Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery...Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.展开更多
This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers...This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers for any M> 0.Moreover,the limiting behavior of minimizers as M→∞ is also analyzed rigorously.展开更多
In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO a...In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.展开更多
In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded ...In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].展开更多
For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing m...For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.展开更多
Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received in...Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.展开更多
Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the mac...Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.展开更多
In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple e...In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.展开更多
It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly eval...It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.展开更多
BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidat...BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).展开更多
Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small...Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.展开更多
BACKGROUND Thrombocytopenia 2,an autosomal dominant inherited disease characterized by moderate thrombocytopenia,predisposition to myeloid malignancies and normal platelet size and function,can be caused by 5’-untran...BACKGROUND Thrombocytopenia 2,an autosomal dominant inherited disease characterized by moderate thrombocytopenia,predisposition to myeloid malignancies and normal platelet size and function,can be caused by 5’-untranslated region(UTR)point mutations in ankyrin repeat domain containing 26(ANKRD26).Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1)have been identified as negative regulators of ANKRD26.However,the positive regulators of ANKRD26 are still unknown.AIM To prove the positive regulatory effect of GATA binding protein 2(GATA2)on ANKRD26 transcription.METHODS Human induced pluripotent stem cells derived from bone marrow(hiPSC-BM)INTRODUCTION Ankyrin repeat domain containing protein 26(ANKRD26)acts as a regulator of adipogenesis and is involved in the regulation of feeding behavior[1-3].The ANKRD26 gene is located on chromosome 10 and shares regions of homology with the primate-specific gene family POTE.According to the Human Protein Atlas database,the ANKRD26 protein is localized to the Golgi apparatus and vesicles,and its expression can be detected in nearly all human tissues[4].Moreover,UniProt annotation revealed that ANKRD26 is localized in the centrosome and contains coiled-coil domains formed by spectrin helices and ankyrin repeats[5,6].The most common disease related to ANKRD26 is thrombocytopenia 2(THC2),which is a rare autosomal dominant inherited disease characterized by lifelong mild-to-moderate thrombocytopenia and mild bleeding[7-9].Caused by the variants in the 5’-untranslated region(UTR)of ANKRD26,THC2 is defined by a decrease in the number of platelets in circulating blood and results in increased bleeding and decreased clotting ability[8,10].Due to the point mutations that occur in the 5’-UTR of ANKRD26,its negative transcription factors(TFs),Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1),lose their repression effect[11].The persistent expression of ANKRD26 increases the activity of the mitogen activated protein kinase and extracellular signal regulated kinase 1/2 signaling pathways,which are potentially involved in the regulation of thrombopoietin-dependent signaling and further impair proplatelet formation by megakaryocytes(MKs)[11].However,the positive regulators of ANKRD26,which might be associated with THC2 pathology,are still unknown.展开更多
Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.展开更多
Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-c...Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.展开更多
The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching proper...The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching property of antiferroelectric domains.Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains,and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops.Moreover,we introduce charge defects into the sample and numerically investigate their influence.It is also shown that charge defects can induce local ferroelectric domains,which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop.Our results give insights into understanding the antiferroelectric phase transformation and optimizing the energy storage property in experiments.展开更多
From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect o...From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.展开更多
BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function...BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function BET by recognizing acetylated lysine residues,thereby downregulating the expression of MYC.AIM To investigate the inhibitory effect and mechanism of a BET inhibitor on CRC cells.METHODS The effect of the BET inhibitor JAB-8263 on the proliferation of various CRC cell lines was studied by CellTiter-Glo method and colony formation assay.The effect of JAB-8263 on the cell cycle and apoptosis of CRC cells was studied by propidium iodide staining and Annexin V/propidium iodide flow assay,respectively.The effect of JAB-8263 on the expression of c-MYC,p21 and p16 in CRC cells was detected by western blotting assay.The anti-tumor effect of JAB-8263 on CRC cells in vivo and evaluation of the safety of the compound was predicted by constructing a CRC cell animal tumor model.RESULTS JAB-8263 dose-dependently suppressed CRC cell proliferation and colony formation in vitro.The MYC signaling pathway was dose-dependently inhibited by JAB-8263 in human CRC cell lines.JAB-8263 dose-dependently induced cell cycle arrest and apoptosis in the MC38 cell line.SW837 xenograft model was treated with JAB-8263(0.3 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P<0.001).The MC38 syngeneic murine model was treated with JAB-8263(0.2 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P=0.003).CONCLUSION BET could be a potential effective drug target for suppressing CRC growth,and the BET inhibitor JAB-8263 can effectively suppress c-MYC expression and exert anti-tumor activity in CRC models.展开更多
基金Shaanxi Province key Research and Development Plan-Listed project(2022-JBGS-07)。
文摘Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.
基金supported by the Graduate Education Innovation Funds(2022CXZZ088)at Central China Normal University in Chinasupported by the NSFC(12225106,11931012)the Fundamental Research Funds(CCNU22LJ002)for the Central Universities in China。
文摘This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers for any M> 0.Moreover,the limiting behavior of minimizers as M→∞ is also analyzed rigorously.
基金supported by the National Natural Science Foundation of China(12271101)。
文摘In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.
文摘In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].
基金supported in part by the National Natural Science Foundation of China,China(Grant No.52102420)the National Key Research and Development Program of China,China(Grant No.2022YFE0102700)the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。
文摘For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.
基金the Natural Science Foundation of Henan Province(232300420094)the Science and TechnologyResearch Project of Henan Province(222102220092).
文摘Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.
基金Shanxi Scholarship Council of China(2022-141)Fundamental Research Program of Shanxi Province(202203021211096).
