In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.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
At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standar...At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.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
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
Background: Radiofrequency has been proven to penetrate deeper than optical light sources independent of skin color allowing a safer treatment for the Asian skin type. Many studies have indicated the efficacy of vario...Background: Radiofrequency has been proven to penetrate deeper than optical light sources independent of skin color allowing a safer treatment for the Asian skin type. Many studies have indicated the efficacy of various types of devices, but have not included a sufficient objective evaluation. Multisource radiofrequency uses multiple phase controlled radiofrequency generators with real time impedance control, allowing painless, deeper dermal heating with better adaptation to differences in individual skin impedance. In this study we used three-dimensional imaging for the objective evaluation of facial skin tightening by multisource phase-controlled radiofrequency. Methods: Twenty Japanese patients were treated with a multisource phase-controlled radiofrequency device. Three-dimensional imaging was performed with a Canfield Scientific Vectra camera and software, and quantitative volume measurements were taken to evaluate the change in the post-treatment volume. The patients then provided subjective assessments. Results: Objective assessments of the treated cheek volume evaluated by a three-dimensional color schematic representation with quantitative volume measurements showed significant improvement three months after the final treatment. The mean volume reduction at the last post-treatment visit was 3.878 ± 2.86 mL. The post-treatment volume was significantly reduced compared to the pretreatment volume in all of the volunteers (P = 0.0007). Ninety-five percent of volunteers reported satisfaction with the improvement of skin laxity, and ninety percent of volunteers reported satisfaction with the improvement of wrinkles, such as the nasolabial folds. Conclusions: The advantages of these multisource phase-controlled radiofrequency treatments are its high efficacy for skin tightening associated with minimal level of discomfort, minimal side effects, and low cost. Taken together, these characteristics facilitate the ability to give repeated treatments as a stand alone treatment or adjunct to surgery. This study provides for the first time a qualitative and quantitative volumetric assessment, proving the ability of the technology to reduce the volume through non invasive skin tightening.展开更多
In this paper, some necessary and sufficient optimality conditions are obtained for a fractional multiple objective programming involving semilocal E-convex and related functions. Also, some dual results are establish...In this paper, some necessary and sufficient optimality conditions are obtained for a fractional multiple objective programming involving semilocal E-convex and related functions. Also, some dual results are established under this kind of generalized convex functions. Our results generalize the ones obtained by Preda[J Math Anal Appl, 288（2003） 365-382].展开更多
AIM:To compare objective dry retinoscopy and subjective refraction measurements in patients with mild keratoconus(KCN)and quantify any differences.METHODS:This cross-sectional study was done on 68 eyes of 68 patients ...AIM:To compare objective dry retinoscopy and subjective refraction measurements in patients with mild keratoconus(KCN)and quantify any differences.METHODS:This cross-sectional study was done on 68 eyes of 68 patients diagnosed with mild KCN.Objective dry retinoscopy using autorefractometer and subjective refraction measurements were performed.Sphere,cylinder,J0,J45,and spherical equivalent values were compared between the two techniques.RESULTS:The mean age of 68 patients with mild KCN was 21.32±5.03y(12–35y).There were 37(54.4%)males.Objective refraction yielded significantly more myopic sphere(-1.44 D vs-0.57 D),higher cylinder magnitude(-2.24 D vs-1.48 D),and more myopic spherical equivalent(-2.56 D vs-1.31 D)compared to subjective refraction(all P<0.05).The mean differences were-0.87 D for sphere,-0.76 D for cylinder,and-1.25 D for spherical equivalent.No significant differences were found for J0 and J45 values,indicating agreement in astigmatism axis(P>0.05).CONCLUSION:In patients with mild KCN,objective dry retinoscopy overestimates the degree of myopia and astigmatism compared to subjective refraction.The irregular cornea in KCN likely impacts objective measurements.Subjective refraction allows compensation for irregularity,providing a more accurate correction.When determining refractive targets,the tendency of objective methods to overcorrect should be considered.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
The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Cent...The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Center(EC model)from February to December in 2022 were used.Based on the data of the national intelligent grid forecast,the intelligent grid forecast of the regional bureau,EC model,etc.,temperature was predicted.According to the research of the grid point forecast synthesis algorithm with the highest accuracy rate in the recent three days,the temperature grid point correction was conducted in two forms of stations and grids.In order to reduce the deviation caused by the seasonal system temperature difference,a temperature prediction model was established by using the rolling forecast errors of 5,10,15,20,25 and 30 d as the basis data.The verification and evaluation of objective correction results show that the accuracy rate of temperature forecast by the intelligent grid of the regional bureau,the national intelligent grid,and EC model could be increased by 10%,8%,and 12%,respectively.展开更多
