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This paper presents an isolated single-stage on-board electric vehicle charger without an intermediate DC-link. Based on an isolated current-source topology, the converter features soft-switching in semiconductors regardless of load variation for the full AC line voltage range. Moreover, it requires no external snubber or clamp circuits. The power factor correction and voltage regulation are provided by a relatively simple phase shift modulation, while the amount of circulating energy is kept at minimum. The charger is distinguished by its efficiency characteristic – the maximum is achieved in the constant power charging mode. The control method, component stresses, and design constraints of the topology are analyzed. The concept is verified using a 3 kW experimental SiC-based prototype, which reaches a peak efficiency of 96.4%. Moreover, the charger has demonstrated efficiencies above 95% with the THD of 4.1% when operating in constant power mode at the maximal power. [ABSTRACT FROM AUTHOR]

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: The soft-affinity that allows VNFs to be executed by either CPUs or GPUs in cloudlets; and the hard-affinity that only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finally, we evaluate the performance of the proposed algorithms, through simulations and implementations in a real test-bed. Experimental results show that the performance of the proposed algorithms outperform their existing counterparts and achieve higher throughput. [ABSTRACT FROM AUTHOR]

This paper studies the problem of cHarging tAsk Scheduling for direcTional wireless chargEr networks (HASTE), i.e., given a set of rotatable directional wireless chargers on a 2D area and a series of offline (online) charging tasks, scheduling the orientations of all the chargers with time in a centralized offline (distributed online) fashion to maximize the overall charging utility for all the tasks. We prove that HASTE is NP-hard. Then, we prove that a relaxed version of HASTE falls within the realm of maximizing a submodular function subject to a partition matroid constraint, and propose a centralized offline algorithm that achieves $(1-\rho)(1-\frac{1}{e})$ (1 - ρ) (1 - 1 e) approximation ratio to address HASTE where $\rho$ ρ is the switching delay of chargers. Further, we propose a distributed online algorithm and prove it achieves $\frac{1}{2}(1-\rho)(1-\frac{1}{e})$ 1 2 (1 - ρ) (1 - 1 e) competitive ratio. We conduct simulations and field experiments on a testbed consisting of eight off-the-shelf power transmitters and 8 rechargeable sensor nodes. The results show that our distributed online algorithm achieves 92.97 percent of the optimal charging utility, and outperforms the comparison algorithms by up to 15.28 percent in terms of charging utility. [ABSTRACT FROM AUTHOR]

The electromagnetic repulsion model of 400 V/630 A/12.5 kA fast vacuum transfer switch (FVTS) is established using finite element method. The speed and characteristics of the contact bounce of the FVTS are taken as constraints, and the objective is to keep the reliable opening of the mechanism with allowable bounce. The deceleration of the fast operating mechanism in the process of opening and closing is realized by applying the reverse electromagnetic repulsion force. Moreover, the simulation results demonstrate that the electromagnetic buffer effect is available, and the allowable bounce opening of the operating mechanism has been obtained. Furthermore, this paper compares the buffering effects of three kinds of conditions, such as no electromagnetic buffering, 110 V electromagnetic buffering and 220 V electromagnetic buffering. It can be found that the electromagnetic buffering is a necessary part of the 400 V FVTS, and the dynamic characteristics are the optimal scheme when the 220 V electromagnetic buffering is applied. Based on the results of simulation, a FVTS experimental prototype is developed to prove the effectiveness of the design scheme. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper. [ABSTRACT FROM AUTHOR]

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data). [ABSTRACT FROM AUTHOR]

Identifying the location of partial discharges (PDs) and providing clear information on insulation vulnerabilities in power systems can help utility companies to improve their maintenance efficiency. This paper presents a PD monitoring and locating system for medium-voltage switchgears using a set of transient earth voltage (TEV) detectors. A time-of-flight methodology was used to identify the switchgear affected by PD among a row of switchgears. A time sharing access mechanism, which was implemented by multiple high-frequency surface mounting relays integrated into each TEV detector, was applied to automatically identify the site of PD and minimize the number of expensive high-speed acquisition cards. The operation principle and implementation of the modular system were formulated, and the efficiency of the proposed system in identifying the switchgear with PD among three 10 kV metal-clad switchgears was confirmed experimentally. [ABSTRACT FROM AUTHOR]

