Microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process

Chen, Qian; Zhao, Zhiyao; Wang, Xiaoyi; Xiong, Ke; Shi, Ce

doi:10.1016/j.ifset.2021.102912

Cited by 24 publications

(19 citation statements)

References 58 publications

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“…The proposed prediction approaches of time series models in the paper can combine other parameter estimation algorithms [61][62][63][64][65][66][67][68] with studying the parameter identification problems of linear and nonlinear systems with different disturbances [69][70][71][72][73][74][75][76][77]. The proposed prediction approach can build the soft sensor models and prediction models based on the time series data and can be applied to other fields [78][79][80][81][82][83][84][85][86], such as signal processing and engineering application systems [87][88][89][90][91].…”

Section: Summary and Future Workmentioning

confidence: 99%

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

et al. 2022

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Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.

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Section: Summary and Future Workmentioning

confidence: 99%

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

et al. 2022

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“… Storage and transportation links: improper warehouse storage makes rice prone to mildew. In addition to changing the color of rice, mildew can produce harmful molds and microorganisms [ 32 ]. The sales link of rice is widely distributed and contains a large amount of information.…”

Section: Analysis Of Supply Chain Process and Key Informationmentioning

confidence: 99%

Blockchain-Based Information Supervision Model for Rice Supply Chains

Wang

Zhang

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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Rice is a major food crop around the world, and its various quality and safety problems are closely related to human health. As an important area of food safety research, the rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated problems of data privacy and circulation efficiency caused by complex rice supply networks, long circulation cycles, and various risk factors in each link. First, we deconstructed the quality and safety of each link of the rice supply chain at the information level and established a key information classification table for each link. On that basis, we built a rice supply chain information supervision model based on blockchain. Various encryption algorithms are used to secure the sensitive data of enterprises in the supply chain to meet regulators’ needs for efficient supervision. Moreover, we propose a practical Byzantine fault-tolerant consensus algorithm that scores the credit of enterprise nodes, optimizes the selection strategy of master nodes, and ensures high efficiency and low cost. Then, we built a prototype system based on the open-source framework of hyperledger fabric, analyzed the model’s viability, and implemented the system using cases. The results indicated that the proposed system can optimize the information supervision process of rice supply chain regulators and provide a feasible solution for the quality and safety supervision of grain and oil.

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“…The prediction and verification experiment of Beijing air quality data, considering the indicators such as RMSE, MSE, and MAE, shows that the model is superior to other models in terms of prediction accuracy and calculation speed. The proposed prediction approaches of time-series models in the paper can combine other parameter estimation algorithms [52][53][54][55][56][57][58] with studying the parameter identification problems of linear and nonlinear systems with different disturbances [59][60][61][62][63][64] to build soft sensor models and prediction models based on time-series data that can be applied to other fields [65][66][67][68][69][70] such as signal processing and engineering application systems [71][72][73][74][75][76][77][78].…”

Section: Discussionmentioning

confidence: 99%

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

Jin

Gong

Kong

et al. 2022

Entropy

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Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing’s air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.

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Microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process

Cited by 24 publications

References 58 publications

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

Blockchain-Based Information Supervision Model for Rice Supply Chains

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

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