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A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated crossdomain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed with insufficient theoretical support. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is a series of MAP-based cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized.We prove the general multiplicative inapproximability, and thus propose two heuristics, which, respectively, enjoy an -additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are also empirically validated, with one of the datasets extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations manifest the importance of community information in enhancing privacy inferencing.

This study investigates the problem of fault estimation and control for unknown discrete-time systems. Such a problem was first formulated as an multiobjective optimization problem. Then, a data-driven parameterization controller design method was proposed to optimize both fault estimation and robust control performances. In terms of the single-objection control problem, necessary and sufficient conditions for designing the suboptimal controller were presented, and the performance index optimized by the developed data-driven method was shown to be consistent with that of the model-based method. In addition, by introducing additional slack variables into the controller design conditions, the conservatism of solving the multiobjective optimization problem was reduced. Furthermore, contrary to the existing data-driven controller design methods, the initial stable controller was not required, and the controller gain was directly parameterized by the collected state and input data in this work. Finally, the effectiveness and advantages of the proposed method are shown in the simulation results.

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements---this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude.

As a combination of fuzzy sets and covering rough sets, fuzzy β covering has attracted much attention in recent years. The fuzzy β neighborhood serves as the basic granulation unit of fuzzy β covering. In this article, a new discernibility measure with respect to the fuzzy β neighborhood is proposed to characterize the distinguishing ability of a fuzzy covering family. To this end, the parameterized fuzzy β neighborhood is introduced to describe the similarity between samples, where the distinguishing ability of a given fuzzy covering family can be evaluated. Some variants of the discernibility measure, such as the joint discernibility measure, conditional discernibility measure, and mutual discernibility measure, are then presented to reflect the change of distinguishing ability caused by different fuzzy covering families. These measures have similar properties as the Shannon entropy. Finally, to deal with knowledge reduction with fuzzy β covering, we formalize a new type of decision table, that is, fuzzy β covering decision tables. The data reduction of fuzzy covering decision tables is addressed from the viewpoint of maintaining the distinguishing ability of a fuzzy covering family, and a forward attribute reduction algorithm is designed to reduce redundant fuzzy coverings. Extensive experiments show that the proposed method can effectively evaluate the uncertainty of different types of datasets and exhibit better performance in attribute reduction compared with some existing algorithms.

This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the H∞ state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are represented as linear combinations of weighting parameters. In the reformulated form, the constraints of the parameters for the activation function are considered in terms of linear matrix inequalities. Based on the Lyapunov-Krasovskii function and the improved reciprocally convex inequality, enhanced conditions for designing a new state estimator that guarantees H∞ performance are derived through a parameterization technique. The compared results with recent studies demonstrate the superiority and effectiveness of the presented method.

Considering disturbances, noise, and sensor faults, this paper investigates interval estimation for discrete-time Takagi-Sugeno fuzzy systems. To obtain precise estimation results and attenuate disturbances and noise in the system simultaneously, we integrate robust observers based on the H technique and reachability analysis. Two novel observer gain computation methods are proposed for different purposes. The time-invariant method relaxes the original design conditions by transforming the parameterized linear matrix inequality into a series of linear matrix inequalities to increase computational speed, while the time-varying method employs the parameterized linear matrix inequality directly and conducts calculation online. Furthermore, reachable set representations for error dynamics are formulated by making use of both time-invariant observer gain and time-varying observer gain. Two comparative numerical simulation examples are studied to illustrate the effectiveness and superiority of the developed methods.

The aim of this article is to develop a new cooperative path following control scheme for a team of autonomous underwater vehicles (AUVs) which track one curve with nonlinear uncertainties. Individual path-following controllers are designed to guarantee that each AUV meets the desired tracking performance. For the cooperative path following, a containment control approach is incorporated into the design, where each AUV is forced to evenly disperse on a path that is parameterized by a continuous variable over a communication network. The main features of the paper that unlike the existing designs are that: first, by employing the command filtered control technique, the assumption of second-order derivative of the reference path is removed. Second, a globally uniformly ultimately bounded (GUUB) path following control structure is proposed that enables two kinds of controllers to switch under different cases. Third, coordination between multiple AUVs is achieved through a containment design, and a path variable containment cooperative path following controller is derived that enables multiple AUVs to be evenly dispersed and guided by the virtual leaders. Finally, the stability analysis and simulation example are given to verify the proposed control strategy.

