Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine l...Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.展开更多
Maize(Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight(NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica(Luttrell) Leonard a...Maize(Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight(NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica(Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks(DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, Google Net, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model Google Net, some of the recent loss functions developed for deep facial recognition tasks such as Arc Face, Cos Face, and A-Softmax were applied to detect NCLB. We found that a pre-trained Google Net architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.展开更多
Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(S...Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.展开更多
This paper focuses on the maintenance of automotive stamping automation equipment.Through long-term self-study and accumulated experience,we independently developed a process monitoring system based on the three pumpi...This paper focuses on the maintenance of automotive stamping automation equipment.Through long-term self-study and accumulated experience,we independently developed a process monitoring system based on the three pumping stations of clutch,tension pad and lubrication in the stamping automation production line,which is used for real-time monitoring and diagnosis in the automatic production process without stopping the machine,and for the detection of oil temperature change,high-pressure pipeline leakage and oil return pipe In this paper,the improved case has strong practicability,low development cost,and has been recognized by peers in terms of cost efficiency improvement,which is easy to be popularized.展开更多
Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation,and its working reliability directly affects the safety of oil and gas storage and transportation.Intelligent d...Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation,and its working reliability directly affects the safety of oil and gas storage and transportation.Intelligent diagnosis is a key technical method to reduce failure rate of oil transfer pump,ensure the safety of gathering and transportation process,and avoid major safety accidents caused by oil transfer pump failure.Various oil transfer pumps have been emerged in recent decades,and the common fault types and characteristics of oil transfer pump have been brought out in the review.This article highlights on the research of the fault signal and processing methods of oil transfer pump.Firstly,the fault signal of the oil transfer pump is discussed and the advantages and disadvantages of different signal extraction are analyzed.Secondly,the intelligent diagnosis method of oil transfer pump and the shortcomings of the existing methods are pointed out.Finally,the conclusions are given and the future development perspectives of oil transfer pumps are suggested.The main contribution of this review is to give a syn-thetic understanding on oil transfer pumps.展开更多
The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotatio...The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.展开更多
Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physici...Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.展开更多
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potent...Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.展开更多
Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extra...Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.展开更多
After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and ...After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results.展开更多
The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge ...The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning(MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.展开更多
Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even ...Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.展开更多
Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when ...Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.展开更多
The first International Symposium on Dynamics,Monitoring,and Diagnostics was held in Chongqing,China,in April 2022.The Symposium,which was attended both virtually and in person,had an audience of 2000 and was aimed at...The first International Symposium on Dynamics,Monitoring,and Diagnostics was held in Chongqing,China,in April 2022.The Symposium,which was attended both virtually and in person,had an audience of 2000 and was aimed at enhancing the intelligence of condition monitoring for engineering systems.During the Symposium,five keynote addresses were delivered by world leading experts,and this paper is comprised of summaries of these addresses to ensure that the important messages of these speakers are properly on record and readily able to be referenced.展开更多
The study focuses on developing mobile applications based on intelligent acupuncture, classified into education and intelligent diagnosis and treatment. The mobile application function is divided into two directions: ...The study focuses on developing mobile applications based on intelligent acupuncture, classified into education and intelligent diagnosis and treatment. The mobile application function is divided into two directions: assisting acupoint positioning and perfecting acupoint knowledge system. The study does a relative review on Android and IOS, showing that the number of Android users is rather more prevailing than IOS. It suggests that intelligent acupuncture mobile applications should comply with the trend of acupuncture globalization in the future, developing multilingual versions. Further, the content should be characterized by the experience of different famous doctors to avoid homogenization. Technically, intelligent applications should continue to develop three-dimensional and augmented reality technology, to optimize the accuracy of acupoint positioning.展开更多
AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN ...AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.展开更多
Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of col...Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.展开更多
Objective:Community health services are an emerging trend.We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct.The respiratory department’s daily work involves ...Objective:Community health services are an emerging trend.We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct.The respiratory department’s daily work involves a number of outpatient registration items and a vast workload.The routine manual operation is inefficient and it is not convenient to make effective statistical analysis of the outpatient data to identify the risk factors closely related to diseases.Therefore,it is imperative to process the outpatient information of patients with respiratory diseases effectively and efficiently in a unified manner by means of computer technology.Methods:The design and realization of the Community Health Service-oriented computerassisted Information System for Diagnosis and Treatment of Respiratory Diseases(CHS-DTRD)was completed as part of the community intervention study on bronchial asthma that was carried out jointly by the Nanjing First Hospital Affiliated to Nanjing Medical University and the Hospital of Nanjing University of Science&Technology,and based on 2 years of experience and the needs of an overall analysis.Results:The computer-assisted information system for diagnosis and treatment was developed using Java Server Page(JSP)technology and introducing the advanced Asynchronous JavaScript XML(AJAX)technique and MS-SQL Server was used in the background database.CHS-DTRD was composed of eight functional modules(outpatient data maintenance,outpatient appointment,intelligent analysis for disease risk factors,query and statistics,data dictionary maintenance,database manipulation,access control,and system configuration).CHS-DTRD featured a friendly interface,convenient operation,and stability and reliability.Conclusion:Community health-oriented diagnosis and treatment of respiratory diseases is simple,programmable,and intuitive,thus the workload of physicians is significantly reduced and the work efficiency is improved.This system facilitates an intelligent analysis of disease risk factors using data mining technology,and provides physicians with suggestions on intelligent analysis for diagnosis of disease and conclusion of disease causes.展开更多
Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault inform...Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification method.However,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information.To improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance method.Then,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify faults.Different from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.Findings-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model.The experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification method.Consequently,the model can intuitively classify faults with higher accuracy.Originality/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is obtained.The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.展开更多
The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the av...The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data.However,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and laborious.As some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual machines.As a potential tool,transfer learning adapts a network trained in a source domain to its application in a target domain.Therefore,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes.In the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification.Various groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.展开更多
基金Supported by National Natural Science Foundation of China(62102036).
