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As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. In this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption. [ABSTRACT FROM AUTHOR]

The presence of condensate inside the dryer cylinders results in a significant increase in the power and torque required until the condensate transitions to the rimming stage. This paper evaluates the effect of the condensate during the starting of a dryer section using the experimental curves reported by several authors, as well as a dimensionless model of the input power as a function of the condensate quantity and roll velocity. Through the evaluation of a nine-cylinder dryer section for various condensate quantities and varying number of cylinders containing condensate, a comparison is made to the power requirements of several actual paper machine dryer sections undergoing various starting procedures. These comparisons indicate that if the percent condensate (\condensate volume/full cylinder volume \times 100) in the range of 0.5%–1%, or greater, is nominally assumed to be present, then the sizing of the section drive capacity must take into consideration the additional indrive load resulting from this condensate. Options are proposed to consider the effect of the condensate without increasing the Technical Association of the Pulp and Paper Industry recommended drive capacity of dryer sections. [ABSTRACT FROM PUBLISHER]

The five papers in this special section focus on collaboration and innovation dynamics in software ecosystems. Over the past years, there has been an exponential growth in the number of articles mentioning "ecosystem" as their topic, and among them, ecosystems in the context of information and communication technologies have received particular attention. Much of this research is motivated by the intuition that ecosystems facilitate innovation. Yet, despite this burgeoning research activity accompanied by a flourish of reviews, actual empirical investigations of the collaboration and innovation dynamics in an ecosystem in general and in software ecosystems specifically remain few and far between. [ABSTRACT FROM AUTHOR]

This paper presents F2C, a cooperative resource management system for Infrastructure-as-a-Service (IaaS) clouds. Inspired by group-buying mechanisms in real product and service markets, F2C advocates a group of cloud tenants (called tenant coalition) to buy resource capacity in bulk and share the resource pool in the form of virtual machines (VMs). Tenant coalitions leads to vast opportunities for fine-grained resource sharing among multiple tenants. However, resource sharing, especially for multiple resource types, poses several challenging problems in pay-as-you-use cloud environments, such as sharing incentive, free-riding, lying and economic fairness. To address those problems, we propose Reciprocal Resource Fairness (RRF) , a novel resource allocation mechanism to enable fair sharing on multiple resource types within a tenant coalition. RRF is implemented in two complementary and hierarchical mechanisms: inter-tenant resource trading and intra-tenant weight adjustment. RRF satisfies several highly desirable properties to ensure fairness. We implement F2C in Xen platform. The experimental results show F2C is promising for both cloud providers and tenants. For cloud providers, F2C improves VM density and cloud providers’ revenue by 2.2X compared to the current IaaS cloud models. For tenants, F2C improves application performance by 45 percent and guarantees 95 percent economic fairness among multiple tenants. [ABSTRACT FROM PUBLISHER]

The Marching Cubes paper by Bill Lorensen and Harvey Cline, "Marching Cubes: A High Resolution 3D Surface Construction Algorithm," was published at SIGGRAPH 1987.1 According to Google Scholar, their paper has 15,667 citations (as of January 17, 2020), the most highly cited paper in computer graphics. Sadly, while writing this article Bill Lorensen passed away on December 12, 2019. Origins Department Editor Chris Johnson contributed the text in italics. EARLY [ABSTRACT FROM AUTHOR]

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric-based template that lacks the surface details. As such resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of the parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. Our method can restore detailed human body shapes with complete textures beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance. [ABSTRACT FROM AUTHOR]

Two decades have passed since the introduction of Markov chain Monte Carlo (MCMC) into light transport simulation by Veach and Guibas, and numerous follow-up works have been published since then. However, up until now no survey has attempted to cover the majority of these methods. The aim of this paper is therefore to offer a first comprehensive survey of MCMC algorithms for light transport simulation. The methods presented in this paper are categorized by their objectives and properties, while we point out their strengths and weaknesses. We discuss how the methods handle the main issues of MCMC and how they could be combined or improved in the near future. To make the paper suitable for readers unacquainted with MCMC methods, we include an introduction to general MCMC and its demonstration on a simple example. [ABSTRACT FROM AUTHOR]

The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron cannot be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data. [ABSTRACT FROM PUBLISHER]