Object occlusion is a common issue in Light Detection and Ranging (LiDAR)-based vehicle tracking technology. The occlusions can cause variance in vehicle location and speed calculation. How to link the vehicle trajectories caused by occlusion issues is a challenge for traffic engineers and researchers. This paper developed an augmented vehicle tracking method under occlusions with the roadside LiDAR data. The proposed method can be divided into two parts. The first part based on the corner point is used to choose a representative vehicle tracking point. And the second part based on the GNN algorithm is employed to link the vehicles’ trajectories under two occlusion situations. The performance of the proposed method has been evaluated using roadside lidar data collected from four different scenarios. The test results showed that more than 89% of disconnected trajectories can be fixed with the proposed method, which is superior compared to the state-of-the-art method. The proposed method can benefit a lot of transportation areas, such as traffic volume count, vehicle speed tracking, and traffic safety analysis. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable electrocardiogram (ECG) analog front-end (AFE) exploiting bio-signals' inherent low activity and quasi-periodicity to reduce power consumption. This is realized by an agile, on-the-fly dynamic noise-power trade-off performed over specific cardiac cycle regions guided by a least mean squares (LMS)-based adaptive predictor leading to ∼2.5× data-dependent power savings. Implemented in 65 nm CMOS, the AFE has tunable performance exhibiting an input-referred noise ranging from 2.38 – 3.64 μ Vrms while consuming 307 – 769 nW from a 0.8 V supply. A comprehensive system performance verification was performed using ECG records from standard databases to establish the feasibility of the proposed predictor-based approach for power savings without compromising the system's anomaly detection capability or ability to extract pristine ECG features. [ABSTRACT FROM AUTHOR]

The implementation of finite-control-set model predictive control (FCS-MPC) in voltage source inverters (VSIs) can make the system suffer from poor current harmonics performance, which may complicate the design of the required AC filter. To overcome this shortcoming, a carrier-based modulated model predictive control (CB-MMPC) strategy is proposed in this paper. This method enables the utilization of existing PWM modulation techniques with FCS-MPC, where a modulation waveform with zero-sequence signal injection is generated and compared to a triangular carrier wave, while optimizing the selection of the switching states. As it is shown, the studied CB-MMPC strategy not only considerably improves the current total harmonic distortion (THD) but also attains the performance of fast current dynamic response and robustness as the traditional FCS-MPC. Herein, the detailed implementation of the CB-MMPC control strategy is given, while considering its application to the current feedback control loop of a three-phase three-wire two-level VSI modulated at constant switching frequency. Finally, PLECS circuit simulation and a 3-kW VSI prototype are used to verify the superiority and the effectiveness of the presented CB-MMPC strategy. This is also benchmarked to the FCS-MPC and dead-beat based controllers. [ABSTRACT FROM AUTHOR]

Conventional model predictive control (MPC) for three-level active neutral point clamped converter (ANPC) utilizes weighting factors to achieve the optimal control of multiple control objectives in a single cost function. However, the selection of these weighting factors has an impact on the performance of the controlled objectives significantly. Tuning of weighting factors is usually tedious and lacks theoretical background. Moreover, the predictive and evaluation process for multiple objectives optimization have to be carried out by enumerating all admissible switching states within one loop. To simplify the complexity of this process and eliminate the effect of weighting factors, this paper proposes a cascaded predictive control scheme for a 3L-ANPC inverter fed induction machine (IM) drive. With the proposed approach, three separate cost functions for three control objectives, stator flux vector, neutral point voltage and device loss distribution are evaluated in a cascaded way, so that the optimization of these control objectives is performed independently. A 3L-ANPC inverter driven IM drive is studied in this paper to verify the effectiveness of the proposed algorithm. Experimental results in different operating conditions confirm that the proposed method achieves satisfactory steady-state and transient performances comparable to the conventional predictive control approach. The weighting factors used in the conventional method are eliminated and the computational effort is reduced by 68% compared to the conventional method without any sacrifice in the drive performance. [ABSTRACT FROM AUTHOR]

The network switches and routers need to forward the network packets at a line-rate. The underlying hardware and software implementations may limit the performance of these packet processing devices. In this paper, we propose a novel model, called Lookaside, to increase the performance of the network switches by extracting the payload from the packet and storing it in a nearby standard memory, such as dynamic random access memory (DRAM), before the packet is sent for processing. We know that the packet payload has no role in packet processing. We implemented the prototypes of our Lookaside switch model on the state-of-the-art ONetSwitch45 hardware field-programmable gate array board. The simulation results show an increase in the performance of the switch in terms of latency, throughput, switching chip resources, and on-chip resources power consumption as compared to SimpleSwitch. [ABSTRACT FROM AUTHOR]

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system. [ABSTRACT FROM AUTHOR]

