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DeepClassifier:A Data Sampling-Based Hybrid BiLSTM-BiGRU Neural Network for Enhanced Type 2 Diabetes Prediction 认领 引用
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作者 Abdullahi Abubakar Imam Sahalu Balarabe Junaidu +9 位作者 Hussaini Mamman Ganesh Kumar Abdullateef Oluwagbemiga Balogun Sunder Ali Khowaja Shuib Basri Luiz Fernando Capretz Asmah Husaini Hanif Abdul Rahman Usman Ali Fatoumatta Conteh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期1017-1049,共33页
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O... Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics. 展开更多
关键词 Diabetes deep learning prediction BiLSTM BiGRU classification data sampling
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ISTIRDA:An Efficient Data Availability Sampling Scheme for Lightweight Nodes in Blockchain 认领 引用
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作者 Jiaxi Wang Wenbo Sun +3 位作者 Ziyuan Zhou Shihua Wu Jiang Xu Shan Ji 《Computers, Materials & Continua》 SCIE EI 2026年第4期685-700,共16页
Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re... Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes. 展开更多
关键词 Blockchain scalability data availability sampling lightweight nodes
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Cooperative Iteration Matching Method for Aligning Samples from Heterogeneous Industrial Datasets 认领 引用
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作者 LI Han SHI Guohong +1 位作者 LIU Zhao ZHU Ping 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期375-384,共10页
Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining s... Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method. 展开更多
关键词 dynamic time warping mismatched samples sample alignment industrial data data missing
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A two-sample KS test with m out of n bootstrap for massive data 认领 引用
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作者 XIE Xiaoyue TIAN Xinyu SHI Jian 《纯粹数学与应用数学》 2025年第4期744-760,共17页
In the era of big data,traditional statistical inference methods are faced with great challenges.Taking the two-sample distribution test scenario of big data as an example,this paper proposes the BB-KS test based on m... In the era of big data,traditional statistical inference methods are faced with great challenges.Taking the two-sample distribution test scenario of big data as an example,this paper proposes the BB-KS test based on m out of n bootstrap for solving a single-machine memory and computing constraints.It is verified to the feasibility and effectiveness of the proposed test method through theoretical analysis and numerical simulation.The results show that the BB-KS test can improve the calculation efficiency of the test to a certain extent in the single machine scenario. 展开更多
关键词 massive data two sample test the BB-KS method m out of n bootstrap
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Fuzzy data envelopment analysis approach based on sample decision making units 认领 引用 被引量：12
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作者 Muren Zhanxin Ma Wei Cui 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期399-407,共9页
The conventional data envelopment analysis （DEA） measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs ty... The conventional data envelopment analysis （DEA） measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit （DMU） with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit （FSDEA）. Five eval- uation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches. 展开更多
关键词 fuzzy mathematical programming sample decision making unit fuzzy data envelopment analysis efficiency α-cut.
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Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions 认领 引用
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作者 Qiang Ma Zhuopei Wei +2 位作者 Kai Yang Long Tian Zepeng Li 《Structural Durability & Health Monitoring》 EI 2025年第4期1011-1035,共25页
An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra... An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance. 展开更多
关键词 Deep learning Wasserstein deep convolutional generative adversarial network small sample learning feature fusion multidimensional data enhancement small sample fault diagnosis
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Data processing of small samples based on grey distance information approach 认领 引用 被引量：15
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作者 Ke Hongfa Chen Yongguang Liu Yi 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第2期281-289,共9页
Data processing of small samples is an important and valuable research problem in the electronic equipment test.Because it is difficult and complex to determine the probability distribution of small samples,it is diff... Data processing of small samples is an important and valuable research problem in the electronic equipment test.Because it is difficult and complex to determine the probability distribution of small samples,it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty.Using the grey relational theory and the norm theory,the grey distance information approach,which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples,is proposed in this article.The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples,with their characteristics and algorithms,are introduced.The correlative problems,including the algorithm of estimated value,the standard deviation,and the acceptance and rejection criteria of the samples and estimated results,are also proposed.Moreover,the information whitening ratio is introduced to select the weight algorithm and to compare the different samples.Several examples are given to demonstrate the application of the proposed approach.The examples show that the proposed approach,which has no demand for the probability distribution of small samples,is feasible and effective. 展开更多
关键词 Data processing Grey theory Norm theory,Small samples Uncertainty assessments Grey distance measure,Information whitening ratio
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Progressive prediction method for failure data with small sample size 认领 引用 被引量：2
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作者 WANG Zhi-hua FU Hui-min LIU Cheng-rui 《航空动力学报》 EI CAS CSCD 北大核心 2011年第9期2049-2053,共5页