This brief presents a novel one-step adaptive parameter estimation framework for identification of unknown asymmetric dead-zone characteristic parameters (e.g., width and slopes) in the sandwich systems, which avoids using the intermediate variables or carrying out a two-step recursive identification procedure. By applying a continuous piecewise linear neural network (CPLNN), the asymmetric dead-zone nonlinearities can be represented into a parameterized form, where the dead-zone characteristic parameters can be derived based on the online updated CPLNN weights. Moreover, after representing the sandwich system into an augmented system, an adaptive observer is designed to reconstruct the immeasurable internal variables, and an adaptive law with guaranteed convergence is used to online update the CPLNN weights so as to estimate the dead-zone parameters. In this adaptive law, the parameter estimation error information is extracted using simple filters and introducing several auxiliary variables. It is proven that the observation error and estimation error both converge to a small set around zero. The efficiency of the estimation method is exemplified via practical experiments on an X-Y positioning platform.

This article focuses on the target search problem in a curve-shape area using multiple unmanned aerial vehicles (UAVs), with the demand for obtaining the maximum cumulative detection reward, as well as the constraint of maneuverability and obstacle avoidance. First, the prior target probability map of the curve-shape area, generated by Parzen windows with Gaussian kernels, is approximated by the 1-D Gaussian mixture model (GMM) in order to extract some high-value curve segments corresponding to Gaussian components. Based on the parameterized curve segments from GMM, the self-organizing map (SOM) neural network is then established to achieve the coverage search. The step of winner neuron selection in SOM will prioritize and allocate the curve segments to UAVs, with the comprehensive consideration of multiple evaluation factors and allocation balance. The following step of neuron weight update will plan the UAV paths under the constraint of maneuverability and obstacle avoidance, using the modified Dubins guidance vector field. Finally, the good performance of GMM-SOM is evaluated on a coastline map.

Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting the adversarial attack, self-driving car. As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation to the true posterior. Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, and evaluate the model repeatedly, and average multiple model outputs. In many real-world cases, this is computationally prohibitive. In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by the model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Theoretically, we show that the idea of amortization incurs no additional costs on approximation performance. Empirical results validate the strong practical performance of our approach.

Many modern neural network architectures with over parameterized regime have been used for identification of skin cancer. Recent work showed that network, where the hidden units are polynomially smaller in size, showed better performance than overparameterized models. Hence, in this paper, we present multistage unit-vise deep dense residual network with transition and additional supervision blocks that enforces the shorter connections resulting in better feature representation. Unlike ResNet, We divided the network into several stages, and each stage consists of several dense connected residual units that support residual learning with dense connectivity and limited the skip connectivity. Thus, each stage can consider the features from its earlier layers locally as well as less complicated in comparison to its counter network. Evaluation results on ISIC-2018 challenge consisting of 10,015 training images show considerable improvement over other approaches achieving 98.05% accuracy and improving on the best results achieved in the International Skin Imaging Collaboration (ISIC-17 and ISIC-18) skin cancer competitions. The code of Unit-vise network is publicly available.

Left-turning at unsignalized intersection is one of the most challenging tasks for urban automated driving, due to the various shapes of intersections and rapidly changing nature of the driving scenarios. This paper addresses the challenges of decision-making at highly uncertain intersections of different shapes by proposing a generalized critical turning point (CTP) based hierarchical decision-making and planning method. The high-level planner takes the road map information and generates CTPs using a parameterized extraction model which is proposed and verified by naturalistic driving data. The CTPs are used to generate behavior-oriented paths that could be adapted to various intersections. These modifications help to assure high searching efficiency of the planning process. The low-level planner makes real-time, two-dimensional planning using a partially observable Markov decision process solver, which can handle the uncertainties of the intersections and make less conservative yet safe actions. With proper modifications, our proposed method can make commute-efficient two-dimensional planning decisions at unsignalized intersections of various shapes in real time.

This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.

It is well known that the "fixed spectrum" {i.e., the set of fixed modes} of a multi-channel linear system plays a central role in the stabilization of such a system with decentralized control. A parameterized multi-channel linear system is said to be "structurally complete" if it has no fixed spectrum for almost all parameter values. Necessary and sufficient algebraic conditions are presented for a multi-channel linear system with dependent parameters to be structurally complete. An equivalent graphical condition is also given for a certain type of parameterization.

Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log interpretation is usually with low efficiency and bad consistency. Therefore, the automatic well log interpretation tools based on machine learning and deep learning have been developed. Although the state-of-the-art sophisticated models can show fine interpretation performance with acceptable accuracies, ``blind'' tests do not always exhibit satisfactory results because of the complexity of lithology interpretation with respect to subsurface rock properties and the data-labeling quality. To solve this generalization challenge, we propose to leverage the parameterized quantum circuits in the deep-learning model. The quantum computing takes advantages of the superposition and entanglement quantum systems, which could potentially endow the generalization power or capability to the deep-learning model. Using the proposed quantum-enhanced deep-learning (QEDL) model, we have tested the model performance on field well log data from different wells. Compared with the classic fine convolutional neural network (CNN) model and the long short-term memory (LSTM) model, the proposed QEDL model achieves comparable model performance with a clearly improved generalization power for interpreting both thin and thick lithology layers. In addition, because of the quantum circuit structure, the QEDL model needs much fewer model parameters than LSTM and CNN models, i.e., the QEDL parameter number in our study can be approximately 75% less than that of LSTM and 89% less than that of CNN.

Buildings serve as the main places of human activities, and it is essential to automatically extract each building instance for a wide range of applications. Recently, automatic building segmentation approaches have made great progress in both detection and segmentation accuracy due to the rapid development of deep learning. However, these approaches struggle to delineate regular and accurate building boundaries due to the limitations in inferring overall structure of the building instance; this might lead to inconsistency in building geometry and difficulty in being applied directly to practical engineering. To tackle this challenge, this article presents an adaptive polygon generation algorithm (APGA), a novel method that aims at directly generating a polygonal output, parameterized as a sequence of building vertices, to outline each building instance. To achieve this, APGA predicts the candidate locations of building vertices and determines the arrangement of these vertices with the help of the position and orientation of the building boundary. Moreover, to introduce local context features and achieve improved performance of the predicted building polygon, APGA integrates finer structures around the candidate vertices to refine their positions. Experiments on several challenging building extraction datasets demonstrated that APGA outperformed state-of-the-art methods in terms of building coverage and geometric similarity.

As the control tasks become complex, fulfilling such tasks cooperatively is the first choice in practice. In this article, the output consensus problem of heterogeneous multiagent systems is studied by deploying distributed observers in follower agents. Each observer in the follower only measures part of the leader's output, which relieves the burden of a simple agent when the leader's output is of large-scale dimensions. Then, all followers in the system work cooperatively to estimate the full state of the leader. By using parameterized Riccati equation and output regulation theory, sufficient conditions are given to design the distributed observers and the output consensus protocol. Finally, a numerical example is conducted to verify the obtained result.

In this article, a distributed adaptive iterative learning control for a group of uncertain autonomous vehicles with a time-varying reference is presented, where the autonomous vehicles are underactuated with parametric uncertainties, the actuators are subject to faults, and the control gains are not fully known. A time-varying reference is adopted, the assumption that the trajectory of the leader is linearly parameterized with some known functions is relaxed, and the control inputs are smooth. To design distributed control scheme for each vehicle, a local compensatory variable is generated based on information collected from its neighbors. The composite energy function is used in stability analysis. It is shown that uniform convergence of consensus errors is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed control scheme.

This paper studies energy-efficient precoding designs for multi-user visible light communication (VLC) systems from the perspective of physical layer security where users’ messages must be kept mutually confidential. For such systems, we first derive a lower bound on the achievable secrecy rate of each user. Next, the total power consumption for illumination and data transmission is thoroughly analyzed. We then tackle the problem of maximizing energy efficiency, given that each user’s secrecy rate satisfies a certain threshold. The design problem is shown to be non-convex fractional programming, which renders finding the optimal solution computationally prohibitive. Our aim in this paper is, therefore, to find sub-optimal yet low complexity solutions. For this purpose, the traditional Dinkelbach algorithm is first employed to reformulate the original problem to a non-fractional parameterized one. Two different approaches based on the convex-concave procedure (CCCP) and Semidefinite Relaxation (SDR) are utilized to solve the non-convex parameterized problem. In addition, to further reduce the complexity, we investigate a design using the zero-forcing (ZF) technique. Numerical results are conducted to show the feasibility, convergence, and performance of the proposed algorithms depending on different parameters of the system.