A new method is proposed to identify the location of transient events, including incipient faults, in power distribution systems, by using synchronized measurements from an emerging class of sensors, called waveform measurement units (WMUs). WMUs capture the voltage and current waveforms in time domain. The proposed method consists of three steps. The first step is to characterize the oscillatory modes of the transient components of all the captured synchronized voltage and current waveforms from all WMUs, by conducting a multi-signal modal analysis. The second step is to construct a circuit model for the underlying distribution feeder at the identified dominant mode(s) of the transient event. The final step is to identify the location of the transient event with the means of a method that involves certain forward and backward analyses of the constructed circuit model. The proposed method requires installing as few as only two WMUs. It can also utilize several synchronized waveform measurements when several WMUs are available. The performance of the proposed method is assessed on the IEEE 33-bus test system; for different cases of transient events, such as sub-cycle incipient faults, multi-cycle incipient faults, permanent faults, as well as benign yet informative events such as capacitor bank switching. Both the accuracy and the robustness of the proposed method are verified. The analysis and results in this paper provide new insights on possible applications of synchronized WMU measurements; while they also address a highly challenging problem in power distribution networks. [ABSTRACT FROM AUTHOR]

Widespread and large-scale WiFi systems have been deployed in many corporate locations, while the backhual capacity becomes the bottleneck in providing high-rate data services to a tremendous number of WiFi users. Mobile edge caching is a promising solution to relieve backhaul pressure and deliver quality services by proactively pushing contents to access points (APs). However, how to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous APs can have heterogeneous traffic characteristics, and future traffic conditions are unknown ahead. In this paper, given the cache storage budget, we explore the cache deployment in a large-scale WiFi system, which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain. Specifically, we first collect two-month user association records and conduct intensive spatio-temporal analytics on WiFi traffic consumption, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the proportion of AP distributes evenly within the range, indicating that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LeaD (i.e., Large-scale WiFi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize long-term traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven experiments are carried out, and the results demonstrate that LeaD is able to achieve the near-optimal caching performance and can outperform other benchmark strategies significantly. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]

For Electro-Magnetic-Transient (EMT) simulations of power networks with switches, techniques such as linear interpolation and Critical Damping Adjustment (CDA) are widely used for improving numerical robustness. This paper analyzes the numerical stability of simulations with these techniques. Firstly, it is mathematically shown that the interpolation or CDA step is equivalent to the introduction of additional switching states. Subsequently, Common Quadratic Lyapunov Function (CQLF) theory is used to investigate the numerical stability of the whole simulation considering these new switching states. It is proved that the widely used strategies like linear interpolation and CDA always result a stable simulation if the original switched system is strictly passive in all switching states. Finally, it is shown that the developed approach can be used to determine the stability of other practical interpolation methods. Examples are provided to verify the proposed technique. [ABSTRACT FROM AUTHOR]

Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers. [ABSTRACT FROM AUTHOR]

In photovoltaic systems, common-mode voltage (CMV) generates leakage current, which shortens the insulation lifespan and causes safety problems. As for neutral-point-clamped (NPC) inverters, zero CMV PWM (ZCMV PWM) can be implemented to eliminate leakage current, but unable to balance neutral-point (NP) voltage in dc side. This article presents a novel modulation technique for NPC PV inverter. The strategy contains ZCMV PWM and another two modulation modes which keeps CMV constant in one switching period, so as to eliminate leakage current. In order to balance NP voltage, NP current is adjusted by selection of optimal modulation mode and the leakage current is still well eliminated during the process of balancing NP voltage. Compared with conventional ZCMV PWM, the proposed scheme can remove both leakage current and NP voltage deviation. The output performance and limitations of the proposed method are also introduced in the paper. Experiments are provided to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, very high frequency (VHF) converter has attracted much attention. However, with rich parasitic parameters and complex resonance links, there are some great difficulties to the modeling of such systems. Taking the on-off controlled VHF resonant boost converter as an example, this paper presents an accurate modeling and analysis method that considers multiple parasitic parameters. First, the closed-loop VHF converter system is divided into a main network and a parasitic oscillation network. Then, based on the operation analysis, an equivalent circuit model characterized by a time-varying input voltage source and two variable duty-cycle controlled switches is proposed. It worth noting that both the time-varying input and the controlled switches take into account the influences of the parameters. Furthermore, the periodic approximate analytical solution of the output voltage in the on stage is obtained by using the equivalent small parameter method to the proposed circuit model. And then combining the solution of the parasitic oscillation network and on-off state switching conditions, the steady-state waveform of output voltage can be obtained quickly. Finally, a prototype with operating frequency of 21.44 MHz is built to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize the service confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30 to 50 percent improvement in the confidence levels of fog and cloud latencies, respectively. [ABSTRACT FROM AUTHOR]