The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and co... The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and confidence limits formula of the progressive auto regressive(PAR) method were discussed in great detail.PAR model not only inherits the simple linear features of auto regressive(AR) model,but also has applicability for nonlinear systems.An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors.Forecasting results of PAR model were compared with auto regressive moving average(ARMA) model,and it can be seen that the PAR method can be considered good and shows a promise for future applications. 展开更多
关键词 failure data forecast system reliability small sample progressive prediction nonlinear system
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Model-data-driven seismic inversion method based on small sample data 认领 引用 被引量：4
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作者 LIU Jinshui SUN Yuhang LIU Yang 《Petroleum Exploration and Development》 SCIE 2022年第5期1046-1055,共10页
As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this prob... As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data. 展开更多
关键词 small sample data space-variant objective function model-data-driven neural network seismic AVO inversion thin interbedded sandstone identification Paleocene Lishui sag
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CONTROL CHAOS IN TRANSITION SYSTEM USING SAMPLED-DATA FEEDBACK 认领 引用 被引量：2
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作者 陆君安 谢进 +1 位作者 吕金虎 陈士华 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2003年第11期1309-1315,共7页
The method for controlling chaotic transition system was investigated using sampled- data . The output of chaotic transition system was sampled at a given sampling rate , then the sampled output was used by a feedback... The method for controlling chaotic transition system was investigated using sampled- data . The output of chaotic transition system was sampled at a given sampling rate , then the sampled output was used by a feedbacks subsystem to construct a control signal for controlling chaotic transition system to the origin . Numerical simulations are presented to show the effectiveness and feasibility of the developed controller. 展开更多
关键词 sampled-data feedback transition system control
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A Hybrid System Approach to Robust Fault Detection for a Class of Sampled-data Systems 认领 引用 被引量：7
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作者 QIU Ai-Bing WEN Cheng-Lin JIANG Bin 《自动化学报》 EI CAS 北大核心 2010年第8期1182-1188,共7页
In this paper,a novel direct design methodology of robust fault detection for a class of sampled-data systems with both continuous-time process noise and discrete-time measurement noise is presented.First,by using a l... In this paper,a novel direct design methodology of robust fault detection for a class of sampled-data systems with both continuous-time process noise and discrete-time measurement noise is presented.First,by using a linear system with finite discrete jumps as residual generator,the design of robust fault detection filter is formulated as a sampled-data filtering problem.Then,a bounded real lemma for the linear system with finite discrete jumps is developed in terms of linear matrix inequalities （LMIs）.Based on this,a sufficient condition for the existence of the fault detection filter as well as the design parameters is derived.Furthermore,the case of sampled-data systems with model uncertainties is extended.The designed fault detection filter cannot only make the error between residual and weighted fault as small as possible but also exhibit robustness to all uncertainties including continuous-time process noise,discrete-time measurement noise,and model uncertainties.Finally,simulation results are provided to demonstrate the feasibility of the proposed method. 展开更多
关键词 鲁棒故障检测 自动化系统 设计方案 采样数据
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Sampled-data modeling and dynamical effect of output-capacitor time-constant for valley voltage-mode controlled buck-boost converter 认领 引用 被引量：5
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作者 周述晗 周国华 +2 位作者 曾绍桓 冷敏瑞 徐顺刚 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第11期515-525,共11页
By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering... By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering the fact that the increasing and decreasing slopes of the inductor current are assumed to be constant during each switching cycle, an especial sampleddata model of valley voltage-mode controlled buck-boost converter is established. Based on this model, the dynamical effect of an output-capacitor time-constant on the valley voltage-mode controlled buck-boost converter is revealed and analyzed via the bifurcation diagrams, the movements of eigenvalues, the Lyapunov exponent spectra, the boundary equations,and the operating-state regions. It is found that with gradual reduction of output-capacitor time-constant, the buck-boost converter in continuous conduction mode（CCM） shows the evolutive dynamic behavior from period-1 to period-2, period-4, period-8, chaos, and invalid state. The stability boundary and the invalidated boundary are derived theoretically by stability analysis, where the stable state of valley voltage-mode controlled buck-boost converter can enter into an unstable state, and the converter can shift from the operation region to a forbidden region. These results verified by time-domain waveforms and phase portraits of both simulation and experiment indicate that the sampled-data model is correct and the time constant of the output capacitor is a critical factor for valley voltage-mode controlled buck-boost converter, which has a significant effect on the dynamics as well as control stability. 展开更多
关键词 buck-boost converter valley voltage-mode control sampled-data modeling dynamics
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ADS: Adaptive Dataset Selection for Fine-Tuning in Anomalous Text 认领 引用
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作者 Xiaoyong Zhao Jiamin Wu Lei Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期770-784,共15页
