The distribution automation system has become a critical component of the modern power system due to the rapid development of information and communication technology. However, safety issues have become prominent due ...
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Due to the strong attenuation effect of electromagnetic wave propagation in water, GNSS cannot be used underwater. The underwater acoustic navigation methods based on long baseline (LBL), short baseline (SBL) and ultr...
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Integrating task and motion planning is challenging as task planners work in the discrete domain and computationally expensive motion planners in the continuous domain. A single-task action can be translated to infini...
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This paper studies an adaptive fixed-time output tracking control problem for nonlinearly parametrized hypersonic vehicles. To deal with unknown nonlinearly parametrized dynamics, fuzzy logic systems (FLSs) are used t...
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With the rapid development of industrial automation, industrial robots have become increasingly widespread in production lines. Most existing transfer systems rely on traditional mechanical structures, which makes it ...
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Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing r...
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Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (IoT) automation. However, the exceptional interactions between automation applications may result in inter...
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ISBN:
(纸本)9798350329964
Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (IoT) automation. However, the exceptional interactions between automation applications may result in interferences, such as conflicts and infinite loops, which cause undesirable consequences and even security and safety risks. While several techniques have been proposed to address this problem, they are often restricted in handling explicit and simple conflicts without considering contextual influences. In addition, they suffer from performance issues when applying to large-scale applications. To address these challenges, we design an effective and practical tool KnowDetector with comprehensive domain knowledge to detect application interferences. To detect application interferences, KnowDetector constructs an automation graph with 1) events, conditions, and actions from automation applications, 2) vertices representing physical environment channels, and 3) edges derived from potential semantic relations between the vertices. In order to make the graph extensively capture the interactions between automation applications, we propose a knowledge model named KnowIoT that accurately characterizes IoT devices with command-level IoT services and the intricate relations between these services and the contextual environment. We abstract the interference detection into a graph pattern-matching problem and summarize ten application interference patterns of four types. Finally, KnowDetector can efficiently detect application interferences by searching for sub-graphs matching the patterns within the automation graph. We evaluated KnowDetector on three real-world datasets. The results demonstrated that it outperformed the other state-of-the-art tools with the highest precision, recall, and F-measure. In addition, KnowDetector is scalable to detect application interferences within a large number of applications with a minimal time overhead.
In order to avoid the harm caused by uncontrolled charging and discharging of EVs to the power grid, this paper takes the EV charging and discharging scheduling within the microgrid (MG) as the research object and est...
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In this paper, we introduce multiple Reconfigurable Intelligent Surfaces (RISs) aided secure MIMO Dual-Function Radar-Communication (DFRC) system for enhanced dual-functionality performance. We aim to optimize the Sig...
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Physiological status diagnosis plays an important role in clinical practice. Different personal information hinders the practical application heavily. To address this issue, we propose a tensor-based physiological sta...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
Physiological status diagnosis plays an important role in clinical practice. Different personal information hinders the practical application heavily. To address this issue, we propose a tensor-based physiological status diagnosis approach, fused the subject-variant information with physiological data. The subject-variant information guided similarity information matrix is employed to regularize the tensor-based formulation so that the subject-variant information can be appropriately adopted. We proposed an alternating direction method of multipliers (ADMM) inbuilt with the block coordinate descent (BCD) algorithm to solve this formulation. A real-case dataset has been used to validate the proposed diagnosis method, which shows satisfactory results compared with other existing methods.
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