This paper explores the dynamic properties of oscillating and propagating patterns in concentration of the information molecule and proposes a new modulation scheme for molecular communication. In the proposed modulation scheme, information molecules transmitted from a sender bio-nanomachine propagate in the environment, chemically react with the molecules in the environment, change their concentration, and form an oscillating and propagating pattern in the environment. A sender bio-nanomachine modulates information onto the dynamic properties of the pattern, such as the amount of change in concentration of the information molecule and the time duration of one cycle of a periodic pattern in concentration of the information molecule. Receiver bio-nanomachines receive the oscillating and propagating pattern that the sender bio-nanomachine generates and detect its dynamic properties to demodulate information. This paper implements the proposed modulation scheme using Ca2+ oscillation and propagation, a communication mechanism found in biological cells. It also demonstrates that the proposed modulation scheme allows information to be simultaneously modulated onto multiple dynamic properties of an oscillating and propagating pattern that a sender bio-nanomachine generates. The main contributions of this paper are to open a new research area of modulation schemes in molecular communication and to illustrate the possibility that biological cells use such modulation schemes for cell-to-cell communication. [ABSTRACT FROM PUBLISHER]

This paper introduces a possible mechanism to explain the changes in neurons following extremely low frequency magnetic fields (ELF-MFs). The model in this paper contains two parts: the physical part and the bio-trigger part. The first part is an electric current equation, which is a generalization of the Nernst–Plank equation with the exposure of ELF-MF, and the second bio-part is a phenomenological model, which is a simple two-state channel with two steady state open (O) state and closed state (C). The two parts are complementary. The first part is utilized to explain changes about synaptic plasticity following ELF-MF exposure at embryonic and postnatal age. The second part is used to simulate the process of three different types of neurons reported by previous papers. According to the results, the present model is feasible for the simulation process. The concept of ion mobility or permeability is emphasized in this paper in part I, and channel state transition probabilities are paid much attention to in part II. In this paper, the ion mobility or permeability is supposed to have close relationships with ion channel transition probabilities, which connects the two parts of the model together. [ABSTRACT FROM PUBLISHER]

Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study the problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose novel models, including $\sigma$ σ -periodic $k$ k -core and $\sigma$ σ -periodic $k$ k -clique, that represent periodic communities in temporal networks. Specifically, a $\sigma$ σ -periodic $k$ k -core (or $\sigma$ σ -periodic $k$ k -clique) is a $k$ k -core (or clique with size larger than $k$ k ) that appears at least $\sigma$ σ times periodically in the temporal graph. The problem of searching periodic core is efficient but the resulting communities may be not enough cohesive; the problem of enumerating all periodic cliques is not efficient (NP-hard) but the resulting communities are very cohesive. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we transform the temporal graph into a static graph and prove that mining the periodic communities in the temporal graph equals mining communities in the transformed graph. Subsequently, we propose a decomposition algorithm to search maximal $\sigma$ σ -periodic $k$ k -core, a Bron-Kerbosch style algorithm to enumerate all maximal $\sigma$ σ -periodic $k$ k -cliques, and a branch-and-bound style algorithm to find the maximum $\sigma$ σ -periodic clique. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty non-connected lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles’ position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, “safe following distance” is based on the ego vehicle’s ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations. [ABSTRACT FROM AUTHOR]

This paper presents a new mathematical framework for the economic impact analysis of topology data attacks in electricity markets with virtual bidding activities. The network topology information is very important for system operators to manage the grid in a secure manner. However, this network topology can be manipulated by an adversary through the change of the circuit breaker’s on/off status. This, combined with virtual bids submitted by the attackers, could lead to financial misconduct and potential profit for the attacker in the power market. This paper aims at developing an analytical framework to evaluate the economic profit of an attacker who conducts topology data attack and submits virtual bids accordingly. This framework can be used for system operators as a cybersecurity tool to quickly find the most profitable pair of virtual bidding buses and the most influential transmission line on the change in profit once topology data attack is initiated. Furthermore, the sensitivity of the adversary’s profit with respect to the change in generator’s marginal cost and susceptance for any targeted line is assessed in the proposed framework. IEEE 14-bus system is used to validate and test the proposed framework in various system operation conditions and attack scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of quality improvement for sensing models in various applications is considered. A choice of sensing model strongly influences the design of wireless sensor networks and the performance of protocols, such as traffic aggregation, broadcasting, energy-efficient routing, target or barrier coverage, and so on. For these reasons, it is useful to investigate the nature of the sensing ability and the manner in which it depends on the characteristics of wireless sensor networks. This paper investigates the impact of experiment design on the evaluation of sensing quality function. This paper provides an approach to a statistically efficient estimation of sensing model parameters. [ABSTRACT FROM PUBLISHER]