With the continuous improvement of the performance of large language models,how to further enhance their ability in complex tasks has become a key issue.The task of abnormal text detection poses a challenge to the mod... With the continuous improvement of the performance of large language models,how to further enhance their ability in complex tasks has become a key issue.The task of abnormal text detection poses a challenge to the model in identifying non-standard semantics due to its semantic complexity and high-risk features.However,existing fine-tuning methods rely heavily on static data selection strategies,making it difficult to adapt to the dynamic evolution of model capabilities,resulting in low training efficiency.This article proposes ADS(Adaptive Dataset Selection),an adaptive framework for selecting data in anomaly text detection.ADS performs model-aware data selection prior to fine-tuning,adapting the initial state of pre-trained language models by selecting samples that are most informative for the target anomaly detection task.Empirical results on mainstream large language model architectures show that ADS significantly compresses data size while still outperforming existing static strategies and mainstream compression methods.When using only 1000 fine-tuning samples,ADS achieves a 92%F1 score,with an accuracy improvement of over 22%compared to the baseline,demonstrating excellent performance.This study proposes an efficient data selection mechanism from the perspective of model capability and dynamic adaptation of data,providing theoretical support and a practical path for fine-tuning large models in low-resource scenarios. 展开更多
关键词 Adaptive dataset selection anomalous text detection fine-tuning large language models dynamic sample optimization data diversity
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Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements 认领 引用 被引量：2
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作者 Shuang-Shuang Wu Zhi-Hai Wu +1 位作者 Li Peng Lin-Bo Xie 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第1期591-596,共6页
This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to parti... This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results. 展开更多
关键词 asymptotic bounded consensus tracking multi-agent systems without velocity measurements sampled-data control
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Power Analysis and Sample Size Determination for Crossover Trials with Application to Bioequivalence Assessment of Topical Ophthalmic Drugs Using Serial Sampling Pharmacokinetic Data 认领 引用
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作者 YU Yong Pei YAN Xiao Yan +1 位作者 YAO Chen XIA Jie Lai 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2019年第8期614-623,共10页
Objective To develop methods for determining a suitable sample size for bioequivalence assessment of generic topical ophthalmic drugs using crossover design with serial sampling schemes.Methods The power functions of ... Objective To develop methods for determining a suitable sample size for bioequivalence assessment of generic topical ophthalmic drugs using crossover design with serial sampling schemes.Methods The power functions of the Fieller-type confidence interval and the asymptotic confidence interval in crossover designs with serial-sampling data are here derived.Simulation studies were conducted to evaluate the derived power functions.Results Simulation studies show that two power functions can provide precise power estimates when normality assumptions are satisfied and yield conservative estimates of power in cases when data are log-normally distributed.The intra-correlation showed a positive correlation with the power of the bioequivalence test.When the expected ratio of the AUCs was less than or equal to 1, the power of the Fieller-type confidence interval was larger than the asymptotic confidence interval.If the expected ratio of the AUCs was larger than 1, the asymptotic confidence interval had greater power.Sample size can be calculated through numerical iteration with the derived power functions.Conclusion The Fieller-type power function and the asymptotic power function can be used to determine sample sizes of crossover trials for bioequivalence assessment of topical ophthalmic drugs. 展开更多
关键词 Serial-sampling data Crossover design Topical ophthalmic drug Bioequivalence Sample size
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Physically-consistent-WGAN based small sample fault diagnosis for industrial processes 认领 引用 被引量：1
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作者 Siyu Tang Hongbo Shi +2 位作者 Bing Song Yang Tao Shuai Tan 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第2期163-174,共12页
In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversa... In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods. 展开更多
关键词 Chemical processes Fault diagnosis Physical consistency Generative adversarial networks Small sample data
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Sensor Fault Diagnosis Observer Design for Linear Sampled-Data Descriptor System with Time-Vary Delay 认领 引用 被引量：1
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作者 Mao Wang Tiantian Liang Zhenhua Zhou 《Journal of Harbin Institute of Technology(New Series)》 CAS 2019年第6期8-18,共11页
In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model... In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model with time-vary delay is proposed and transformed into a discrete-time non-singular one. Then, a robust sensor fault diagnosis observer is proposed based on the state estimation error and the measurement residual, this observer can guarantee the robustness of the residual against the augmented disturbance and the sensor fault, which means the H∞ performance index is satisfied. As the confining matrix of the designed observer parameters does not meet the Linear Matrix Inequality (LMI), a cone complementary linearization (CCL) algorithm is proposed to solve this problem. The decision logic of the residual is obtained by the residual evaluation function. Simulation results show the effectiveness of the method. 展开更多
关键词 descriptor system sampled-data system time-vary delay sensor fault diagnosis observer design
