Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Ufmylation is an ubiquitin-like post-translational modification characterized by the covalent binding of mature UFM1 to target proteins.Although the consequences of ufmylation on target proteins are not fully understo...Ufmylation is an ubiquitin-like post-translational modification characterized by the covalent binding of mature UFM1 to target proteins.Although the consequences of ufmylation on target proteins are not fully understood,its importance is evident from the disorders resulting from its dysfunction.Numerous case reports have established a link between biallelic loss-of-function and/or hypomorphic variants in ufmylation-related genes and a spectrum of pediatric neurodevelopmental disorders.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurode...The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.展开更多
LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quad...LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.展开更多
Recently,the evolution of Generative Adversarial Networks(GANs)has embarked on a journey of revolutionizing the field of artificial and computational intelligence.To improve the generating ability of GANs,various loss...Recently,the evolution of Generative Adversarial Networks(GANs)has embarked on a journey of revolutionizing the field of artificial and computational intelligence.To improve the generating ability of GANs,various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples,and the effectiveness of the loss functions in improving the generating ability of GANs.In this paper,we present a detailed survey for the loss functions used in GANs,and provide a critical analysis on the pros and cons of these loss functions.First,the basic theory of GANs along with the training mechanism are introduced.Then,the most commonly used loss functions in GANs are introduced and analyzed.Third,the experimental analyses and comparison of these loss functions are presented in different GAN architectures.Finally,several suggestions on choosing suitable loss functions for image synthesis tasks are given.展开更多
The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence...The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.展开更多
Much research effort has been devoted to economic design of X ＆ S control charts,however,there are some problems in usual methods.On the one hand,it is difficult to estimate the relationship between costs and other m...Much research effort has been devoted to economic design of X ＆ S control charts,however,there are some problems in usual methods.On the one hand,it is difficult to estimate the relationship between costs and other model parameters,so the economic design method is often not effective in producing charts that can quickly detect small shifts before substantial losses occur;on the other hand,in many cases,only one type of process shift or only one pair of process shifts are taken into consideration,which may not correctly reflect the actual process conditions.To improve the behavior of economic design of control chart,a cost ＆ loss model with Taguchi＇s loss function for the economic design of X ＆ S control charts is embellished,which is regarded as an optimization problem with multiple statistical constraints.The optimization design is also carried out based on a number of combinations of process shifts collected from the field operation of the conventional control charts,thus more hidden information about the shift combinations is mined and employed to the optimization design of control charts.At the same time,an improved particle swarm optimization（IPSO） is developed to solve such an optimization problem in design of X ＆ S control charts,IPSO is first tested for several benchmark problems from the literature and evaluated with standard performance metrics.Experimental results show that the proposed algorithm has significant advantages on obtaining the optimal design parameters of the charts.The proposed method can substantially reduce the total cost（or loss） of the control charts,and it will be a promising tool for economic design of control charts.展开更多
The effective energy loss functions for Al have been derived from differential i nverse inelastic mean free path based on the extended Landau approach. It has be en revealed that the effective energy loss function is ...The effective energy loss functions for Al have been derived from differential i nverse inelastic mean free path based on the extended Landau approach. It has be en revealed that the effective energy loss function is very close in value to th e theoretical surface energy loss function in the lower energy loss region but g radually approaches the theoretical bulk energy loss function in the higher ener gy loss region. Moreover, the intensity corresponding to surface excitation in e ffective energy loss functions decreases with the increase of primary electron e nergy. These facts show that the present effective energy loss function describe s not only surface excitation but also bulk excitation. At last, REELS spectra s imulated by Monte Carlo method based on use of the effective energy loss functio ns has reproduced the experimental REELS spectra with considerable success.展开更多
Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be di...Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be difficult. This paper investigates Neyman- Pearson classification with convex loss function in the arbitrary class of real measurable functions. A general condition is given under which Neyman-Pearson classification with convex loss function has the same classifier as that with indicator loss function. We give analysis to NP-ERM with convex loss function and prove it＇s performance guarantees. An example of complexity penalty pair about convex loss function risk in terms of Rademacher averages is studied, which produces a tight PAC bound of the NP-ERM with convex loss function.展开更多
We present a fitting calculation of energy-loss function for 26 bulk materials, including 18 pure elements （Ag, A1, Au, C, Co, Cs, Cu, Er, Fe, Ge, Mg, Mo, Nb, Ni, Pd, Pt, Si, Te） and 8 compounds （AgCl, Al2O3, AlAs,...We present a fitting calculation of energy-loss function for 26 bulk materials, including 18 pure elements （Ag, A1, Au, C, Co, Cs, Cu, Er, Fe, Ge, Mg, Mo, Nb, Ni, Pd, Pt, Si, Te） and 8 compounds （AgCl, Al2O3, AlAs, CdS, SiO2, ZnS, ZnSe, ZnTe） for application to surface electron spectroscopy analysis. The experimental energy-loss function, which is derived from measured optical data, is fitted into a finite sum of formula based on the Drude-Lindhard dielectric model. By checking the oscillator strength-sum and perfect- screening-sum rules, we have validated the high accuracy of the fitting results. Further-more, based on the fitted parameters, the simulated reflection electron energy-loss spec- troscopy （REELS） spectrum shows a good agreement with experiment. The calculated fitting parameters of energy loss function are stored in an open and online database at http：//micro.ustc.edu.cn/ELF/ELF.html.展开更多
