Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are ...unable to learn the relevant information of input data when the input gap is large. By introducing gate functions into the cell structure, the long short-term memory (LSTM) could handle the problem of long-term dependencies well. Since its introduction, almost all the exciting results based on RNNs have been achieved by the LSTM. The LSTM has become the focus of deep learning. We review the LSTM cell and its variants to explore the learning capacity of the LSTM cell. Furthermore, the LSTM networks are divided into two broad categories: LSTM-dominated networks and integrated LSTM networks. In addition, their various applications are discussed. Finally, future research directions are presented for LSTM networks.
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Videos, especially short videos, have become an increasingly important source of information in these years. However, many videos spread on video sharing platforms are misleading, which have negative ...social impacts. Therefore, it is necessary to find methods to automatically identify misleading videos. In this paper, three categories of features (content features, uploader features and environment features) are proposed to construct a convolutional neural network (CNN) for misleading video detection. The experiment showed that all the three proposed categories of features play a vital role in detecting misleading videos. Our proposed approach that combines three categories of features achieved the best performance with the accuracy of 0.90 and the F1 score of 0.89. It also outperformed other baselines such as SVM, k-NN, decision tree and random forest models by more than 22%.
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Synthetic aperture radar (SAR) ship detection based on deep learning has been widely applied in recent years. However, two main obstacles are hindering SAR ship detection. First, the identification ...of ships in a port is seriously disrupted by the presence of onshore buildings. It is difficult for the existing detection algorithms to effectively distinguish the targets from such a complex background. Additionally, it appears more complicated to accurately locate densely arranged ships. Second, the ships in SAR images exist at a variety of scales due to multiresolution imaging modes and the variety of ship shapes; these pose a much greater challenge to ship detection. To solve the above problems, this paper proposes an object detection network combined with an attention mechanism to accurately locate targets in complex scenarios. To address the diverse scales of ship targets, we construct a loss function that incorporates the generalized intersection over union (GIoU) loss to reduce the scale sensitivity of the network. For the final processing of the results, soft nonmaximum suppression (Soft-NMS) is also introduced into the model to reduce the number of missed detections for ship targets in the presence of severe overlap. The experimental results reveal that the proposed model exhibits excellent performance on the extended SAR ship detection dataset (SSDD) while achieving real-time detection.
► We investigated to make use of CFBC fly ash and slag containing about 10% SO3. ► Up to 97% of CFBC fly ash and slag can be used to make an autoclaved brick. ► The compressive strength of the ...autoclaved brick reached 14.3MPa. ► The autoclaved brick had no destructive expansion even cured in water for 270days. ► There was no AFt and dihydrate gypsum in the autoclaved brick.
Circulating fluidized bed combustion (CFBC) fly ash and slag are wastes from CFBC power plant, and have such a high sulfur content (usually more than 5% SO3 by weight) that would result in a great risk of destructive expansion when used in building materials, which are produced in normal temperature. This work investigated the preparation conditions of autoclaved brick with CFBC fly ash and slag, and compared the long-term volume stability and the hydration products between the autoclaved brick and adobe one. It is shown that the autoclaved brick could be made up of 77% CFBC fly ash, 20% CFBC slag and 3% cement by weight, and exhibited good long-term volume stability, and achieved the compressive strength of up to 14.3MPa, and that there was no dihydrate gypsum and ettringite formation in the autoclaved brick so that the destructive expansion could be avoided. This work has carried out practical production and application of the autoclaved brick. It is concluded that CFBC fly ash and slag can be used to make high-quality brick as long as it is autoclaved.
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The tracking control of a multi-input multioutput nonlinear nonminimum phase system in general form is discussed. This system is assumed to be suffering from parameter uncertainties and unmodeled ...dynamics, and the priori information of them is unknown. By considering both the exact model and uncertain model, the sliding mode-based learning controller is proposed. By designing an appropriate sliding surface and a learning controller, the stability of the closed-loop system is guaranteed for both the exact model and uncertain model. To overcome the disadvantage caused by parameter uncertainties and unmodeled dynamics, a fuzzy logical system is adopted here. A numerical simulation result carried on vertical takeoff and landing aircraft is taken as an example to validate the effectiveness of the presented controller.
This paper is concerned with the adaptive sliding mode control (ASMC) design problem for a flexible air-breathing hypersonic vehicle (FAHV). This problem is challenging because of the inherent ...couplings between the propulsion system, the airframe dynamics and the presence of strong flexibility effects. Due to the enormous complexity of the vehicle dynamics, only the longitudinal model is adopted for control design in the present paper. A linearized model is established around a trim point for a nonlinear, dynamically coupled simulation model of the FAHV, then a reference model is designed and a tracking error model is proposed with the aim of the ASMC problem. There exist the parameter uncertainties and external disturbance in the model, which are not necessary to satisfy the so-called matched condition. A robust sliding surface is designed, and then an adaptive sliding mode controller is designed based on the tracking error model. The proposed controller can drive the error dynamics onto the predefined sliding surface in a finite time, and guarantees the property of asymptotical stability without the information of upper bound of uncertainties as well as perturbations. Finally, simulations are given to show the effectiveness of the proposed control methods.
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Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, ...which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime's closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on Expectation Maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration.
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Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and ...environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.
Abstract
Spammer detection is essentially a process of judging the authenticity of users, and thus can be regarded as a classification problem. In order to improve the classification performance, ...multi-classifier information fusion is usually used to realize the automatic detection of spammers by utilizing the information from multiple classifiers. However, the existing fusion strategies do not reasonably take the uncertainty from the results of different classifiers (views) into account, and the relative importance and reliability of each classifier are not strictly distinguished. Therefore, in order to detect spammers effectively, this paper develops a novel multi-classifier information fusion model based on the evidential reasoning (ER) rule. Firstly, according to the user's characterization strategy, the base classifiers are constructed through the profile-based, content-based and behavior-based. Then, the idea of multi-classifier fusion is combined with the ER rule, and the results of base classifiers are aggregated by considering their weights and reliabilities. Extensive experimental results on the real-world dataset verify the effectiveness of the proposed model.
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In this paper, the problem of adaptive finite-time control is considered for a class of nonlinear systems with parametric uncertainties. A novel adaptive command filtered backstepping control method ...is proposed, and the adverse impact caused by the command filter is eliminated by introducing modified error compensation mechanism with consideration of parametric uncertainties. Combined with the designed adaptation laws, the error compensation mechanism can be finite-time stable. Rigorous proof is achieved to show that the tracking error converges to a small neighborhood of zero in finite time with online parameters adaptation and error compensation. Finally, numeral simulations are presented to validate the effectiveness of the proposed adaptive finite-time control scheme.
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