At the 2006 IEEE International Conference on Systems of Systems Engineering, Boardman and Sauser presented a set of characteristics based on an integrative review of the literature. These characteristics are the foundation of the Boardman–Sauser system of systems (SoS) theory. We accept this theory as the basis of collaborative SoS and desire a means to validate this assertion. While numerous simulations have been published, the target SoS is usually a specific implementation. These specialized simulations produce results applicable primarily to the system under study but not necessarily SoS in general. Simulating the mathematical model of the Boardman–Sauser SoS theory in the form of an agent-based model produces results that can be compared with chemical reactions. This paper contends that the formation of molecules from atoms is an analog of systems forming any collaborative SoS, which we call our molecule organization model. The result is a supported logical argument that validates the Boardman–Sauser SoS theory as definitive of a generic SoS with behavior applicable to collaborative SoS. This paper seeks to provide a unique contribution to the SoS body of knowledge by increasing our understanding of collaboration in an SoS, so we may better understand their health, maintenance, replication, and evolution. [ABSTRACT FROM PUBLISHER]

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors. [ABSTRACT FROM AUTHOR]

In this paper, we study the stabilization and finite-time stabilization of probabilistic Boolean control networks (PBCNs). A complete family of reachable sets is defined first, based on which, feedback stabilization conditions are obtained. Then a way to find all possible state feedback controllers are presented for the stabilization of PBCNs accordingly. Moreover, it has been stated that the approach in this paper can also be applied to finite-time stabilization via some changes in the construction of set sequence. Finally, an evolutionary networked game is given as an example to illustrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

Consider observing a collection of discrete events within a network that reflect how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the past events. This paper addresses the problem of estimating self-excitation parameters and inferring the underlying functional network structure from self-exciting point process data. Past work in this area was limited by strong assumptions which are addressed by the novel approach here. Specifically, in this paper we 1) incorporate saturation in a point process model which both ensures stability and models non-linear thresholding effects; 2) impose general low-dimensional structural assumptions that include sparsity, group sparsity, and low-rankness that allows bounds to be developed in the high-dimensional setting; and 3) incorporate long-range memory effects through moving average and higher-order auto-regressive components. Using our general framework, we provide a number of novel theoretical guarantees for high-dimensional self-exciting point processes that reflect the role played by the underlying network structure and long-term memory. We also provide simulations and real data examples to support our methodology and main results. [ABSTRACT FROM AUTHOR]

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman–Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents’ actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect $\epsilon $ -Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators. [ABSTRACT FROM AUTHOR]

In the wide field of tumor treatment, thermal ablation procedures are very promising. Alternating magnetic fields are used to transfer the energy from outside the patient to the tumor area located anywhere in the body. This energy is converted to heat by implanted devices located in the tumor area. In this paper, the spatial distribution of the magnetic field of different circular coil configurations is analyzed and optimized with focus on patients’ safety and on coil configuration performance. The analysis is based on several performance criteria and is conducted with respect to the worst case scenario of a contactless thermal tumor ablation of deep-seated tumors, in which the energy has to be transferred over a considerably large distance. The magnetic field and the specific absorption rate (SAR) are calculated numerically and the performance criteria are evaluated based on a model of a human body including a tumor area. The most suitable coil configurations for different application scenarios are presented and a thermal analysis is done. Based on this, the minimum required heating power, coil current and number of coil windings, and the corresponding maximum SAR to achieve an adequate rise of tissue temperature are evaluated. For a heating power of 1.45 W, a minimum SAR of 130 mW/kg, a maximum power transfer efficiency of 1.05%, and a maximum coupling coefficient of 0.00695 is achieved. This paper shows the potential to enhance the safety of the patients significantly by choosing the appropriate coil configuration for a specific application scenario. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles within the issue on topics including insulation, high energy density storage, and high power microwave devices.