文摘Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.
基金financially supported by the Key Planning Projects on Science and Technology of Jilin Province,China(20180201012NY)the Inter-Governmental International Cooperation Special Project of National Key R&D Program of China(2019YFE0114200)the National Key R&D Program of China(2017YFD0201802)。
文摘Maize(Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight(NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica(Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks(DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, Google Net, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model Google Net, some of the recent loss functions developed for deep facial recognition tasks such as Arc Face, Cos Face, and A-Softmax were applied to detect NCLB. We found that a pre-trained Google Net architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.
基金Project Supported by National Natural Science Foundation of China(51177104)Liaoning Province Natural Science Foundation of China(201102169)
文摘Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.
文摘This paper focuses on the maintenance of automotive stamping automation equipment.Through long-term self-study and accumulated experience,we independently developed a process monitoring system based on the three pumping stations of clutch,tension pad and lubrication in the stamping automation production line,which is used for real-time monitoring and diagnosis in the automatic production process without stopping the machine,and for the detection of oil temperature change,high-pressure pipeline leakage and oil return pipe In this paper,the improved case has strong practicability,low development cost,and has been recognized by peers in terms of cost efficiency improvement,which is easy to be popularized.
基金This study is supported by Sichuan science and technology program(2021ZHCG0013,22ZDYF3009)China National Petroleum Corporation science and technology planning project(2020B-4121)Such supports are greatly appreciated by the authors.
文摘Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation,and its working reliability directly affects the safety of oil and gas storage and transportation.Intelligent diagnosis is a key technical method to reduce failure rate of oil transfer pump,ensure the safety of gathering and transportation process,and avoid major safety accidents caused by oil transfer pump failure.Various oil transfer pumps have been emerged in recent decades,and the common fault types and characteristics of oil transfer pump have been brought out in the review.This article highlights on the research of the fault signal and processing methods of oil transfer pump.Firstly,the fault signal of the oil transfer pump is discussed and the advantages and disadvantages of different signal extraction are analyzed.Secondly,the intelligent diagnosis method of oil transfer pump and the shortcomings of the existing methods are pointed out.Finally,the conclusions are given and the future development perspectives of oil transfer pumps are suggested.The main contribution of this review is to give a syn-thetic understanding on oil transfer pumps.
基金the National Key R&D Program of China(2022YFB3402100)the National Science Fund for Distinguished Young Scholars of China(52025056)+4 种基金the National Natural Science Foundation of China(52305129)the China Postdoctoral Science Foundation(2023M732789)the China Postdoctoral Innovative Talents Support Program(BX20230290)the Open Foundation of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment(2022JXKF JJ01)the Fundamental Research Funds for Central Universities。
文摘The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.
基金This research was supported by the National Key Research and Development Program of China(No.2016YFC0901501)CAMS Innovation Fund for Medical Science(No.CAMS-2017-I2M–1-011)the Research Project of the Institute of Internet Industry,Tsinghua University,titled“DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG.”。
文摘Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.
基金Supported by National Natural Science Foundation of China(No.61906066)Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202250196)+4 种基金Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z)Natural Science Foundation of Ningbo City(No.202003N4072)Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Fundamental Research Program(No.JCYJ20220818103207015).
文摘Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
基金deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number (IFP-2020-133).
文摘Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.
文摘After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results.
基金Supported by Hebei Provincial Natural Science Foundation of China(Grant No.F2016203421)
文摘The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning(MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.
基金funded by the National Natural Science Foundation,China(No.62172123)the Key Research and Development Program of Heilongjiang(Grant No.2022ZX01A36)+1 种基金the Special Projects for the Central Government to Guide the Development of Local Science and Technology,China(No.ZY20B11)the Harbin Manufacturing Technology Innovation Talent Project(No.CXRC20221104236).
