Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for ...Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.展开更多
In this review, we highlight the recent development of organic π-functional materials as buffer layers in constructing efficient perovskite solar cells（PVSCs）. By following a brief introduction on the PVSC developm...In this review, we highlight the recent development of organic π-functional materials as buffer layers in constructing efficient perovskite solar cells（PVSCs）. By following a brief introduction on the PVSC development, device architecture and material design features, we exemplified the exciting progresses made in field by exploiting organic π-functional materials based hole and electron transport layers（HTLs and ETLs） to enable high-performance PVSCs.展开更多
In this study, we report narrow-size distribution Zn_2SnO_4（ZSO） nanoparticles, which are produced by low-temperature solution-processed used as the electron extraction layer（EEL） in the inverted polymer solar ce...In this study, we report narrow-size distribution Zn_2SnO_4（ZSO） nanoparticles, which are produced by low-temperature solution-processed used as the electron extraction layer（EEL） in the inverted polymer solar cells（i-PSCs）. Moreover, poly[（9,9-bis（30-（N,N-dimethylamino）propyl）-2,7-fluorene）-alt-2,7-（9,9-dioctylfluorene）]（PFN） is used to modify the surface properties of ZSO thin film. By using the ZSO NPs/PFN as the EEL, the i-PSCs fabricated by poly[4,8-bis（2-ethylhexyloxyl）benzo[1,2-b：4,5-b0] dithio-phene-2,6-diyl-altethylhexyl-3-fluorothithieno [3,4-b]thiophene-2-carboxylate-4,6-diyl]（PTB7） blended with（6,6）-phenyl-C_（71）-butyric acid methylester（PC_（71）BM） bulk heterojunction（BHJ） composite, exhibits a power conversion efficiency（PCE） of 8.44%, which is nearly 10% enhancement as compared with that of7.75% observed from the i-PSCs by PTB7：PC_（71）BM BHJ composite using the ZnO/PFN EEL. The enhanced PCE is originated from improved interfacial contact between the EEL with BHJ active layer and good energy level alignment between BHJ active layer and the EEL. Our results indicate that we provide a simple way to boost efficiency of i-PSCs.展开更多
Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a sh...Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.展开更多
基金suported by the Fundamental Research Grant Scheme(FRGS)of Universiti Sains Malaysia,Research Number:FRGS/1/2024/ICT02/USM/02/1.
文摘Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.
基金financial support from the 973 program(No.2014CB643503)the National Natural Science Foundation of China(No.21474088)+2 种基金financial support from NSFC(No.21674093)the National 1000 Young Talents Program hosted by China100 Talents Program by Zhejiang University
文摘In this review, we highlight the recent development of organic π-functional materials as buffer layers in constructing efficient perovskite solar cells（PVSCs）. By following a brief introduction on the PVSC development, device architecture and material design features, we exemplified the exciting progresses made in field by exploiting organic π-functional materials based hole and electron transport layers（HTLs and ETLs） to enable high-performance PVSCs.
基金supported by National Natural Science Foundation of China (No. 51329301)
文摘In this study, we report narrow-size distribution Zn_2SnO_4（ZSO） nanoparticles, which are produced by low-temperature solution-processed used as the electron extraction layer（EEL） in the inverted polymer solar cells（i-PSCs）. Moreover, poly[（9,9-bis（30-（N,N-dimethylamino）propyl）-2,7-fluorene）-alt-2,7-（9,9-dioctylfluorene）]（PFN） is used to modify the surface properties of ZSO thin film. By using the ZSO NPs/PFN as the EEL, the i-PSCs fabricated by poly[4,8-bis（2-ethylhexyloxyl）benzo[1,2-b：4,5-b0] dithio-phene-2,6-diyl-altethylhexyl-3-fluorothithieno [3,4-b]thiophene-2-carboxylate-4,6-diyl]（PTB7） blended with（6,6）-phenyl-C_（71）-butyric acid methylester（PC_（71）BM） bulk heterojunction（BHJ） composite, exhibits a power conversion efficiency（PCE） of 8.44%, which is nearly 10% enhancement as compared with that of7.75% observed from the i-PSCs by PTB7：PC_（71）BM BHJ composite using the ZnO/PFN EEL. The enhanced PCE is originated from improved interfacial contact between the EEL with BHJ active layer and good energy level alignment between BHJ active layer and the EEL. Our results indicate that we provide a simple way to boost efficiency of i-PSCs.
基金funded by the Shanxi Provincial Science and Technology Department Surface Project(Grant No.202303021211330)Innovation Platform Project of Science and Technology Innovation Program of Higher Education Institutions in Shanxi Province(Grant No.2022P009)+2 种基金Shanxi Province Basic Research Program Projects(Grant No.202303021212244)the Datong City Shanxi Province Key Research&Development(Agriculture)Program Projects(Grants No.2023006,2023015)the 2024 Basic Research Program of Shanxi Province(Free Exploration Category)Program Projects(Grant No.202403021221181).
文摘Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.
