Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving local...4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.展开更多
Soft(flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs...Soft(flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs, which maximizes data fidelity and minimizes biological interference.Most of the early soft sensors focused on sensing physical signals. Recently, it is becoming a trend that novel soft sensors are developed to sense and monitor biochemical signals in situ in real biological environments, thus providing much more meaningful data for studying fundamental biology and diagnosing diverse health conditions. This is essential to decentralize the healthcare resources towards predictive medicine and better disease management. To meet the requirements of mechanical softness and complex biosensing, unconventional materials, and manufacturing process are demanded in developing biosensors. In this review, we summarize the fundamental approaches and the latest and representative design and fabrication to engineer soft electronics(flexible and stretchable) for wearable and implantable biochemical sensing. We will review the rational design and ingenious integration of stretchable materials, structures, and signal transducers in different application scenarios to fabricate high-performance soft biosensors. Focus is also given to how these novel biosensors can be integrated into diverse important physiological environments and scenarios in situ, such as sweat analysis, wound monitoring, and neurochemical sensing. We also rethink and discuss the current limitations,challenges, and prospects of soft biosensors. This review holds significant importance for researchers and engineers, as it assists in comprehending the overarching trends and pivotal issues within the realm of designing and manufacturing soft electronics for biochemical sensing.展开更多
Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conserva...Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.展开更多
Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new s...Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.展开更多
Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensin...Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.展开更多
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
Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of t...Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.展开更多
Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is pr...Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.展开更多
Aiming at the water temperature measuring problem for controlled cooling system of rolling plant,a new water temperature measuring method based on soft-sensing method with a water temperature model of on-line self cor...Aiming at the water temperature measuring problem for controlled cooling system of rolling plant,a new water temperature measuring method based on soft-sensing method with a water temperature model of on-line self correction parameter was built.A water temperature compensation factor model was also built to improve coiling temperature control precision.It was proved that the model meets production requirements.The soft-sensing technique has extensive applications in the field of metal forming.展开更多
Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but ...Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.展开更多
The sensing capabilities of a soft arm are ofparamount importance to its overall performance as they allow precise control of the soft arm and enhance its interactionwith the surrounding environment. However, the actu...The sensing capabilities of a soft arm are ofparamount importance to its overall performance as they allow precise control of the soft arm and enhance its interactionwith the surrounding environment. However, the actuationand sensing of a soft arm are not typically integrated into amonolithic structure, which would impede the arm’s movement and restrict its performance and application scope. Toaddress this limitation, this study proposes an innovativemethod for the integrated design of actuator structures andsensing. The proposed method combines the art of kirigamiwith soft robotics technology. In the proposed method, sensorsare embedded in the form of kirigami structures into actuatorsusing laser cutting technology, achieving seamless integrationwith a soft arm. Compared to the traditional amanogawakirigami and fractal-cut kirigami structures, the proposedmiddle-cut kirigami (MCK) structure does not buckle duringstretching and exhibits superior tensile performance. Based onthe MCK structure, an advanced interdigitated capacitivesensor with a high degree of linearity, which can significantlyoutperform traditional kirigami sensors, is developed. Theexperimental results validate the effectiveness of the proposedsoft arm design in actual logistics sorting tasks, demonstratingthat it is capable of accurately sorting objects based on sensorsignals. In addition, the results indicate that the developedcontinuum soft arm and its embedded kirigami sensors havegreat potential in the field of logistics automation sorting.This work provides a promising solution for high-precisionclosed-loop feedback control and environmental interaction ofsoft arms.展开更多
A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cuttin...A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.展开更多
Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,f...Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,for which co-training based semi-supervised learning offers a potential solution.So in this paper,a novel soft sensing method for mechanical properties based on improved co-training(ICO)is proposed.Compared with the existing co-training framework,the proposed ICO introduces improvements from the aspects of multiple view partition,confidence estimation,and pseudo-label assignment.Specifically,(ⅰ)in the stage of multiple view partition,ICO integrates metallurgical mechanisms of hot rolling processes and statistical mutual information to achieve a balance between view sufficiency and independence,which improves model performance and interpretability;(ⅱ)in the stage of confidence estimation,ICO evaluates the confidence of unlabeled samples at the cluster level rather than at the level of a single sample,which facilitates the exploration of sample distribution and the selection of representative samples;(ⅲ)in the pseudo-label assignment stage,ICO adopts a safe pseudo-label algorithm(which is called SAFER by its author and originally used for each single sample)to assign pseudo-labels for cluster of samples with the highest confidence determined in the previous step stage,to take advantage of the merit of handling unlabeled samples at the cluster level mentioned above on one hand,and the merit of SAFER in enhancing the quality of pseudo-labels on the other hand.The proposed soft sensing method effectively predicts mechanical properties on the real hot rolling dataset,achieving approximately 5%improvement in R~2 compared to traditional supervised learning.展开更多
