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Day-ahead prediction of wind speed is a basic and key problem of large-scale wind power penetration. Many current techniques fail to satisfy practical engineering requirements because of wind speed's strong nonlinear features, influenced by many complex factors, and the general model's inability to automatically learn features. It is well recognized that wind speed varies in different patterns. In this paper, we propose a deep feature learning (DFL) approach to wind speed forecasting because of its advantages at both multi-layer feature extraction and unsupervised learning. A deep belief network (DBN) model for regression with an architecture of 144 input and 144 output nodes was constructed using a restricted Boltzmann machine (RBM). Day-ahead prediction experiments were then carried out. By comparing the experimental results, it was found that the prediction errors with respect to both size and stability of a DBN model with only three hidden layers were less than those of the other three typical approaches including support vector regression (SVR), single hidden layer neural networks (SHL-NN), and neural networks with three hidden layers (THL-NN). In addition, the DBN model can learn and obtain complex features of wind speed through its strong nonlinear mapping ability, which effectively improves its prediction precision. In addition, prediction errors are minimized when the number of DBN model's hidden layers reaches a threshold value. Above this number, it is not possible to improve the prediction accuracy by further increasing the number of hidden layers. Thus, the DBN method has a high practical value for wind speed prediction.

Robustness studies on integrated urban public transport networks have attracted growing attention in recent years due to the significant influence on the overall performance of urban transport system. In this paper, topological properties and robustness of a bus–subway coupled network in Beijing, composed of both bus and subway networks as well as their interactions, are analyzed. Three new models depicting cascading failure processes on the coupled network are proposed based on an existing binary influence modeling approach. Simulation results show that the proposed models are more accurate than the existing method in reflecting actual passenger flow redistribution in the cascading failure process. Moreover, the traffic load influence between nodes also plays a vital role in the robustness of the network. The proposed models and derived results can be utilized to improve the robustness of integrated urban public transport systems in traffic planning.

Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model’s robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.

Transmission efficiency and robustness are two important properties of various networks and a number of optimization strategies have been proposed recently. We propose a scheme to enhance the network performance by adding a small fraction of links (or edges) to the currently existing network topology, and we present four edge addition strategies for adding edges efficiently. We aim at minimizing the maximum node betweenness of any node in the network to improve its transmission efficiency, and a number of experiments on both Barabási–Albert (BA) and Erdös–Rényi (ER) networks have confirmed the effectiveness of our four edge addition strategies. Also, we evaluate the effect of some other measure metrics such as average path length, average betweenness, robustness, and degree distribution. Our work is very valuable and helpful for service providers to optimize their network performance by adding a small fraction of edges or to make good network planning on the existing network topology incrementally.

A new 1-bit hybrid Full Adder cell is presented in this paper with the aim of reaching a robust and high-performance adder structure. While most of recent Full Adders are proposed with the purpose of using fewer transistors, they suffer from some disadvantages such as output or internal non-full-swing nodes and poor driving capability. Considering these drawbacks, they might not be a good choice to operate in a practical environment. Lowering the number of transistors can inherently lead to smaller occupied area, higher speed and lower power consumption. However, other parameters, such as robustness to PVT variations and rail-to-rail operation, should also be considered. While the robustness is taken into account, HSPICE simulation demonstrates a great improvement in terms of speed and power-delay product (PDP).

The optimum design of distributed tuned mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improved hybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.

After light absorption the primary process in light harvesting is the transfer of excitation to a reaction center which facilitates a separation of charge across a cell membrane. The physical principles underlying excitation transfer are explained. Theoretical methods for the description of the excitation migration process, including an expansion for excitation lifetime in terms of repeated trapping and subsequent detrapping events, and the construction of representative pathways for excitation transfer based on mean first passage times, are presented. Measures for robustness and optimality of excitation transfer in terms of quantum yield are introduced. Photosystem I (PSI) is used as an example to illustrate the methods discussed. Some conclusions for the design of artificial light harvesting systems are also discussed.

