Variational auto-encoders (VAEs) have been widely used in soft sensing due to their ability to provide a probabilistic description of the hidden space. However, VAEs are static models that do not consider process dynamics, which can limit the ability of VAEs to accurately model complex industrial processes. To tackle this problem, this paper proposes a model called adaptive multi-head self-attention based supervised VAE (AMSA-SVAE). In AMSA-SVAE, an adaptive multi-head self-attention mechanism (AMSA) is proposed based on the multi-head self-attention mechanism (MSA). AMSA can dynamically extract different attention information depending on specific tasks. By adjusting the attention weights based on the input sequence, AMSA allows for more accurate and efficient modeling of complex industrial processes. Then, AMSA is used as the encoder and decoder of SVAE for soft sensing. Furthermore, with the data generation capabilities of VAE, an adaptive multi-head self-attention based VAE (AMSA-VAE) framework is proposed to address the issue of missing data. The AMSA-VAE is used to dynamically fill in missing data, thereby extending the capabilities of AMSA-SVAE. Finally, the performance of AMSA-SVAE is verified by a set of real industrial data, and the ability of AMSA-VAE framework is demonstrated by simulating different degrees of data missing rates. By combining the dynamic modeling capabilities of AMSA-SVAE with the data generation capabilities of AMSA-VAE, the proposed approach provides a robust solution to the challenges of incomplete data in soft sensing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners — Soft sensors are widely used to measure key parameters in industrial processes, but missing values in the data are common due to sensor failures or transmission signal interference. This poses a significant challenge for traditional soft sensors, which require complete data to accurately model. Meanwhile, the dynamic nature of industrial process data further complicates the modeling process. To solve these challenges, this paper proposes an AMSA-SVAE model for soft sensing and an AMSA-VAE framework for filling in the missing values in the data, thereby extending the capabilities of AMSA-SVAE to handle missing data. When facing a dataset with missing values, AMSA-VAE framework is first used to fill in the missing values before the filled complete data is fed into AMSA-SVAE for modeling. Finally, the proposed approaches are evaluated through two sets of experiments using a real industrial dataset, showing the excellent performance of AMSA-SVAE and AMSA-VAE framework in modeling dynamic industrial process data and addressing the missing data problem.

Adaptive Multi-Head Self-Attention Based supervised VAE for Industrial Soft Sensing With Missing Data

Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques

The increased demand for food is accelerating plant diseases globally. Hence, a manual process of detection of plant diseases is almost impossible. Artificial intelligence (AI) can offer several solutions to farmers’ problems. AI is facile to mitigate farmer’s agriculture challenges. With the unpredictable changing climate, plants are often affected by several diseases where AI can play an important role. AI techniques such as Machine learning and deep Learning have been employed in literature to detect, predict, and design recommendation systems for plant diseases. Significant work has been done in this area in the last two decades, which can change farmer’s lives in the coming years. This paper presents a systematic multi-fold survey and analysis of such work focusing on recent AI techniques developed to combat plant diseases. This article discusses various challenges faced by farmers and their AI solutions. It analyzes several applications of AI in agriculture and current trends. Recent advancements in AI for plant disease detection, like Identification Model Improvement (IMI), Few Shot Learning (FSL), Generative Adversarial Networks (GANs), and Self Supervised Learning (SSL), are also discussed in this article. Several challenges while employing AI in plant disease detection are also discussed in this article. It will serve researchers as a valuable document for further research to solve farmer’s issues.

Role of Artificial Intelligence in Agriculture: An Analysis and Advancements with Focus on Plant Diseases

Nowadays, the deep learning technique has been widely applied in soft sensing benefiting from its strong ability of feature representation. However, classic dynamic soft sensing methods based on deep learning do not consider spatial-temporal and quality-relevant information simultaneously in the process data. Additionally, feature extraction methods encounter the challenge of information redundancy when dealing with large datasets. To address these challenges, we propose a novel hybrid self-attention mechanism with distributed GRUs (HSAM-dGRUs) within an encoder-decoder framework. The HSAM-dGRUs architecture contains a hybrid self-attention encoder, integrating multi-channel multi-head self-attention (MC-MSA) and quality-related self-attention (QR-SAM). The MC-MSA method effectively extracts local features through multi-channels to capture spatio-temporal characteristics in sequence data. Subsequently, the QR-SAM method introduces supervisory knowledge into the features and capture the quality-relevant information adaptively. The distributed GRUs decoder is designed to extract local dynamic hidden states from multi-channel features for accurate prediction. Two case studies on real industrial process datasets demonstrate the effectiveness and superiority of the proposed HSAM-dGRUs soft sensing approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners —In the process industry, monitoring quality variables is beneficial for tracking process status, saving energy, and reducing emissions. However, it is difficult to implement accurate measurement and real-time control for process quality in general. In this work, we propose a novel hybrid self-attention mechanism utilizing distributed gated recurrent units for soft sensing modeling of dynamic processes. The proposed method integrates an multi-channel multi-head self-attention mechanism and a quality self-attention mechanism into the encoder. The encoder is responsible for adaptively extracting spatio-temporal features and assigning weights based on quality-related information. Furthermore, a decoder based on distributed GRUs is employed to capture the dynamic information from the extracted features, enhancing the prediction task. First, the proposed methods can be trained offline based on historical dataset. Then, the well-trained model can be uploaded and use the new data for accurate online prediction. Preliminary experiments in this paper have demonstrated the feasibility of the proposed method, but it has not been tested in actual industrial production. In the future, we will work on developing a quality prediction system based on the proposed method so that it can be applied in actual industrial production.