The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology pro...The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.展开更多
In this paper, the admissibility of multivariate linear regression coefficient with respect to an inequality constraint under balanced loss function is investigated. Necessary and sufficient conditions for admissible ...In this paper, the admissibility of multivariate linear regression coefficient with respect to an inequality constraint under balanced loss function is investigated. Necessary and sufficient conditions for admissible homogeneous and inhomogeneous linear estimators are obtained, respectively.展开更多
In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric qu...In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.展开更多
With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the...With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance.So far,one of our mainstream loss function optimization methods is to add penalty terms,such as orthogonal loss,to further constrain the original loss function.The other is to optimize using the loss based on angular/cosine margin.The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss.In this paper,based on the three methods with good practical performance and the joint optimization method,various loss functions are thoroughly reviewed.展开更多
Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the r...Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the remote sensing technic,large-scale satellite data become available for surface monitoring.Due to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges.Recent developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing segmentation.However,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high robustness.Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative learning.There are three improvements in this article.First,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the input.Second,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss function.Third,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training.We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve.The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorithms.As a result of this investigation,the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.展开更多
Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivatio...Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivation of touch and proprioceptive inputs consequently impair skilled hand use.展开更多
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performan...In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.展开更多
To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on...To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A threelevel function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.展开更多
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
文摘Ufmylation is an ubiquitin-like post-translational modification characterized by the covalent binding of mature UFM1 to target proteins.Although the consequences of ufmylation on target proteins are not fully understood,its importance is evident from the disorders resulting from its dysfunction.Numerous case reports have established a link between biallelic loss-of-function and/or hypomorphic variants in ufmylation-related genes and a spectrum of pediatric neurodevelopmental disorders.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
文摘The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.
基金Supported by the NNSF of China(71001046)Supported by the NSF of Jiangxi Province(20114BAB211004)
文摘LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.
文摘Recently,the evolution of Generative Adversarial Networks(GANs)has embarked on a journey of revolutionizing the field of artificial and computational intelligence.To improve the generating ability of GANs,various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples,and the effectiveness of the loss functions in improving the generating ability of GANs.In this paper,we present a detailed survey for the loss functions used in GANs,and provide a critical analysis on the pros and cons of these loss functions.First,the basic theory of GANs along with the training mechanism are introduced.Then,the most commonly used loss functions in GANs are introduced and analyzed.Third,the experimental analyses and comparison of these loss functions are presented in different GAN architectures.Finally,several suggestions on choosing suitable loss functions for image synthesis tasks are given.
文摘The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.
基金supported by Defense Industrial Technology Development Program of China (Grant No. A2520110003)
文摘Much research effort has been devoted to economic design of X ＆ S control charts,however,there are some problems in usual methods.On the one hand,it is difficult to estimate the relationship between costs and other model parameters,so the economic design method is often not effective in producing charts that can quickly detect small shifts before substantial losses occur;on the other hand,in many cases,only one type of process shift or only one pair of process shifts are taken into consideration,which may not correctly reflect the actual process conditions.To improve the behavior of economic design of control chart,a cost ＆ loss model with Taguchi＇s loss function for the economic design of X ＆ S control charts is embellished,which is regarded as an optimization problem with multiple statistical constraints.The optimization design is also carried out based on a number of combinations of process shifts collected from the field operation of the conventional control charts,thus more hidden information about the shift combinations is mined and employed to the optimization design of control charts.At the same time,an improved particle swarm optimization（IPSO） is developed to solve such an optimization problem in design of X ＆ S control charts,IPSO is first tested for several benchmark problems from the literature and evaluated with standard performance metrics.Experimental results show that the proposed algorithm has significant advantages on obtaining the optimal design parameters of the charts.The proposed method can substantially reduce the total cost（or loss） of the control charts,and it will be a promising tool for economic design of control charts.
基金This work was supported by the National Natural Science Foundation of China(No.10025420,No.20075026,No.60306006 and No.90206009)the post-doctoral fellowship provided by a Grant-in-Aid for Creative Scientific Research of Japanese govermment(No.13GS0022).The authors would also like to thank Dr.H.Yoshikawa,National Institute for Materials Science of Japan,and Dr.T.Nagatomi,Osaka University,for their helpful comments.
