Examining the impact of domain and cognitive complexity on query formulation and reformulation

The purpose of this analysis was to evaluate an existing set of search tasks in terms of their effectiveness as part of a “shared infrastructure” for conducting interactive IR research. Twenty search tasks that varied in their cognitive complexity and domain were assigned to 47 study participants; the 3,101 moves used to complete those tasks were then analyzed in terms of frequency of each type of move and the sequential patterns they formed. The cognitive complexity of the tasks influenced the number of moves used to complete the tasks, with the most complex (i.e., Create) tasks requiring more moves than tasks at other levels of complexity. Across the four domains, the Commerce tasks elicited more search moves per search. When sequences of moves were analyzed, seven patterns were identified; some of these patterns were associated with particular task characteristics. The findings suggest that search tasks can be designed to elicit particular types of search behaviors and, thus, allow researchers to focus attention on particular aspects of IR interactions.     

Impulsive effects on the stability and stabilization of positive systems with delays

This study addresses the exponential stability and positive stabilization problems of impulsive positive systems (IPSs) with time delay. Specially, three types of impulses, namely, disturbance, “neutral”, and stabilizing impulses, are considered. For each type of impulsive effect, the exponential stability criterion is established utilizing the Lyapunov–Razumikhin techniques. Moreover, on the basis of the obtained stability results, the state-feedback controller design problem is investigated to positively stabilize the IPSs with time delay under different types of impulsive effects. Finally, numerical examples are provided to illustrate the effectiveness of the theoretical results.     

Optimal configuration and energy management scheme of an isolated micro-grid using Cuckoo search optimization algorithm

This paper proposes an optimization scheme for optimal configuration and energy management of the micro-grid (MG), using the Cuckoo search optimization algorithm (CSOA). The selected MG supplies a load profile located between 30.119 latitude and 31.605 longitude. The energy produced by the MG generation sources, according to meteorological data of the proposed location, is calculated using MATLAB. The objective/fitness function is modeled and designed for minimizing the total investment cost (TIC) including capital, investment, operation and maintenance costs. A novel weighted goal attainment function (WGAF) has been proposed to reduce CO₂ emissions and their associated costs. Moreover, WGAF also applies higher taxes on the amount of emissions that exceed governmental approved limits. To investigate the effects of WGAF on the TIC ($/year), annual cost of energy ($/kWh), and CO₂ emissions, various weighted coefficients are analyzed. The simulation results have shown that the designed optimization scheme can robustly and efficiently produce the optimal MG configuration that is both eco-friendly and generates economic benefits.     

Chaotic behavior of discrete-time linear inclusion dynamical systems

Given any finite family of real d-by-d nonsingular matrices $\{S_1,\dots ,S_l\},$ by extending the well-known Li–Yorke chaos of a deterministic nonlinear dynamical system to a discrete-time linear inclusion or hybrid or switched system:

$\begin{array}{c}\hfill x_n\in \{S_k{x}_{n?1};1\le k\le l\},x_0\in {\mathbb{R}}^d\mathrm{and}n\ge 1,\end{array}$

we study the chaotic dynamics of the state trajectory (x_n(x₀, σ))_{n ≥ 1} with initial state $x_0\in {\mathbb{R}}^d,$ governed by a switching law $\sigma :\mathbb{N}\to \{1,\dots ,l\}$. Two sufficient conditions are given so that for a “large” set of switching laws σ, there exhibits the scrambled dynamics as follows: for all $x_0,y_0\in {\mathbb{R}}^d,x_0\ne y_0,$

$\begin{array}{c}\hfill \underset{n\to +\infty }{lim\; inf}\parallel x_n(x_0,\sigma )?x_n(y_0,\sigma )\parallel =0\mathrm{and}\underset{n\to +\infty }{lim\; sup}\parallel x_n(x_0,\sigma )?x_n(y_0,\sigma )\parallel =\infty .\end{array}$

This implies that there coexist positive, zero and negative Lyapunov exponents and that the trajectories (x_n(x₀, σ))_{n ≥ 1} are extremely sensitive to the initial states $x_0\in {\mathbb{R}}^d$. We also show that a periodically stable linear inclusion system, which may be product unbounded, does not exhibit any such chaotic behavior. An explicit simple example shows the discontinuity of Lyapunov exponents with respect to the switching laws.     