The capacitor condition monitoring is an important issue for the reliable operation of modular multilevel converters (MMCs) in high-voltage applications. The numerous capacitors make the monitoring for the capacitor condition complex and computation-consuming. This paper proposes a hierarchic capacitor condition monitoring strategy to assure the reliability of high-voltage MMCs. In the strategy, the switching times of the switching signals are counted to detect the condition of the capacitor in each submodule (SM). Then, the subsequent capacitance calculation method is only conducted to the abnormal capacitors indicated by the above condition detection algorithm. Meanwhile, the variations of the measured and reference capacitor voltages are employed to calculate the capacitance in the proposed method. The condition detection algorithm shrinks the scope where the abnormal capacitors locate, which avoids calculating the total capacitances in MMCs. In the proposed capacitance calculation method, the complex operations are further saved. Both the two measures reduce the computation burden effectively. The implementation of the whole monitoring strategy is simple and fit for high-voltage MMCs. Moreover, the strategy has no adverse influence on the normal operation of MMCs. The effectiveness of the proposed strategy is verified by the simulation studies with the professional tool Matlab/Simulink. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for calculating the DC source amplitude in an extended multilevel inverter (MLI) structure so that the maximum number of levels and the output voltage waveform are as close as possible to the sinusoidal wave with minimum total harmonic distortion (THD). For the developed structure, three algorithms are suggested to determine the amounts of DC voltage resources. The first important point about choosing the right amounts for the DC resources is that the number of levels should be as large as possible, and the second important point is that the intervals between the levels should be the same throughout the waveform. By observing these two points, the output voltage waveform can be as near as possible to the sinusoidal wave that we want. In this study, we used iteration-based methods to find suitable values for DC sources. Simulation results are offered to confirm the capability of the extended multilevel converter. After we solved the problem through calculation and analysis, a code was written in MATLAB with the aim that this time the code will tell us for what values of DC sources we will have the largest number of levels and as we expected, the output of the MATLAB code confirmed the correctness of the calculations. [ABSTRACT FROM AUTHOR]

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods. [ABSTRACT FROM AUTHOR]

High-performance multicast packet switching technologies are evolving to meet the growing demand for scalability on the Internet and datacenters, etc. Implementing a high-performance switch/router relies on a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether a packet is forwarded through an egress. Among these, Bloom filter (BF)-based and Residue Number System (RNS)-based are being considered as two representatives of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as false-positive probability and time inefficiencies, especially for a carrier-grade PFE with high port-density features. According to similar properties of the RNS, we propose a simplified forwarding algorithm in this paper, named Per-Port Prime Filter Array (P3FA). The simulation results indicate that the P3FA can significantly improve space efficiencies under specific lower egress-diversities conditions. Under the same space constraints, P3FA improves multicast and unicast time efficiency by 1 to 4 orders of magnitude in the port-density 16–1024 range compared to previous works. Although it comes at the expense of hardware cost, it is still acceptable compared to recently improved previous work. [ABSTRACT FROM AUTHOR]

Resilient fuzzy stabilization is capable of providing much less conservative results than conventional fuzzy stabilization while the alert threshold condition should be always satisfied at each sampling instant. In order to make the alert threshold condition more easily to be guaranteed, the short paper employs the switching-type gain-scheduling control law so that the real-time information, which is specific to the current sampling instant, can be integrated into resilient fuzzy stabilization. More importantly, a new kind of time-variant balanced matrix is introduced for the first time for adjusting positive/negative terms of different monomials in a more flexible way. As a result, the conservatism of resilient fuzzy stabilization can be further reduced even if the alert threshold condition becomes more difficult to be violated. Finally, the advantage of the developed method is tested and validated via related comparisons on a benchmark example. [ABSTRACT FROM AUTHOR]

This paper presents a novel active power control scheme for SRMs that are used as S/G units at MEAs; the proposed method uses a vector-controlled SAF, facilitating the acceleration of the SRM during starter mode and the efficient power control of the SRG at various operational points during generator mode. Moreover, an energy storage unit is incorporated in the SAF, aiming at enhancing the SRM's starting torque production and taking full advantage of the SRG's energy generation capability, through the appropriate phases’ magnetization regulation that is imposed by the developed adaptive SAF vector control. In addition, the proposed scheme provides enhanced FRTC during both modes of operation. The mathematical analysis is verified through MATLAB/Simulink simulations, while the performance of the S/G concept under study is evaluated via real-time CHIL tests, with the use of the dSPACE 1202 (MicroLabBox) platform and the 16-bit dsPIC30F4011 microcontroller. [ABSTRACT FROM AUTHOR]

