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Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network 认领 引用
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作者 Zihao Song Yan Zhou +2 位作者 Wei Cheng Futai Liang Chenhao Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第3期3349-3376,共28页
The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random mis... The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design. 展开更多
关键词 Missing value imputation time-series tracks probabilistic sparsity diagonal masking self-attention weight fusion
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EGAIN: Enhanced Generative Adversarial Networks for Imputing Missing Values 认领 引用
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作者 Abolfazl Saghafi Soodeh Moallemian +1 位作者 Miray Budak Rutvik Deshpande 《Computers, Materials & Continua》 SCIE EI 2026年第8期2241-2255,共15页
Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible de... Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible deep-learning approach for missing value imputation,but their practical use is limited by convergence instability,sensitivity to hyperparameter selection,and dependence on outdated software implementations.To address these limitations,we propose Enhanced Generative Adversarial Imputation Networks(EGAIN),a modernized extension of GAIN implemented in TensorFlow 2.x.EGAIN incorporates convolution-based generator and discriminator networks,a channel-stacked representation of the data and mask,and checkpoint-based training diagnostics to improve stability and usability.EGAIN was evaluated on five benchmark datasets under multiple Missing Completely At Random(MCAR)settings and compared with the original GAIN implementation and median imputation.Across most evaluated conditions,EGAIN achieved lower root mean squared error(RMSE)and showed greater robustness,particularly when missingness was concentrated in a subset of variables.These results indicate that EGAIN provides a more stable and reproducible framework for missing data imputation in tabular datasets. 展开更多
关键词 Missing value imputation generative adversarial network tabular data imputation missing completely at random convolutional architectures training stability
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Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data 认领 引用
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作者 Sun Hee KIM Hyung Jeong YANG Kam Swee NG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2011年第8期687-697,共11页
Missing values occur in bio-signal processing for various reasons,including technical problems or biological char-acteristics.These missing values are then either simply excluded or substituted with estimated values f... Missing values occur in bio-signal processing for various reasons,including technical problems or biological char-acteristics.These missing values are then either simply excluded or substituted with estimated values for further processing.When the missing signal values are estimated for electroencephalography (EEG) signals,an example where electrical signals arrive quickly and successively,rapid processing of high-speed data is required for immediate decision making.In this study,we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set.The proposed method solves the problem of a biased model,which inevitably results from simply removing incomplete data rather than estimating them,and thus reduces the loss of information by incorporating missing values in real time.By using an incremental approach,the proposed method alsominimizes memory usage and processing time of continuously arriving data.Experimental results show that the proposed method assigns more accurate missing values than previous methods. 展开更多
关键词 Electroencephalography (EEG) Missing value imputation Hidden pattern discovery Expectation maximization Principal component analysis
Missing Data Imputation for Traffic Flow Based on Improved Local Least Squares 认领 引用 被引量：7
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作者 Gang Chang Yi Zhang Danya Yao 《Tsinghua Science and Technology》 EI CAS 2012年第3期304-309,共6页
Complete and reliable field traffic data is vital for the planning, design, and operation of urban traf- fic management systems. However, traffic data is often very incomplete in many traffic information systems, whic... Complete and reliable field traffic data is vital for the planning, design, and operation of urban traf- fic management systems. However, traffic data is often very incomplete in many traffic information systems, which hinders effective use of the data. Methods are needed for imputing missing traffic data to minimize the effect of incomplete data on the utilization. This paper presents an improved Local Least Squares （LLS） ap- proach to impute the incomplete data. The LLS is an improved version of the K Nearest Neighbor （KNN） method. First, the missing traffic data is replaced by a row average of the known values. Then, the vector angle and Euclidean distance are used to select the nearest neighbors. Finally, a regression step is used to get weights of the nearest neighbors and the imputation results. Traffic flow volume collected in Beijing was analyzed to compare this approach with the Bayesian Principle Component Analysis （BPCA） imputation ap- proach. Tests show that this approach provides slightly better performance than BPCA imputation to impute missing traffic data. 展开更多
关键词 Local Least Squares （LLS） vector angle missing value imputation traffic flow
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Unsupervised anomaly detection of industrial building energy consumption 认领 引用
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作者 Yi Song Sennan Kuang +1 位作者 Junling Huang Da Zhang 《Energy and Built Environment》 EI CSCD 2026年第2期351-363,共13页
Detecting anomalies in building energy consumption can reduce unnecessary energy waste and improve energy efficiency.The role of anomaly detection has become particularly pivotal in industrial buildings because of the... Detecting anomalies in building energy consumption can reduce unnecessary energy waste and improve energy efficiency.The role of anomaly detection has become particularly pivotal in industrial buildings because of their high energy consumption and the potential risks associated with abnormal events.Although extensive data collected through smart meters has indicated the advantages of anomaly detection using data mining techniques,labeled data are often unavailable in practical situations.Therefore,this study develops an ensemble framework that combines three unsupervised learning algorithms,including Local Outlier Factor,Deep Isolation Forest,and Anomaly Transformer,to identify anomalous power consumption with a focus on subsequence anomaly.The transformer-based network is established to precisely impute missing values and enhance the reliability of anomaly detection.The experimental results based on hourly cooling energy consumption in the two industrial buildings confirmed the effectiveness of the proposed method.To better interpret the anomaly detection results,the Extreme Gradient Boosting is applied to construct the relationship between influencing factors and anomalous consumption.The area under the Receiver Operating Characteristic curve is used as a metric for the classification task,and an average of over 0.96 indicates robust performance.Weekday and dew point temperatures are found to have significant impacts on the electricity usage pattern.The research findings provide valuable insights for developing effective solutions to identify unexpected trends in building energy consumption and support efficient energy management. 展开更多
关键词 Industrial building Deep abnormality detection Energy management Unsupervised learning Missing value imputation
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