The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the...The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet Engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).展开更多
According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation dat...According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.展开更多
Automated detection of suspended anomalous objects on high-speed railway catenary systems using computer vision-based technology is a critical task for ensuring railway transportation safety. Despite the critical impo...Automated detection of suspended anomalous objects on high-speed railway catenary systems using computer vision-based technology is a critical task for ensuring railway transportation safety. Despite the critical importance of this task, conventional vision-based foreign object detection methodologies have predominantly concentrated on image data, neglecting the exploration and integration of textual information. The currently popular multimodal model Contrastive Language-Image Pre-training (CLIP) employs contrastive learning to enable simultaneous understanding of both visual and textual modalities. Drawing inspiration from CLIP’s capabilities, this paper introduces a novel CLIP-based multimodal foreign object detection model tailored for railway applications, referred to as Railway-CLIP. This model leverages CLIP’s robust generalization capabilities to enhance performance in the context of catenary foreign object detection. The Railway-CLIP model is primarily composed of an image encoder and a text encoder. Initially, the Segment Anything Model (SAM) is employed to preprocess raw images, identifying candidate bounding boxes that may contain foreign objects. Both the original images and the detected candidate bounding boxes are subsequently fed into the image encoder to extract their respective visual features. In parallel, distinct prompt templates are crafted for both the original images and the candidate bounding boxes to serve as textual inputs. These prompts are then processed by the text encoder to derive textual features. The image and text encoders collaboratively project the multimodal features into a shared semantic space, facilitating the computation of similarity scores between visual and textual representations. The final detection results are determined based on these similarity scores, ensuring a robust and accurate identification of anomalous objects. Extensive experiments on our collected Railway Anomaly Dataset (RAD) demonstrate that the proposed Railway-CLIP outperforms previous state-of-the-art methods, achieving 97.25% AUROC and 92.66% F1-score, thereby validating the effectiveness and superiority of the proposed approach in real-world high-speed railway anomaly detection scenarios.展开更多
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
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 Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_1136)the National Natural Scientific Foundation of China(No.42275037)+2 种基金the Basic Research Fund of CAMS(No.2023Z016)the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202202)supported by the Jiangsu Collaborative Innovation Center for Climate Change。
文摘At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.
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Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
文摘Background: Radiofrequency has been proven to penetrate deeper than optical light sources independent of skin color allowing a safer treatment for the Asian skin type. Many studies have indicated the efficacy of various types of devices, but have not included a sufficient objective evaluation. Multisource radiofrequency uses multiple phase controlled radiofrequency generators with real time impedance control, allowing painless, deeper dermal heating with better adaptation to differences in individual skin impedance. In this study we used three-dimensional imaging for the objective evaluation of facial skin tightening by multisource phase-controlled radiofrequency. Methods: Twenty Japanese patients were treated with a multisource phase-controlled radiofrequency device. Three-dimensional imaging was performed with a Canfield Scientific Vectra camera and software, and quantitative volume measurements were taken to evaluate the change in the post-treatment volume. The patients then provided subjective assessments. Results: Objective assessments of the treated cheek volume evaluated by a three-dimensional color schematic representation with quantitative volume measurements showed significant improvement three months after the final treatment. The mean volume reduction at the last post-treatment visit was 3.878 ± 2.86 mL. The post-treatment volume was significantly reduced compared to the pretreatment volume in all of the volunteers (P = 0.0007). Ninety-five percent of volunteers reported satisfaction with the improvement of skin laxity, and ninety percent of volunteers reported satisfaction with the improvement of wrinkles, such as the nasolabial folds. Conclusions: The advantages of these multisource phase-controlled radiofrequency treatments are its high efficacy for skin tightening associated with minimal level of discomfort, minimal side effects, and low cost. Taken together, these characteristics facilitate the ability to give repeated treatments as a stand alone treatment or adjunct to surgery. This study provides for the first time a qualitative and quantitative volumetric assessment, proving the ability of the technology to reduce the volume through non invasive skin tightening.