This paper proposes a leader–follower-based model of mobile molecular communication networks for target detection applications. The proposed model divides the application functionalities of molecular communication networks into two types of mobile bio-nanomachine: leader and follower bio-nanomachines. Leader bio-nanomachines distribute in the environment to detect a target and create an attractant gradient around the target. Follower bio-nanomachines move according to the attractant gradient established by leader bio-nanomachines; they approach the target and perform necessary functionalities, such as releasing drug molecules. This paper develops mathematical expressions for the proposed model, describes wet laboratory experiments designed to estimate model parameters, and performs biologically realistic computer simulation experiments to evaluate the performance of the proposed model. The main contributions of this paper are to demonstrate the functional division of molecular communication networks, which will facilitate the design and development of molecular communication networks. Furthermore, insight into the application-level performance of molecular communication networks will be provided based on the proposed model. [ABSTRACT FROM PUBLISHER]

This paper presents a time-series classification method based on fuzzy cognitive maps. We advocate that fuzzy cognitive maps provide a sound representation of time series, and we can construct a classification mechanism based on them. The classifier has to distinguish maps constructed for time series belonging to different classes. The proposed classification procedure evaluates similarity of fuzzy cognitive maps, and it is done by comparing weight matrices based on the same set of concepts. A weight matrix describes relationships between concepts in a map. Concepts represent the underlying data, because they are extracted via a data-driven clustering procedure. Each data point of a time series is related to each concept, and we evaluate the strength of relationships with a membership function. This paper investigates performance of the proposed approach on a suite of real-world datasets. We compare classification accuracy of our method with 37 state-of-the-art time-series classification methods. Experiments show that the proposed method is performing well. In many cases, it is better than its competitors. [ABSTRACT FROM AUTHOR]

The coordinated solution of AC optimal power flow (ACOPF) in the integrated transmission and distribution grid utilizes controllable energy resources in distribution grids for introducing additional operational economy and security benefits of power grids. However, it is rather impractical to compute the ACOPF centrally considering the information privacy in the operation of transmission and distribution grids. Due to the large capacity of distributed generators integrated to distribution grids, existing decentralized methods may encounter numerical problems and fail to converge when nonlinear ACOPF models are applied. In this paper, we propose a decentralized method to solve the ACOPF for integrated transmission and distribution grids. The proposed ACOPF model is characterized through polar-coordinate equations and branch flow equations for transmission grids and distribution grids respectively. A distribution-cost-correction framework is developed for a fast-convergent solution which is based on approximated distribution cost functions. A rigorous proof is provided for convergence and numerical simulations are analyzed for standard IEEE systems which demonstrate the advantages of the coordinated approach in reducing generation dispatch costs and mitigating overvoltage problems. The paper validates through simulations that the accuracy, computational efficiency, and scalability of the proposed approach are superior to those of traditional methods. [ABSTRACT FROM AUTHOR]

This paper models the cyber-social system as a cyber-network of agents monitoring states of individuals in a social network. The state of each individual is represented by a social node, and the interactions among individuals are represented by a social link. In the cyber-network, each node represents an agent, and the links represent information sharing among agents. The agents make an observation of social states and perform distributed inference. In this direction, the contribution of this paper is threefold: First, a novel distributed inference protocol is proposed that makes no assumption on the rank of the underlying social system. This is significant as most protocols in the literature only work on full-rank systems. Second, a novel agent classification is developed, where it is shown that the connectivity requirement of the cyber-network differs for each type. This is particularly important in finding the minimal number of observations and minimal connectivity of the cyber-network as the next contribution. Third, the cost-optimal design of the cyber-network constraint with distributed observability is addressed. This problem is subdivided into sensing cost optimization and networking cost optimization, where both are claimed to be NP-hard. We solve both the problems for certain types of social networks and find polynomial-order solutions. [ABSTRACT FROM AUTHOR]