The $\mathcal {H}_2$ -guaranteed cost decentralized control problem is investigated in this article. More specifically, on the basis of an appropriate $\mathcal {H}_2$ reformulation that we put in place, the optimal control problem in the presence of parameter uncertainties is then suitably characterized by convex restriction and solved in parameter space. It is shown that a set of stabilizing decentralized controller gains for the uncertain system is parameterized in a convex set through appropriate convex restriction, and then an approximated conic optimization problem is constructed. This facilitates the use of the symmetric Gauss–Seidel (sGS) semi-proximal augmented Lagrangian method (ALM), which attains high computational effectiveness. A comprehensive analysis is given on the application of the approach in solving the optimal decentralized control problem; and subsequently, the preserved decentralized structure, robust stability, and robust performance are all suitably guaranteed with the proposed methodology. Furthermore, an illustrative example is presented to demonstrate the effectiveness of the proposed optimization approach.

Timing violations in high performance communication channels in system-on-chips (SoC) may occur in the late stages of the physical design process. To address that, latency insensitive systems (LISs) employ pipelining in the communication channels through the insertion of relay stations. Although the functionality of an LIS is robust with respect to the communication latencies, imbalances in relay station insertion may degrade the throughput of the system. While having a large number of buffer queues can eliminate such performance loss, the system may not have adequate area to accommodate these buffers. The problem of buffer queue sizing for maximizing throughput while meeting buffer area constraints has been solved using a mixed-integer linear program (MILP) formulation; however, such an approach is not scalable. In this work, we formulate the buffer queue sizing problem as a parameterized graph optimization problem where for every communication channel there is a parameterized edge with buffer counts as the edge weight. We then use a minimum cycle mean algorithm to determine from which edges buffers can be removed safely. Experimental results on large LISs suggest that the proposed approach is scalable. Moreover, quality of the solutions, in terms of the throughput and the size of buffer queues, is observed to be as good as that of the MILP-based approach.

Reliability in petabyte-scale file transfers is critical for data collected from scientific instruments. We introduce a Multi-Layer Error Detection (MLED) architecture that significantly reduces the Undetected Error Probability (UEP) in file transfer. MLED is parameterized by a number of layers $n$ , and a policy $P_{i}$ for each layer $1 \le i \le n$ that describes its operation. MLED generalizes existing error detection approaches used in file transfer. We show conditions under which MLED reduces UEP. Analytical results show that a petabyte-size file transfer in MLED with $n=2$ using CRC-32s improves UEP by $2.49E\,+\,28$ compared to a single-layer CRC-64, when the BER is 10−10.

This article presents a new approach to construct reduced-order models for large nonlinear circuits excited by periodical or quasi-periodical sources. The key feature in the constructed reduced model is that it enables, through solving a much smaller system, tracing the variations in the periodical steady-state response of the original circuit with regards to variations in selected design parameters. The proposed approach is based on the notion of using discrete empirical interpolation to project the nonlinear operator of the circuit equation onto a smaller subspace. In addition, the original system equations are projected onto the subspace spanned by the first few derivatives of the solution with respect to the selected design parameters. Computational savings are made possible through solving a reduced system of equations instead of the full system. Numerical examples are presented to validate the accuracy and efficiency of the proposed approach.

Fuzzy integrals (FIs) are powerful aggregation operators that fuse information from multiple sources. The aggregation is parameterized using a fuzzy measure (FM), which encodes the worths of all subsets of sources. Since the FI is defined with respect to an FM, much consideration must be given to defining the FM. However, in practice this is a difficult task—the number of values in an FM scales as $2^n$ , where $n$ is the number of input sources, thus manually specifying an FM quickly becomes tedious. In this article, we review an automatic, data-supported method of learning the FM by minimizing a sum-of-squared error objective function in the context of decision-level fusion of classifiers using the Choquet FI. While this solves the specification problem, we illuminate an issue encountered with many real-world data sets; i.e., if the training data do not contain a significant number of all possible sort orders, many of the FM values are not supported by the data. We propose various regularization strategies to alleviate this issue by pushing the learned FM toward a predefined structure; these regularizers allow the user to encode knowledge of the underlying FM to the learning problem. Furthermore, we propose another regularization strategy that constrains the learned FM's structure to be a linear order statistic. Finally, we perform several experiments using synthetic and real-world data sets and show that our proposed extensions can improve the learned FM behavior and classification accuracy. A previously proposed visualization technique is employed to simultaneously quantitatively illustrate the FM as well as the FI.

This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network. The simulation results are discussed to substantiate the efficacy of the proposed path-guided output-feedback ASV flocking control based on data-driven adaptive ESOs without measured velocity information.

