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This paper presents the design of a novel flexure-based precision positioning stage with constant output force for biological cell micromanipulation. One uniqueness of the proposed design is that it produces a constant force without using a force controller. Only a motion control is needed to produce a constant output force, which significantly simplifies the system design process. The stage is driven by a piezoelectric actuator through a displacement amplifier. Analytical models of the displacement amplifier and the zero-stiffness structure are established and verified by conducting finite-element analysis simulations. The structure parameters are optimally designed to guarantee the requirement on output force, motion range, and physical size. A prototype stage is fabricated by 3-D printing process and a series of experiments is carried out. Experimental results show that the developed positioning stage delivers a near constant output force with slight fluctuation in the reachable constant-force motion range of $138~\mu \text{m}$. The applications of the developed constant-force stage in biological cell manipulation have been demonstrated through experimental investigations. Note to Practitioners—A constant-force stage can produce a constant output force without using a force control. It is attractive for biological micromanipulation. This paper presents the design and testing of a novel constant-force flexure stage. The constant force indicates a zero stiffness for the mechanism. The stage mechanism is devised using modified leaf flexure (MLF) to achieve positive-stiffness structure. Bistable beams are used to design negative-stiffness structure by making use of their postbuckling characteristics. Two bistable beams and two MLFs are combined together to construct a zero-stiffness structure. A conventional stage is also fabricated for comparison study. The performance of the proposed constant-force stage has been verified by simulation and experimental studies. Results indicate that the developed stage system has great superiority over conventional one in terms of reducing driving force, increasing motion range, and reducing force fluctuation. Experimental demonstration of bio-micromanipulation has been presented to reveal its potential applications. [ABSTRACT FROM AUTHOR]

Application of the nonthermal plasma (NTP) technology for NOX removal from diesel engine exhaust has already been experimentally proved to be an efficient method at laboratory level. The NOX removal efficiency depends on various electrical and physical parameters, and very few studies have been carried out to predict it with the variation in those parameters. In this paper, an approach has been made using dimensional analysis to predict the NOX concentration with the NTP treatment, based on its dependence on various operating parameters. A relation has been obtained between NOX concentration and 11 numbers of parameters affecting NOX removal so as to form dimensionless groups. A mathematical model has been established using the experimental data and nonlinear regression technique. Experiments are conducted to validate this model with variations in the parameters voltage, flow rate, temperature, discharge gap width, and initial NOX concentration. The predicted NOX concentration values have been found to be well matched with the experimental results. The rms errors for the test data are found to be 4.64%, and in ppm, it is 9.58. [ABSTRACT FROM AUTHOR]

In this paper, we present a surface potential-based explicit continuous model for a metal-ferroelectric-insulator-semiconductor (MFIS) type gate-all-around negative capacitance transistor (GAA-NCFET). Unlike previously reported models, an explicit formulation to calculate the electrical characteristics of GAA-NCFET is proposed. Our model includes the radial dependence of the electric field in the ferroelectric, ignored in the previous works and accurately captures ferroelectric material parameter variations in the nonhysteretic regime. In contrast to bulk NCFETs, GAA-NCFET characteristics show different bias dependence due to the absence of bulk charge. We also present analytical expressions for the terminal charges which are essential to obtain the trans-capacitances for transient simulations. We find that, compared with conventional MOSFETs, the gate charge saturates to a different fraction of its maximum value. Furthermore, the modeling of quantum mechanical effect and overlap capacitances in an MFIS NCFET structure is discussed. Finally, the proposed model has been implemented in Verilog-A and tested for the transient response of ring oscillator in a commercial circuit simulator. [ABSTRACT FROM AUTHOR]

This paper presents a shutdown dose rate (SDR) calculation to estimate a newly developed preliminary concept of the equatorial port model for the Korean fusion demonstration reactor (K-DEMO). The equatorial port model was designed to provide effective radiation shielding abilities adopting the port shielding block and labyrinth structure, which was integrated into the modified K-DEMO neutronic analysis model by the Monte Carlo automatic modeling system. A rigorous two-step method was applied to couple transport (MCNP) and activation (FISPACT) codes according to three steps of calculation procedures. The results of neutron flux and decay gamma transport calculation for the equatorial port area have been indicated in this paper. The SDR calculation result near the equatorial port area reveals that a dose level in the equatorial port interspace was below $100~\mu $ Sv/h after 12 days of the shutdown. Shielding blocks and two labyrinth structures prove that they effectively attenuate neutrons on the current concept of the equatorial port model. [ABSTRACT FROM AUTHOR]