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique.     

Asymptotic stability of delayed fractional-order fuzzy neural networks with impulse effects

In this paper, we investigate the asymptotic stability of fractional-order fuzzy neural networks with fixed-time impulse and time delay. According to the fractional Barbalat’s lemma, Riemann–Liouville operator and Lyapunov stability theorem, some sufficient conditions are obtained to ensure the asymptotic stability of the fractional-order fuzzy neural networks. Two numerical examples are also given to illustrate the feasibility and effectiveness of the obtained results.     

Synchronization of stochastic discrete-time complex networks with partial mixed impulsive effects

In this paper, the synchronization problem is studied for a class of stochastic discrete-time complex networks with partial mixed impulsive effects. The involving impulsive effects, called partial mixed impulses, can be regarded as local and time-varying impulses, which means that impulses are not only injected into a fraction of nodes in networks but also contain synchronizing and desynchronizing impulses at the same time. In order to handle this case, several mathematical techniques are proposed to tackle mixed impulsive effects in discrete-time dynamical systems. Based on the variation of parameters formula, several sufficient criteria are derived to ensure that synchronization of the addressed networks can be achieved in mean square. The obtained criteria not only rely on the strengths of mixed impulses and the impulsive intervals, but also can reduce conservativeness. Finally, a numerical example is presented to show the effectiveness of our results for neural networks.     

Nonlinear stochastic position and attitude filter on the Special Euclidean Group 3

This paper formulates the pose (attitude and position) estimation problem as nonlinear stochastic filter kinematics evolved directly on the Special Euclidean Group 3 ($\mathbb{SE}\left(3\right)$). This work proposes an alternate way of potential function selection and handles the problem as a stochastic filtering problem. The problem is mapped from $\mathbb{SE}\left(3\right)$ to vector form, using the Rodriguez vector and the position vector, and then followed by the definition of the pose problem in the sense of Stratonovich. The proposed filter guarantees that the errors present in position and Rodriguez vector estimates are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and that they converge to small neighborhood of the origin in probability. Simulation results show the robustness and effectiveness of the proposed filter in presence of high levels of noise and bias associated with the velocity vector as well as body-frame measurements.     

Robust predictive control based on the Meixner-like model

This paper is devoted to the issue of a robust predictive control for linear discrete-time systems by using Meixner-like model. The Meixner-like functions are an extension of Laguerre functions and convenient when the system has a slow start or delay. To ensure the reduction of the parameter number in the Meixner-like model, the optimization of parameters characterizing the Meixner-like functions is proposed. This proposed robust predictive control copes with physical constraints and geometrical constraints due to parameter uncertainties, which are estimated by using the Unknown But Bounded Error (UBBE) approach, and leads to the min-max optimization problem.     

Improving the performance of networked control systems with time delay and data dropouts based on fuzzy model predictive control

This paper proposes a fuzzy model predictive control (FMPC) combined with the modified Smith predictor for networked control systems (NCSs). The network delays and data dropouts are problems, which greatly reduce the controller performance. For the proposed controller, the model of the controlled system is identified on-line using the Takagi – Sugeno (T-S) fuzzy models based on the Lyapunov function. There are two internal loops in the proposed structure. The first is the loop around the FMPC, which predicts the future outputs. The other is the loop around the plant to give the error between the system model and the actual plant. The proposed controller is designed for controlling a DC servo system through a wireless network to improve the system response. The practical results based on MATLAB/SIMULINK are established. The practical results are indicated that the proposed controller is able to respond the networked time delay and data dropouts compared to other controllers.     

Study on stability and stabilizability of discrete-time mean-field stochastic systems

This paper is to study the mean square stabilizability and regional stability of discrete-time mean-field stochastic systems. Firstly, a necessary and sufficient condition is presented via the spectrum of linear operator to illustrate the stabilizability of discrete-time mean-field stochastic systems. B(0, γ)-stabilizability is introduced and transformed into solving linear matrix inequalities (LMIs). Secondly, B_M-stability is characterized, especially, the stabilities of circular region, sector region and annulus regions are discussed extensively. Finally, as applications, it is shown that B(0, γ₁; γ₂)-stability has close relationship with the decay rate of the system state response and the Lyapunov exponent.     

