International Journal of Intelligent Systems最新文献

This study was conducted to enhance the efficiency of chemical process systems and address the limitations of conventional methods through hyperparameter optimization. Chemical processes are inherently continuous and nonlinear, making stable operation challenging. The efficiency of processes often varies significantly with the operator’s level of expertise, as most tasks rely on experience. To move beyond the constraints of traditional simulation approaches, a new machine learning-based simulation model was developed. This model utilizes a recurrent neural network (RNN) algorithm, which is ideal for analyzing time-series data from chemical process systems, presenting new possibilities for applications in systems with special chemical reactions or those that are continuous and complex. Hyperparameters were optimized using a grid search method, and optimal results were confirmed when the model was applied to an actual distillation process system. By proposing a methodology that utilizes machine learning for the optimization of chemical process systems, this research contributes to solving new problems that were previously unaddressed. Based on these results, the study demonstrates that a machine learning simulation model can be effectively applied to continuous chemical process systems. This application enables the derivation of unique hyperparameters tailored to the specificities of a limited control volume system.

Intelligent decision-making in air defense operations has attracted wide attention from researchers. Facing complex battlefield environments, existing decision-making algorithms fail to make targeted decisions according to the hierarchical decision-making characteristics of air defense operational command and control. What’s worse, in the process of problem-solving, these algorithms are beset by defects such as dimensional disaster and poor real-time performance. To address these problems, a new hierarchical reinforcement learning algorithm named Hierarchy Asynchronous Advantage Actor-Critic (H-A3C) is developed. This algorithm is designed to have a hierarchical decision-making framework considering the characteristics of air defense operations and employs the hierarchical reinforcement learning method for problem-solving. With a hierarchical decision-making capability similar to that of human commanders in decision-making, the developed algorithm produces many new policies during the learning process. The features of air situation information are extracted using the bidirectional-gated recurrent unit (Bi-GRU) network, and then the agent is trained using the H-A3C algorithm. In the training process, the multihead attention mechanism and the event-based reward mechanism are introduced to facilitate the training. In the end, the proposed H-A3C algorithm is verified in a digital battlefield environment, and the results prove its advantages over existing algorithms.

In this paper, the vibration suppression and trajectory tracking control of a flexible joint manipulator (FJM) based on particle swarm optimization (PSO) and fixed-time nonsingular terminal sliding mode control (NTSMC) are studied. Firstly, in order to suppress the residual vibration of the FJM, an optimal trajectory planning method based on higher-order trajectory planning (HOTP) and the PSO algorithm is proposed. Then, to ensure that the FJM can track the optimized trajectory without being affected by the initial value of the trajectory, a novel fixed-time NTSMC scheme is proposed. Compared with the cubic spline trajectory, the proposed HOTP is smoother and can more accurately suppress the residual vibration of the FJM. By combining the HOTP with the PSO algorithm, the vibration amplitude of FJM can be suppressed to around 0.002 mm. Unlike finite-time NTSMC, the rate of convergence of the proposed fixed-time NTSMC does not depend on the initial value of FJM’s joint trajectory. Especially when the initial value of the trajectory is large, the FJM can still quickly track the optimal trajectory within 0 to 1 s. Finally, the effectiveness of this method is verified through simulation and comparison.

Industrial control systems (ICSs), as critical national infrastructures, are increasingly susceptible to sophisticated security threats. To address this challenge, our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring in ICS. The CAE-T utilizes unsupervised deep learning, employing a convolutional autoencoder for spatial feature extraction from multidimensional time-series data, and combines this with a transformer architecture to capture long-term temporal dependencies. The design of the model facilitates rapid training and inference, while its dual-component approach, utilizing an optimization function based on support vector data description (SVDD), enhances detection accuracy. This integration synergistically combines spatiotemporal feature extraction, significantly improving the robustness and precision of anomaly detection in ICS environments. The CAE-T model demonstrated notable performance enhancements across three industrial control system datasets. Notably, the CAE-T model achieved approximately a 70.8% increase in F1 score and a 9.2% rise in AUC on the WADI dataset. On the SWaT dataset, the model showed improvements of approximately 2.8% in F1 score and 5% in AUC. The power system dataset saw more modest gains, with an approximately 0.1% uptick in F1 score and a 1% increase in AUC. These improvements validate the CAE-T model’s efficacy and robustness in anomaly detection across various scenarios.

The misleading information during the coronavirus disease 2019 (COVID-19) pandemic’s peak time is very sensitive and harmful in our community. Analyzing and detecting COVID-19 information on social media are a crucial task. Early detection of COVID-19 information is very helpful and minimizes the risk of psychological security which leads to inconvenience in daily life. In this paper, a deep ensemble transfer learning framework with an understanding of the context of Arabic text COVID-19 information is proposed. This framework is inspired to spontaneously analyze and recognize the text about COVID-19. The ArCOVID-19Vac dataset has been used to train and test our proposed model. A comprehensive experimental study for each scenario is performed. For the binary classification scenario, the proposed framework records better evaluation results with 83.0%, 84.0%, 83.0%, and 84.0% in terms of accuracy, precision, recall, and F1-score, respectively. For the second scenario (three classes), the overall performance is recorded with an accuracy of 82.0%, precision of 80.0%, recall of 82.0%, and F1-score of 80.0%, respectively. In the last scenario with ten classes, the best evaluation performance results are recorded with an accuracy of 67.0%, a precision of 58.0%, a recall of 67.0%, and F1-score of 59.0%, respectively. In addition, we have applied an ensemble transfer learning model for this scenario to get 64.0%, 66.0%, 66.0%, and 65.0% in terms of accuracy, precision, recall, and F1-score, respectively. The results show that the proposed model through transfer learning provides better results for Arabic text than all state-of-the-art methods.

This paper proposes an anime audio retrieval method based on audio separation and feature recognition techniques, aiming to help users conveniently locate their desired audio segments and enhance the overall user experience. Additionally, by establishing an audio fingerprint database and a corresponding copyright information management system, it becomes possible to track and manage the audio content within anime, effectively preventing piracy and unauthorized use, thereby improving the management and protection of audio resources. Traditional methods for anime audio feature recognition suffer from issues like low efficiency and subjective factors. In contrast, the proposed approach overcomes these limitations by automatically separating and extracting audio fingerprints from different audio sources within anime and creating an anime audio fingerprint database for fast retrieval. The paper utilizes an improved audio separation model based on the efficient channel attention mechanism to separate the anime audio. Subsequently, feature recognition is performed on the separated anime audio, employing a contrastive learning-based audio fingerprint retrieval method for anime audio fingerprinting. Experimental results demonstrate that the proposed algorithm effectively alleviates the issue of poor audio separation performance in anime audio, while also improving retrieval efficiency and accuracy, meeting the demands for anime audio content retrieval.