文摘The effective energy loss functions for Al have been derived from differential i nverse inelastic mean free path based on the extended Landau approach. It has be en revealed that the effective energy loss function is very close in value to th e theoretical surface energy loss function in the lower energy loss region but g radually approaches the theoretical bulk energy loss function in the higher ener gy loss region. Moreover, the intensity corresponding to surface excitation in e ffective energy loss functions decreases with the increase of primary electron e nergy. These facts show that the present effective energy loss function describe s not only surface excitation but also bulk excitation. At last, REELS spectra s imulated by Monte Carlo method based on use of the effective energy loss functio ns has reproduced the experimental REELS spectra with considerable success.
基金This is a Plenary Report on the International Symposium on Approximation Theory and Remote SensingApplications held in Kunming, China in April 2006Supported in part by NSF of China under grants 10571010 , 10171007 and Startup Grant for Doctoral Researchof Beijing University of Technology
文摘Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be difficult. This paper investigates Neyman- Pearson classification with convex loss function in the arbitrary class of real measurable functions. A general condition is given under which Neyman-Pearson classification with convex loss function has the same classifier as that with indicator loss function. We give analysis to NP-ERM with convex loss function and prove it＇s performance guarantees. An example of complexity penalty pair about convex loss function risk in terms of Rademacher averages is studied, which produces a tight PAC bound of the NP-ERM with convex loss function.
文摘We present a fitting calculation of energy-loss function for 26 bulk materials, including 18 pure elements （Ag, A1, Au, C, Co, Cs, Cu, Er, Fe, Ge, Mg, Mo, Nb, Ni, Pd, Pt, Si, Te） and 8 compounds （AgCl, Al2O3, AlAs, CdS, SiO2, ZnS, ZnSe, ZnTe） for application to surface electron spectroscopy analysis. The experimental energy-loss function, which is derived from measured optical data, is fitted into a finite sum of formula based on the Drude-Lindhard dielectric model. By checking the oscillator strength-sum and perfect- screening-sum rules, we have validated the high accuracy of the fitting results. Further-more, based on the fitted parameters, the simulated reflection electron energy-loss spec- troscopy （REELS） spectrum shows a good agreement with experiment. The calculated fitting parameters of energy loss function are stored in an open and online database at http：//micro.ustc.edu.cn/ELF/ELF.html.
基金Jilin Science and Technology Development Plan Project(No.20200403075SF)Doctoral Research Start-Up Fund of Northeast Electric Power University(No.BSJXM-2018202).
文摘The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.
基金Supported by the National Natural Science Foundation of China (Grant No.11201005)the Natural Science Foundation of Anhui Province (Grant Nos.1308085QA13+2 种基金1208085MA11)the Key Project of Natural Science Foundation of Universities in Anhui Province (Grant No.KJ2012A135)the Key Project of Distinguished Young Talents of Universities in Anhui Province (Grant No.2012SQRL028ZD)
文摘In this paper, the admissibility of multivariate linear regression coefficient with respect to an inequality constraint under balanced loss function is investigated. Necessary and sufficient conditions for admissible homogeneous and inhomogeneous linear estimators are obtained, respectively.
基金Supported by the National Natural Science Foundation of China（11271189） Supported by the Scientific Research Innovation Project of Jiangsu Province（KYZZ116_0175）
文摘In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.41875184)Innovation Team of“Six Talent Peaks”In Jiangsu Province(Grant No.TD-XYDXX-004).
文摘With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance.So far,one of our mainstream loss function optimization methods is to add penalty terms,such as orthogonal loss,to further constrain the original loss function.The other is to optimize using the loss based on angular/cosine margin.The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss.In this paper,based on the three methods with good practical performance and the joint optimization method,various loss functions are thoroughly reviewed.
基金supported by the following funds:Basic Research Program of Qinghai Province under Grants No.2020-ZJ-709National Key R&D Program of China (2018YFF01010100)+1 种基金Natural Science Foundation of Beijing (4212001)Advanced information network Beijing laboratory (PXM2019_014204_500029).
文摘Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the remote sensing technic,large-scale satellite data become available for surface monitoring.Due to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges.Recent developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing segmentation.However,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high robustness.Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative learning.There are three improvements in this article.First,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the input.Second,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss function.Third,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training.We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve.The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorithms.As a result of this investigation,the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.
基金supported by NIH grants NS067017 to HXQNS16446 to JHK
文摘Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivation of touch and proprioceptive inputs consequently impair skilled hand use.
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
基金supported by the National Key Research and Development Program of China(Grant numbers:2021YFF0901705,2021YFF0901700)the State Key Laboratory of Media Convergence and Communication,Communication University of China+1 种基金the Fundamental Research Funds for the Central Universitiesthe High-Quality and Cutting-Edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China).
文摘In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.
基金The National Key Research and Development Program of China(No.2023YFC3805003)。
文摘To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A threelevel function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.