Using matrix norms to estimate the direction of arrival of planar waves on an ULA

SEAD method estimates the direction-of-arrival angles on an uniform linear array based on the difference between the two largest singular values, what is called differential spectrum. Although it presented an outstanding performance, the ability to indicate the source positions was not elucidated yet. Inspired by the differential spectrum formulation we derived a total differential spectrum and found out that the matrix norm induced by the vector 2-norm of a modified spatial covariance matrix can be used to estimate the direction-of-arrival of multiple plane waves. Indeed we show that matrix norms are estimators and we propose their use instead of the singular value decomposition in SEAD-based methods. We present a general mathematical expression in order to explicit the operating principles of the proposed methods. Consequently, we were able to explain how the relation between the arriving and the search angles produces the larger peaks on the differential spectrum. To evaluate the important role played by matrix norms, a thousand experiments were carried out. They showed that the proposed approach proved to be as accurate as the previous SEAD-based methods, while providing a significant reduction on runtime. It also outperformed well-established methods like MODEX regarding the estimation error.     

Denial of service attack and defense method on load frequency control system

The application of the network technology in the power grid makes the Load Frequency Control (LFC) system more vulnerable to various kinds of network attacks. The Denial of Service (DOS) attack can block the data collected by the Phasor measurement unit from being transmitted to the LFC center, thereby affecting the decision of the control center and generation of control signals, and can not adjust the frequency of the power grid timely. Aiming at the DOS attack on LFC, a defense method based on data prediction is proposed. Through the combination of the deep learning algorithm and the Extreme Learning Machine (ELM) algorithm, the Deep auto-encoder Extreme Learning Machine (DAELM) algorithm combines the advantages of the fast speed of the extreme learning machine and the advantages of high accuracy of the deep learning. We can predict and supplement the lost data based on the DAELM algorithm, and ensure the normal operation of the LFC system, thus can prevent DOS attacks. The experiments verified the effectiveness of the proposed method.     

Towards quantifying the impact of randomly occurred attacks on a class of networked control systems

In this paper, the impacts from both cyber and physical attacks are analyzed for a class of networked control systems via a delta operator approach. A comprehensive attack model involving randomly occurred deception attack, denial-of-service attack and physical attack is established. The multi-tasking optimal control strategy is developed in the delta-domain such that the individual cost function for each player is minimized. For quantifying the impacts from attacks, an ?-Nash equilibrium is employed to describe the performance degradation. An explicit upper bound for the ?-Nash equilibrium is provided such that the maximum performance-loss induced by the considered all-around attacks is estimated. Some convex optimization algorithms are provided to compute an upper bound of the ?-Nash equilibrium. Finally, a numerical example is presented to illustrate the validity of the design scheme.     

Using matrix norms to estimate the direction of arrival of planar waves on an ULA

SEAD method estimates the direction-of-arrival angles on an uniformly linear array based on the difference between the two largest singular values, what is called differential spectrum. Although it presented an outstanding performance, the ability to indicate the source positions was not elucidated yet. Inspired by the differential spectrum formulation we derived a total differential spectrum and found out that the matrix norm induced by the vector 2-norm of a modified spatial covariance matrix can be used to estimate the direction-of-arrival of multiple plane waves. Indeed we show that matrix norms are estimators and we propose their use instead of the singular value decomposition in SEAD-based methods. We present a general mathematical expression in order to explicit the operating principles of the proposed methods. Consequently, we were able to explain how the relation between the arriving and the search angles produces the larger peaks on the differential spectrum. To evaluate the important role played by matrix norms, a thousand experiments were carried out. They showed that the proposed approach proved to be as accurate as the previous SEAD-based methods, while providing a significant reduction on runtime. It also outperformed well-established methods like MODEX regarding the estimation error.     