锂离子电池的荷电状态(state of charge,SOC)在电池均衡、优化能量使用等方面具有重要作用。针对基于模型的SOC估计方法中状态空间方程非线性导致计算量大的问题,提出了使用门控循环单元(gated recurrent units,GRU)软测量SOC,并以此为...锂离子电池的荷电状态(state of charge,SOC)在电池均衡、优化能量使用等方面具有重要作用。针对基于模型的SOC估计方法中状态空间方程非线性导致计算量大的问题,提出了使用门控循环单元(gated recurrent units,GRU)软测量SOC,并以此为观测量构建线性状态空间方程,进而使用卡尔曼滤波(Kalman filter,KF)估计SOC的方法。在随机驾驶循环工况下,所提出方法的SOC估计最大绝对误差为0.017,同时具有较快的估计速度。进一步研究发现,不同充放电倍率下电池模型的参数具有很大差异,导致基于模型的SOC估计方法在复杂情况下的估计精度较低,而所提出的GRU-KF方法因为不需要精确的电池模型,更能适应复杂的工况。展开更多
Humanoid robots have garnered substantial attention recently in both academia and industry.These robots are becoming increasingly sophisticated and intelligent,as seen in health care,education,customer service,logisti...Humanoid robots have garnered substantial attention recently in both academia and industry.These robots are becoming increasingly sophisticated and intelligent,as seen in health care,education,customer service,logistics,security,space exploration,and so forth.Central to these technological advancements is tactile perception,a crucial modality through which humanoid robots exchange information with their external environment,thereby facilitating human‐like behaviors such as object recognition and dexterous manipulation.Texture perception is particularly vital for these tasks,as the surface morphology of objects significantly influences recognition and manipulation abilities.This review addresses the recent progress in tactile sensing and machine learning for texture perception in humanoid robots.We first examine the design and working principles of tactile sensors employed in texture perception,differentiating between touch‐based and sliding‐based approaches.Subsequently,we delve into the machine learning algorithms implemented for texture perception using these tactile sensors.Finally,we discuss the challenges and future opportunities in this evolving field.This review aims to provide insights into the state‐of‐the‐art developments and foster advancements in tactile sensing and machine learning for texture perception in humanoid robotics.展开更多
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
基金supported in part by National Natural Science Foundation of China under Grant 52305304Jilin Youth Growth Technology Project under Grant 20230508147RC+2 种基金the Science and Technology Research Project of Jilin Provincial Education Department(No.JJKH20231193KJ)supported in part by the National Natural Science Foundation of China under Grant 52021003in part by the Natural Science Foundation of Jilin Province under Grant 20210101053JC.
文摘4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.
基金support from the National Science Foundation under Award Nos. EFMA-2318057, ECCS-2339495, ECCS-2334134, ECCS-2216131, and CMMI-2323917。
文摘Soft(flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs, which maximizes data fidelity and minimizes biological interference.Most of the early soft sensors focused on sensing physical signals. Recently, it is becoming a trend that novel soft sensors are developed to sense and monitor biochemical signals in situ in real biological environments, thus providing much more meaningful data for studying fundamental biology and diagnosing diverse health conditions. This is essential to decentralize the healthcare resources towards predictive medicine and better disease management. To meet the requirements of mechanical softness and complex biosensing, unconventional materials, and manufacturing process are demanded in developing biosensors. In this review, we summarize the fundamental approaches and the latest and representative design and fabrication to engineer soft electronics(flexible and stretchable) for wearable and implantable biochemical sensing. We will review the rational design and ingenious integration of stretchable materials, structures, and signal transducers in different application scenarios to fabricate high-performance soft biosensors. Focus is also given to how these novel biosensors can be integrated into diverse important physiological environments and scenarios in situ, such as sweat analysis, wound monitoring, and neurochemical sensing. We also rethink and discuss the current limitations,challenges, and prospects of soft biosensors. This review holds significant importance for researchers and engineers, as it assists in comprehending the overarching trends and pivotal issues within the realm of designing and manufacturing soft electronics for biochemical sensing.
基金Item Sponsored by National Natural Science Foundation of China (50474086,60843007)
文摘Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.
基金This project is supported by Special Foundation for Major State Basic Research of China (No.G1998030415).
文摘Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.
基金Supported by the National Natural Science Foundation of China(61273160)the Fundamental Research Funds for the Central Universities(14CX06067A,13CX05021A)
文摘Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.
基金the support from the National Natural Science Foundation of China(No.21676086).
文摘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.
基金the National Natural Science Foundation of China(Nos.61374110 and 61074060)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20120073110017)
文摘Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.
基金National Natural Science Foundation of China(No.51467008)。
文摘Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.