The reflectometry diagnostic for demonstration fusion reactor (DEMO) is envisaged to provide the electron density profile and to be used as a control diagnostic for the real-time vertical position controller. An initial conceptual study has been performed defining the position of the antennas and the routing of the waveguides. In the present design, the integration has been driven by the remote handling and blanket interfaces. To progress with the system integration, neutronics simulations were performed to assess the cooling requirements. This paper presents a nuclear and thermal analysis for the initial design of the reflectometry diagnostic for DEMO. The neutronics simulations were performed using the Monte Carlo simulation program MCNP6 and FENDL 2.1 cross sections, while ANSYS mechanical v18 was used to perform the thermal analysis. Simulation results show that the nuclear heat loads reach 8 W/cm3 at the surface of the diagnostics section. Without an active cooling system, the operating temperatures of the components under such heat loads would be well above the acceptable from the thermomechanical point of view. The thermal analysis here presented provides the temperature distribution in the components when subjected to neutron/gamma irradiation and thermal radiation from the plasma, after the implementation of a preliminary design of the active cooling system. The operation temperatures of the plasma-facing antennas, as well as the performance of the diagnostics system from the neutron shielding point of view, are analyzed and discussed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we consider the norm-optimal iterative learning control (NO-ILC) framework and study its robust monotonic convergence (RMC) against model uncertainties in single-input-single-output linear time-invariant systems. Modeling errors in general degrade the convergence performance of NO-ILC, and hence ensuring RMC against model uncertainties is important. Although the robustness of NO-ILC has been studied in the literature, determining the allowable range of modeling errors for a given NO-ILC design is still an open research question. To fill this gap, a frequency-domain analysis with a multiplicity formulation of model uncertainty is developed in this paper to quantify and visualize the allowable modeling errors. Compared with the traditional formulation, the proposed new uncertainty formulation provides a less conservative representation of the allowable model uncertainty range by taking additional phase information into account and thus allows for a more complete evaluation of the robustness of NO-ILC. The analysis also clarifies how the RMC region changes as a function of NO-ILC weighting parameters and therefore can be used as a frequency-domain design tool to achieve RMC for given model uncertainties. Simulation examples are given to confirm the theoretical conclusions and demonstrate the utility of the developed analysis. [ABSTRACT FROM PUBLISHER]

In-band full-duplex (FD) communication provides a promising alternative to half-duplex (HD) for wireless systems, due to increased spectral efficiency and capacity. In this paper, HD and FD radio implementations of two way, two hop, and two way two hop communication are compared in terms of degrees of freedom (DoF) under a realistic residual self-interference (SI) model. DoF analysis is carried out for each communication scenario for HD, antenna conserved (AC), and RF chain conserved (RC) FD radio implementations. The DoF analysis indicates that for the two way channel, the achievable AC FD with imperfect SI cancellation performs strictly below HD, and RC FD DoF tradeoff is superior when the SI can be sufficiently cancelled. For the two hop channel, FD is better when the relay has a large number of antennas and enough SI cancellation. For the two way two hop channel, when both nodes require similar throughput, the achievable DoF pairs for FD do not outperform HD. FD still can achieve better DoF pairs than HD, provided the relay has sufficient number of antennas and SI suppression. [ABSTRACT FROM PUBLISHER]

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a \lambda -domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the \lambda -domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R- \lambda model developed in our initial work, a D- \lambda model is properly constructed to complete a comprehensive framework of \lambda -domain R-D analysis. Based on this comprehensive \lambda -domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software. [ABSTRACT FROM PUBLISHER]

This paper concerns the force density optimization for medium-stroke reluctance actuators applied in antivibration applications. The force density in a conventional E-core reluctance actuator is limited for medium strokes by the nonlinear force–displacement characteristic. In this paper, different tooth topologies are analyzed to maximize the force density along the stroke using the finite-element analysis. Teeth parameters are tuned in each topology to analyze the influences on the force density over a stroke of $\pm$12.5 mm. An analytic thermal model is used to estimate the surface temperature and is verified with finite-element simulations. The optimal topology is validated by experiments on a prototype. [ABSTRACT FROM AUTHOR]

In this paper, the procedure for designing a sliding-mode controller for maximum power point tracking photovoltaic (PV) applications is proposed. It is applied to a single-ended primary inductor converter, thus to a fourth-order topology, but it can be extended to a wide class of converters suitable for PV applications. The reachability and existence conditions give rise to a number of design inequalities that add to the classical steady-state conditions in order to have the desired closed-loop converter's performances. The superiority of the control technique over the classical tracking methods is analytically demonstrated through the procedure proposed in this paper. The analytical results have been validated by means of both simulations and experiments. [ABSTRACT FROM PUBLISHER]