In this paper, we develop approximation error estimates as well as corresponding inverse inequalities for B-splines of maximum smoothness, where both the function to be approximated and the approximation error are measured in standard Sobolev norms and semi-norms. The presented approximation error estimates do not depend on the polynomial degree of the splines but only on the grid size. We will see that the approximation lives in a subspace of the classical B-spline space. We show that for this subspace, there is an inverse inequality which is also independent of the polynomial degree. As the approximation error estimate and the inverse inequality show complementary behavior, the results shown in this paper can be used to construct fast iterative methods for solving problems arising from isogeometric discretizations of partial differential equations.

In recent years, because complex networks can be used to model real-world complex systems, such as the Internet, urban infrastructure networks, and gene interaction networks, such research has been widely applied in engineering, social sciences, and life sciences and has caused widespread concern. Fractal dimension, as a concept concerning the filling ability and complexity of an object space, has great significance for the study of the robustness of complex networks. This paper studies the relationship between fractal dimension and the robustness of different types of complex networks from the perspective of network structure and network scale. We find that fractal dimension is strongly correlated with robustness under certain conditions and can be used as an important index to evaluate the robustness of complex networks.

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.

Human solid tumors are believed to be caused by a sequence of genetic abnormalities arising in normal and premalignant cells. The understanding of these sequences is important for improving cancer treatment. Models for the occurrence of the abnormalities include linear structure and a recently proposed tree-based structure. We will describe the oncogenetic tree model and an efficient algorithm for its estimation. We also discuss methods for estimating the reliability and goodness-of-fit of this reconstruction. An R package “Oncotree” implementing the described methodology is available from the authors.

With load-based model, considering the loss of capacity on nodes, we investigate how the coupling strength (many-to-many coupled pattern) and link patterns (one-to-one coupled pattern) can affect the robustness of interdependent networks. In one-to-one coupled pattern, we take into account the properties of degree and betweenness, and adopt four kinds of inter-similarity link patterns and random link pattern. In many-to-many coupled pattern, we propose a novel method to build new networks via adding inter-links (coupled links) on the existing one-to-one coupled networks. For a full investigation on the effects, we conduct two types of attack strategies, i.e. RO-attack (randomly remove only one node) and RF-attack (randomly remove a fraction of nodes). We numerically find that inter-similarity link patterns and bigger coupling strength can effectively improve the robustness under RO-attacks and RF-attacks in some cases. Therefore, the inter-similarity link patterns can be applied during the initial period of network construction. Once the networks are completed, the robustness level can be improved via adding inter-links appropriately without changing the existing inter-links and topologies of networks. We also find that BA–BA topology is a better choice and that it is not useful to infinitely increase the capacity which is defined as the cost of networks.

In recent decades, many countries have suffered power outages and these accidents were often caused by a small disturbance, but because of the connection structure between the circuits, even a small mistake will cause the power grid cascade to fail and paralyze the network in a large area. In order to find a way to enhance the network robustness, this paper proposes three defense methods based on different ratio: the k-shell value ratio, degree ratio, and residual load ratio. We would compare the quality of the three defense methods by the relative size of the largest connected component after cascading failure. Besides, we also compare the time, the total number of crashed nodes, and the speed of cascading failure progress under three defense methods. From the experimental results, it is found that the defense methods based on the k-shell value ratio and the degree ratio have their own advantages in different situations.

In this paper, we propose a scheme to implement hybrid encryption of images using generalized Arnold transformation and matrix nonlinear operations. This scheme consists of two stages. In the first stage, the pixels scrambling encryption is achieved by using generalized Arnold transformation. In the second stage, the pixels sequence encryption is carried out by a nonlinear matrix operation based on the key stream generated by a random matrix. Accordingly, the decryption process is completed by two steps inverse transformations. The hybrid encryption algorithm is one-time pad, and therefore has good anti-attack performance. The algorithm is featured by good confidentiality, low computational complexity, and easy-to-program processing. Moreover, the effectiveness, security and robustness of the algorithm are demonstrated by an image encryption simulation and the encryption performance analysis. Compared with the traditional Arnold scrambling encryption scheme and the chaotic encryption method based on the generalized standard mapping, the superiority of the proposed algorithm is demonstrated.