This paper is concerned with control-oriented modeling of a class of wireless power transfer systems with a phase-shift-controlled inverter. Two methods are investigated: the analytical modeling method, which is based on physical laws, and the refined instrumental variable method, which is based on sampled data. It shows that the former provides physical insights into the system, and that the resulting models will be accurate if the true circuit component parameters are a priori known. Otherwise, the model accuracy may decline. By contrast, the latter provides a cheap solution for accurate modeling, based only on a set of sampled input–output data, and the resulting model can capture very well the dynamic behavior of the system within a defined operating range. In real applications, it is always hard to know the actual circuit component parameters, since they may drift from the nominal values, due to some reasons such as the aging of circuit components and the variation of coil placement, making the analytical modeling method not very accurate. Therefore, to preserve the virtues of both methods, it is suggested to use them as a combination, that is, using the analytical modeling method to determine the model structure while using the data-driven modeling method to estimate the model parameters. All results derived in this paper are verified by means of both numerical simulation and experimental validation. [ABSTRACT FROM AUTHOR]

The finite-iteration tracking for singular coupled systems with packet-dropping learning controllers is discussed in this paper. The singular coupled systems are constructed to describe systems with some algebraic constraints, and iterative learning controllers are then designed to achieve the finite-iteration tracking of singular systems. The definition of the finite-iteration tracking is first proposed and the settling iteration is explicitly calculated. Moreover, the iterative learning controllers are designed to consider the case of packet losses. Simulation results are given to elaborate the correctness of the given theorems. [ABSTRACT FROM AUTHOR]

This paper proposes a bioinspired reference model based fuzzy adaptive tracking control for active suspension systems. A general bioinspired nonlinear structure, which can present ideal nonlinear quasi-zero stiffness for vibration isolation, is adopted as tracking reference model. Fuzzy logic systems are used to approximate unknown nonlinear terms in nonlinear suspension systems. Particularly, a nonlinear damping is designed to improve damping characteristics of the bioinspired reference model. With beneficial nonlinear stiffness and improved nonlinear damping of the bioinspired reference model, the proposed fuzzy adaptive controller can effectively suppress vibration of suspension systems with less actuator force and much improved ride comfort, thus energy saving performance can be achieved. Finally, a quarter-vehicle active suspension system with considering payload uncertainties, general disturbance, and actuator saturation is provided for evaluating the validity and superiority of the bioinspired nonlinear dynamics based fuzzy adaptive control approach proposed in this paper. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading cause of mortality worldwide and its incidence is rising due to an aging population. The development and progression of CVD is directly linked to adverse vascular hemodynamics and biomechanics, whose in-vivo measurement remains challenging but can be simulated numerically and experimentally. The ability to evaluate these parameters in patient-specific CVD cases is crucial to better predict future disease progression, risk of adverse events, and treatment efficacy. While significant progress has been made toward patient-specific hemodynamic simulations, blood vessels are often assumed to be rigid, which does not consider the compliant mechanical properties of vessels whose malfunction is implicated in disease. In an effort to simulate the biomechanics of flexible vessels, fluid-structure interaction (FSI) simulations have emerged as promising tools for the characterization of hemodynamics within patient-specific cardiovascular anatomies. Since FSI simulations combine the blood's fluid domain with the arterial structural domain, they pose novel challenges for their experimental validation. This paper reviews the scientific work related to FSI simulations for patient-specific arterial geometries and the current standard of FSI model validation including the use of compliant arterial phantoms, which offer novel potential for the experimental validation of FSI results. [ABSTRACT FROM AUTHOR]