On the wheel-rail supported hybrid railway system, the linear synchronous motor (LSM) for very high-speed transit must be a system that can maintain a large air gap, and the maximum vertical displacement must be taken into consideration in designing the LSM. As such, this paper designs two small-scale LSM models that produce equal output power—one with the normal-conducting magnet and the other with the superconducting magnet. In addition, this paper conducts a comparative analysis of the various characteristics by applying the electromagnetic numerical analysis method. Furthermore, this paper manufactures two small-scale LSM prototypes which have the normal-conducting magnet and the superconducting magnet, while also conducting a no-load and load test. Through such comparisons, this paper attempts to verify the characteristic values of the two models. [ABSTRACT FROM AUTHOR]

Acoustic noise modeling, analysis, and reduction are critical in the design of switched reluctance machines for noise-sensitive applications. Although the analytical method can provide a fast simulation of the acoustic noise, it is generally less accurate compared to the numerical method. In order to improve the simulation accuracy, a novel analytical method is presented in this paper. The proposed method considers the excitation of the high-axial-order vibration modes to simulate the vibration behavior and the acoustic noise in the three-dimensional (3D) domain. Methods of obtaining the input data for the 3D analytical simulations are presented. The acoustic noise results obtained from the numerical simulations and experiments from an 8/6 switched reluctance motor drive are used to validate the analytical method presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic thermal model which predicts the winding temperature of electrical machines at steady-state and transient conditions where windings are used in integrated battery chargers (IBCs) for DC charging in a stationary position. Using winding in high current charging process causes rising winding temperature which affects insulation life. Switched reluctance machines are a proper choice for using in electric vehicles (EVs) because of their advantages such as simple and sturdy structures. IBCs can reduce volume and weight of EVs and total cost is diminished because of integration. The analytical thermal model proposed in this paper can calculates the winding temperature in balanced and unbalanced currents injection because of the phase inductance in each rotor angle is distinct, so current excitation in each phase should be different for obtaining zero total torque and keeping rotor in a stationary position, also a reduced-order model is proposed with minor impact on the accuracy of the model. Special attention has been paid to monitor the winding temperature with considering the motor lifetime in DC charging mode, this view has not been addressed in IBCs before. The proposed model is then validated against experimental results and 3D finite element method. [ABSTRACT FROM AUTHOR]

The Network Function Virtualization (NFV) paradigm has been devised as an enabler of next generation network infrastructures by speeding up the provisioning and the composition of novel network services. The latter are implemented via a chain of virtualized network functions, a process known as Service Function Chaining. In this paper, we evaluate the availability of multi-tenant SFC infrastructures, where every network function is modeled as a multi-state system and is shared among different and independent tenants. To this aim, we propose a Universal Generating Function (UGF) approach, suitably extended to handle performance vectors, that we call Multidimensional UGF. This novel methodology is validated in a realistic multi-tenant telecommunication network scenario, where the service chain is composed by the network elements of an IP Multimedia Subsystem implemented via NFV. A steady-state availability evaluation of such an exemplary system is presented and a redundancy optimization problem is solved, so providing the SFC infrastructure which minimizes deployment cost while respecting a given availability requirement. [ABSTRACT FROM AUTHOR]

This paper presents some results on the accuracy of normal form analysis for large deviations from the stable equilibrium point (SEP) in a power system. The results of studies on the region of convergence of Neumann series, which is required for normal form analysis, are presented. A polynomial function approximation is proposed to model the generator exciter limiter. Studies on the accuracy of higher order normal form analysis for large deviations from SEP are presented. Use of Prony analysis in addition to simulation is proposed for the verification of accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic and general approach to model correlated stochastic processes in power systems by means of stochastic differential-algebraic equations. The paper discusses the theoretical background of stochastic differential-algebraic equations and provides a variety of examples of correlated stochastic models for power system applications. With this aim, stochastic processes with Normal and Weibull distributions are considered. The case study utilizes the well-known two-area system to demonstrate that the presence of correlation between the stochastic processes can cause instability, despite the fact that the same system is stable if the same processes are uncorrelated. The case study also considers a 1479-bus dynamic model of the all-island Irish transmission system to show the scalability of the proposed technique, and to compare scenarios with different levels of correlation among stochastic processes. Results indicate that correlation has a non-negligible impact on short-term dynamics. A high level of correlation among the processes, in fact, can give raise to instability. [ABSTRACT FROM AUTHOR]