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keffuncertainty quantification and analysis due to nuclear data during the full lifetime burnup calculation for a small-sized prismatic high temperature gas-cooled reactor 认领 引用 被引量：8
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作者 Rong-Rui Yang Yuan Yuan +2 位作者 Chen Hao Ji Ma Guang-Hao Liu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2021年第11期105-118,共14页
To benefit from recent advances in modeling and computational algorithms,as well as the availability of new covariance data,sensitivity and uncertainty analyses are needed to quantify the impact of uncertain sources o... To benefit from recent advances in modeling and computational algorithms,as well as the availability of new covariance data,sensitivity and uncertainty analyses are needed to quantify the impact of uncertain sources on the design parameters of small prismatic high-temperature gascooled reactors(HTGRs).In particular,the contribution of nuclear data to the keffuncertainty is an important part of the uncertainty analysis of small-sized HTGR physical calculations.In this study,a small-sized HTGR designed by China Nuclear Power Engineering Co.,Ltd.was selected for keffuncertainty analysis during full lifetime burnup calculations.Models of the cold zero power(CZP)condition and full lifetime burnup process were constructed using the Reactor Monte Carlo Code RMC for neutron transport calculation,depletion calculation,and sensitivity and uncertainty analysis.For the sensitivity analysis,the Contribution-Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance Characterization(CLUTCH)method was applied to obtain sensitive information,and the "sandwich" method was used to quantify the keffuncertainty.We also compared the keffuncertainties to other typical reactors.Our results show that 235U is the largest contributor to keffuncertainty for both the CZP and depletion conditions,while the contribution of 239Pu is not very significant because of the design of low discharge burnup.It is worth noting that the radioactive capture reaction of 28Si significantly contributes to the keffuncertainty owing to its specific fuel design.However,the keffuncertainty during the full lifetime depletion process was relatively stable,only increasing by 1.12%owing to the low discharge burnup design of small-sized HTGRs.These numerical results are beneficial for neutronics design and core parameters optimization in further uncertainty propagation and quantification study for small-sized HTGR. 展开更多
关键词 Small-sized HTGR SU analysis Nuclear data Burnup
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Irregularly seismic data interpolation based on deep learning with integrated channel-spatial attention mechanism 认领 引用
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作者 Chao Ma Jian-Ping Huang +3 位作者 Zi-Xuan Qiao San-Fu Li Wen-Sheng Duan Gang-Lin Lei 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1182-1196,共15页
To address the challenges of irregular sampling and insufficient spatial sampling in field seismic data,this study proposed a deep learning-based interpolation method incorporating dual channel spatial attention mecha... To address the challenges of irregular sampling and insufficient spatial sampling in field seismic data,this study proposed a deep learning-based interpolation method incorporating dual channel spatial attention mechanisms(CSAM).The proposed model establishes a collaborative framework of channel and spatial attention,enhancing feature representation by establishing connections between local reflection characteristics and global structural features.The performance of the method was evaluated through synthetic data experiments,including sparsity sensitivity tests,noise sensitivity tests,and field data validation,using metrics such as signal to noise ratio(SNR),mean absolute error(MAE),and structural similarity index(SSIM).Comparative analyses were conducted with Fourier projection onto convex sets(Fourierpocs),the classic U-net,and the efficient channel attention U-net(ECAUnet).Results demonstrate that the proposed method outperforms existing methods in reconstructing seismic reflection events and preserving amplitude fidelity,particularly in scenarios with extensive random data missing. 展开更多
关键词 Seismic data Deep learning Irregular sampling Channel-spatial attention mechanism Interpolation
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Machine learning for adsorption-related parameters prediction of electronic specialty gases:DFT-based dataset construction and balanced data augmentation 认领 引用
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作者 Zhikang Wu Ying Wu +4 位作者 Guang Miao Runze Chen Lingjun Ma Hongxia Xi Jing Xiao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期261-271,共11页
Electronic specialty gases play vital roles in key chip manufacturing processes like lithography,etching,deposition and cleaning.While their ultra-high purity(≥99.999%)creates challenging separation requirements,insu... Electronic specialty gases play vital roles in key chip manufacturing processes like lithography,etching,deposition and cleaning.While their ultra-high purity(≥99.999%)creates challenging separation requirements,insufficientphysicochemical data has hindered adsorbent development.To bridge this gap,we constructed a multidimensional database covering 101 semiconductor-related molecules with 19 physical parameters,and developed a Bayesian regression-based collaborative prediction model demonstrating high accuracy(R2=0.95-0.97)on test sets.We further constructed the balanced dataaugmented Transformer-based molecular property prediction(BD-TMPP)model to address the overfittingproblem in small-sample learning.This model achieves the end-to-end prediction of molecular quadrupole moment(R2=0.99),and polarizability(R2=0.98)via the capture of interatomic spatial correlations.Compared with traditional density functional theory calculations,the model achieves a five-orders-of-magnitude improvement in computational efficiency while maintaining accuracy,demonstrating a successful application of the"structure-property relationship"theory in chemical machine learning. 展开更多
关键词 Molecular property database Small sample machine learning Data augmentation Molecular property prediction Adsorption
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