This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal f...This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.展开更多
With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional...With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional detection methods fail to satisfy modern production needs.A non-destructive method based on machine vision and machine learning technology was proposed for detecting chicken breast freshness.A self-designed machine vision system was first used to collect images of chicken breast samples stored at 4℃ for 1-7 d.The Region of Interest(ROI)for each image was then extracted and a total of 700 ROI images were obtained.Six color features were extracted from two different color spaces RGB(red,green,blue)and HSI(hue,saturation,intensity).Six main Gray Level Co-occurrence Matrix(GLCM)texture feature parameters were also calculated from four directions.Principal Component Analysis(PCA)was used to reduce the dimension of these 30 extracted feature parameters for multiple features image fusion.Four principal components were taken as input and chicken breast freshness level as output.A 10-fold cross-validation was used to partition the dataset.Four machine learning methods,Particle Swarm Optimization-Support Vector Machine(PSO-SVM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Naive Bayes Classifier(NBC),were used to establish a chicken breast freshness level prediction model.Among these,SVM had the best prediction effect with prediction accuracy reaching 0.9867.The results proved the feasibility of using a detection method based on multiple features image fusion and machine learning,providing a theoretical reference for the nondestructive detection of chicken breast freshness.展开更多
Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the ...Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy.展开更多
The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain str...The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain streaks have different appearances even in one image.Regions where rain accumulates appear foggy or misty,while rain streaks can be clearly seen in areas where rain is less heavy.We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network(MSGMFFNet).Specially,to deal with rain streaks,our method generates a rain streak attention map,while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation.Using these tools,the model can restore a result with abundant details.Furthermore,a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information.Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.展开更多
文摘This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.
基金funded by the International Science and Technology Cooperation Program of Jiangsu Province(Grant No.BZ2023013)the Xuzhou Key Research and Development Project(Modern Agriculture)(Grant No.KC21135).
文摘With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional detection methods fail to satisfy modern production needs.A non-destructive method based on machine vision and machine learning technology was proposed for detecting chicken breast freshness.A self-designed machine vision system was first used to collect images of chicken breast samples stored at 4℃ for 1-7 d.The Region of Interest(ROI)for each image was then extracted and a total of 700 ROI images were obtained.Six color features were extracted from two different color spaces RGB(red,green,blue)and HSI(hue,saturation,intensity).Six main Gray Level Co-occurrence Matrix(GLCM)texture feature parameters were also calculated from four directions.Principal Component Analysis(PCA)was used to reduce the dimension of these 30 extracted feature parameters for multiple features image fusion.Four principal components were taken as input and chicken breast freshness level as output.A 10-fold cross-validation was used to partition the dataset.Four machine learning methods,Particle Swarm Optimization-Support Vector Machine(PSO-SVM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Naive Bayes Classifier(NBC),were used to establish a chicken breast freshness level prediction model.Among these,SVM had the best prediction effect with prediction accuracy reaching 0.9867.The results proved the feasibility of using a detection method based on multiple features image fusion and machine learning,providing a theoretical reference for the nondestructive detection of chicken breast freshness.
基金This research work is supported in part by Chiang Mai University and HITEC University.
文摘Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy.
基金This work was supported in part by the National Key R&D Program of China under No.2017YFB1003000the National Natural Science Foundation of China under No.61872047 and No.61720106007+2 种基金the Beijing Nova Program under No.Z201100006820124the Beijing Natural Science Foundation(L191004)the 111 Project(B18008).
文摘The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain streaks have different appearances even in one image.Regions where rain accumulates appear foggy or misty,while rain streaks can be clearly seen in areas where rain is less heavy.We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network(MSGMFFNet).Specially,to deal with rain streaks,our method generates a rain streak attention map,while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation.Using these tools,the model can restore a result with abundant details.Furthermore,a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information.Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.