Although much work has been done and significant progress has been made on partial discharges (PD) measurement and detection techniques, this is not the case for PD modeling. Four types of internal PD modeling, in sequential order from first to last developed, are three-capacitance (abc), induced charge concept (ICC), finite element analysis (FEA), and Multiphysics models. The abc model is too simple to provide an understanding of the physical phenomena affecting PD. In contrast, Multiphysics models are immature. Multiphysics models require a high number of physical parameters that need to be experimentally determined, which is not a trivial task. Moreover, adjusting the physical parameters to achieve a good agreement between simulation results and measurement results for a specific geometry and dimension may not work for other geometries and dimensions. The FEA model, a relatively recent model, and the ICC model, which fall between the abc and Multiphysics models in terms of modeling complexity, can partly explain mechanisms and physical phenomena associated with internal PD. However, to the best of our knowledge, there is only one paper comparing ICC and FEA models for a case study; thus, further research is needed to elucidate various aspects of these two models. In this paper, 1) an improved ICC and a FEA capable of describing phenomena occurring during PD are developed, 2) both developed models are coded and implemented either in MATLAB or in COMSOL Multiphysics linked with MATLAB, and their simulation results are compared and analyzed, and 3) the influence of different parameters including void shape, void size, and void air pressure on PD parameters are studied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis. [ABSTRACT FROM AUTHOR]

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: “what visualization processes can be assisted by ML?” and “how ML techniques can be used to solve visualization problems? ”This survey reveals seven main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this article can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed. [ABSTRACT FROM AUTHOR]

In this work we study the distance distribution computation problem. It has been widely used in many real-world applications, e.g., human genome clustering, cosmological model analysis, and parameter tuning. The straightforward solution for the exact distance distribution computation problem is unacceptably slow due to (i) massive data size, and (ii) expensive distance computation. In this paper, we propose a novel method to compute approximate distance distributions with error bound guarantees. Furthermore, our method is generic to different distance measures. We conduct extensive experimental studies on three widely used distance measures with real-world datasets. The experimental results demonstrate that our proposed method outperforms the sampling-based solution (without error guarantees) by up to three orders of magnitude. [ABSTRACT FROM AUTHOR]

The high penetration of renewables has sparked new oscillatory concerns in hybrid AC/DC grids. Knowledge of the oscillation regarding its source and propagation mechanism, is crucial to system operation. In this respect, the frequency-domain modal analysis (FMA) provides a way (e.g., participation factor) to extract these oscillation properties, and has been widely applied in converter-based AC grids. Compared to AC grids, oscillation properties of hybrid AC/DC grids are much more complex. Such complexity mainly originates from the diversity of oscillation propagation behaviors in hybrid AC/DC grids, where a distinctive local-area oscillation will emerge. Therefore, a dedicated oscillation propagation analysis for hybrid AC/DC grids is desired, which is the main focus of this paper. First, the mechanism of the distinctive local-area oscillation is analyzed considering AC/DC coupled dynamics of converters. Then, a systematic method for the oscillation propagation analysis is presented, where indices for characterizing the local-area oscillation and its impact area are established. Finally, some propagation properties are discussed, by which a strategy for the online oscillation source locating is designed. Case studies are carried out in a typical hybrid AC/DC grid to validate the propagation analysis in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a cascade blockage model for analyzing the vision that a user has of a wireless network. This model, inspired by the classical multiplicative cascade models, has a radial structure meant to analyze blockages seen by the receiver at the origin in different angular sectors. The main novelty is that it is based on the geometry of obstacles and takes the joint blockage phenomenon into account. We show on a couple of simple instances that the Laplace transforms of total interference satisfies a functional equation that can be solved efficiently by an iterative scheme. This is used to analyze the coverage probability of the receiver and the effect of blockage correlation and penetration loss in both dense and sparse blockage environments. Furthermore, this model is used to investigate the effect of blockage correlation on user beamforming techniques. Another functional equation and its associated iterative algorithm are proposed to derive the coverage performance of the best beam selection in this context. In addition, the conditional coverage probability is also derived to evaluate the effect of beam switching. The results not only show that beam selection is quite efficient for multi-beam terminals, but also show how the correlation brought by blockages can be leveraged to accelerate beam sweeping and pairing. [ABSTRACT FROM AUTHOR]