基金Item Sponsored by National Natural Science Foundation of China(59995440)Doctoral Program of Higher Education Foundation of China(97014515)
文摘Aiming at the water temperature measuring problem for controlled cooling system of rolling plant,a new water temperature measuring method based on soft-sensing method with a water temperature model of on-line self correction parameter was built.A water temperature compensation factor model was also built to improve coiling temperature control precision.It was proved that the model meets production requirements.The soft-sensing technique has extensive applications in the field of metal forming.
文摘Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.
基金supported in part by the National Science Foundation for Young Scientists of China (51705098)。
文摘The sensing capabilities of a soft arm are ofparamount importance to its overall performance as they allow precise control of the soft arm and enhance its interactionwith the surrounding environment. However, the actuationand sensing of a soft arm are not typically integrated into amonolithic structure, which would impede the arm’s movement and restrict its performance and application scope. Toaddress this limitation, this study proposes an innovativemethod for the integrated design of actuator structures andsensing. The proposed method combines the art of kirigamiwith soft robotics technology. In the proposed method, sensorsare embedded in the form of kirigami structures into actuatorsusing laser cutting technology, achieving seamless integrationwith a soft arm. Compared to the traditional amanogawakirigami and fractal-cut kirigami structures, the proposedmiddle-cut kirigami (MCK) structure does not buckle duringstretching and exhibits superior tensile performance. Based onthe MCK structure, an advanced interdigitated capacitivesensor with a high degree of linearity, which can significantlyoutperform traditional kirigami sensors, is developed. Theexperimental results validate the effectiveness of the proposedsoft arm design in actual logistics sorting tasks, demonstratingthat it is capable of accurately sorting objects based on sensorsignals. In addition, the results indicate that the developedcontinuum soft arm and its embedded kirigami sensors havegreat potential in the field of logistics automation sorting.This work provides a promising solution for high-precisionclosed-loop feedback control and environmental interaction ofsoft arms.
基金Projects(61203287,61302138,11126274)supported by the National Natural Science Foundation of ChinaProject(2013CFB414)supported by Natural Science Foundation of Hubei Province,ChinaProject(CUGL130247)supported by the Special Fund for Basic Scientific Research of Central Colleges of China University of Geosciences
文摘A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.
基金supported in part by National Key Research&Development Program of China(2021YFB3301200)in part by the National Natural Science Foundation of China(61933015)。
文摘Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,for which co-training based semi-supervised learning offers a potential solution.So in this paper,a novel soft sensing method for mechanical properties based on improved co-training(ICO)is proposed.Compared with the existing co-training framework,the proposed ICO introduces improvements from the aspects of multiple view partition,confidence estimation,and pseudo-label assignment.Specifically,(ⅰ)in the stage of multiple view partition,ICO integrates metallurgical mechanisms of hot rolling processes and statistical mutual information to achieve a balance between view sufficiency and independence,which improves model performance and interpretability;(ⅱ)in the stage of confidence estimation,ICO evaluates the confidence of unlabeled samples at the cluster level rather than at the level of a single sample,which facilitates the exploration of sample distribution and the selection of representative samples;(ⅲ)in the pseudo-label assignment stage,ICO adopts a safe pseudo-label algorithm(which is called SAFER by its author and originally used for each single sample)to assign pseudo-labels for cluster of samples with the highest confidence determined in the previous step stage,to take advantage of the merit of handling unlabeled samples at the cluster level mentioned above on one hand,and the merit of SAFER in enhancing the quality of pseudo-labels on the other hand.The proposed soft sensing method effectively predicts mechanical properties on the real hot rolling dataset,achieving approximately 5%improvement in R~2 compared to traditional supervised learning.
基金supported by the National Natural Science Foundation of China under Grant No.12302248 and No.12272146the Fundamental Research Funds for the Central Universities under Grant No.2024BRA009the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ23F010015.
文摘Humanoid robots have garnered substantial attention recently in both academia and industry.These robots are becoming increasingly sophisticated and intelligent,as seen in health care,education,customer service,logistics,security,space exploration,and so forth.Central to these technological advancements is tactile perception,a crucial modality through which humanoid robots exchange information with their external environment,thereby facilitating human‐like behaviors such as object recognition and dexterous manipulation.Texture perception is particularly vital for these tasks,as the surface morphology of objects significantly influences recognition and manipulation abilities.This review addresses the recent progress in tactile sensing and machine learning for texture perception in humanoid robots.We first examine the design and working principles of tactile sensors employed in texture perception,differentiating between touch‐based and sliding‐based approaches.Subsequently,we delve into the machine learning algorithms implemented for texture perception using these tactile sensors.Finally,we discuss the challenges and future opportunities in this evolving field.This review aims to provide insights into the state‐of‐the‐art developments and foster advancements in tactile sensing and machine learning for texture perception in humanoid robotics.
