No abstract is provided for this article.
Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.
The quality of signals plays a critical role in the diagnosis of faults in rotating machinery. It significantly impacts the accuracy of fault Identification and the precision of predicting the remaining useful life. During the signal collection process, it is inevitable that noise is acquired from the surrounding environment or other components within the rotating machinery. This noise can pose a significant challenge when attempting to extract meaningful features from vibration signals. Therefore, there is a pressing need to create a denoising model that can effectively remove noise from collected vibration signals, even in the absence of pristine, noise-free reference signals. In this study, we first developed a self-supervised denoising method for vibration signals that differs markedly from the traditional supervised learning denoising models in the field of rotating machinery, which rely on noise-clean signal pairs for training.
This paper deals with a convex optimization approach to the problem of robust network-based H ∞ control for linear systems connected over a common digital communication network with static quantizers. Both the polytopic and the norm-bounded uncertainties are taken into consideration separately. First, the effect of both the output quantization levels and the network conditions under static quantizers is investigated. Second, by introducing a descriptor technique, using a Lyapunov—Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-range-dependent linear matrix inequalities for the existence of the desired network-based quantized controllers with simultaneous consideration of network-induced delays and measurement quantization. The explicit expression of the controllers is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm-bounded uncertainties. Two examples are utilized to illustrate the design procedure proposed in this paper.
This paper studies the problem of fault detection for linear discrete time-varying systems with multiplicative noise in finite-horizon, where our main object is to provide an optimal fault detection filter (FDF) design scheme such that stochastic sensitivity/robustness ratio for fault diagnosis is maximized in the sense of probability 1. An operator-aided optimization approach and a generalized FDF are proposed such that solutions to the filter design issues are derived in the operator forms. The relationships among the deduced solutions are explicitly revealed via the proposed operator-aided methodology. The parameter matrices of the filter are computed in an analytical way by solving some recursive matrix equations. It is shown that the addressed approaches establish an operator-based framework of optimal FDF design for some categories of linear discrete-time systems. An example is given to illustrate the efficacy of our algorithms.
One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Trust can be improved once we understand how and why deep models carve out a particular form of decision boundary and thus make particular decisions. Robustness against adversarial examples is directly related to the decision boundary as adversarial examples are basically 'missed out' by the decision boundary between two classes. Investigating the decision boundary of deep neural networks, nevertheless, faces tremendous challenges. First, how we can generate instances near the decision boundary that are similar to real samples? Second, how we can leverage near decision boundary instances to characterize the behaviour of deep neural networks? Motivated to solve these challenges, we focus on investigating the decision boundary of deep neural network classifiers. In particular, we propose a novel approach to generate instances near decision boundary of pre-trained DNNs and then leverage these instances to characterize the behaviour of deep models.
Summary This paper is focused on the problem of adaptive sliding mode control design for uncertain neutral‐type stochastic systems under a prescribed H ∞ performance. A simplified state observer is put forward to estimate the unknown state variables, which could be properly incorporated for establishing a new linear‐type switching surface and the associated adaptive variable structure controller. By virtue of the adaptive control design, unknown matched perturbation and potential uncertainties can be counteracted, and the system trajectories are guaranteed to reach the predefined switching surface within finite moment in almost surely sense, and performance analysis of the closed‐loop dynamics during the sliding surface is carried out with a specified H ∞ performance. At last, two illustrative examples through computer simulations are provided to verify the effectiveness and applicability of the proposed scheme.
Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. In this paper, we study local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. An appropriate Lyapunov–Razumikhin function and the small gain condition are utilized to establish some conditions for stability analysis of the network under consideration. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate an application of the proposed approach.
Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.
No abstract is provided for this article.
This paper deals with a computational optimization approach to the problem of state-feedback control design for a wind turbine system. The first step of the study is to develop a reduced order model for the system by considering the most important physical phenomena of aerodynamics and structural dynamics. Moreover, the behavior of the system can be influenced by the coupled dynamics between the tower motions and the blade pitch and turbine speed which can cause instabilities in the control loops in the worst case. By using a suitable wavelet funcation, called Haar functions, a recursive computational procedure is established for finding the system dynamics approximately by solving only algebraic equations instead of solving the Riccati differential. Simulation results are given to illustrate the usefulness of the proposed control methodology.
This paper presents the issue of robust disturbance attenuation and robust asymptotic stability problem for finite-dimensional linear parameter-dependent state-delayed systems. The use of polynomial parameter-dependent quadratic Lyapunov functions and linear matrix inequalities (LMIs) formulations for robust H ∞ control are considered. It is shown that the state feedback control can be determined to guarantee the stability of the closed-loop system independently of the time-delay. We present an illustrative example to demonstrate the applicability of the proposed design approach. Acknowledgements This research project was partially financially supported under Research Grant 8101004-1-1 provided by University of Tehran and partially by the German Academic Exchange Service (DAAD).
