzhou as a case,the authors...It has become a trend of the city development in the world to build up the ecological city with an ecological system that is balanced in structure,high-efficient in service function.Taking Suzhou as a case,the authors believe that the construction of an ecological city is a process of the progressive and well-organized systematic development and of the ecological function consummation,which will require five stages to complete,including ecological hygiene,ecological security,ecological industry,ecological landscape,and ecological culture.The problems presently encountered in the construction of ecological city in Suzhou can be solved through the following approaches:(1) To rebuild the relationship between man and nature when the planning is worked out;(2) To observe the principle of ecology in city design;(3) To choose the local plant species in city afforestation;(4) To choose those plantlets with vigorous growth,good shape,and high survival rate,observing the growth rule of plant and paying much attention to forest landscape,ecological system and biological diversity,rationally allocating the plant species resources;(5) To aim at the implementation of the less-labor management even the zero-labor management.展开更多
This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line ...This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line strip laser and one color CCD camera. The scanned object is pictured twice by a color CCD camera. First, the texture of the scanned object is taken by a color CCD camera. Then the 3D information of the scanned object is obtained from laser plane equations. This paper presents a practical way to implement the three dimensional measuring method and the automatic registration of a large 3D object and a pretty good result is obtained after experiment verification.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
基金National Scientific and Technology Supporting Program of 11th 5-Year Plan(No.2006BAG04B06)
文摘Objectives define the boundaries of complex engineering system.It is a hard work to identify the specific objectives of a complex engineering system.The objectives system development needs a complicated process,from nix to prototype,and to final definition.The total process will cover the following course:from chaos to well-ordered;from qualitativeness to combination of quantitativeness and qualitativenss,then from qualitativeness to quantitativeness(a recurrent process),expert experience and theoretical science,rationality and sensibility,synthesis analysis and meta-synthesis,routinization and non-routinization.Such process is explicit in phase development yet overlapped;mutually confined yet mutually independent;permeated conflicts yet pregnant in harmony.This article explores the complexity of Sutong Bridge's objectives development and the process of meta-synthesis in the Sutong Bridge engineering.
基金Supported by the National Natural Science Foundation of China(52074273)Natural Science Research Project of Universities in Anhui Province(2023AH050343)+4 种基金Anhui Innovative Team for Pollutant Sensitive Monitoring and Application(2023AH010043)Anhui Province Graduate Education Quality Project(2024jyjxggyjY204)Innovation and Entrepreneurship Training Programme for College Students in Anhui Province(S202410373037)Huaibei Normal University’s Postgraduate Education Quality Project(2024jgxm003)Open Project Funded by Anhui Province Key Laboratoryof Intelligent Computing and Applications(AFZNJS2025KF08)。
文摘Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low efficiency,poor identification accuracy,severe environmental pollution,and other drawbacks.This paper proposes a machine vision-based intelligent coal gangue sorting robot system.Firstly,the OpenMV captures images of coal gangue and utilizes the MobileNetV20.35 lightweight convolutional neural network to train the FOMO(Faster Objects,More Objects)target detection model in the cloud.This enables prediction and recognition of gangue,along with the acquisition of its center point pixel coordinates.Secondly,the position information of the gangue is sent to the STM32 microcontroller using the serial communication protocol for coordinate system conversion,pose algorithm,and path planning.Finally,the STM32 microcontroller controls the start and stop of the conveyor belt through the working status of the relay.When the relay is absorbed,the conveyor belt stops,and at the same time,the robotic arm grasps the gangue for transfer action,thus realizing the sorting of coal and gangue.The experimental results demonstrate that the cloud-trained FOMO neural network model achieves an F1 score of 95.5%and a recall of 91.3%,with a test accuracy of 97.56%.The quantified model deployed on OpenMV can accurately identify multiple gangues and output their position information.The success rate of the robotic arm in tracking and sorting gangue reaches 90.13%,and the positioning error of the robotic arm is[9,12.5]mm.This system realizes high-precision identification,positioning,and intelligent sorting of coal and gangue,meeting the basic requirements for gangue sorting in coal mines.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.
文摘The purpose of this paper is to propose an innovation system of managerial accounting reports, which is actually on the basis of accounting objectives. On the one hand, as managerial accounting is one important branch of accounting(the other important branch is financial accounting), some of its characters should be closely connected with accounting. On the other hand, managers need managerial accounting information for enterprise operation(especially for internal management control) decisions, so, managerial accounting should also be in accordance with the enterprise's operation and its management control. Therefore, combined with the existed research of accounting(especially financial accounting research) and for the development requirement of Chinese enterprises, this paper will mainly discuss the relation between accounting objectives and managerial accounting's system and put forward an idea of constructing an applicable reporting system of managerial accounting based on the operation mode in Chinese modern enterprises. This study will develop the accounting reports research(including external reports and internal reports) both in the field of theory and that of practice.
文摘Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.B240201068)the National Natural Science Foundation of China(Grant No.42361144861)the National Basic Research Program of China(Grant No.2014CB954303).