This paper models a warm standby system where a single element is online performing a specified mission task (e.g., a computing task) and subject to corrective replacement (CR) by an available standby element upon its failure. During the mission, preventive replacements (PRs) are also performed to renew the aged or worn online operating element before its actual failure according to a predetermined policy. In addition, to facilitate an effective restoration of system function in case of CR or PR happening, backups are also performed periodically so that the mission task can be resumed from the last successful backup point instead of from scratch. The mission succeeds if the specified mission task is accomplished; in other words, the mission fails when no operating elements remain prior to the mission task completion. In this paper, we make new contributions by first proposing an event transition-based numerical method to evaluate mission performance indices of the considered standby system subject to periodic backups, CR and PR. Mission success probability (MSP), expected mission completion time, expected mission operation cost (EMC), and expected uncompleted work fraction are evaluated. Based on the suggested evaluation algorithm, we make another contribution by formulating and solving optimization problems that help to determine the optimal backup-PR policy or the optimal combination of element activation sequencing and backup-PR policy to maximize MSP or minimize EMC. Influence of element performance and reliability parameters, data backup and retrieval complexity parameters on the optimal operation policy is investigated. Findings from this paper can guide the optimal decision making on policies related to element sequencing, backups as well as preventive maintenance planning, contributing toward reliable and cost-effective design and operation of standby computing systems. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study, which leads to a theoretical framework that links organizational learning and capital productivity. The approach described in this paper helps fab managers make fundamental strategic decisions concerning capital investment and point of entry by engaging in scenario planning. Three strategic options for semiconductor manufacturing are analyzed in detail—leading-edge manufacturer, fast follower, and slow follower. The study concludes that profitability and capital productivity can be in conflict with each other. Leading-edge manufacturers can make large profits, if they ramp up to volume production in a timely manner, but their return on investment and thus their capital productivity are relatively low. Generally, manufacturers that do not run state-of-the-art processes are less profitable than those that do, but their return on investment and thus their capital productivity is comparatively high. Fast followers, which import part of their manufacturing process and ramp to volume production rapidly but with a delay, neither break even nor recover their investment. [ABSTRACT FROM AUTHOR]

Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual’s kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective. [ABSTRACT FROM AUTHOR]

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). Prior work has shown that FDI attacks can be designed via an attacker-defender bi-level linear program (ADBLP) to cause physical overflows after re-dispatch using DCOPF. In this paper, it is shown that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable systems because the system response modeled is inaccurate. An ADBLP that accurately models the system response is proposed to find the worst-case physical consequences, thereby modeling a strong attacker with system level knowledge. Simulation results on the synthetic Texas system with 2000 buses show that even with the new enhanced attacks, for systems operated conservatively due to $N-1$ constraints, the designed attacks only lead to post-contingency overflows. Moreover, the attacker must control a large portion of measurements and physically create a contingency in the system to cause consequences. Therefore, it is conceivable but requires an extremely sophisticated attacker to cause physical consequences on $N-1$ reliable power systems operated with RTCA and SCED. [ABSTRACT FROM AUTHOR]

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors. [ABSTRACT FROM AUTHOR]

Accurate prediction of the traffic state can help to address the issue of traffic congestion, providing guiding advices for people’s travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction approach based on empirical mode decomposition and combination model fusion. First, we explore the amplitude-frequency characteristics of short-term traffic flow series, and use empirical mode decomposition to decompose traffic flow to several components with different frequency. Second, based on the results of self-similarity analysis of each component, improved extreme learning machine, seasonal auto regressive integrated moving average and auto regressive moving average are selected to predict different components. Meanwhile, an improved fruit fly optimization algorithm is proposed to optimize the weight coefficient of the combination model. Third, the prediction results of each prediction model are multiplied by their respective weight coefficient to get the final prediction results. We evaluate our prediction approach by doing thorough experiment on a real traffic data set. Moreover, experimental results show that the proposed approach has superior performance than state-of-the-art prediction methods or models in short-term traffic flow prediction. [ABSTRACT FROM AUTHOR]

The mobile traffic explosion has caused spectrum shortage and polarization of data usage among users, which will eventually decrease user welfare (UW) in mobile communication services. Governments around the world are planning to make more spectrum available for mobile broadband use, and the key policy issue is to find an efficient spectrum allocation method that will improve UW. In this paper, we propose a data subsidy scheme where the regulator offers a spectrum price discount to mobile network operators (MNOs) in return for imposing the responsibility of providing a predefined data amount to users free of charge. To analyze the subsidy effect, we adopt the two-stage approach of Cournot and Bertrand competition and find a Nash equilibrium (NE) of the competition. An interesting observation is that the increase in UW does not involve MNO profit (MP) loss and that the increasing amount is higher than the regulator's expenses for implementing the data subsidy scheme. Most of the paper concerns the duopoly competition, which is finally extended to the general case. [ABSTRACT FROM PUBLISHER]

