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Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS). [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new data‐driven fixed‐order H∞ controller design method based on convex optimisation is proposed for linear single input single output systems. The proposed non‐parametric frequency domain data based approach renders the need for a mathematical model of the controlled plant unnecessary. First, a semi‐definite convex optimisation algorithm is proposed to simultaneously compute a minimal uncertainty model and an optimal nominal model from the experimental data. Then the H∞ robust performance condition, control input constraints and the closed‐loop model matching objective are described by convex functions with respect to the parameters of the controller. The usefulness and efficiency of the proposed approach are verified experimentally with application to the control of a thrust vector control system.

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.

This work aims to introduce a tuning-free PID controller for stable FOPTD and FOLIPD systems. The objective was achieved using a partial model-matching strategy; matching the coefficients of corresponding powers of s of the closed loop response of the model to the desired closed loop transfer function. Closed-loop performance is evaluated by considering a unit step change in the process model and compared the results with other methods reported earlier in the literature. Input and output performances were evaluated using error analyses and total variation. The robustness of the proposed method was assessed via incorporation of uncertainties in the process model parameters. The proposed method was also experimentally validated for FOPTD model using a temperature controlled lab. It was concluded that the method was analytically derived and model based with a key advantage that the resultant PID required no tuning parameter.

Process Model Matching (PMM) aims to automatically identify corresponding activities from two process models that exhibit similar behaviors. Recognizing the diverse applications of process model matching, several techniques have been proposed in the literature. Typically, the effectiveness of these matching techniques has been evaluated using three widely used performance measures, Precision, Recall, and F1 score. In this study, we have established that the values of these three measures for each dataset do not provide deeper insights into the capabilities of the matching techniques. To that end, we have made three significant contributions. Firstly, we have enhanced four benchmark datasets by classifying their corresponding activities into three sub-types. The enhanced datasets can be used for surface-level evaluation, as well as a deeper evaluation of matching techniques. Secondly, we have conducted a systematic search of the literature to identify an extensive set of 27 matching techniques and subsequently proposed a taxonomy for these matching techniques. Finally, we have performed 432 experiments to evaluate the effectiveness of all the matching techniques, and key observations about the effectiveness of the techniques are presented.

Recently, in regards to vehicle steering control system based on steer by wire (SBW), the relationship between the vehicle response and steering input could change flexibly. There are some previous studies (e.g. H∞, LQG, Model Matching etc. ) to similar researches, but these researches are mutually exclusive for the desired response and robustness. We report the evaluation results of the robust steering control law by combination of model matching and optimal observer, which can realize compatible of vehicle desired response and robust characteristics to steer input disturbance simultaneously. Generally, this offered system can accommodate to develop the SBW system which realize compatible of vehicle desired response and robust characteristics to steer input disturbance simultaneously. Especially, the function to eliminate the input disturbance allows the efficient design to develop the vehicle control law realize compatible of the maneuverability and dynamic stability involving the static stability characteristics. The control plant, which abstracts the actual vehicle characteristics by linear bicycle model using the linear cornering stiffness, is positioned as the evaluation tool to verify the desired response and the robustness of offered control law. Although this linear bicycle model plant is positioned for the evaluation tool, in accordance with this research developing, we would verify the effect of this control law to the actual vehicle characteristics.