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Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling 被引量:1
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作者 Yuan-Peng Zhang Xin-Yun Zhang +11 位作者 Yu-Ting Cheng Bing Li Xin-Zhi Teng Jiang Zhang Saikit Lam Ta Zhou Zong-Rui Ma Jia-Bao Sheng Victor CWTam Shara WYLee Hong Ge Jing Cai 《Military Medical Research》 SCIE CAS CSCD 2024年第1期115-147,共33页
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of... Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research. 展开更多
关键词 Artificial intelligence Radiomics feature extraction feature selection modeling INTERPRETABILITY Multimodalities Head and neck cancer
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Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model
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作者 Wenbo XUE Hui YU +1 位作者 Shengming TANG Wei HUANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第6期1161-1170,共10页
Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SM... Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study. 展开更多
关键词 surface wind speed terrain features error analysis MOS calibration model
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Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models
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作者 Robert KOSK Richard SOUTHERN +3 位作者 Lihua YOU Shaojun BIAN Willem KOKKE Greg MAGUIRE 《虚拟现实与智能硬件(中英文)》 EI 2024年第5期383-395,共13页
Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition sys... Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods. 展开更多
关键词 Shape modelling Deep 3D morphable models Representation learning feature engineering Perceptual metrics
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RESEARCH ON PARAMETRIC MODELING SYSTEM BASED ON FEATURE
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作者 刘苏 钱晓峰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 1999年第2期160-164,共5页
Feature modeling is the key to the realization of CAD/CAPP/CAM and the information integration of concurrent engineering. This paper describes the method for the advanced development of the parametric modeling system ... Feature modeling is the key to the realization of CAD/CAPP/CAM and the information integration of concurrent engineering. This paper describes the method for the advanced development of the parametric modeling system based on features by using I DEAS 5 system. It elaborates the modeling technique based on the features and generates the product information models based on the features providing abundant information for the process of the ensuing applications. The development of the feature modeling system on the commercial CAD software platform can take a great advantage of the solid modeling resources of the existing software, save the input of funds and shorten the development cycles of the new systems. 展开更多
关键词 featureS feature modeling modeling system
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Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis 被引量:12
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作者 LI Yungong ZHANG Jinping +2 位作者 DAI Li ZHANG Zhanyi LIU Jie 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2010年第3期391-397,共7页
It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory... It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory(EA) model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect. 展开更多
关键词 faults diagnosis feature extraction auditory model early auditory model
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A Feature-Based Parametric Product Modeling System in CIMS Environment 被引量:4
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作者 李海龙 《High Technology Letters》 EI CAS 1997年第1期13-16,共4页
This paper proposes an approach of developing the feature based parametric product modeling system which is suitable for integrated engineering design in CIMS environment.The architecture of ZD--MCADII and the charact... This paper proposes an approach of developing the feature based parametric product modeling system which is suitable for integrated engineering design in CIMS environment.The architecture of ZD--MCADII and the characteristics of its each module are introduced in detail. ZD--MCADII’s product data is managed by an object--oriented database management system OSCAR, and the product model is built according to the standard STEP. The product design is established on a unified product model, and all the product data are globally associated in ZD--MCADII. ZD--MCADII provides various design features to facilitate the product design, and supports the integrity of CAD, CAPP and CAM. 展开更多
关键词 CIMS feature based modeling PARAMETRIC design PRODUCT model OODB
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Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model 被引量:4
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作者 LIU Yueming YANG Xiaomei +3 位作者 WANG Zhihua LU Chen LI Zhi YANG Fengshuo 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2019年第6期1941-1954,共14页
Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area... Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts. 展开更多
关键词 AQUACULTURE area VULNERABILITY assessment Richer Convolutional features(RCF)network model deep learning HIGH-RESOLUTION REMOTE SENSING
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A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS 被引量:3
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作者 Lu Mingquan Xiao Xianci Li Lemin (University of Electronic Science and Technology of China, Chengdu 610054) 《Journal of Electronics(China)》 1999年第3期244-250,共7页
Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognitio... Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed. 展开更多
关键词 MODULATION RECOGNITION GAR model feature extraction NEURAL network CLASSIFIER
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Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction 被引量:4
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作者 孟月波 邹建华 +1 位作者 甘旭升 刘光辉 《Journal of Central South University》 SCIE EI CAS 2013年第4期931-941,共11页
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr... In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles. 展开更多
关键词 WAVELET neural network fuzzy C-means clustering kernel principal components analysis feature extraction aerodynamic modeling
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Mesomechanics coal experiment and an elastic-brittle damage model based on texture features 被引量:3
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作者 Sun Chuanmeng Cao Shugang Li Yong 《International Journal of Mining Science and Technology》 EI CSCD 2018年第4期634-642,共9页
To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage ... To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result. 展开更多
关键词 Mesomechanics experiment Image processing Texture feature Damage variable Damage model
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Rethinking the image feature biases exhibited by deep convolutional neural network models in image recognition 被引量:2
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作者 Dawei Dai Yutang Li +2 位作者 Yuqi Wang Huanan Bao Guoyin Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2022年第4期721-731,共11页