文摘The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the influences of indicators,contexts,and scales.Yet,another potential factor,which has been overlooked,may be the mixed use of spatial and temporal approaches.Using twelve ES and seven well-being indicators and multiple statistical methods,we quantified and compared the spatial and temporal ES–HWB relationships for Inner Mongolia,China.The spatial and temporal relationships differed in both correlation direction and strength.Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative.Some relationships of economic and employmentrelated indicators with water retention,sandstorm prevention,and wind erosion were temporally negative but spatially complex.However,the spatial and temporal ES–HWB relationships could also be similar in some cases.We conclude that although both the spatial and temporal approaches have merits,space generally cannot substitute for time in the study of ES–HWB relationship.Our study helps reconcile the seemingly conflicting findings in the literature,and suggests that future studies should explicitly distinguish between the spatial and temporal ES–HWB relationships.
基金supported by Guangdong Province Rural Science and Technology Commissioner Project,Zen Tea Reliable Traceability and Intelligent Planting Key Technology Research and Development,Promotion and Application(KTP20210199)Special Project of Guangdong Provincial Education Department,Research on Abnormal Behavior Recognition Technology of Pregnant Sows Based onGraph Convolution(2021ZDZX1091)+2 种基金Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515110729Shenzhen Science and Technology Program under Grant 20231128093642002the Research Foundation of Shenzhen Polytechnic University under Grant 6023312007K.
文摘This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.
文摘Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system for these patients. Methods: Ninety patients with type 2 diabetes admitted to the Department of Endocrinology of the hospital from January 2024 to June 2024 were selected. The control group (n = 45) received routine nursing care, while the observation group (n = 45) received whole-course nursing. Indicators such as glucose metabolism and compliance behavior were measured before and after care, and the health and quality of life of patients in both groups were evaluated. Results: A comparison of blood glucose levels and compliance behavior showed that the observation group had lower blood glucose levels than the control group (P < 0.05). Additionally, the compliance behavior score of the observation group was higher than that of the control group (P < 0.05). Conclusion: The holistic nursing model demonstrates significant nursing effects for patients with type 2 diabetes. This approach not only assists in blood sugar control, prevents disease progression, and reduces complications, but also enhances patients’ knowledge of health management, aiding in their recovery.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金the Scientific Research Project of Education Department of Hunan Province of China under grant number 22B0549International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Province Undergraduate Innovation and Entrepreneurship Training Program(grant number S202410531015).
文摘The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.
基金by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515120064.
文摘In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying prohibited items that are not visible in one view due to rotation or overlap.However,existing work still focuses mainly on single-view,and the limited dual-viewbasedwork only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view.To this end,this paper proposes an end-to-end dual-view prohibited item detection method,the core of which is an adaptive material-aware coordinate-aligned attention module(MACA)and an adaptive adjustment strategy(AAS).Specifically,we observe that in X-ray images,the material information of an object can be represented by color and texture features,and remains consistent across views,even under complex backgrounds.Therefore,our MACA first integrates the material information of the prohibited items in each view and then smoothly transfers these clearmaterial clues along the shared axis to the corresponding locations in the other view to enhance the feature representation of the blurred prohibited items in the other view.In addition,AAS can autonomously adjust the importance of the two views during feature learning to make joint optimizationmore stable and effective.Experiments on the DvXray dataset demonstrate that the proposed MACA and AAS can be plug-and-played into various detectors,such as Faster Region-based Convolutional Neural Network(Faster R-CNN)and Fully Convolutional One-Stage Object Detector(FCOS),and bring consistent performance gains.The entire framework performs favorably against state-of-the-art methods,especially on small-sized prohibited items,highlighting its potential application in reality.
文摘The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation 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.
文摘The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.
基金supported in part by the National Natural Science Foundation of China(62276119)the Natural Science Foundation of Jiangsu Province(BK20241764)the Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_2860)
文摘Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
基金National Natural Science Foundation of China,Grant/Award Number:62303275International Alliance for Cancer Early Detection,Grant/Award Numbers:C28070/A30912,C73666/A31378Wellcome/EPSRC Centre for Interventional and Surgical Sciences,Grant/Award Number:203145Z/16/Z。
文摘Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.
文摘In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.
文摘It has become a trend of the city development in the world to build up the ecological city with an ecological system that is balanced in structure,high-efficient in service function.Taking Suzhou as a case,the authors believe that the construction of an ecological city is a process of the progressive and well-organized systematic development and of the ecological function consummation,which will require five stages to complete,including ecological hygiene,ecological security,ecological industry,ecological landscape,and ecological culture.The problems presently encountered in the construction of ecological city in Suzhou can be solved through the following approaches:(1) To rebuild the relationship between man and nature when the planning is worked out;(2) To observe the principle of ecology in city design;(3) To choose the local plant species in city afforestation;(4) To choose those plantlets with vigorous growth,good shape,and high survival rate,observing the growth rule of plant and paying much attention to forest landscape,ecological system and biological diversity,rationally allocating the plant species resources;(5) To aim at the implementation of the less-labor management even the zero-labor management.
文摘This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line strip laser and one color CCD camera. The scanned object is pictured twice by a color CCD camera. First, the texture of the scanned object is taken by a color CCD camera. Then the 3D information of the scanned object is obtained from laser plane equations. This paper presents a practical way to implement the three dimensional measuring method and the automatic registration of a large 3D object and a pretty good result is obtained after experiment verification.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