This paper describes the application of high-confidence-interval prediction methods to the aircraft trajectory prediction problem, more specifically to the altitude prediction during climb. We are interested in methods for finding two-sided intervals that contain, with a specified confidence, at least a desired proportion of the conditional distribution of the response variable. This paper introduces two-sided Bonferroni-quantile confidence intervals, which is a new method for obtaining high-confidence two-sided intervals in quantile regression. This paper also uses the Bonferroni inequality to propose a new method for obtaining tolerance intervals in least squares regression. The latter has the advantages of being reliable, fast, and easy to calculate. We compare physical point-mass models to the introduced models on an air traffic management data set composed of traffic at major French airports. Experimental results show that the proposed interval prediction models perform significantly better than the conventional point-mass model currently used in most trajectory predictors. When comparing with a recent state-of-the-art point-mass model with adaptive mass estimation, the proposed methods give altitude intervals that are slightly wider but more reliable. [ABSTRACT FROM PUBLISHER]

The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies. [ABSTRACT FROM PUBLISHER]

The paper introduces a research program formulated to uncover and describe a complex nature of the sleep apnea disorders. This study include the physiological sensing and the signal processing oriented towards the mapping of a dynamical profile of physiological system represented by its complexity and variability. To reconstruct a heatmap of the dynamical features significant for triggering sleep disorders we collected a set of procedures dedicated to qualitative and quantitative depiction of the intra- and inter-events, and then adapted them to the use with a polysomnography data. Research protocol was organized with reference to the patients and modified PNEUMA model, and the COMPASS Toolbox devoted to time series exploration. The outcome novelty consists in the complementary characterization of the sleep apnea dynamics, measured at various levels of the system, but also the original statements on the sensitivity of fractal and network oriented algorithms applied to physiological data has been formulated in the report in reference to the temporal patterns encoded in polysomnography data, e.g. a detection of the central sleep apnea with the use of nasal airflow has been documented. The complementary approach proposed in the paper is a prerequisite to understand the SAS phenotyping, predict that modes and the SAS states, and formulate an efficient procedures for personalized patient care. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data. [ABSTRACT FROM AUTHOR]

Real-time dynamic line rating forecasting can be classified into two critical steps of evaluating the operation risk, and avoiding the overload operation of transmission lines. The dynamic capacity of the transmission line is generally predicted for scheduling power networks in the day-ahead market. However, the forecasting error motivates researchers to propose real-time forecasting models to correct the day-ahead scheduling results based on the accurate data close to real-time values in the balancing market. Among the methods presented for real-time dynamic line rating forecasting, the single-point estimation type has been considered more than other forecasting types. Despite its safety, and efficiency, a single point estimation type suffers from several significant drawbacks. For instance, this forecasting type cannot indicate the probabilistic distribution function of forecasted value, which is used in novel balancing methods of market scheduling. The main purpose of this paper is to present a density forecast method to predict real-time dynamic line rating for covering the balancing market requirements. Dynamic stochastic general equilibrium (DSGE) is applied to achieve this aim. Due to incorporating stochastic volatility in the proposed real-time forecasting model, evaluating simulation results, and reference models highlight a significant improvement in the performance of the real-time density forecast of DLR. [ABSTRACT FROM AUTHOR]

Palmprint recognition is one of the most popular biometric technologies. Recent researches mainly focus on the recognition performance, while pay less attention to the data protection issues. In this paper, we propose an active biometric data protection model for securing palmprint images in transmission or storage scenarios, called Dynamic Random Invisible Watermark Embedding (DRIWE) model. The DRIWE model implicitly embeds a watermark in each original palmprint ROI image, and then separates the embedded watermark from the watermarked image before identification. If the separated watermark is consistent with the original watermark, it indicates that the image is trustworthy and can be used in the subsequent recognition process. Otherwise, it proves that the image has been illegally tampered with. Furthermore, a two-dimensional image information entropy loss is proposed to enhance the generalization of the model to different watermarks. It ensures that the model can always assign enough information to the host image (i.e., original palmprint image) when different watermarks are applied. Thus, it enables the separator to extract the complete watermark from the watermarked image. This greatly enhances the dynamics and randomness of the watermark embedding process and further improves the ability to secure the data. Adequate experiments are conducted on two benchmark palmprint databases. The results show that the proposed DRIWE model has satisfactory attack resistance and strong generalization ability: even if only one watermark is used in training stage, it can be generalized to a dozen of other new watermark images in the testing stage. In addition, the optimal accuracy of the watermarked data is only reduced by 0.07% compared with the original data. [ABSTRACT FROM AUTHOR]