基金Foundation item： Supported by Hunan Provincial Natural Science Foundation of China（05JJ40103） Supported by Soft Science Research Fund of Hunan Province（2006ZK3028） Supported by Scientific Research Fund of Hunan Provincial Education Department（105B0707, 08C470）
文摘In this paper, some necessary and sufficient optimality conditions are obtained for a fractional multiple objective programming involving semilocal E-convex and related functions. Also, some dual results are established under this kind of generalized convex functions. Our results generalize the ones obtained by Preda[J Math Anal Appl, 288（2003） 365-382].
文摘AIM:To compare objective dry retinoscopy and subjective refraction measurements in patients with mild keratoconus(KCN)and quantify any differences.METHODS:This cross-sectional study was done on 68 eyes of 68 patients diagnosed with mild KCN.Objective dry retinoscopy using autorefractometer and subjective refraction measurements were performed.Sphere,cylinder,J0,J45,and spherical equivalent values were compared between the two techniques.RESULTS:The mean age of 68 patients with mild KCN was 21.32±5.03y(12–35y).There were 37(54.4%)males.Objective refraction yielded significantly more myopic sphere(-1.44 D vs-0.57 D),higher cylinder magnitude(-2.24 D vs-1.48 D),and more myopic spherical equivalent(-2.56 D vs-1.31 D)compared to subjective refraction(all P<0.05).The mean differences were-0.87 D for sphere,-0.76 D for cylinder,and-1.25 D for spherical equivalent.No significant differences were found for J0 and J45 values,indicating agreement in astigmatism axis(P>0.05).CONCLUSION:In patients with mild KCN,objective dry retinoscopy overestimates the degree of myopia and astigmatism compared to subjective refraction.The irregular cornea in KCN likely impacts objective measurements.Subjective refraction allows compensation for irregularity,providing a more accurate correction.When determining refractive targets,the tendency of objective methods to overcorrect should be considered.
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.
文摘The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Center(EC model)from February to December in 2022 were used.Based on the data of the national intelligent grid forecast,the intelligent grid forecast of the regional bureau,EC model,etc.,temperature was predicted.According to the research of the grid point forecast synthesis algorithm with the highest accuracy rate in the recent three days,the temperature grid point correction was conducted in two forms of stations and grids.In order to reduce the deviation caused by the seasonal system temperature difference,a temperature prediction model was established by using the rolling forecast errors of 5,10,15,20,25 and 30 d as the basis data.The verification and evaluation of objective correction results show that the accuracy rate of temperature forecast by the intelligent grid of the regional bureau,the national intelligent grid,and EC model could be increased by 10%,8%,and 12%,respectively.
文摘The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet Engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).
文摘According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.
基金supported by the Technology Research and Development Program of China National Railway Group(Q2024T002)the Open Project Fund of National Engineering Research Center of Digital Construction and Evaluation Technology of Urban Rail Transit(2024023).
文摘Automated detection of suspended anomalous objects on high-speed railway catenary systems using computer vision-based technology is a critical task for ensuring railway transportation safety. Despite the critical importance of this task, conventional vision-based foreign object detection methodologies have predominantly concentrated on image data, neglecting the exploration and integration of textual information. The currently popular multimodal model Contrastive Language-Image Pre-training (CLIP) employs contrastive learning to enable simultaneous understanding of both visual and textual modalities. Drawing inspiration from CLIP’s capabilities, this paper introduces a novel CLIP-based multimodal foreign object detection model tailored for railway applications, referred to as Railway-CLIP. This model leverages CLIP’s robust generalization capabilities to enhance performance in the context of catenary foreign object detection. The Railway-CLIP model is primarily composed of an image encoder and a text encoder. Initially, the Segment Anything Model (SAM) is employed to preprocess raw images, identifying candidate bounding boxes that may contain foreign objects. Both the original images and the detected candidate bounding boxes are subsequently fed into the image encoder to extract their respective visual features. In parallel, distinct prompt templates are crafted for both the original images and the candidate bounding boxes to serve as textual inputs. These prompts are then processed by the text encoder to derive textual features. The image and text encoders collaboratively project the multimodal features into a shared semantic space, facilitating the computation of similarity scores between visual and textual representations. The final detection results are determined based on these similarity scores, ensuring a robust and accurate identification of anomalous objects. Extensive experiments on our collected Railway Anomaly Dataset (RAD) demonstrate that the proposed Railway-CLIP outperforms previous state-of-the-art methods, achieving 97.25% AUROC and 92.66% F1-score, thereby validating the effectiveness and superiority of the proposed approach in real-world high-speed railway anomaly detection scenarios.