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network. Side operation is introduced for bypassing (non-sampled) channels to guarantee the performance of searched architectures under extremely low sampling rates. In the spatial level, input features are down-sampled to eliminate spatial redundancy and enhance the efficiency of the mixed computation for operation selection. Furthermore, edge normalization is developed to maintain the consistency of edge selection based on channel sampling with the architectural parameters for edges. Theoretical analysis shows that partial channel connection and parameterized side operation are equivalent to regularizing the super-network on the weights and architectural parameters during bilevel optimization. Experimental results demonstrate that the proposed approach achieves higher search speed and training stability than DARTS. PC-DARTS obtains a top-1 error rate of 2.55 percent on CIFAR-10 with 0.07 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.1 percent on ImageNet (under the mobile setting) within 2.8 GPU-days.

This article presents a fully automated algorithm for the extraction of parameterized macromodels from frequency responses. The reference framework is based on a frequency-domain rational approximation combined with a parameter-space expansion into Gaussian radial basis functions (RBFs). An iterative least-squares fitting with positivity constraints is used to optimize model coefficients, with a guarantee of uniform stability over the parameter space. The main novel contribution of this work is a set of algorithms, supported by strong theoretical arguments with associated proofs, for the automated determination of all the hyperparameters that define model orders, placement, and width of RBFs. With respect to standard approaches, which tune these parameters using time-consuming tentative model extractions following a trial-and-error strategy, the presented technique allows much faster tuning of the model structure. The numerical results show that models with up to ten independent parameters are easily extracted in few minutes.

Persistent actuator failures can cause uncertain system dynamics mutation including minimum and nonminimum phase switching, which motivates us to develop a robust adaptive control scheme using a control-separation-based linear quadratic (LQ) design. It is shown that such a system can be expressed by a parameterized time-varying input–output model plus an actuator failure dependent signal and a persistent signal which captures the effect of the system dynamics mutation. A control-separation LQ design has the specific desired functions for output regulation and failure compensation as well, and is applied and analyzed for solving the persistent actuator failure compensation problem. Both nominal control design for system known and adaptive control design for system unknown are developed. The desired control performance is ensured by a robust adaptive control technique and is fully characterized in terms of a small in the mean output regulation, that is, the mean value of the system output is bounded by the average number of actuator failures over time and the average actuator failure value. Such desired system performance is shown by a complete analytical study and demonstrated by an illustrative simulation study.

In this article, we present a statistical narrowband mobile satellite maritime propagation channel model at the ${L}$ -band based on empirical observations using GPS $C/N_{0}$ measurements from a receiver onboard a vessel in different sea conditions. The model is fully parameterized for all possible elevation angles. In the measurements, we have used a low-gain, 6.2 dBi, hemispherical antenna. The parameters provided, thus, are valid for this and similar antenna gains.

Electric microgrids require accurate dynamic models for operation, control, stability, and protection studies, then adequate load modeling plays an important role. This paper presents a two-stage adaptive approach to improve the generalization capability of load models obtained with the measurement-based modeling. The load model and their respective parameters are obtained through machine learning tools like decision trees (DTs) and optimization algorithms as ant colony (ACO). In the off-line stage of the proposed approach, several parameterized load models are optimally obtained using a database of microgrid disturbances. Then, the best model to represent each disturbance is defined using a similarity criterion. This model and the disturbance characteristics are integrated into a DT (classifier), while the characteristics and the model parameters are related in a second DT (predictor). These DTs are used in an on-line stage to swiftly determine the adequate parameterized load model in the case of a new disturbance in the microgrid. The approach’s performance is compared with the conventional measurement-based load modeling in a modified CIGRE benchmark low voltage microgrid. The results evidence the advantages of the proposed adaptive approach for dynamic load modeling.

An algorithm is proposed to determine output feedback policies that solve finite-horizon linear-quadratic (LQ) optimal control problems without requiring knowledge of the system dynamical matrices. To reach this goal, the $Q$ -factors arising from finite-horizon LQ problems are first characterized in the state feedback case. It is then shown how they can be parameterized as functions of the input–output vectors. A procedure is then proposed for estimating these functions from input/output data and using these estimates for computing the optimal control via the measured inputs and outputs.

To support the massive machine-type communications (mMTC) scenario for Internet of Things (IoTs) applications featured by large-scale device connectivity and low device activity, grant-free non-orthogonal multiple access (GF-NOMA) and compressive sensing (CS)-based multi-user detection methods (MUD) are developed. In this paper, we develop two Bayesian CS-based methods, i.e., sparse Bayesian Learning (SBL) and fast inverse-free sparse Bayesian Learning (FI-SBL), for joint MUD and channel estimation (CE) in GF-NOMA with Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology. SBL is investigated for robust MUD and CE by utilizing the parameterized Gaussian prior information. Then to resolve the high computational complexity of SBL, FI-SBL is proposed, which replaces matrix reversion operations with relaxed evidence lower bound. Simulation results show that the two proposed algorithms outperform the traditional methods, and FI-SBL reduces the computational complexity significantly.