Emerging device-based digital processing-in-memory (PIM) architectures have been actively studied due to their energy and area efficiency derived from analog to digital converter (ADC)-less PIM hardware. However, digital PIM architectures generally need large extra memories to copy parameters, and they also suffer from low computation per memory-cycle efficiencies. In this paper, we present a novel spin-orbit torque magnetic random access memory (SOT-MRAM) based digital PIM architecture to alleviate the extra memory size burden and computation cycle issues. First, we propose the spintronics-assisted logic-in-memory (SLIM) cells to support efficient digital logic operations inside memories, where the voltage-controlled magnetic anisotropy (VCMA) is exploited to enhance the computation per memory-cycle efficiencies. In addition, crossed input source PIM (CRISP) architecture is proposed to extend the merits of SLIM cells by eliminating the extra memories for parameter copying while significantly improving the degree of parallel processing. An intra-memory pipelining scheme is also considered to further increase the throughput of CRISP. The proposed CRISP architecture has been implemented using 28 nm CMOS process, and it presents 1.10 TOPS/W and 0.95 TOPS/mm2, showing considerable improvements of energy efficiency and throughput per area, compared to the state-of-the-art digital PIM architecture. Finally, to evaluate the impact of computation errors induced from the SOT devices and circuits in CRISP architecture, classification accuracy simulations have been performed while applying computation errors. [ABSTRACT FROM AUTHOR]

Aiming at the scheduling problem for regional power grids under both outage and power uncertainties, this paper puts forward a multistage robust optimization (RO) method that ensures the reliable power supply in system operations. Firstly, a multistage RO model is proposed according to the practical scheduling request of the regional power grid. The startup and shutdown of slow-acting generators are optimized in the first stage. The optimal topology is then determined in the second stage in preparation to the outage uncertainty of grid-connected lines. In the third to 2+ ${T}$ stages, the scheduling plans of generation units in each time period are further formulated regarding the sequential realizations of renewable-load power uncertainties, which ensures the nonanticipativity of the obtained robust schedules. Secondly, a tailored and efficient transformation-decomposition algorithm is exploited for the solution of multistage RO problem. Based on the affine control function, the nested max-min optimization of the third to 2+ ${T}$ stages is explicitly transformed to a single-stage max-min model via the multi-to-one strategy. A column and constraint generation (C&CG) algorithm with looped alternative optimization procedure (AOP) handles the equivalent three-stage RO problem with binary recourses, thus avoiding numerous forward and backward iterations as well as speeding up the modeling solution. Numerical experiments of real-world regional power grids verify the effectiveness, superiorities and scalability of the proposed multistage RO scheduling method, which indicates great guidance for engineering applications. [ABSTRACT FROM AUTHOR]

Modern data centers often host multiple applications with diverse network demands. To provide fair bandwidth allocation to several thousand traversing flows, Approximate Fair Queueing (AFQ) utilizes multiple priority queues in switch to approximate ideal fair queueing. However, due to limited number of queues in programmable switches, AFQ easily experiences high packet loss and low link utilization. In this paper, we propose Elastic Fair Queueing (EFQ), which leverages limited priority queues to flexibly achieve both high network utilization and fair bandwidth allocation. EFQ dynamically assigns the free buffer space in priority queues for each packet to obtain high utilization without sacrificing flow-level fairness. The results of simulation experiments and real implementations show that EFQ reduces the average flow completion time by up to 82% over the state-of-the-art fair bandwidth allocation mechanisms. [ABSTRACT FROM AUTHOR]

Packet classification, i.e., the process of categorizing packets into flows, is a first-class citizen in any networking device. Every time a new packet has to be processed, one or more header fields need to be compared against a set of pre-installed rules. This is done for basic forwarding operations, to apply security policies, application-specific processing, or quality-of-service guarantees. A lot of research efforts have identified better lookup techniques, i.e., finding the best match between packet headers and rules, by capitalizing on the rule sets characteristics. Here, ClassBench has greatly served the community by enabling the generation of IPv4 rule sets. In this paper, we present a new tool, ClassBench-ng, that creates synthetic IPv4, IPv6, and OpenFlow rules. We start from an analysis of classification rules deployed in-the-wild and we use the findings to craft our solution. ClassBench-ng can generate a user-defined number of rules as well as an associated header trace matching them. Compared to state-of-the-art solutions, the rule set generation process is usually more accurate and it is able to produce rules matching a number of different use cases, i.e., from an IPv4 router to an OpenFlow switch, which is unique among current rule set generation tools. [ABSTRACT FROM AUTHOR]

