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
针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际...针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际背景的负样本;使用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)训练背景感知相关滤波器,减少计算复杂度,在保证跟踪速度的前提下,提升跟踪侵限异物的准确性,从而适应铁路沿线环境中由于侵限异物的形变、快速移动或天气等原因造成的目标丢失及跟踪框漂移等情况。实验结果表明,该方法对铁路侵限异物的跟踪精确度和AUC(Area Under Curve)值分别达到93%和71.9%,均高于SRDCF、KCF、ASLA和CSK等算法,具有更好的准确性。展开更多
A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatm...A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatment outcomes,develop more effective medical devices,or arrive at a more accurate diagnosis.This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction.The classification process was conducted with the aid of a convolu-tional neural network(CNN)with dual graphs.Evaluation of the performance of the fused model is carried out with various methods.In the initial input Com-puter Tomography(CT)image,150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules.The geometrical,statistical,struc-tural,and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix(GLCM),Histo-gram-oriented gradient features(HOG),and Gray-level dependence matrix(GLDM).To select the optimal features,a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed.For the classification of lung cancer,dual graph convolutional neural networks have been employed.A com-parison of classification algorithms and optimization algorithms has been con-ducted.According to the evaluated results,the proposed fused algorithm is successful with an accuracy of 98.72%in predicting lung tumors,and it outper-forms other conventional approaches.展开更多
复杂干扰条件下的红外空中目标识别技术是空战对抗领域的热点研究课题,复杂人工干扰严重遮蔽目标,导致目标特征的连续性与显著性遭到破坏,无法全面描述识别对象的特性,造成空中目标识别准确率下降。针对此问题,提出一种基于图像混合深...复杂干扰条件下的红外空中目标识别技术是空战对抗领域的热点研究课题,复杂人工干扰严重遮蔽目标,导致目标特征的连续性与显著性遭到破坏,无法全面描述识别对象的特性,造成空中目标识别准确率下降。针对此问题,提出一种基于图像混合深度特征的空中目标抗干扰识别算法。首先,基于卷积神经网络进行图像深度特征的提取,将深度特征与梯度直方图(Histogram of Gradient,HOG)特征进行有效融合,构建混合深度特征。针对作战场景中的目标与干扰的对抗态势多样性,将支持向量机的二分类模型改进为三分类模型,对目标、干扰以及目标干扰粘连三种状态进行精确分类。实验结果表明:在复杂干扰环境下,基于混合深度特征的空中目标抗干扰识别算法正确率为92.29%,该算法可以有效地解决目标被干扰遮蔽、形成目标干扰粘连状态时的抗干扰识别问题。展开更多
The automatic and accurate identification of apoptosis facilitates large-scale cell analysis.Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters.However,the...The automatic and accurate identification of apoptosis facilitates large-scale cell analysis.Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters.However,these parameters cannot completely describe nuclear morphology,thus limiting the identification accuracy of models.This paper proposes a new feature extraction method to improve the performance of the model for apoptosis identification.The proposed method uses a histogram of oriented gradient(HOG)of high-frequency wavelet coefficients to extract internal and edge texture information.The HOG vectors are classified using support vector machine.The experimental results demonstrate that the proposed feature extraction method well performs apoptosis identification,attaining 95:7% accuracy with low cost in terms of time.We confirmed that our method has potential applications to cell biology research.展开更多
During Covid pandemic,many individuals are suffering from suicidal ideation in the world.Social distancing and quarantining,affects the patient emotionally.Affective computing is the study of recognizing human feeling...During Covid pandemic,many individuals are suffering from suicidal ideation in the world.Social distancing and quarantining,affects the patient emotionally.Affective computing is the study of recognizing human feelings and emotions.This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face.Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance.In this paper,a new method is proposed for emotion recognition and suicide ideation detection in COVID patients.This helps to alert the nurse,when patient emotion is fear,cry or sad.The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm.The proposed Convolution Neural Networks(CNN)architecture with DnCNN preprocessing enhances the performance of recognition.The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras.The proposed method accuracy is more when compared to other methods.It detects the chances of suicide attempt based on stress level and emotional recognition.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.