A parameterized finite-element model of braided cable shields is developed to investigate the analytical model and study the influence of parameter and material dependencies of the transfer impedance of braided cable shields. The parameter influences and their interrelations are analyzed by the use of global sensitivity analysis. Measurements are only needed for the validation of the finite-element model but not for parameter variations. This reduces time and costs and eliminates measurement uncertainties occurring at different measurement procedures.

We start with a detailed review of the PACT approach for model order reduction of RC networks. We then develop a method that uses PACT as a preprocessing step to transform a generic lumped RC transmission line of some nominal order, based on a nominal (r,c) setting, into a parameterized circuit captured in a SPICE sub-circuit description. Then, given any other lumped RC line of the same order, we pass its (r,c) setting as parameters to this sub-circuit so as to automatically transform and reduce the line into a reduced order model without having to rerun PACT. In this way, we effectively characterize lumped RC transmission lines in a way that allows them to be reduced on-the-fly without any expensive processing.

This article considers dynamic output feedback model predictive control for quasi-linear parameter varying model with norm-bounded disturbance. The infinite horizon control moves are parameterized as the parameter-dependent dynamic output feedback (PDDOF) laws. In contrast to the traditional single-step approach where one PDDOF law is utilized, in this article a periodic approach where a sequence of PDDOF laws, which are invoked periodically in the future predictions, are applied. The regions of attraction for the periodic approach are considerably larger, and the control performance improved, as compared with the single-step approach. The recursive feasibility, and convergence of augmented state and output/input, are guaranteed. An illustrative example is given to show the effectiveness of this approach.

In this article, we are concerned with the consensus problem of multiagent system where the channels from each agent's control input to the plant are fading nonidentically. Two problems have been extensively studied. One is the consentable problem when the current state information is available for the controller. The other is that the state information is delayed for the controller in which we propose a novel predictorlike control protocol. It is obtained that the multiagent system with system matrices $A=B=1$ is mean-square consentable for both problems. In particular, the mean-square consensus value is the average of all agents’ initial states. Furthermore, for the multiagent system with general dynamics, sufficient conditions have been obtained in terms of two parameterized Riccati inequality/equation for both problems, respectively.

In genetic regulatory networks (GRNs), gene mutations often occur in a stochastic manner. As an important model of GRNs, gene mutations of Boolean networks are always described as function perturbations. This article studies the robust stability and stabilization of Boolean networks with stochastic function perturbations. A kind of parameterized set is constructed, and it is revealed that under the stochastic function perturbations, the property of finite-time stability remains unchanged when the perturbed set and the parameterized set are disjoint. In addition, it is proved that the finite-time stability is reduced to stability in distribution when the intersection of perturbed set and complement set of parameterized set is nonempty. As an application, the robust stabilization problem of Boolean control networks with stochastic function perturbations is discussed, and several necessary and sufficient conditions are presented for the robustness of feedback stabilizers. Finally, the obtained results are used to study the Drosophila melanogaster segmentation polarity gene network and the lac operon in the bacterium Escherichia coil.

This paper investigates the synthesis of parameterized systems that are self-stabilizing by construction. To this end, we present several significant results. First, we show a counterintuitive result that despite the undecidability of verifying self-stabilization for parameterized unidirectional rings, synthesizing self-stabilizing unidirectional rings is decidable! This is surprising because it is known that, in general, the synthesis of distributed systems is harder than their verification. Second, we present a topology-specific synthesis method (derived from our proof of decidability) that generates the state transition system of template processes of parameterized self-stabilizing systems with elementary unidirectional topologies (e.g., rings, chains, trees). We also provide a software tool that implements our synthesis algorithms and generates interesting self-stabilizing parameterized unidirectional rings in less than 50 microseconds on a regular laptop. We validate the proposed synthesis algorithms for decidable cases in the context of several interesting distributed protocols. Third, we show that synthesis of self-stabilizing bidirectional rings remains undecidable.

