Cast blanks with large-scale free form surfaces are very difficult tomanufacture because of significant casting distortions. It is concerned that the development andapplication of a hogging algorithm for preparing the...Cast blanks with large-scale free form surfaces are very difficult tomanufacture because of significant casting distortions. It is concerned that the development andapplication of a hogging algorithm for preparing the blanks for an extended rough cutting. Theprocedure includes three main phases. They are the reconstruction of the free form surface withscattered points based on a special Hermite's interpolation, intersection of curved surfaces todefine the hogging areas, and the tool path planning. The result shows that the algorithm is greatlyvalid in reducing the invalid tool paths so that the work efficiency can be improved remarkably.展开更多
Through the experiments of 7 T-section composite beams, steel fiber reinforced self-stressing concrete （SFRSC） as the composite beam in the composite layer was studied under the hogging bending. The tests simulated ...Through the experiments of 7 T-section composite beams, steel fiber reinforced self-stressing concrete （SFRSC） as the composite beam in the composite layer was studied under the hogging bending. The tests simulated composite layer tensile strain under the hogging bending of inverted loading composite beams, giving the relationship under the different fatigue stress ratios between fatigue cycles and steel bar’s stress range, crack width, stiffness loss and damage, etc., in composite layer. This article established fatigue life equation, and analyzed SFRSC reinforced mechanism to crack width and stiffness loss. The results show that SFRSC as the composite beam concrete has excellent properties of crack resistance and tensile, can reinforce the fatigue crack width and stiffness loss of composite beams, and improve the durability and in normal use of composite beams in the hogging bending zone.展开更多
针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特...针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。展开更多
Autophagy is crucial for maintaining cellular homeostasis and is linked to various dis-eases.In Saccharomyces cerevisiae,the Polymyxin B Sensitivity 2(Pbs2)protein is a member of the mitogen-activated protein kinase(M...Autophagy is crucial for maintaining cellular homeostasis and is linked to various dis-eases.In Saccharomyces cerevisiae,the Polymyxin B Sensitivity 2(Pbs2)protein is a member of the mitogen-activated protein kinase(MAPK)family and plays a role in mitophagy.To explore the potential role of Pbs2 in macroautophagy,we engineered wild-type and PBS2-deficient cells using plasmid construction and yeast transforma-tion techniques,followed by a series of autophagy assays.First,after nitrogen star-vation,the levels of autophagic activity were evaluated with the classical GFP-Atg8 cleavage assay and the Pho8Δ60 activity assay at different time points.Deleting PBS2 significantly decreased both GFP-Atg8 protein cleavage and Pho8Δ60 activity,indicat-ing that Pbs2 is essential for macroautophagy.Furthermore,the influence of Pbs2 on macroautophagy was shown to be independent of Hog1,a well-known downstream factor of Pbs2.Second,the Atg8 lipidation assay demonstrated that Atg8 lipidation levels increased upon PBS2 deletion,suggesting that Pbs2 acts after Atg8 lipidation.Third,the proteinase K protection assay indicated that the loss of PBS2 led to a higher proportion of closed autophagosomes,implying that Pbs2 impacts the later stages of macroautophagy following autophagosome closure.In conclusion,Pbs2 regulates the late stages of macroautophagy induced by nitrogen starvation.展开更多
Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting...Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.展开更多
Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the in...Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the interpretation of GPR echo images often relies on manual recognition by experienced engineers.In order to address the automatic interpretation of cavity targets in GPR echo images,a recognition-algorithm based on Gaussian mixed model-hidden Markov model(GMM-HMM)is proposed,which can recognize three dimensional(3D)underground voids automatically.First,energy detection on the echo images is performed,whereby the data is preprocessed and pre-filtered.Then,edge histogram descriptor(EHD),histogram of oriented gradient(HOG),and Log-Gabor filters are used to extract features from the images.The traditional method can only be applied to 2D images and pre-processing is required for C-scan images.Finally,the aggregated features are fed into the GMM-HMM for classification and compared with two other methods,long short-term memory(LSTM)and gate recurrent unit(GRU).By testing on a simulated dataset,an accuracy rate of 90%is obtained,demonstrating the effectiveness and efficiency of our proposed method.展开更多
基金This project is supported by Visiting Scholar Foundation of Key Laboratory in University, Ministry of Education of China.
