By overlaying preoperative models and their internal structures onto endoscopic images, cross-modal registration methods restore a surgeon’s ability to perceive three-dimensional information in laparoscopic scenes, p...
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The deep clustering method has been successfully employed for fault diagnosis due to its remarkable ability to extract deep representation features, particularly in scenarios where labeled data is unavailable. However...
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Recently, inverse design approach, which directly generates optimal aerodynamic shape with neural network models to meet designated performance targets, has drawn enormous attention. However, the current state-of-the-...
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Time-varying traffic conditions are crucial features of urban logistics. Overlooking these conditions will pose a high coordination risk for drone-assisted routing problems. In this paper, a time-dependent multiple tr...
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Cross-efficiency evaluation in data envelopment analysis (DEA) assumes that decision making units (DMUs) have full flexibility in choosing weights according to their individual preferences. However, this total autonom...
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This study focuses on the learning-based asynchronous sliding mode control for switching systems, operating under a general switching rule and partially unknown probability information. A novel switching rule is const...
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This paper introduces a vehicle-assisted multi-drone inspection routing problem (VAMDIRP), which enables the vehicle to repeatedly traverse roads, thereby reducing task completion time. Firstly, a mixed-integer linear...
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As unmanned aerial vehicles(UAVs) are used more and more in military operations, increasing their level of autonomous decision making becomes necessary. In uncertain battlefield environments, when making sovereign dec...
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As unmanned aerial vehicles(UAVs) are used more and more in military operations, increasing their level of autonomous decision making becomes necessary. In uncertain battlefield environments, when making sovereign decisions, UAVs must choose low-risk options. An integrated framework is proposed for UAV robust decision making in air-to-ground attack missions under severe uncertainty. In the offline part of the framework, the battlefield scenarios are analyzed and an influence diagram is built to represent the decision situation. In the online part, the UAV evaluates the alternative actions for every scenario, and then the optimal robust action is chosen, using the robust decision model. Results of simulation show that the proposed approach is feasible and effective. The framework can support UAVs in making independent robust decisions under circumstances which require immediate responses under severe uncertainty, and it can also be extended to applications in more complex situations.
In this paper, a probabilistic-linguistic-information-driven decision-making method is proposed to help decision makers make decisions with historical data. Historical individual assessments and collective assessments...
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In this study, we consider the generation of optimal persistent formations for heterogeneous multi-agent systems, with the leader constraint that only specific agents can act as leaders. We analyze three modes to cont...
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In this study, we consider the generation of optimal persistent formations for heterogeneous multi-agent systems, with the leader constraint that only specific agents can act as leaders. We analyze three modes to control the optimal persistent formations in two-dimensional space, thereby establishing a model for their constrained generation. Then, we propose an algorithm for generating the optimal persistent formation for heterogeneous multi-agent systems with a leader constraint (LC-HMAS-OPFGA), which is the exact solution algorithm of the model, and we theoretically prove its validity. This algorithm includes two kernel sub-algorithms, which are optimal persistent graph generating algorithm based on a minimum cost arborescence and the shortest path (MCA-SP-OPGGA), and the optimal persistent graph adjusting algorithm based on the shortest path (SP-OPGAA). Under a given agent formation shape and leader constraint, LC-HMAS-OPFGA first generates the network topology and its optimal rigid graph corresponding to this formation shape. Then, LC-HMAS- OPFGA uses MCA-SP-OPGGA to direct the optimal rigid graph to generate the optimal persistent graph. Finally, LC- HMAS-OPFGA uses SP-OPGAA to adjust the optimal persistent graph until it satisfies the leader constraint. We also demonstrate the algorithm, LC-HMAS-OPFGA, with an example and verify its effectiveness.
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