Service Overlay Network (SON) is an efficient solution for ensuring the end-to-end Quality of Service (QoS) in different real-world applications, including Video-on-Demand, Voice over IP, and other value-added Internet-based services. Although SON offers many advantages, such as ease of deployment and resilience to the node failures, it has to face the challenge of overlay network configuration that needs to dynamically adjust to the change in communication requirements. In this paper, we propose a novel method for adaptive overlay topology configuration, called AOTC based on Software-Defined Networks, deep learning, and reinforcement learning. The intuitive motivation is to address the above challenge, maximize the QoS from two aspects of customer preference and network cost. The obtained experimental results demonstrate the superiority of AOTC. Such a method can significantly reduce network cost while providing an improvement of 50% and 60% in terms of average delay and packet loss rate as compared to other traditional approaches. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC), owing to the presence of a large number of submodules (SMs) and energy storing elements, requires a large footprint and initial investment. To address these issues, this paper proposes the switched modular multilevel converter (SMMC). It consists of two major elements; the chain-links (CLs), consisting of cascade connection of SMs as in MMC, and the single-pole-triple-throw (SPTT) switches, which can be realized using either the T-type or NPC-type switch strings. The SMMC works on the principle of connecting all the phase-legs in series and their respective positions are mutually interchanged. It offers a compact structure as the SMs count is reduced to 33% of MMC, which results in a much lower energy storage requirement. Furthermore, it does not have inter-phase circulating current problem and hence the CL inductors can also be avoided. The SMMC structure, operating principles, control, and modulation schemes are explained in detail. A detailed comparison of SMMC with the other major existing converters is carried out to highlight the important features of SMMC. The efficacy of SMMC is validated using PSCAD simulation studies with a 19-level grid-connected SMMC model. A 3-phase 19-level SMMC laboratory prototype is developed to further validate its feasibility. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) in high- voltage applications contain large numbers of power switches, which increases the fault probability inside MMCs. This paper proposes a detection method for the faulty arm containing switch open-circuit failures in high-voltage MMCs to improve the reliable operations of MMCs. The proposed method uses the periodicity of the deviation between the maximum and minimum capacitor voltages to provide the detection reference. To yield a reliable reference, the repetitive signal generator is employed. Then, the fault is identified by the difference between the referenced and measured capacitor voltage deviations. An auxiliary criterion is also designated to avoid the false detection caused by the mutations in MMCs. The proposed method can reduce the difficulty in the selection of thresholds. Moreover, the proposed method is robust in high-voltage MMCs with inferior capacitor voltage balancing performance, which ensures detection rapidity and accuracy. Finally, the feasibility of the method is verified by simulation results in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Piezoelectrically actuated fast mechanical switches provide a low-loss conduction path in hybrid circuit breakers for protecting medium-voltage direct-current systems. As the desired actuation speed approaches the driving limit of a piezoelectric actuator, excessive vibration starts to dominate its underdamped travel curves, resulting in insulation failures and delayed switching in the fast mechanical switch. To improve the underdamped travel curves into critically damped travel curves, several switching motion control schemes with active damping filters are proposed in this paper. The switching motion controllers are based on an electromechanical model of a fast mechanical switch with experimentally identified parameters. According to hardware implementation tests, the proposed closed-loop control schemes optimized the switch travel curves with a 56% reduction in the undershoot and a 75% reduction in the settling time. Equipped with the critically damped travel curves, the piezoelectrically actuated fast mechanical switch is better suited for sequential submodule operations to reduce the interruption time, the peak current and the fault energy to be absorbed in hybrid circuit breakers. [ABSTRACT FROM AUTHOR]

Uncertainties in power system studies are typically managed by Monte Carlo methods or by applying some worst case assumptions. As the number ofuncertainties increases, there is a need for methods that can estimate statistical parameters from a limited number of calculations. This paper utilizes a method called the Unscented Transform together with Cornish-Fisher expansion to estimate the 2%-value of switching overvoltages using only about one tenth the number of calculations as a Monte Carlo method, with similar accuracy. The uncertainties considered are the energizing instants of the three phases. The method is illustrated through EMT simulations in PSCAD and it is shown to provide a good approximation of the 2%-value in most of the studied cases. [ABSTRACT FROM AUTHOR]

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively. [ABSTRACT FROM AUTHOR]

In this paper, we address the finite-time and fixed-time synchronization of a general class of switched neural networks (SNNs) with time delays subject to stochastic disturbances. Considering two types of switching in this class of SNNs: 1) intra-SNN state-dependent switching and 2) inter-SNN Markovian switching, we develop three control laws and derive three sets of sufficient conditions for both finite-time and fixed-time synchronization of SNNs subject to stochastic disturbances. We make two remarks on the effects of control-law parameters on synchronization settling time. Moreover, we derive several upper bounds of synchronization settling time and evaluate their pros and cons. Finally, we elaborate on two numerical examples to illustrate the viability of the theoretical results. [ABSTRACT FROM AUTHOR]