Device-edge co-inference opens up new possibilities for resource-constrained wireless devices (WDs) to execute deep neural network (DNN)-based applications with heavy computation workloads. In particular, the WD executes the first few layers of the DNN and sends the intermediate features to the edge server that processes the remaining layers of the DNN. By adapting the model splitting decision, there exists a tradeoff between local computation cost and communication overhead. In practice, the DNN model is re-trained and updated periodically at the edge server. Once the DNN parameters are regenerated, part of the updated model must be placed at the WD to facilitate on-device inference. In this paper, we study the joint optimization of the model placement and online model splitting decisions to minimize the energy-and-time cost of device-edge co-inference in presence of wireless channel fading. The problem is challenging because the model placement and model splitting decisions are strongly coupled, while involving two different time scales. We first tackle online model splitting by formulating an optimal stopping problem, where the finite horizon of the problem is determined by the model placement decision. In addition to deriving the optimal model splitting rule based on backward induction, we further investigate a simple one-stage look-ahead rule, for which we are able to obtain analytical expressions of the model splitting decision. The analysis is useful for us to efficiently optimize the model placement decision in a larger time scale. In particular, we obtain a closed-form model placement solution for the fully-connected multilayer perceptron with equal neurons. Simulation results validate the superior performance of the joint optimal model placement and splitting with various DNN structures. [ABSTRACT FROM AUTHOR]

In social networks, individuals’ decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: $(i)$ (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; $(ii)$ (i i) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a small-signal dynamic model for a modular multilevel converter (MMC) based HVDC grid, and then systematically i) identifies the system dominant oscillatory modes, ii) quantifies their propensity to instability phenomena, and iii) devises appropriate countermeasures. Based on the developed model, the eigen structure of an HVDC grid is characterized and the impact of the system parameters, configuration, AC system strength and operating point on the damping ratios of the oscillatory modes, and consequently the HVDC grid stability, are investigated. The model is also applied to systematically determine the sensitive parameters and optimize them to mitigate oscillatory modes and prevent instability. The eigen analysis results are verified based on time-domain simulation studies in the PSCAD/EMTDC platform. [ABSTRACT FROM AUTHOR]

This paper proposes a reconstruction algorithm for the geometric shapes of static overhead transmission lines that is based on the spatial coordinate data (obtained by field mapping) and the corresponding inclination data (obtained by attitude sensors). The algorithm takes the piecewise cubic Hermite interpolation function as the basic framework and corrects the aforementioned two types of data based on the “minimum energy principle” of the transmission line system. The introduction of a new energy function in modified energy method (MEM) overcame the shortcomings of conventional energy method (CEM) in fairing strategy, and the new Fairing Criterion made result curves more consistent with the mechanical properties of transmission lines. In the numerical simulation tests, the two energy methods were compared in terms of load type, horizontal span, elevation difference, and anti-disturbance ability, which showed that MEM has a higher reconstruction accuracy (average Euclidean distance is less than 1 cm) and better robustness than CEM. Meanwhile, the key parameters involved in the algorithm were discussed to guide the application in practical engineering. In addition, measurement tests under non-uniform icing were carried out in the field test station, and the results showed that the maximum relative error is less than 0.1%. [ABSTRACT FROM AUTHOR]

The periodic characteristic of modular multilevel converter (MMC), which originates from the multi-harmonic couplings among internal dynamics, results in challenges to develop the linear time-invariant (LTI) model of MMC. The linearization of MMC is inherently around time-periodic equilibrium trajectory. In this paper, two different methods to assess small-signal stability of MMC are compared. Both are based on the linear time-periodic (LTP) systems theory, but one is in the time domain (TD) and the other is in the frequency domain (FD). The LTP model of MMC is derived from the linearized arm averaged model. Then, an LTI model for the LTP system is constructed in the FD through the harmonic state-space method. Small-signal stability analysis based on Floquet theory and generalized Nyquist criterion, that accompany TD and FD, are introduced and compared. It is demonstrated that models developed in both domains can effectively capture the small-signal behavior of MMC. Third and higher-order harmonic components of the states have significant effect on the impedance-based stability analysis of MMC around mid-frequency range. Analytical results are validated by electromagnetic transient simulations based on the MMC aggregated model. [ABSTRACT FROM AUTHOR]

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, we first introduce the truncated Huber penalty function which shows strong flexibility under different parameter settings. A generalized framework is then proposed with the introduced truncated Huber penalty function. When combined with its strong flexibility, our framework is able to achieve diverse smoothing natures where contradictive smoothing behaviors can even be achieved. It can also yield the smoothing behavior that can seldom be achieved by previous methods, and superior performance is thus achieved in challenging cases. These together enable our framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. A simple yet effective approach is further proposed to reduce the computational cost of our method while maintaining its performance. The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications. Our code is available at https://github.com/wliusjtu/Generalized-Smoothing-Framework . [ABSTRACT FROM AUTHOR]