In this paper, Lagrangian-based method has been proposed for tuning the parameters of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller. In this method, the five parameters ($K_p$, $K_i$, $K_d$, $\alpha$ and $\beta$) of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller (FOPID) are suitably optimized, and successfully applied to a benchmark stable second-order feedback system. To prove the performance of the proposed method, several state-of-the-art approaches were compared. The computational complexity, robustness and stability analysis has been performed to investigate the performance of all these algorithms. Moreover, the precision and flexibility analysis among all these approaches has also been carried out in this paper. The closed loop response of the second-order bench mark stable plant in Simulink has also been depicted in this paper.

A central problem of systems biology is the reconstruction of Gene Regulatory Networks (GRNs) by the use of time series data. Although many attempts have been made to design an efficient method for GRN inference, providing a best solution is still a challenging task. Existing noise, low number of samples, and high number of nodes are the main reasons causing poor performance of existing methods. The present study applies the ensemble Kalman filter algorithm to model a GRN from gene time series data. The inference of a GRN is decomposed with p genes into p subproblems. In each subproblem, the ensemble Kalman filter algorithm identifies the weight of interactions for each target gene. With the use of the ensemble Kalman filter, the expression pattern of the target gene is predicted from the expression patterns of all the remaining genes. The proposed method is compared with several well-known approaches. The results of the evaluation indicate that the proposed method improves inference accuracy and demonstrates better regulatory relations with noisy data.

This paper deals with the asymptotic behavior of solutions to non-autonomous, fractional, stochastic $p$-Laplacian equations driven by additive white noise and random terms defined on the unbounded domain ${ℝ}^N$. We first prove the existence and uniqueness of solutions for polynomial drift terms of arbitrary order. We then establish the existence and uniqueness of pullback random attractors for the system in $L^2({ℝ}^N)$. This attractor is further proved to be a bi-spatial $(L^2({ℝ}^N),L^r({ℝ}^N))$-attractor for any $r\in [2,\infty )$, which is compact, measurable in $L^r({ℝ}^N)$ and attracts all random subsets of $L^2({ℝ}^N)$ with respect to the norm of $L^r({ℝ}^N)$. Finally, we show the robustness of these attractors as the intensity of noise and the random coefficients approach zero. The idea of uniform tail-estimates as well as the method of higher-order estimates on difference of solutions are employed to derive the pullback asymptotic compactness of solutions in $L^r({ℝ}^N)$ for $r\in [2,\infty )$ in order to overcome the non-compactness of Sobolev embeddings on ${ℝ}^N$ and the nonlinearity of the fractional $p$-Laplace operator.

The double-pendulum overhead crane system belongs to a class of underactuated mechanical systems, which is challenging to control due to its strong nonlinearities. This study deals with the nonlinear controller design for a double-pendulum overhead crane based on hierarchical sliding mode control with state-dependent switching gain. The equivalent and switching controls are obtained with the aid of first-level and second-level sliding surfaces. The asymptotic stability of sliding surfaces is proved theoretically and also shown graphically. Numerical simulations show that the proposed controller performs better than conventional hierarchical sliding mode control. The proposed controller effectively suppresses the load and hook swing and precisely controls the trolley’s displacement. It has strong robustness to the payload displacement and the change of the crane parameters.

The explosive growth in medical imaging technologies has been matched by a tremendous increase in the number of investigations centered on the structural and functional organization of the human body. A pivotal first step towards elucidating the correlation between structure and function, accurate and robust segmentation is a major objective of computerized medicine. It is also a substantial challenge in view of the wide variety of shapes and appearances that organs, anatomical structures and tissues can exhibit in medical images.

This chapter surveys the actively expanding field of medical image segmentation. We discuss the main issues that pertain to the remarkably diverse range of proposed techniques. Among others, the characteristics of a suitable segmentation paradigm, the introduction of a priori knowledge, robustness and validation are detailed and illustrated with relevant techniques and applications.