Hybrid beamforming for Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems is a promising technology for 6G space-air-ground integrated networks, which can overcome huge propagation loss and offer unprecedented data rates. With ultra-wide bandwidth and ultra-large-scale antennas array in THz band, the beam squint becomes one of the critical problems which could reduce the array gain and degrade the data rate substantially. However, the traditional phase-shifters-based hybrid beamforming architectures cannot tackle this issue due to the frequency-flat property of the phase shifters. In this paper, to combat the beam squint while keeping high energy efficiency, a novel dynamic-subarray with fixed true-time-delay (DS-FTTD) architecture is proposed. Compared to the existing studies which use the complicated adjustable TTDs, the DS-FTTD architecture has lower power consumption and hardware complexity, thanks to the low-cost FTTDs. Furthermore, a low-complexity row-decomposition (RD) algorithm is proposed to design hybrid beamforming matrices for the DS-FTTD architecture. Extensive simulation results show that, by using the RD algorithm, the DS-FTTD architecture achieves near-optimal array gain and significantly higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of DS-FTTD architecture with the RD algorithm is robust to the imperfect channel state information. [ABSTRACT FROM AUTHOR]

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly $\mathcal {NP}$ -hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time $\left({1 - \frac {1}{e}}\right)$ -approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless $\mathcal {P} = \mathcal {NP}$ , and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks. [ABSTRACT FROM AUTHOR]

IPC (Inter-Process Communication) is a widely used operating system (OS) technique that allows one process to invoke the services of other processes. The IPC participants may share the same OS (internal IPC) or use a separate OS (external IPC). Even though a long line of researches has optimized the performance of IPC, it is still a major factor of the run-time overhead of IPC-intensive applications. Furthermore, there is no one-size-fits-all solution for both internal and external IPC. This paper presents SkyBridge, a general communication technique designed and optimized for both types of IPC. SkyBridge requires no involvement of the privileged software (the kernel or the hypervisor) and enables a process to directly switch to the virtual address space of the target process, regardless of whether they are running on the same OS or not. We have implemented SkyBridge on two microkernels (seL4 and Google Zircon) as well as an open-source serverless hypervisor (Firecracker). The evaluation results show that SkyBridge improves the latency of internal IPC and external IPC by up to 19.6x and 1265.7x, respectively. [ABSTRACT FROM AUTHOR]

The demand for real-time data processing has become increasingly attractive in Cyber-Physical Systems(CPSs), especially for data-intensive embedded real-time applications. In order to timely perceive and respond to environmental changes, the basic design requirement in such systems is to provide data service with high freshness. As modern CPSs become more complex, there are a broad set of system mode switch behaviors, some unforeseen, in a dynamic computational environment. However, conventional control algorithms can hardly handle such new scenarios, since most of them assume that the operational behavior is fixed. In this paper, we study the problem of how to maximize the freshness of data in multi-modal systems. We first use a recently proposed new conception, namely Age of Information (AoI) to quantify the freshness of data by combining the AoI metric with real-time constraints. Then, we propose, to our knowledge, the first freshness-aware scheduling solution to settle the problem via deep reinforcement learning(RL). To be specific, we develop an RL framework that can continuously update its scheduling strategies and maximize the freshness of data in the long term. Extensive simulation experiments are conducted and the results demonstrate that the proposed FAS-DQN outperforms other traditional state-of-the-art methods in terms of data freshness. [ABSTRACT FROM AUTHOR]

The digital implementation of all the control loops of a versatile buck-boost (VBB) dc–dc converter used in a stand-alone photovoltaic application is proposed in this paper to improve existing digital-analog sliding-mode-based implementations. All three control loops: maximum power point tracking (MPPT), fast input voltage regulation, and inner high-bandwidth current control, have been programmed in the same digital signal controller (DSC). A Model Predictive Control (MPC) based algorithm has satisfactorily solved the challenge of implementing the nominal 100 kHz switching frequency current loop. The MPC cost function is distributed throughout the algorithm to achieve three specific goals: the tracking of the reference current (G1), a quasi-constant steady-state switching frequency (G2), and the assurance that the duration of an interval is larger than the time required to calculate it (G3). The third goal requires the current control to toggle between peak- and valley-modes depending on the operating point. The correct fulfillment of these control objectives on the proposed MPC-based algorithm has been validated through simulations and experimental tests performed on a purpose built-prototype. [ABSTRACT FROM AUTHOR]