This paper aims to reveal the uncertainty of traffic flow by introducing a new quantity based on the concept of information entropy (IE). We discover the existence and analyze the properties of IE of traffic flows. It is revealed by both real-world trajectory data and simulation data that the IE of traffic flows can be clearly measured and observed. More importantly, the relationships between IE and other key quantities, those are space mean speed and density, in traffic flow analysis can be described by linear and parabolic functions. We also discover that these relationships are not sensitive to traffic volume. With the inspiration from IE, another new quantity termed speed entropy (SE) is then proposed. Tests with aggregated traffic data from Performance Measurement System (PeMS) show that the pattern of the relationship between SE and flow-weighted average speed illustrates different traffic conditions. In general, a key achievement of the IE analysis is that it gives us a new pathway to better capture the intricate traffic flows from the dimension of uncertainty, thus it has the potential to enhance existing models for traffic data analysis. [ABSTRACT FROM AUTHOR]

Recently, dual-rotor motor (DRM) has emerged as an alternative for an induction motor (IM) or a permanent-magnet (PM) motor for electric vehicle application, due to its better drive performance and better torque density. This paper presents an analytical model for the hybrid IM-PM configuration of DRM. The analytical model can be used to predict induced bar currents, electromagnetic torque generated, and magnetic fields in various regions of the motor. The mentioned calculations are performed under steady-state conditions. The analytical method is developed using the subdomain model approach in 2-D polar coordinates. Appropriate governing equations along with boundary conditions across all the sub-domain regions of the DRM are employed to calculate the magnetic vector potential in all the regions. The developed analytical model has been validated with finite-element analysis for various operating conditions, and high accuracy has been achieved for all the results. [ABSTRACT FROM AUTHOR]

The use of VSC-HVDC grids for offshore wind farm integration will require the use of dc breaker systems and presently they require dc reactors to limit the rate of rise of fault current. The introduction of large dc reactors throughout a VSC-HVDC system can have a significant impact on its stable operation and will require additional control. This paper analyzes this problem and proposes a PSS-like control (DCPSS) to aid dc grid stability and cope with this effect. A generalized analytical model for studies on dc voltage control is presented. Key stability and transient performance issues caused by the use of the dc reactors in a multiterminal system are investigated by analyzing poles, zeros, and frequency responses of open-loop and closed-loop models. Design and location identification methods for the DCPSS are provided. An excellent damping enhancement is achieved by this controller. The analytical studies and time-domain simulations in this paper are performed based on two VSC-HVDC models. [ABSTRACT FROM AUTHOR]

In large and crowded transportation sites such as ports, parking lots, or busy streets, the unsafe actions of a moving object (such as a pedestrian, a ship, or a car) may compromise the safety and security of the entire infrastructure. It follows that a full comprehension of the situations taking place in the area could significantly decrease the amount of work performed by human administrators and operators in charge of the zone, reducing also the impact of human errors. The idea behind this paper is to use trajectory data to analyze behaviors (atomic actions without any external influence) and interactions (actions inducted by the presence of another entity) of each target in the scene. Probabilistic techniques applied on a topological map of the scene allow tackling the problem in a robust way, handling the uncertainties arising in such environments. The system is tested in a real maritime scenario, where trajectory data of ships and vessels are used to determine normal and abnormal situations occurring in a canal. [ABSTRACT FROM PUBLISHER]

Electrical machines that can run at high speeds are more and more studied as they can respond to the increasing need of power onboard of aircrafts. However, to allow high-speed operability mechanical handling of the rotating parts need to be insured. In this paper an analytic design process of a novel high-speed induction machine is presented. The analytical magnetic and mechanical models developed are presented and validated with finite element simulations. The magnetic model is based on a classic equivalent electrical diagram of induction machine with a specific adaptation for the rotor leakage inductance as the squirrel cage is buried. The mechanical model is based on a field displacement approach leading to the stress tensor in all the rotating part. A four degrees of freedom vibration analysis model considering gyroscopic effects based on Euler–Lagrange equation allows to identify the critical speeds of the system. It is shown that some geometrical parameters will have opposed effects on the two physics. Thus, an optimization-based coupling between the different physics allows to design rapidly the desired machine regarding any technical specifications as analytical models are being used. [ABSTRACT FROM AUTHOR]