文摘Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.
基金Science and Technology Innovation 2030-Major Project of“New Generation Artificial Intelligence”granted by Ministry of Science and Technology,Grant Number 2020AAA0109300.
文摘In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.
基金supported by the National Natural Science Foundation of China (12072365)the Natural Science Foundation of Hunan Province of China (2020JJ4657)。
文摘It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
基金This study was approved by the Clinical Research Ethics Committee of Zhongshan Hospital,Fudan University.
文摘BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).
文摘Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.
基金Supported by General Program of National Natural Science Foundation of China,No.81770197Scientific and Technological Research Major Program of Chongqing Municipal Education Commission,No.KJZD-M202312802+1 种基金Chongqing Natural Science Foundation of China,No.CSTB2022NSCQ-MSX0190,No.CSTB2022NSCQ-MSX0176,and No.cstc2020jcyj-msxmX0051Xinqiao Young Postdoc Talent Incubation Program,No.2022YQB098.
文摘BACKGROUND Thrombocytopenia 2,an autosomal dominant inherited disease characterized by moderate thrombocytopenia,predisposition to myeloid malignancies and normal platelet size and function,can be caused by 5’-untranslated region(UTR)point mutations in ankyrin repeat domain containing 26(ANKRD26).Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1)have been identified as negative regulators of ANKRD26.However,the positive regulators of ANKRD26 are still unknown.AIM To prove the positive regulatory effect of GATA binding protein 2(GATA2)on ANKRD26 transcription.METHODS Human induced pluripotent stem cells derived from bone marrow(hiPSC-BM)INTRODUCTION Ankyrin repeat domain containing protein 26(ANKRD26)acts as a regulator of adipogenesis and is involved in the regulation of feeding behavior[1-3].The ANKRD26 gene is located on chromosome 10 and shares regions of homology with the primate-specific gene family POTE.According to the Human Protein Atlas database,the ANKRD26 protein is localized to the Golgi apparatus and vesicles,and its expression can be detected in nearly all human tissues[4].Moreover,UniProt annotation revealed that ANKRD26 is localized in the centrosome and contains coiled-coil domains formed by spectrin helices and ankyrin repeats[5,6].The most common disease related to ANKRD26 is thrombocytopenia 2(THC2),which is a rare autosomal dominant inherited disease characterized by lifelong mild-to-moderate thrombocytopenia and mild bleeding[7-9].Caused by the variants in the 5’-untranslated region(UTR)of ANKRD26,THC2 is defined by a decrease in the number of platelets in circulating blood and results in increased bleeding and decreased clotting ability[8,10].Due to the point mutations that occur in the 5’-UTR of ANKRD26,its negative transcription factors(TFs),Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1),lose their repression effect[11].The persistent expression of ANKRD26 increases the activity of the mitogen activated protein kinase and extracellular signal regulated kinase 1/2 signaling pathways,which are potentially involved in the regulation of thrombopoietin-dependent signaling and further impair proplatelet formation by megakaryocytes(MKs)[11].However,the positive regulators of ANKRD26,which might be associated with THC2 pathology,are still unknown.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
基金supported by the National Natural Science Foundation of China(NSFC)(U1704158)Henan Province Technologies Research and Development Project of China(212102210103)+1 种基金the NSFC Development Funding of Henan Normal University(2020PL09)the University of Manitoba Research Grants Program(URGP)。
文摘Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.
基金Project supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20190405)the LOEWE program of the State of Hesse,Germany,within the project FLAME(Fermi Level Engineering of Antiferroelectric Materials for Energy Storage and Insulation Systems)。
文摘The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching property of antiferroelectric domains.Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains,and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops.Moreover,we introduce charge defects into the sample and numerically investigate their influence.It is also shown that charge defects can induce local ferroelectric domains,which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop.Our results give insights into understanding the antiferroelectric phase transformation and optimizing the energy storage property in experiments.
基金support provided by the Zhejiang Province Planning Project of Philosophy and Social Science[Grant No.22NDJC107YB]Zhejiang Provincial Natural Science Foundation of China[Grant No.LY21G020009].
文摘From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.
基金Supported by the National Natural Science Foundation of China,No.81871317.
文摘BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function BET by recognizing acetylated lysine residues,thereby downregulating the expression of MYC.AIM To investigate the inhibitory effect and mechanism of a BET inhibitor on CRC cells.METHODS The effect of the BET inhibitor JAB-8263 on the proliferation of various CRC cell lines was studied by CellTiter-Glo method and colony formation assay.The effect of JAB-8263 on the cell cycle and apoptosis of CRC cells was studied by propidium iodide staining and Annexin V/propidium iodide flow assay,respectively.The effect of JAB-8263 on the expression of c-MYC,p21 and p16 in CRC cells was detected by western blotting assay.The anti-tumor effect of JAB-8263 on CRC cells in vivo and evaluation of the safety of the compound was predicted by constructing a CRC cell animal tumor model.RESULTS JAB-8263 dose-dependently suppressed CRC cell proliferation and colony formation in vitro.The MYC signaling pathway was dose-dependently inhibited by JAB-8263 in human CRC cell lines.JAB-8263 dose-dependently induced cell cycle arrest and apoptosis in the MC38 cell line.SW837 xenograft model was treated with JAB-8263(0.3 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P<0.001).The MC38 syngeneic murine model was treated with JAB-8263(0.2 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P=0.003).CONCLUSION BET could be a potential effective drug target for suppressing CRC growth,and the BET inhibitor JAB-8263 can effectively suppress c-MYC expression and exert anti-tumor activity in CRC models.