In this paper, a fault-tolerant control of a symmetrical six-phase induction generator based on a variable structure strategy is experimentally tested and compared with a classical proportional-integral controller. For this, we use a linear feedback sliding mode controller with switched feedback gains in such a way to control the inner current loops of this machine. One main drawback of the classical three-phase induction generator is that it is no longer possible to generate electrical power if only one phase is lost on the stator side. To overcome this issue, we will use a squirrel cage six-phase induction machine that can still operate when at least three phases remain on the stator side. This article proposes to use a variable structure control in order to cope with unbalanced currents and also maintain the generation of power when a loss of the stator's phases occurs. The proposed controller shows its robustness and good regulation performance in healthy and faulty modes. The simulation and experimental tests were carried out with a 24 kW induction machine that represents 1/100 of the electrical power of a real wind turbine. [ABSTRACT FROM AUTHOR]

In this paper a control method for a matrix converter (MC) based on the heterodyne concept from the communication field is presented. The method is used to convert the high frequency of a microturbine generator’s (MTG) output voltage to the utility grid’s frequency. In this control method a frequency modulation (FM) of the MC’s switching frequency is achieved using a DQ0 analysis of the converter’s filtered output voltage with the utility grid’s frequency as a reference. After filtration, the MC achieves a conversion to the reference frequency with a minimal total harmonic distortion (THD) of the output voltage while using a low switching frequency. In addition, the proposed method decouples the output frequency, phase angle and voltage amplitude, which allows them to be controlled separately. This significantly simplifies the required control, and enables a robust and low-cost design. The theoretical analysis, simulation and experimental results of the method are reported. [ABSTRACT FROM AUTHOR]

Differential power processing (DPP) converters have been developed for photovoltaic (PV) panels to preclude energy yield reduction due to partial shading. In large-scale PV systems comprising numerous series-connected panels, DPP converters with good modularity are preferable because the number of series-connected panels varies depending on applications. This paper proposes a switched capacitor converter (SCC)-based modular DPP architecture for large-scale PV systems. Four panels are grouped as a module, each having a panel-level DPP converter, and modules are connected in series to form a string. The design of the panel-level DPP converter is fixed while the system can be arbitrarily extended by stacking modules, achieving good modularity. Adjacent panel-level DPP converters are dc-coupled, by which modules can be connected using long cables, mitigating installation limitations. The experimental results of the laboratory and field testing demonstrated the energy yield enhancement by 10%–20%, depending on shading conditions. [ABSTRACT FROM AUTHOR]

This paper presents a sigma-modified adaptive control algorithm to enhance the charging profile in a multi-objective electric vehicle (EV) charging installation. The present algorithm takes care of multiple parametric uncertainties and grid non-idealities to provide an instantaneous control updation in order to achieve well-regulated charging dynamics. With the support of renewable energy and battery energy storage (BES), the present algorithm also ensures an uninterrupted charging profile with controller robustness and stability for bi-directional EV charging. The sigma-mod adaptive controller provides an iterative error convergence at each clock interval of supply voltage dynamics to guarantee improved power quality operation in presence of grid distortions. To further improve the reliability of EV charging opportunities, a solar photovoltaic (PV) array in conjunction with the battery energy storage supports the ancillary services through maximum power point operation. Multivariable sliding mode control and rule-based phase-shift adaptation at different stages of power transformation assure faster convergence, parameter uncertainty and controller stability for the bi-directional EV charging operation. A 3.3 kW PV-integrated off-board charging facility is designed and developed as a laboratory prototype to validate the multi-mode charging architecture with minimal grid dependency. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dual input nine-level inverter (DINLI) topology which requires only seven controllable devices. The dual inputs are un-identical DC voltage sources in the ratio 1:3. A simple control scheme is derived by appropriately modifying the phase disposition pulse width modulation technique to generate high-quality 9-level output from the DINLI topology. At any given instant, a maximum of four switches conduct out of which only a maximum of two switches operate at high frequency, bringing down the switching and conduction losses significantly and improving the overall system efficiency. The bidirectional DINLI (BDINLI) topology with reactive power capability can be realized by replacing the unidirectional devices with the controlled switches (i.e., total nine switches). In addition, the proposed DINLI topology does not require extra voltage balancing circuits which further improves the efficiency. The DINLI operation is verified through MATLAB simulations for grid-connected applications and its extension to an n-level, 3-phase application is also investigated. Experiments performed on a 1KVA laboratory prototype to validate the theoretical claims. [ABSTRACT FROM AUTHOR]

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow. [ABSTRACT FROM AUTHOR]