The hybrid modular multilevel converter (HMMC), consisting of half-bridge and full-bridge submodules (SMs), has become a competitive topology due to the DC fault ride-through capability (FRTC). Redundancy schemes need to be employed to improve the reliability, including fixed-level and voltage-sharing active schemes, as well as passive scheme. However, different schemes affect reliability differently, which entails specific models. The contributions of this paper involve that analytical reliability models of HMMCs are proposed with considerations of voltage stress in active scheme, as well as imperfect bypass switch in passive scheme, and the comparison of different schemes is conducted to help the redundancy design. The reliability of HMMCs is divided into basic and extra components by the requirement of FRTC, which are modeled through the Markov chain and iteration method. A case study is conducted to validate the feasibility and accuracy of the proposed models, and the reliability analysis is presented, together with comparisons of different redundancy schemes. The results illustrate that reliability improvements of both fixed-level mode and passive scheme are prone to suffer saturation effect with the increase of redundant half-bridge SMs, which may even cause reliability decline in voltage-sharing mode. [ABSTRACT FROM AUTHOR]

Recently, the development of the electric power industry has led to an increase in the fault current when a fault occurs. It is important to reduce the fault current, since fault currents exceeding the capacity of protective devices can result in large blackouts. In order to reduce the fault current, there is a method to separate the bus line of main substation. However, this method has the disadvantage of complicating the power system. Another method is to install a superconducting fault current limiter (SFCL). The SFCL has the advantage that the fault current can be quickly limited within 1/4 cycle immediately after the fault occurrence without affecting the power system since its impedance is zero in a normal time. However, the application of SFCL affects the operation of the over-current relay (OCR) due to the reduced fault current such as trip time delay. The trip time delay of OCR was reported to be suppressed by using SFCL's voltage component in previous studies. In this paper, the impedance compensation method was suggested to exclude the effect of SFCL on OCR. To prevent OCR from the influence of SFCL, the component that can exclude the impedance of SFCL was considered to be added to the characteristic formula of OCR. In addition, the effect of correcting this characteristic equation was analyzed by constructing a power distribution system using PSCAD / EMTDC. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies. This paper presents multi-dimensional discrete-time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the IEEE 802.11p standard and C-V2X Mode 4, considering periodic cooperative awareness messages (CAMs) and event-driven decentralized environmental notification messages (DENMs). Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for several key performance metrics. Numerical results are provided to draw insights on the performance. In particular, a performance comparison between IEEE 802.11p and C-V2X Mode 4 in terms of the average delay, the collision probability, and the channel utilization is presented. The results show that IEEE 802.11p is superior in terms of average delay, whereas C-V2X Mode 4 excels in collision resolution. The paper also includes design insights on possible future MAC layer performance enhancements of both standards. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear analysis model for predicting the performance of eddy current recoil brakes (ECRB) is proposed by combining the magnetic equivalent circuit (MEC) model and the subdomain model. The subdomain model introduces the equivalent region to improve the inherent shortcomings of the equivalent current sheet (ECS) method. The outer tube is divided into a magnetic saturated region and an unsaturated region in the subdomain model to simulate the magnetic saturation of the outer tube during recoil. The MEC model is applied to calculate the air gap magnetic flux density and the relative permeability of the equivalent region. Leakage flux and magnetic saturation are considered in the MEC model. The eddy current resistance of ECRB is calculated by the subdomain model. The model is verified by the finite element model, and it is able to predict the performance of ECRB with different structural parameters. In addition, the prototype of the ECRB was manufactured. A test was carried out on a 155 mm artillery to verify the accuracy of the proposed nonlinear analysis model. [ABSTRACT FROM AUTHOR]

A hybrid analytical and lumped-parameter thermal model is proposed in this paper to estimate the transient and steady-state two- and three-dimensional (2/3-D) spatial magnet temperature distributions for an interior permanent magnet synchronous machine (IPMSM). On the basis of a lumped-parameter thermal model (LPTM), the magnet analytical thermal models (ATMs) are synergized to establish the hybrid thermal model. By utilizing the transient or steady-state boundary conditions calculated by the LPTM, the ATMs are obtained by solving the heat conduction equations to estimate the spatial magnet temperature distributions in the 2-D horizontal-vertical and axial-vertical planes, which can also be extended to the 3-D temperature distribution by utilizing the multi-slice method. Meanwhile, the non-uniform magnet eddy current loss is also considered. Finally, both electromagnetic-thermal coupled finite element analysis method and experiments are used for verification based on a totally enclosed IPMSM. [ABSTRACT FROM AUTHOR]