文摘Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.
基金This study was financially supported by the National Key R&D Program of China(Grant No.2017YFD0400405).
文摘Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.
基金supported in part by the Australian Government through the Australian Research Council Discovery Project DP160103501.
文摘The first International Symposium on Dynamics,Monitoring,and Diagnostics was held in Chongqing,China,in April 2022.The Symposium,which was attended both virtually and in person,had an audience of 2000 and was aimed at enhancing the intelligence of condition monitoring for engineering systems.During the Symposium,five keynote addresses were delivered by world leading experts,and this paper is comprised of summaries of these addresses to ensure that the important messages of these speakers are properly on record and readily able to be referenced.
基金2022 Jiangsu Science and Technology Plan Special Fund(Key Research and Development Plan Social Development)Project(BE2022712)2021 Jiangsu Special Research Project of TCM Science and Technology Development Plan(ZT202120)Research Project of Nanjing University of Traditional Chinese Medicine(XT202001)。
文摘The study focuses on developing mobile applications based on intelligent acupuncture, classified into education and intelligent diagnosis and treatment. The mobile application function is divided into two directions: assisting acupoint positioning and perfecting acupoint knowledge system. The study does a relative review on Android and IOS, showing that the number of Android users is rather more prevailing than IOS. It suggests that intelligent acupuncture mobile applications should comply with the trend of acupuncture globalization in the future, developing multilingual versions. Further, the content should be characterized by the experience of different famous doctors to avoid homogenization. Technically, intelligent applications should continue to develop three-dimensional and augmented reality technology, to optimize the accuracy of acupoint positioning.
基金Supported by Hunan Provincial Science and Technology Department Clinical Medical Technology Innovation Guidance Project(No.2021SK50103)。
文摘AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.
基金supported by the Major National Science and Technology Projects(No.2017-IV-0008-0045)the National Natural Science Foundation of China(Nos.51675262 and 51975276)+1 种基金the Advance Research Field Fund Project of China(No.61400040304)the National Key Research and Development Program of China(No.2018YFB2003300)。
文摘Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.
基金National Natural Science Foundation of China[grant No.61373062]The Fundamental Research Funds for the Central Universities[grant No.30920130111010]Social Development Project of Wujiang City,[grant No.WS201217].
文摘Objective:Community health services are an emerging trend.We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct.The respiratory department’s daily work involves a number of outpatient registration items and a vast workload.The routine manual operation is inefficient and it is not convenient to make effective statistical analysis of the outpatient data to identify the risk factors closely related to diseases.Therefore,it is imperative to process the outpatient information of patients with respiratory diseases effectively and efficiently in a unified manner by means of computer technology.Methods:The design and realization of the Community Health Service-oriented computerassisted Information System for Diagnosis and Treatment of Respiratory Diseases(CHS-DTRD)was completed as part of the community intervention study on bronchial asthma that was carried out jointly by the Nanjing First Hospital Affiliated to Nanjing Medical University and the Hospital of Nanjing University of Science&Technology,and based on 2 years of experience and the needs of an overall analysis.Results:The computer-assisted information system for diagnosis and treatment was developed using Java Server Page(JSP)technology and introducing the advanced Asynchronous JavaScript XML(AJAX)technique and MS-SQL Server was used in the background database.CHS-DTRD was composed of eight functional modules(outpatient data maintenance,outpatient appointment,intelligent analysis for disease risk factors,query and statistics,data dictionary maintenance,database manipulation,access control,and system configuration).CHS-DTRD featured a friendly interface,convenient operation,and stability and reliability.Conclusion:Community health-oriented diagnosis and treatment of respiratory diseases is simple,programmable,and intuitive,thus the workload of physicians is significantly reduced and the work efficiency is improved.This system facilitates an intelligent analysis of disease risk factors using data mining technology,and provides physicians with suggestions on intelligent analysis for diagnosis of disease and conclusion of disease causes.
基金supported by the Mountain and Sea Talents Project of Shandong University of Science and Technology(grant numbers:01040055230)the National Nature Science Foundation of Shandong Province of China(Grant No.ZR2018MEE024).
文摘Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification method.However,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information.To improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance method.Then,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify faults.Different from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.Findings-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model.The experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification method.Consequently,the model can intuitively classify faults with higher accuracy.Originality/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is obtained.The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.
基金Natural Science Foundation of Shanghai (21ZR1425400)Shanghai Rising-Star Program (21QC1400200)+1 种基金National Natural Science Foundation of China (51977128)Shanghai Science and Technology Project (20142202600).
文摘The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data.However,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and laborious.As some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual machines.As a potential tool,transfer learning adapts a network trained in a source domain to its application in a target domain.Therefore,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes.In the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification.Various groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.