In this paper, we investigate the consensus tracking control problem for networked multiagent systems (MASs) with unknown nonaffine dynamics. Our goal is to achieve asymptotic (rather than ultimately uniformly bounded) consensus tracking, which is quite challenging especially if nonvanishing/nonparametric uncertainties are involved and at the same time the control protocol is required to be fully distributed and continuous everywhere. Here, we present a conceptually new and structurally simple solution with distributed and continuous control action. The developed method is capable of ensuring zero-error tracking with a unique converging feature in that the consensus tracking error first converges to a small adjustable residual set around zero within a prescribed finite time, and then further shrinks to zero exponentially. The key technique lies in the utilization of a state transformation based on certain scaling function. Our method also prevents the restrictive requirement that all subsystems have access to the linearly parameterized information as imposed in most existing consensus tracking results for nonlinear MAS.

This paper aims at the safety-critical control synthesis of network systems such that the satisfaction of the safety constraints can be guaranteed. To handle the large state dimension of such systems, an assume-guarantee contract is used to break the large synthesis problem into smaller subproblems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that robust control invariant sets (RCIs) for the subsystems can be composed to form a robust control invariant set for the whole network system under a valid assume-guarantee contract. An epigraph algorithm is proposed to solve for a contract that is valid, ---an approach that has linear complexity for sparse networks, which leads to a robust control invariant set for the whole network system. Implemented with control barrier function (CBF), the state of each subsystem is guaranteed to stay within the safe set. Furthermore, we propose a contingency tube Model Predictive Control approach based on the RCI, which is capable of handling severe contingencies, including topology changes of the network. A power grid example is used to demonstrate the proposed method. The simulation result includes both set point control and contingency recovery, and the safety constraint is always satisfied., arXiv admin note: text overlap with arXiv:1810.10636

Graph neural networks have achieved impressive progress in solving large and complex graph-structured problems. However, existing methods cannot sufficiently explore the advantages of multi-scale information by enlarging the receptive fields through stacking deep layers or multiplying exponentiated graph Laplacian. They are restricted by the discrete scales rigidly dependent on node-hops or suffer from the over-smoothing issue incurred by long-range low-pass filtering. In this paper, we propose a multi-scale graph convolutional network based on the spectral graph wavelet frame to improve multi-scale representation learning. The proposed network flexibly leverages multi-scale neighboring information with continuous scaling to enhance the discrimination ability of the learned multi-scale representations and offer stable feature extraction ensured by frame bounds in theory. The multi-scale spectral convolutional layer is constructed with a low-pass filter and a sequence of dilated band-pass filters to achieve well-established localization in both vertex and frequency domains. The graph convolutional filters can be realized based on spectral graph wavelet kernel functions analytically formulated using parameterized band-limited filters or adaptively learned from training data. Furthermore, fast approximations of exact spectral filtering are developed for memory efficient operations on sparse tensors with guaranteed prediction accuracy and numerical stability. The proposed network is shown to achieve state-of-the-art performance in extensive experiments on citation networks, bioinformatics graphs, and social networks.

A fundamental property of most sea conditions is that sets of large waves alternate with sets of smaller waves, known as quiescent periods. Exploiting the quiescent periods can expand the sea states under which the execution of many maritime operations can be considered safe. Building upon the work of Belmont et al. , we extend the methodology to fully describe the statistical distribution of the quiescent periods in terms of their time duration using sea spectra derived from current ship readings as well as metocean forecast. This provides the basis for a short- or long-term planning tool that complements deterministic wave prediction technology for executing wave-height-limited maritime operations. The method is evaluated using spectra calculated from field measurements in a dedicated sea trial and from standard parameterized oceanographic forms (JONSWAP and Pearson–Moskowitz).

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for medical image segmentation. Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one. However, the existing architectures for segmenting medical images have millions of parameters that require enormous computations which results in memory and cost-inefficiency. To overcome such limitations, we come up with the idea of training small networks in a cascaded form for coarse-to-fine prediction. The proposed adaptive network is extended up to six pyramid levels, and at each level, features are extracted at different scales of the input image. Each lightweight encoder-decoder network is trained independently to minimize loss, where succeeding level networks further refine the prior predictions. Evaluation and comparison of our architecture were performed on four different publicly available medical image segmentation datasets: International Skin Imaging Collaboration (ISIC) challenge 2018 dataset, brain tumor dataset, nuclei dataset, and X-ray dataset. The experimental results of the PMED-Net are either better or on par with other state-of-the-art networks in terms of IoU, F1-Score, and sensitivity metrics. Moreover, PMED-Net is efficient in terms of parameterized complexity as it has 1/21.3, 1/21.1, 1/14.0, 1/11.6, 1/11.2, 1/6.64, and 1/4.95 times fewer parameters than SegNet, U-Net, BCDU-Net, CU-Net, FCN-8s, ORED-Net, and MultiResUNet respectively. The pre-trained models, datasets information, and implementation details are available at https://github.com/kabbas570/Pyramid-Based-Encoder-Decoder.