In recent years,convolutional neural networks(CNNs)have been applied successfully in many fields.However,these deep neural models are still considered as“black box”for most tasks.One of the fundamental issues underl... In recent years,convolutional neural networks(CNNs)have been applied successfully in many fields.However,these deep neural models are still considered as“black box”for most tasks.One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these features.It is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified,however,several recent studies have argued that texture features are more important than other features.In this paper,we assume that the importance of certain features varies depending on specific tasks,that is,specific tasks exhibit feature bias.We designed two classification tasks based on human intuition to train deep neural models to identify the anticipated biases.We designed experiments comprising many tasks to test these biases in the Res Net and Dense Net models.From the results,we conclude that(1)the combined effect of certain features is typically far more influential than any single feature;(2)in different tasks,neural models can perform different biases,that is,we can design a specific task to make a neural model biased towards a specific anticipated feature. 展开更多
关键词 CNNS featureS understandable models
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A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen
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作者 Yulai Zhang Matthew Tsang +4 位作者 Mark Knackstedt Michael Turner Shane Latham Euan Macaulay Rhys Pitchers 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第1期153-166,共14页
Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining indust... Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry.Discrete fracture networks(DFNs)are increasingly used in engineering analyses to spatially model fractures at various scales.The reliability of coal DFNs largely depends on the confidence in the input cleat statistics.Estimates of these parameters can be made from image-based three-dimensional(3D)characterization of coal cleats using X-ray micro-computed tomography(m CT).One key step in this process,after cleat extraction,is the separation of individual cleats,without which the cleats are a connected network and statistics for different cleat sets cannot be measured.In this paper,a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray m CT images.Kernels(filters)representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images.The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin,Queensland,Australia.It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation.Bedding-parallel fractures are also identified and separated,which has his-torically been challenging to delineate and rarely reported.A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling.Finally,variability and heterogeneity with respect to the core axis are investigated.Significant heterogeneity is observed and suggests that the representative elementary volume(REV)of the cleat groups for engineering purposes may be a complex problem requiring careful consideration. 展开更多
关键词 Cleat separation Cleat statistics feature extraction Discrete fracture network(DFN)modeling
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MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI
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作者 Moshe Dayan Sirapangi S.Gopikrishnan 《Computers, Materials & Continua》 SCIE EI 2024年第5期2229-2251,共23页
Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of mu... Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world. 展开更多
关键词 Predictive health modeling Medical Internet of Things explainable artificial intelligence personalized feature selection preemptive analysis
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A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant 被引量:3
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作者 Min Wang Li Sheng +1 位作者 Donghua Zhou Maoyin Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第4期719-727,共9页
With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectiv... With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China. 展开更多
关键词 Abnormality monitoring continuous variables feature weighted mixed naive Bayes model(FWMNBM) two-valued variables thermal power plant
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GENERATIVE CAPP APPROACH BASED ON RESOURCE MODEL AND FEATURE MODEL 被引量:1
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作者 倪前富 戴国洪 +1 位作者 严隽琪 马登哲 《Journal of Shanghai Jiaotong university(Science)》 EI 1998年第1期47-53,共7页
GENERATIVECAPPAPPROACHBASEDONRESOURCEMODELANDFEATUREMODEL*NiQianfu(倪前富)DaiGuohong(戴国洪)YanJunqi(严隽琪)MaDengzhe... GENERATIVECAPPAPPROACHBASEDONRESOURCEMODELANDFEATUREMODEL*NiQianfu(倪前富)DaiGuohong(戴国洪)YanJunqi(严隽琪)MaDengzhe(马登哲)(Dept.ofMech... 展开更多
关键词 严隽琪 ON RESOURCE BASED model feature APPROACH AND
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A subspace ensemble regression model based slow feature for soft sensing application 被引量:1
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作者 Qiong Jia Jun Cai +1 位作者 Xinyi Jiang Shaojun Li 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第12期3061-3069,共9页
A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three asp... A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three aspects.Firstly,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index.Then,an adaptive slow feature regression is presented for sub-models.Finally,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination.Two industrial examples were used to evaluate the proposed method. 展开更多
关键词 Soft sensing Slow feature regression Subspace modeling Ensemble learning
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Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure 被引量:1
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作者 P.S.Anoop V.Sugumaran 《Structural Durability & Health Monitoring》 EI 2017年第2期191-208,共18页
Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A diffe... Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully. 展开更多
关键词 Machine learning Vibration ACCELEROMETER Statistical features Histogram features Logistic model tree(LMT) Tyre pressure monitoring system
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Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model 被引量:1
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作者 Thavavel Vaiyapuri K.Priyadarshini +4 位作者 A.Hemlathadhevi M.Dhamodaran Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin 《Computers, Materials & Continua》 SCIE EI 2022年第8期2429-2444,共16页
Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven deci... Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches. 展开更多
关键词 Financial risks intelligent models financial crisis prediction deep learning feature selection metaheuristics
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2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS 被引量:1
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作者 YE Dapeng DING Qiquan WU Zhaotong 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第1期156-158,共3页
A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tes... A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future. 展开更多
关键词 Fault diagnosis Rotating machinery 2D-hidden Markov model(HMM)feature extraction
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Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills 被引量:3
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作者 汤健 柴天佑 +1 位作者 刘卓 余文 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2020-2028,共9页
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ... Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones. 展开更多
关键词 Nonlinear latent feature extraction Kernel partial least squares Selective ensemble modeling Least squares support vector machines Material to ball volume ratio
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