Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble

Jin, Huaiping; Huang, Shuqi; Wang, Bin; Chen, Xiangguang; Yang, Biao; Qian, Bin

doi:10.1016/j.ces.2023.118958

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…In 2023, the authors of [26] proposed an attention-self-supervised learning-aided classifier generative adversarial network algorithm to expand the samples to improve the defect recognition ability of small sample data sets. In 2023, the authors of [27] proposed a generative model for generating virtual marker samples by combining supervised variational automatic encoders with Wasserstein GAN with a gradient penalty. This model can significantly improve the prediction accuracy of soft sensor models for small-sample problems.…”

Section: Gan-based Data Enhancement Methodsmentioning

confidence: 99%

An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments

Chen,

Wang,

Yang

et al. 2023

Electronics

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Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE (enhancement method based on feature enhancement and data enhancement) to generate adequate training data for expanding few-shot datasets. Training intrusion detection models with an expanded dataset can enhance detection performance. Firstly, the EM-FEDE method adaptively extends the features by analyzing the historical intrusion detection records of smart homes, achieving format alignment of device data. Secondly, the EM-FEDE method performs data cleaning operations to reduce noise and redundancy and uses a random sampling mechanism to ensure the diversity of the few-shot data obtained by sampling. Finally, the processed sampling data is used as the input to the CWGAN, and the loss between the generated and real data is calculated using the Wasserstein distance. Based on this loss, the CWGAN is adjusted. Finally, the generator outputs effectively generated data. According to the experimental findings, the accuracy of J48, Random Forest, Bagging, PART, KStar, KNN, MLP, and CNN has been enhanced by 21.9%, 6.2%, 19.4%, 9.2%, 6.3%, 7%, 3.4%, and 5.9%, respectively, when compared to the original dataset, along with the optimal generation sample ratio of each algorithm. The experimental findings demonstrate the effectiveness of the EM-FEDE approach in completing sparse data.

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Section: Gan-based Data Enhancement Methodsmentioning

confidence: 99%

An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments

Chen,

Wang,

Yang

et al. 2023

Electronics

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“…These innovative tools have transitioned from basic mathematical models to complex systems that leverage advanced data-driven techniques such as AI and ML. AI and ML technologies, notably Gaussian processes, fuzzy logic systems, and neural networks, have played a significant role in improving prediction accuracy, handling missing data effectively, and augmenting the adaptability of soft sensors to shifting data sources [ 34 , 35 , 36 , 37 , 38 ].…”

Section: State Of the Artmentioning

confidence: 99%

Soft Sensing of LPG Processes Using Deep Learning

Sifakis,

Sarantinoudis,

Tsinarakis

et al. 2023

Sensors

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This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

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“…Shuai Liu et al [20] proposed a homogeneous selective ensemble forecasting framework based on an improved differential evolution algorithm to enhance the accuracy of hydrological forecasting. Huaiping Jin et al [21] employed a soft measurement method based on data augmentation and selective ensemble for predicting measurement results, validating the effectiveness and excellence of this approach. Zhang Fan et al [22] introduced a selective ensemble learning method based on the local model prediction accuracy and an adaptive weight calculation method for submodels.…”

Section: Introductionmentioning

confidence: 96%

Prediction of Ship Painting Man-Hours Based on Selective Ensemble Learning

Bu,

Ge,

Zhu

et al. 2024

Coatings

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The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. To solve this problem, this paper proposes a selective integrated learning model (ISA-SE) based on an improved simulated annealing algorithm to predict ship painting man-hours. Firstly, the improved particle swarm optimization (MPSO) algorithm and data grouping techniques are employed to achieve the optimal selection and hyperparameter optimization of base learners, constructing a candidate set of base learners. Subsequently, the simulated annealing algorithm is improved by adding random perturbations and using a parallel perturbation search mechanism to enhance the algorithm’s global search capability. Finally, an optimal set of base learners is composed of the candidate set utilizing the ISA-SE model, and a heterogeneous ensemble learning model is constructed with the optimal set of base learners to achieve the precise prediction of ship painting man-hours. The results indicate that the proposed ISA-SE model demonstrates improvements in accuracy, mean absolute error, and root mean square error compared to other models, validating the effectiveness and robustness of ISA-SE in predicting ship painting man-hours.

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Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble

Cited by 5 publications

References 40 publications

An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments

An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments

Soft Sensing of LPG Processes Using Deep Learning

Prediction of Ship Painting Man-Hours Based on Selective Ensemble Learning

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