A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a li...A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.展开更多
A multi-scale numerical method coupled with the reactor,sheath and trench model is constructed to simulate dry etching of SiO_2 in inductively coupled C_4F_8 plasmas.Firstly,ion and neutral particle densities in the r...A multi-scale numerical method coupled with the reactor,sheath and trench model is constructed to simulate dry etching of SiO_2 in inductively coupled C_4F_8 plasmas.Firstly,ion and neutral particle densities in the reactor are decided using the CFD-ACE＋ commercial software.Then,the ion energy and angular distributions（IEDs and IADs） are obtained in the sheath model with the sheath boundary conditions provided with CFD-ACE＋.Finally,the trench profile evolution is simulated in the trench model.What we principally focus on is the effects of the discharge parameters on the etching results.It is found that the discharge parameters,including discharge pressure,radio-frequency（rf） power,gas mixture ratios,bias voltage and frequency,have synergistic effects on IEDs and IADs on the etched material surface,thus further affecting the trench profiles evolution.展开更多
Multi-scales relaxation processes of short fiber of a nematic liquid crystalline copolymer(LCP)in polycarbonate matrix were investigated.First,the structure relaxation of LCP was studied by rheology.The relaxation spe...Multi-scales relaxation processes of short fiber of a nematic liquid crystalline copolymer(LCP)in polycarbonate matrix were investigated.First,the structure relaxation of LCP was studied by rheology.The relaxation spectrum of the nematic liquid crystalline copolymer at 295℃was calculated from the combined dynamic modulus.There are three kinds of relaxation mechanisms for nematic liquid crystalline copotymer:the relaxation of chain orientation,the relaxation of deformed polydomains and the coalescence of pol...展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Multi-scale casting parts are important components of high-end equipment used in the aerospace,automobile manufacturing,shipbuilding,and other industries.Residual features such as parting lines and pouring risers that...Multi-scale casting parts are important components of high-end equipment used in the aerospace,automobile manufacturing,shipbuilding,and other industries.Residual features such as parting lines and pouring risers that inevitably appear during the casting process are random in size,morphology,and distribution.The traditional manual processing method has disadvantages such as low efficiency,high labor intensity,and harsh working environment.Existing machine tool and serial robot grinding/cutting equipment do not easily achieve high-quality and high-efficiency removal of residual features due to poor dexterity and low stiffness,respectively.To address these problems,a five-degree-of-freedom(5-DoF)hybrid grinding/cutting robot with high dexterity and high stiffness is proposed.Based on it,three types of grinding/cutting equipment combined with offline programming,master-slave control,and other technologies are developed to remove the residual features of small,medium,and large casting parts.Finally,the advantages of teleoperation processing and other solutions are elaborated,and the difficulties and challenges are discussed.This paper reviews the grinding/cutting technology and equipment of casting parts and provides a reference for the research on the processing of multi-scale casting parts.展开更多
Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting fo...Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.展开更多
Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in nume...Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.展开更多
Convenience rice has become widely popular due to its easy availability for cooking. This study investigated the starch structure and composition of leachate and the microstructure of reheated convenience rice using n...Convenience rice has become widely popular due to its easy availability for cooking. This study investigated the starch structure and composition of leachate and the microstructure of reheated convenience rice using novel processing technologies: super-heated steaming(SHS), auto-electric cooking(AEC), and pressurized-steam cooking(PSC). Additionally, the effect of two different target water contents(58% and 63%) was also evaluated. The PSC_63% sample had the highest total solids and amylopectin amount in the leachate. The amylopectin amount in the leachate differed significantly based on the targeted water content. Morphological characterization revealed that the swelling of starch and the coated layer on the surface of rice grains were most pronounced in the PSC_63% sample due to the pressure processing. The textural hardness of the AEC_58% sample was much higher than that of the other samples. The PSC_63% sample had the highest textural adhesiveness value, which can be attributed to the highest amylopectin amount in the leachate. Sensory characterization showed that the PSC_63% sample had the highest glossiness, whiteness, moistness, and overall acceptability. The principal component analysis score plots presented substantial differences in the leachate and textural and sensory characteristics of reheated convenience rice among the different processing technologies.展开更多
In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driv...In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.展开更多
文摘A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.
