Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this lett...
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Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is ***, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.
We study a general maximum prinicple for a kind of partially observed risk-sensitive optimal control problem of mean-field type, where the system contains the mathematical expectation of state. With the use of Girsano...
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Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it...
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Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the product’s performance, minimize change cost, and reduce change duration/time. Focusing on the case where the correlation strength between parts cannot be accurately obtained, in this paper we study an interval multi-objective evolutionary algorithm for finding excellent design change plans. Firstly, on the basis of the established multi-layer product network with interval correlation weights, an interval multi-objective optimization model of the product design change planning problem is established, where three new objective functions regarding product performance, carbon trading cost and supply risk are defined. Then, a constraint multi-objective evolutionary algorithm based on interval Pareto dominance is proposed to search for optimal change plans. Several novel operators, including the problem characteristic-guided population update strategy, the probability-based interval Pareto dominance, and the interval constraint handling strategy, are developed to enhance the algorithm’s performance. Finally, the proposed algorithm is compared with eight existing algorithms on the two design change cases, experimental results revealed its effectiveness. IEEE
The omnidirectional all-wheel drive mobile robot supports single-wheel independent drive and steering, so it can adapt to complex and changeable terrain conditions by multiple sports modes. In the design of omnidirect...
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Cracks are one of the main causes of structural damage to roads and buildings. Compared to traditional crack detection methods, recently the application of deep learning methods for crack detection is gradually increa...
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Current YOLO-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board (PCB) defect detection application scenario. In order to solve this prob...
Current YOLO-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board (PCB) defect detection application scenario. In order to solve this problem， we proposed a new method， which we combined the lightweight network MobileViT with the convolutional block attention module (CBAM) mechanism and the new regression loss function. This method needed less computation resources， making it more suitable for embedded edge detection devices. Meanwhile， the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model. In addition， experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9% across six typical defect detection tasks， while reducing computational costs by nearly 90%. It significantly reduces the model's computational requirements while maintaining accuracy， ensuring reliable performance for edge deployment.
During the transbronchial lung biopsy (TBLB), the bronchoscope may easily collide with the airway, which can cause damage. In order to improve the safety of the surgery, this paper puts forward a tactile sensor integr...
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This paper presents a new online trajectory generation method that enables multiple robots to navigate through the cluttered environment. The proposed method first employs a centralized discrete planner to obtain all ...
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Rail detection is an important part of intelligent rail transportation, and research on rail recognition is of significant importance for both detecting foreign object intrusion in railways and realizing unmanned driv...
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Recently, graph convolutional networks (GCNs) have achieved excellent performances in skeleton-based recognition. However, most of GCN-based methods are adopted with fixed skeleton graphs, which cannot adaptively mode...
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