文摘针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际背景的负样本;使用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)训练背景感知相关滤波器,减少计算复杂度,在保证跟踪速度的前提下,提升跟踪侵限异物的准确性,从而适应铁路沿线环境中由于侵限异物的形变、快速移动或天气等原因造成的目标丢失及跟踪框漂移等情况。实验结果表明,该方法对铁路侵限异物的跟踪精确度和AUC(Area Under Curve)值分别达到93%和71.9%,均高于SRDCF、KCF、ASLA和CSK等算法,具有更好的准确性。
文摘A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatment outcomes,develop more effective medical devices,or arrive at a more accurate diagnosis.This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction.The classification process was conducted with the aid of a convolu-tional neural network(CNN)with dual graphs.Evaluation of the performance of the fused model is carried out with various methods.In the initial input Com-puter Tomography(CT)image,150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules.The geometrical,statistical,struc-tural,and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix(GLCM),Histo-gram-oriented gradient features(HOG),and Gray-level dependence matrix(GLDM).To select the optimal features,a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed.For the classification of lung cancer,dual graph convolutional neural networks have been employed.A com-parison of classification algorithms and optimization algorithms has been con-ducted.According to the evaluated results,the proposed fused algorithm is successful with an accuracy of 98.72%in predicting lung tumors,and it outper-forms other conventional approaches.
文摘复杂干扰条件下的红外空中目标识别技术是空战对抗领域的热点研究课题,复杂人工干扰严重遮蔽目标,导致目标特征的连续性与显著性遭到破坏,无法全面描述识别对象的特性,造成空中目标识别准确率下降。针对此问题,提出一种基于图像混合深度特征的空中目标抗干扰识别算法。首先,基于卷积神经网络进行图像深度特征的提取,将深度特征与梯度直方图(Histogram of Gradient,HOG)特征进行有效融合,构建混合深度特征。针对作战场景中的目标与干扰的对抗态势多样性,将支持向量机的二分类模型改进为三分类模型,对目标、干扰以及目标干扰粘连三种状态进行精确分类。实验结果表明:在复杂干扰环境下,基于混合深度特征的空中目标抗干扰识别算法正确率为92.29%,该算法可以有效地解决目标被干扰遮蔽、形成目标干扰粘连状态时的抗干扰识别问题。
基金This work is supported by the Key Project of the National Natural Science Foundation of China(Grant Number 62135003)the Science and Technology Program of Guangzhou(Grant No.202201010704)Special Carrier Program of Qingyuan Hitech Industrial Development Zone.
文摘The automatic and accurate identification of apoptosis facilitates large-scale cell analysis.Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters.However,these parameters cannot completely describe nuclear morphology,thus limiting the identification accuracy of models.This paper proposes a new feature extraction method to improve the performance of the model for apoptosis identification.The proposed method uses a histogram of oriented gradient(HOG)of high-frequency wavelet coefficients to extract internal and edge texture information.The HOG vectors are classified using support vector machine.The experimental results demonstrate that the proposed feature extraction method well performs apoptosis identification,attaining 95:7% accuracy with low cost in terms of time.We confirmed that our method has potential applications to cell biology research.
文摘During Covid pandemic,many individuals are suffering from suicidal ideation in the world.Social distancing and quarantining,affects the patient emotionally.Affective computing is the study of recognizing human feelings and emotions.This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face.Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance.In this paper,a new method is proposed for emotion recognition and suicide ideation detection in COVID patients.This helps to alert the nurse,when patient emotion is fear,cry or sad.The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm.The proposed Convolution Neural Networks(CNN)architecture with DnCNN preprocessing enhances the performance of recognition.The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras.The proposed method accuracy is more when compared to other methods.It detects the chances of suicide attempt based on stress level and emotional recognition.
