Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., p...
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivi...
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In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy set...
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In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.
Discrete-time fractional-order dynamical systems (DT-FODS) have found innumerable applications in the context of modeling spatiotemporal behaviors associated with long-term memory. Applications include neurophysiologi...
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The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first ...
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Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink ...
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In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In ...
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ISBN:
(纸本)9781665489898
In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository.
Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real-time implementation. Event-triggered MPC aims to reduce this burden by optimi...
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Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real-time implementation. Event-triggered MPC aims to reduce this burden by optimizing the control inputs only when needed instead of every time step. Existing works in literature have been focused on algorithmic development and simulation validation for very specific scenarios. Therefore, event-triggered MPC in real-world full-size vehicle has not been thoroughly investigated. This work develops event-triggered MPC with switching model for autonomous vehicle lateral motion control, and implements it on a production vehicle for real-world validation. Experiments are conducted under both closed road and open road environments, with both low speed and high speed maneuvers, as well as stop-and-go scenarios. The efficacy of the proposed event-triggered MPC, in terms of computational load saving without sacrificing control performance, is clearly demonstrated. It is also demonstrated that event-triggered MPC can sometimes improve the control performance, even with less number of optimizations, thus contradicting to existing conclusions drawn from simulation.
This paper presents a novel optimization-based full-pose trajectory tracking method to control overactuated multirotor aerial vehicles with limited actuation abilities. The proposed method allocates feasible control i...
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Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuo-tactile sensor and the pneuma...
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