Novel Distributed GRUs Based on Hybrid Self-Attention Mechanism for Dynamic Soft Sensing

Industrial soft sensing models have found extensive application in predicting key process variables that are challenging to directly measure. However, the effectiveness of conventional soft sensing models is impacted by the intricate characteristics of process variables, such as high nonlinearity, coupling, and complex dynamicity. To address this limitation, an enhanced bidirectional long short-term memory (Bi-LSTM) model based on distributed nonlinear extensions integrated with parallel inputs (DNEPI-Bi-LSTM) is proposed for constructing the soft sensing model. First, to account for the differential impact between inputs and outputs, partial correlation is employed to segregate the inputs into two categories: positive subinputs and negative subinputs. Subsequently, these two distributed subinputs are transformed into nonlinear space by passing through the hidden layer of the extreme learning machine. The resulting outputs from the hidden layer are considered as distributed nonlinear extensions. Finally, the enhanced DNEPI-Bi-LSTM soft sensing model is developed using parallel inputs integrated with distributed nonlinear extensions. To assess the efficacy of DNEPI-Bi-LSTM, an industrial process known as the sulfur recovery unit is adopted. Simulation results illustrate that DNEPI-Bi-LSTM outperforms other advanced models in terms of accuracy, showcasing its potential in industrial applications.

Improved Bi-LSTM With Distributed Nonlinear Extensions and Parallel Inputs for Soft Sensing

Improved domain adaptive rice disease image recognition based on a novel attention mechanism

Traditional soft sensors typically rely only on labeled data to predict key variables, despite the significant amount of unlabeled data that could provide valuable information. To solve this problem, a quality regularization based semisupervised adversarial transfer model (QR-SATM) is proposed. The idea of transfer learning is used in QR-SATM. QR-SATM comprises a pre-training model and a regression model. The pre-training model is an unsupervised model. And the regression model is a supervised model with a similar structure to the pre-training model, allowing for easy transfer between the two models. Firstly, the pre-training model is trained with unlabeled data to extract features. Then the trained parameters of pre-training model are transferred to the regression model, and the regression model is fine-tuned with labeled data. During fine-tuning the regression model, an improved quality regularization is introduced in order to select useful features and prevent overfitting. QR-SATM is validated by a real industrial dataset of purified terephthalic acid (PTA). The experimental results show the effectiveness of the proposed QR-SATM in accurately predicting key variables. 

Quality Regularization based Semisupervised Adversarial Transfer Model with Unlabeled Data for Industrial Soft Sensing

For a receiver of the BeiDou Satellite Based Augmentation System (BDSBAS), the acquisition aims to coarsely estimate the pseudo-code delays and carrier Doppler shifts of the received signals. The use of downsampling techniques to accelerate this acquisition process for weak signals has been an emerging topic. However, the acquisition probabilities of conventional algorithms with downsampling are inadequate and need to be enhanced to prevent excessive false alarms due to degradation of the peak signal-to-noise ratios (PSNRs) during acquisition. To improve signal acquisition performance, we introduce an enhancer function before downsampling to achieve the minimum PSNR loss during acquisition. Then, we use graphic processing unit (GPU) functions to speed up the processing loop, matrix multiplication and fast Fourier transform operations. We thus present a fast GPU-based acquisition method by using minimum loss and downsampling techniques. Our experimental results show the remarkable effectiveness of our method for receiving weak BDSBAS signals as compared to legacy downsampling algorithms. This work seeks not only to solve the current issue of achieving fast acquisition of weak BDSBAS signals but also to inspire creative new ideas that spur further advances in this promising field in the future. 

Fast GPU based acquisition with minimum loss and downsampling techniques for weak BeiDou SBAS signals

As machine vision involves many fields such as photogrammetry, national defense and military, and security surveillance, fisheye cameras have also been widely used as an ultra-wide-angle lens. Different from ordinary cameras, fisheye cameras can obtain a wider viewing angle, but their imaging laws will produce nonlinear distortion, which needs to be corrected to achieve better visual effects. In this paper, a fisheye distortion correction method based on deep learning is proposed. According to the parameter division model, the distortion dataset is synthesized, the neural network is constructed, and the parameter model is trained. Finally, the fisheye image is corrected by extracting the distortion features and estimating the distortion parameters. Image enhancement. This method overcomes the limitation of traditional correction methods that rely heavily on imaging model or camera calibration, benefits from the learning features of neural networks, and has better universality; The results show that this method has better performance.

A fisheye distortion correction method based on deep learning

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