Chromatic $k$ -median is a frequently encountered problem in the determination of the topological structures of chromosomes. This problem considers a set $\mathcal {C}$ of colored clients and a set $\mathcal {F}$ of facilities located in a metric space, where $|\mathcal {C}\cup \mathcal {F}|=n$ . The goal is to open $k$ facilities and assign each client to an opened facility, such that clients with the same color are assigned to different facilities and the sum of the distance from each client to the corresponding facility is minimized. It was known that the chromatic $k$ -median problem is W[2]-hard if parameterized by $k$ . This rules out the probability of obtaining an exact FPT( $k$ )-time algorithm for the problem. In this paper, we give an FPT( $k$ )-time approximation algorithm for chromatic $k$ -median. The algorithm achieves a $(3+\epsilon)$ -approximation and runs in $(k\epsilon ^{-1})^{O(k)}n^{O(1)}$ time. We propose a different random sampling algorithm for opening facilities, which is the crucial step in getting the constant factor parameterized approximation.

This paper studies the passive multi-objective optimal control of uncertain singularly perturbed systems. First, the uncertainty is described in a norm-bounded way, and then the state space model of the system is determined. Further time, the problem of passivity can also be transformed into a linear quadratic performance index problem. According to the Lyapunov stability theory, the system robust stability criterion is derived, based on the above conclusion, the corresponding parameterized passive multi-objective controller is designed using state feedback, and the weight matrix and the minimum performance index are solved based on the optimal control theory. Finally, a numerical example is given to illustrate the correctness and feasibility of the control method.

The increased penetration of intermittent renewable energy sources and random loads has caused many uncertainties in the power system. It is essential to analyze the effect of these uncertain factors on the behavior of the power system. This study presents a new powerful approach called probability-boxes (p-boxes) to consider these uncertainties by combining interval and probability simultaneously. The proposed method is appropriate for problems with insufficient information. In this paper, the uncertainty in distribution functions is modeled according to the influence of natural factors such as light intensity and wind speed. First, the p-boxes load flow problem is studied using an appropriate point estimation method to calculate statistical moments of probabilistic load flow (PLF) outputs. Then, the Cornish–Fisher expansion series is used to obtain the probability bounds. The proposed approach is analyzed on the IEEE 14-bus, and IEEE 118-bus test systems consist of loads, solar farms, and wind farms as p-boxes input variables. The obtained results are compared with the double-loop sampling (DLS) approach to show the proposed method’s precision and efficiency.

In this brief, bipartite tracking problem is addressed for linear multi-agent systems (MAS) subjected to actuator saturation over a signed directed graph (digraph) where both cooperation and competition exist between the agents. To that end, an observer-based relative output feedback low-gain control law parameterized in a small positive scalar is constructed by designing suitable scalar coupling gain and feedback gain matrices. For an arbitrarily bounded set, the designed control law realizes the bipartite tracking problem for the MAS whose underlying graph contains a directed spanning tree rooted at the leader. Numerical simulation validates the efficacy of the proposed methods.

This paper concerns the deployment problem of wireless sensor networks (WSNs) with mobile robotic sensor nodes for spatiotemporal monitoring. The proposed approach, deep reinforced learning tree (DRLT), utilizes deep reinforcement learning (DRL) to improve the efficiency of searching the most informative sampling locations. The parameterized sampling locations in an infinite horizon space are modeled according to their spatiotemporal correlations and subject to various constraints, including field estimation error and information gain. And the model-based information gain can be calculated efficiently over an infinite horizon. In this manner, the effectiveness of the sampling locations is learned through DRLT during the exploration by the robotic sensors. Then DRLT can instruct the robotic sensors to avoid unnecessary sampling locations in future iterations. Also, it is proved in this paper that the proposed algorithm is capable of searching for the near-optimal sampling locations effectively and guaranteeing a minimum field estimation error. Simulation based on national oceanic and atmospheric administration (NOAA) datasets is presented, which demonstrates the significant enhancements made by the proposed algorithm. Compared with the traditional approaches, such as the information theory-based greedy approach or random sampling, the proposed method shows a superior performance with regard to both estimation error and planning efficiency.