This paper analyzes magnet defect fault signature in the surface-mounted permanent magnet motor (SPMM) using nonlinear analytical model considering the influence of magnet shape and magnetization direction. Based on the surface current method and conformal mapping technique, the magnet defect equivalent current and equivalent nonlinearity current are proposed to represent the magnet defect. Accordingly, the performance of SPMM can be obtained. The proposed model has great potential to investigate SPMM with non-uniform magnet defect and gives theoretical basis for fault diagnosis. The extra sensing coil is added to measure the voltage waveforms, which can also be predicted using the nonlinear analytical model and shows the influence of magnet defect fault. Finally, the finite element analysis and experimental results verify the high accuracy of nonlinear analytical model. [ABSTRACT FROM AUTHOR]

Cooperative vehicle safety (CVS) systems have been receiving significant attention as key enablers of important intelligent transportation system (ITS) applications, such as cooperative collision warning, emergency braking, and auto driving. In CVS systems, vehicles periodically broadcast short packets that include various types of vehicular information, e.g., position and speed. In this paper, we propose an optimization method for the broadcast rate in vehicle-to-vehicle (V2V) broadcast communications at an intersection based on theoretical analysis. We consider a model in which the locations of vehicles are modeled separately as queuing, and running segments and derive key performance metrics of V2V broadcast communications through a stochastic geometry approach. We develop closed-form approximate formulas for the theoretical expressions because they are mathematically intractable. Based on approximate analysis, we optimize the broadcast rate such that the interference at an intersection is mitigated and the overall performance of V2V communications is maximized. Because of the closed-form approximation, the optimal rate can be used as a guideline for a real-time control-method, which cannot be achieved through time-consuming simulations. We evaluate our method through numerical examples and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Resource-constrained end devices can offload computation to backend clouds. The stochastic wireless channel that an end device is connected to can introduce bursty computation demand to the cloud. Specifically, under good channel conditions, a device can transmit more data to the cloud, which consequently yields higher instantaneous computation demand. Conversely, poor channel conditions can result in lower instantaneous demand. The performance indicator for such a mobile cloud computing system is the average of the response time, which is the time span from the arrival of the computation demand at the backend cloud instance to the completion of its execution. The question we target in this paper is how resources should be provisioned for the backend cloud instance to address this bursty computation demand and guarantee a desired quality-of-service (QoS), namely, a user-specified average response time. To answer this question, we model the mobile cloud computing system as two tandem queues. We analyze this queueing network using the fluid flow analysis framework, and derive the analytical relationship between the required resource capacity at the backend cloud instance and the desired QoS, given the workload generation process at the end device and the wireless channel conditions. Having obtained the required resource capacity for a desired QoS, we then determine whether it is economical to provision this resource capacity by subscribing to the traditional static instance or the recently introduced burstable instance offered by public cloud providers. Finally, trace-driven simulations validate our theoretical results. [ABSTRACT FROM AUTHOR]

To compensate for the limitations of existing dedicated short range communications (DSRC), cellular vehicle-to-everything (C-V2X) has been proposed recently, which is also a promising technology for future intelligent transportation systems (ITS). Using stochastic geometry approach, this paper presents the modeling and analysis of success probability in multi-relay cooperative C-V2X networks. The spatial distribution of base stations (BSs) and vehicles in $\mathbb {R}^{2}$ are modeled as a 2D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP), respectively. We focus on the success probability of a source vehicle sending a message to the nearest destination vehicle assisted by the closest BS. Each vehicle is equipped with single antenna whereas each BS is equipped with multiple antennas, which act as independent relays. We consider two decoding schemes, i.e., selection combining (SC) and maximum ratio combing (MRC), and obtain the analytical expressions for joint success probability during two continuous time slots, taking into account the interference correlation (i.e., the spatial correlation of vehicle location). The analytical model is validated using Monte Carlo simulations in MATLAB, and the effects of major parameters on success probability are investigated. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) provide self-organized wireless multihop transmission, where nodes cooperate with each other to support data communication. However, malicious nodes may intercept or discard data packets, which might interfere with the transmission process and cause privacy leakage. We consider historical interaction data of nodes as an important factor of trust. Thus, this paper focuses on the trust node management of VANETs, which aims to quantify node credibility as an assessment method and avoid assigning malicious nodes. First, the integrated trust of each node is proposed, which consists of the direct trust and the recommended trust. The former is dynamically computed by historical interaction records and Bayesian inference considering penalty factors. The latter defines trust by third-party nodes and their reputation. Second, the process of trust calculation and data communication calls for timeliness. Therefore, we introduce a time sliding window and time decay function to ensure that the latest interaction information has a higher weight. We can sensitively identify malicious nodes and make quick responses. Finally, the experimental results demonstrate that our proposed method outperforms bassline methods, especially with respect to the packet delivery ratio and security. [ABSTRACT FROM AUTHOR]