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Stable fault self-healing ensures the smooth operation of intelligent industrial manufacturing processes. However, current research, while capable of inferring fault propagation paths or individually controlling manufacturing processes, still faces some challenges and limitations in addressing issues within manufacturing processes. In particular, it is easy to overlook the impact of fault variables on normal causal relationships in the manufacturing process and how to achieve adaptive fault repair from the source of faults. The biological immune system possesses sensitive nodes and a neuro-endocrine-immune network with interconnected functionalities, bearing remarkable resemblance to the sensor arrays, information networks, and intelligent controller systems in modern self-healing control systems. These problem domains have significant practical implications in industrial manufacturing processes and urgently require new methods and technologies to address them. To address the issue of root cause fault self-healing in intelligent industrial manufacturing processes, this paper proposes an innovative method that utilizes root cause inference and bio-inspired heuristic reinforcement learning models to achieve fault self-healing in industrial processes. First, innovatively introduce the Variational Autoencoder (VAE) into the Time Convolutional Network (TCN) to construct the TCN-VAE network. By performing feature reconstruction, the network's feature extraction capability is enhanced, further exploring the relationships between latent variables, and consequently building a causal graph of faults. A multi-head attention mechanism is introduced into the network, and the inference process is quantitatively evaluated, thereby improving the generalization and accuracy of root cause inference. Root cause determination rules are established to identify the fundamental reasons for fault occurrence, ensuring self-healing from the source of faults. Next, we developed for the first time a fault self-healing model based on reinforcement learning, utilizing the immune repair process of effector T cells to characterize the fault self-healing process. It effectively adapts to eliminate various levels of faults based on the state transition process of antigens in T helper cells. Additionally, stability control analysis is performed on the proposed model. The average reward function curve and Sobol sensitivity index demonstrates the model's strong robustness in practical applications. The validation results from case studies on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) show that the average improvement in fault repair F1 score was 0.0941 and 0.105 respectively, and the fastest improvement in fault repair response time was 0.266s and 0.258s respectively. It was confirmed that the root cause inference and fault repair results align with the actual mechanisms of the manufacturing processes. The proposed method has been verified to achieve stable and accurate root cause fault repair in real-world intelligent industrial manufacturing systems, making a significant contribution to achieving stable and accurate root cause fault repair in intelligent industrial manufacturing systems. • TCN-VAE enables feature reconstruction and latent variable exploration for root cause reasoning. • Multi-head attention enhances generality and accuracy in root cause reasoning. • Effector T-cell immune-inspired RL adapts to eliminate faults effectively. • The fault self-healing model exhibits strong reliability and robustness. • Method validated on Tennessee Eastman and continuous stirred-tank reactor datasets. [ABSTRACT FROM AUTHOR]

Stock forecasting is a very complex non-stationary, nonlinear time series forecasting, and is often affected by many factors, making it difficult to predict it with a simple model. Support vector machine (SVM) is one of the common data mining methods in the field of machine learning. It has outstanding advantages compared with other methods and it is widely used in various fields. However, there are still many problems in the practical application of the method, and the model itself has many fields that need to be improved. The purpose of this paper is to accurately predict the trend of stock prices, providing a reference model for the trend of stock market and the tracking method of stock price prediction, and provide value reference for research on the forecasting model of stock market and investor's investment decision. Research using a combined model to predict stock market trends whether will have a significant improvement compared to using a single model to forecast that. The method of this paper is to analyze the shortcomings of current stock market forecasting methods and standard support vector machines firstly, at the same time, based on this, a cumulative auto-regressive moving average is proposed, which combines the least squares support vector machine synthesis model (ARI-MA-LS-SVM) to make basic predictions for the stock market. Secondly, process the data first for the predictive indicators by using cumulative auto-regressive moving average. Then, use the least squares support vector machine of simple indicator system to predict stock price fluctuations. Therefore, it can be concluded that the combined model based on ARI-MA-LS-SVM is more suitable for stock price forecasting than the single forecasting model, and the actual performance is better. At the same time, a large number of simulation experiments show that the algorithm of multiple model's fusion can achieve the expected effect, which indicate that the model has universal applicability, market applicability and stability feasibility. This model can bring some guidance and reference value for many investors and market regulators. [ABSTRACT FROM AUTHOR]

Process monitoring technology can help make the right decisions in manufacturing, but the complexity and scale of modern process industry processes render process monitoring difficult. Existing data-driven process monitoring methods utilize abundant monitoring data that are accumulated in industrial processes, but nonlinearity, high coupling, noise effects, and other problems continuously appear in process industry monitoring data. This study proposes a process monitoring method based on variational autoencoder and long short-term memory techniques. The method reconstructs the monitoring data by learning their distribution and time series characteristics under the controlled state, and then it monitors the state of the manufacturing process in real time by calculating the statistics. Evaluation is conducted using the Tennessee Eastman process case verification and experimental comparison method. Then, the proposed method is compared with the centralized process via principal component analysis and kernel principal component analysis. The results show that the proposed method can more significantly improve the effect of fault detection in distributed system process monitoring compared with the traditional method, and it has a better process monitoring effect. [ABSTRACT FROM AUTHOR]

Requirements are important in software development. Ambiguous requirements cause inconsistent understanding by developers, which leads to rework, delayed delivery, and other problems, and may even have devastating effects on the project. A large number of requirements text written in natural language are not concise, intuitive, and accurate. This condition increases the workload of designers and the difficulty of their tasks. An effective solution for the aforementioned problems is to extract actors and use cases from the requirement texts. This study proposes a model for extracting actors and using cases automatically, which combines bi-directional long short-term memory (BiLSTM) and conditional random fields. BiLSTM is used to capture the contextual information of the texts, and CRF is used to calculate the tag transfer score and determine the most accurate tag sequence, which aims to extract actors and use cases. Results show that the accuracy of extraction is significantly improved compared with the baseline method, which verifies the effectiveness of the proposed method in extracting actors and use cases. [ABSTRACT FROM AUTHOR]

Effectively identifying self-admitted technical debt (SATD) from project source code comments helps developers quickly find and repay these debts, thereby reducing its negative impact. Previous studies used techniques based on patterns, text mining, natural language processing, and neural networks to detect SATD. Compared with these above, Convolutional Neural Networks (CNN) have the strong feature extraction ability. Deep network ensembles are demonstrated great potential for the task of sentences classification. In order to boost the performance of CNN-based SATD detecting, we propose a deep neural network ensemble contribute to ensemble learning in a simple yet effective way. Specifically, CNN, CNN-LSTM (convolutional neural network and long short-term memory), and DPCNN (Deep Pyramid Convolutional Neural Networks) are used as individual classifiers to diversify the deep network ensembles. In order to improve the explainability, we introduce attention to measure the contribution of feature words to SATD classification. 62,285 source code comments from 10 projects were used in our experiments. The results show that our approach can effectively reduce misjudgment and detect more SATD, especially for cross-project, so as to greatly improve the detection accuracy. [ABSTRACT FROM AUTHOR]