基金supported by National Natural Science Foundation of China(No.11375040)the Important National Science&Technology Specific Project of China(No.2011ZX02403-002)
文摘A multi-scale numerical method coupled with the reactor,sheath and trench model is constructed to simulate dry etching of SiO_2 in inductively coupled C_4F_8 plasmas.Firstly,ion and neutral particle densities in the reactor are decided using the CFD-ACE＋ commercial software.Then,the ion energy and angular distributions（IEDs and IADs） are obtained in the sheath model with the sheath boundary conditions provided with CFD-ACE＋.Finally,the trench profile evolution is simulated in the trench model.What we principally focus on is the effects of the discharge parameters on the etching results.It is found that the discharge parameters,including discharge pressure,radio-frequency（rf） power,gas mixture ratios,bias voltage and frequency,have synergistic effects on IEDs and IADs on the etched material surface,thus further affecting the trench profiles evolution.
基金This work was financially supported by the National Natural Science Foundation of China(Nos. 20174024,20204007 and 50290090).
文摘Multi-scales relaxation processes of short fiber of a nematic liquid crystalline copolymer(LCP)in polycarbonate matrix were investigated.First,the structure relaxation of LCP was studied by rheology.The relaxation spectrum of the nematic liquid crystalline copolymer at 295℃was calculated from the combined dynamic modulus.There are three kinds of relaxation mechanisms for nematic liquid crystalline copotymer:the relaxation of chain orientation,the relaxation of deformed polydomains and the coalescence of pol...
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金National Natural Science Foundation of China(Grant Nos.51875391,51875392)Tianjin Science and Technology Planning Project(Grant Nos.18PTLCSY00080,20YDLZGX00290)State Key Laboratory of Digital Manufacturing Equipment and Technology(Grant No.DMETKF2022007).
文摘Multi-scale casting parts are important components of high-end equipment used in the aerospace,automobile manufacturing,shipbuilding,and other industries.Residual features such as parting lines and pouring risers that inevitably appear during the casting process are random in size,morphology,and distribution.The traditional manual processing method has disadvantages such as low efficiency,high labor intensity,and harsh working environment.Existing machine tool and serial robot grinding/cutting equipment do not easily achieve high-quality and high-efficiency removal of residual features due to poor dexterity and low stiffness,respectively.To address these problems,a five-degree-of-freedom(5-DoF)hybrid grinding/cutting robot with high dexterity and high stiffness is proposed.Based on it,three types of grinding/cutting equipment combined with offline programming,master-slave control,and other technologies are developed to remove the residual features of small,medium,and large casting parts.Finally,the advantages of teleoperation processing and other solutions are elaborated,and the difficulties and challenges are discussed.This paper reviews the grinding/cutting technology and equipment of casting parts and provides a reference for the research on the processing of multi-scale casting parts.
基金supported by Western Research Interdisciplinary Initiative R6259A03.
文摘Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.
文摘Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.
基金supported by the High Value-added Food Technology Development Program in Korea (Grant No. 323002-4)the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry, Republic of Korea。
文摘Convenience rice has become widely popular due to its easy availability for cooking. This study investigated the starch structure and composition of leachate and the microstructure of reheated convenience rice using novel processing technologies: super-heated steaming(SHS), auto-electric cooking(AEC), and pressurized-steam cooking(PSC). Additionally, the effect of two different target water contents(58% and 63%) was also evaluated. The PSC_63% sample had the highest total solids and amylopectin amount in the leachate. The amylopectin amount in the leachate differed significantly based on the targeted water content. Morphological characterization revealed that the swelling of starch and the coated layer on the surface of rice grains were most pronounced in the PSC_63% sample due to the pressure processing. The textural hardness of the AEC_58% sample was much higher than that of the other samples. The PSC_63% sample had the highest textural adhesiveness value, which can be attributed to the highest amylopectin amount in the leachate. Sensory characterization showed that the PSC_63% sample had the highest glossiness, whiteness, moistness, and overall acceptability. The principal component analysis score plots presented substantial differences in the leachate and textural and sensory characteristics of reheated convenience rice among the different processing technologies.
基金supported by National Natural Science Foundation of China(No.61873142)the Science and Technology Research Program of the Chongqing Municipal Education Commission,China(Nos.KJZD-K202201901,KJQN202201109,KJQN202101904,KJQN202001903 and CXQT21035)+2 种基金the Scientific Research Foundation of Chongqing University of Technology,China(No.2019ZD76)the Scientific Research Foundation of Chongqing Institute of Engineering,China(No.2020xzky05)the Chongqing Municipal Natural Science Foundation,China(No.cstc2020jcyj-msxmX0666).
文摘In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.