文摘Cast blanks with large-scale free form surfaces are very difficult tomanufacture because of significant casting distortions. It is concerned that the development andapplication of a hogging algorithm for preparing the blanks for an extended rough cutting. Theprocedure includes three main phases. They are the reconstruction of the free form surface withscattered points based on a special Hermite's interpolation, intersection of curved surfaces todefine the hogging areas, and the tool path planning. The result shows that the algorithm is greatlyvalid in reducing the invalid tool paths so that the work efficiency can be improved remarkably.
基金Project supported by the Science and Technology of Department of Communications of Liaoning Province (Grant No.200514)the Science and Technology of Department of Education of Liaoning Province (Grant No.L2010378)
文摘Through the experiments of 7 T-section composite beams, steel fiber reinforced self-stressing concrete （SFRSC） as the composite beam in the composite layer was studied under the hogging bending. The tests simulated composite layer tensile strain under the hogging bending of inverted loading composite beams, giving the relationship under the different fatigue stress ratios between fatigue cycles and steel bar’s stress range, crack width, stiffness loss and damage, etc., in composite layer. This article established fatigue life equation, and analyzed SFRSC reinforced mechanism to crack width and stiffness loss. The results show that SFRSC as the composite beam concrete has excellent properties of crack resistance and tensile, can reinforce the fatigue crack width and stiffness loss of composite beams, and improve the durability and in normal use of composite beams in the hogging bending zone.
文摘针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。
基金National Natural Science Foundation of China,Grant/Award Number:31970044 and 32370805。
文摘Autophagy is crucial for maintaining cellular homeostasis and is linked to various dis-eases.In Saccharomyces cerevisiae,the Polymyxin B Sensitivity 2(Pbs2)protein is a member of the mitogen-activated protein kinase(MAPK)family and plays a role in mitophagy.To explore the potential role of Pbs2 in macroautophagy,we engineered wild-type and PBS2-deficient cells using plasmid construction and yeast transforma-tion techniques,followed by a series of autophagy assays.First,after nitrogen star-vation,the levels of autophagic activity were evaluated with the classical GFP-Atg8 cleavage assay and the Pho8Δ60 activity assay at different time points.Deleting PBS2 significantly decreased both GFP-Atg8 protein cleavage and Pho8Δ60 activity,indicat-ing that Pbs2 is essential for macroautophagy.Furthermore,the influence of Pbs2 on macroautophagy was shown to be independent of Hog1,a well-known downstream factor of Pbs2.Second,the Atg8 lipidation assay demonstrated that Atg8 lipidation levels increased upon PBS2 deletion,suggesting that Pbs2 acts after Atg8 lipidation.Third,the proteinase K protection assay indicated that the loss of PBS2 led to a higher proportion of closed autophagosomes,implying that Pbs2 impacts the later stages of macroautophagy following autophagosome closure.In conclusion,Pbs2 regulates the late stages of macroautophagy induced by nitrogen starvation.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project Grant Number(BFP/RGP/ICT/22/490).
文摘Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.
基金National Natural Science Foundation of China(62071147)。
文摘Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the interpretation of GPR echo images often relies on manual recognition by experienced engineers.In order to address the automatic interpretation of cavity targets in GPR echo images,a recognition-algorithm based on Gaussian mixed model-hidden Markov model(GMM-HMM)is proposed,which can recognize three dimensional(3D)underground voids automatically.First,energy detection on the echo images is performed,whereby the data is preprocessed and pre-filtered.Then,edge histogram descriptor(EHD),histogram of oriented gradient(HOG),and Log-Gabor filters are used to extract features from the images.The traditional method can only be applied to 2D images and pre-processing is required for C-scan images.Finally,the aggregated features are fed into the GMM-HMM for classification and compared with two other methods,long short-term memory(LSTM)and gate recurrent unit(GRU).By testing on a simulated dataset,an accuracy rate of 90%is obtained,demonstrating the effectiveness and efficiency of our proposed method.