Fabric defect inspection based on lattice segmentation and lattice templates

Automated fabric inspection is a challenging task due to the unpredictable visual forms of the fabric defects and their scarcity compared with the tremendous amount of defect-free fabric products. This paper proposes a novel method based on lattice segmentation and lattice templates which automatically identifies the defects of fabric images. With the proposed method, a fabric image is segmented to lattices by inferring the placement rule of the texture primitives categorized to distinct texture classes. Each texture class is modeled by multiple templates inferred from the defect-free samples based on some metrics determined a priori according to their inspection efficiencies. For a lattice segmented from a given image, the most similar template is identified through a template matching process which compensates the local deformations around the lattice, and the distances between the lattice and the identified template are estimated based on the selected metrics. The lattices of distances exceeding the learnt distance range are identified as defective. The performance of the proposed method is evaluated based on two databases respectively providing pixel-level and image-level evaluations. For both databases, the receiver operating characteristic curves are plotted and the average areas under curves are 0.86 and 0.95, respectively, for pixel-level and image-level databases. The proposed method is further tested on the blurred and noisy version of images from pixel-level database and the resulting area is 0.81 on average. The proposed method outperforms the state-of-the-art methods by comparing corresponding areas.     

Source localization using TDOA and FDOA measurements based on semidefinite programming and reformulation linearization

The problem of source localization using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements has been widely studied. It is commonly formulated as a weighted least squares (WLS) problem with quadratic equality constraints. Due to the nonconvex nature of this formulation, it is difficult to produce a global solution. To tackle this issue, semidefinite programming (SDP) is utilized to convert the WLS problem to a convex optimization problem. However, the SDP-based methods will suffer obvious performance degradation when the noise level is high. In this paper, we devise a new localization solution using the SDP together with reformulation-linearization technique (RLT). Specifically, we firstly apply the RLT strategy to convert the WLS problem to a convex problem, and then add the SDP constraint to tighten the feasible region of the resultant formulation. Moreover, this solution is also extended for cases when there are sensor position and velocity errors. Numerical results show that our solution has significant accuracy advantages over the existing localization schemes at high noise levels.     

Spacecraft output feedback attitude control based on extended state observer and adaptive dynamic programming

This paper investigates spacecraft output feedback attitude control problem based on extended state observer (ESO) and adaptive dynamic programming (ADP) approach. For the plant described by the unit quaternion, an ESO is first presented in view of the property of the attitude motion, and the norm constraint on the unit quaternion can be satisfied theoretically. The practical convergence proof of the developed ESO is illustrated by change of coordinates. Then, the controller is designed with an involvement of two parts: the basic part and the supplementary part. For the basic part, a proportional-derivative control law is designed. For the supplementary part, an ADP method called action-dependent heuristic dynamic programming (ADHDP) is adopted, which provides a supplementary control action according to the differences between the actual and the desired system signals. Simulation studies validate the effectiveness of the proposed scheme.     

Stimulus space complexity determines the ratio of specialist and generalist neurons during pattern recognition

Neural processing layers built on divergent connectivity patterns display two types of stimulus-dependent responses: neurons that react to a few stimuli, specialists, and other ones that respond to a wide range of inputs, generalists. Specialists are essential for the discrimination of stimuli and generalists extract common and generic properties from them. This neural heterogeneity could have emerged because of animal adaptation to the environment. Thus, we suggest that there is a relationship between the percentage of specialists and generalists and the stimulus complexity. In order to study this possible relationship, we use patterns with different complexities in a bio-inspired neural network and calculate their classification errors for different ratios of these types of neurons. This study shows that, when the complexity of the stimuli is low, the minimum classification error is achieved with almost any specialist-generalist ratio. Thus, in this case, the role of these neurons during pattern recognition is unspecific. When this complexity is intermediate, both are needed to minimize the classification error, usually in a similar proportion. For increasing stimulus complexity, the importance of generalists decreases, until their relevance is fully nullified when the complexity is high. Therefore, if we adjust the specialist-generalist ratio to the complexity of patterns, we can build more effective neural networks for pattern recognition. Finally, we propose an estimation of stimulus complexity based on the proportion of these types of neurons observed by neural recordings. This offers the possibility to evaluate the stimulus complexity to which animals are adapted.     

A new approach based on discrete-time high-order neural networks with delays and impulses

This paper is concerned with the stability of discrete-time high-order neural networks (HONNs) with delays and impulses. Without applying the Lyapunov function, some sufficient conditions, which ensure the exponential stability and asymptotic stability of considered networks involving delays and impulses, are derived based on the fixed point theory. Finally, several numerical examples are given to demonstrate the effectiveness of the obtained results.