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The support vector machine (SVM) is a very important machine learning algorithm with state-of-the-art performance on many classification problems. However, on large datasets it is very slow and requires much memory. To solve this defficiency, we propose the fast support vector classifier (FSVC) that includes: 1) an efficient closed-form training free of any numerical iterative procedure; 2) a small collection of class prototypes that avoids to store in memory an excessive number of support vectors; and 3) a fast method that selects the spread of the radial basis function kernel directly from data, without classifier execution nor iterative hyper-parameter tuning. The memory requirements of FSVC are very low, spending in average only 6 $\cdot 10^{-7}$ · 10 - 7 sec. per pattern, input and class, and processing datasets up to 31 millions of patterns, 30,000 inputs and 131 classes in less than 1.5 hours (less than 3 hours with only 2GB of RAM). In average, the FSVC is 10 times faster, requires 12 times less memory and achieves 4.7 percent more performance than Liblinear, that fails on the 4 largest datasets by lack of memory, being 100 times faster and achieving only 6.7 percent less performance than Libsvm. The time spent by FSVC only depends on the dataset size and thus it can be accurately estimated for new datasets, while Libsvm or Liblinear are much slower on “difficult” datasets, even if they are small. The FSVC adjusts its requirements to the available memory, classifying large datasets in computers with limited memory. Code for the proposed algorithm in the Octave scientific programming language is provided.1 [ABSTRACT FROM AUTHOR]

With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively. [ABSTRACT FROM AUTHOR]

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance ($R_{\mathrm {RS}}$) for ocean color applications. Among these is the vicarious calibration coefficient ($g$) of the “long” near infrared band (NIR $_{\mathrm {L}}$) used for atmospheric correction. For this band, the prelaunch calibration has always been deemed sufficient [thus $g$ (NIR $_{\mathrm {L}}) =1.00$ ] as long as other bands are vicariously calibrated. Recent research, however, suggests that Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua $R_{\mathrm {RS}}$ time series is quite sensitive to $g$ (NIR $_{\mathrm {L}}$) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP) $R_{\mathrm {RS}}$ to NIRL calibration and compared our results to previous MODIS/Aqua and Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS)/OrbView2 analysis. In doing so, we note that $g$ (NIR $_{\mathrm {L}}$) sensitivities of mission-averaged $R_{\mathrm {RS}}$ time series are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies (MCs), however, all sensors show prominent $g$ (NIR $_{\mathrm {L}}$) sensitivity with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on $g$ (NIR $_{\mathrm {L}}$) sensitivity. As such, $g$ (NIR $_{\mathrm {L}}$) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within $g$ (NIR $_{\mathrm {L}}$) sensitivity, such studies should be coupled with cross-sensor intercalibrations [e.g., using simultaneous same view (SSV) measurements] toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in $R_{\mathrm {RS}}$. [ABSTRACT FROM AUTHOR]

High-voltage direct-current (HVDC) systems using voltage source converters (VSCs) have been developed to suppress voltage fluctuations in offshore grids due to the intermittent power generation of offshore wind farms (OWFs). This paper proposes a new strategy for the optimal secondary voltage control of HVDC-linked OWFs. In the proposed strategy, optimal coordinated control of an HVDC system and OWFs is achieved to minimize real-time voltage fluctuations throughout an offshore grid. A full-order dynamic model of HVDC-linked OWFs is implemented, and its unobservable and uncontrollable state variables are then eliminated using a model reduction algorithm. Given the dynamic model, a linear quadratic Gaussian regulator is designed for optimal coordinated control, and an eigenvalue analysis is conducted focusing on the effects of communication time delays on the stability of the proposed strategy. Simulation case studies are also carried out to verify that the proposed strategy more effectively improves real-time regulation of offshore grid voltages, compared to conventional strategies, while ensuring voltage stability even for relatively large delays in communications systems. [ABSTRACT FROM AUTHOR]

Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods. Related codes and datasets will be provided at https://github.com/yilun-xu/SVEHDRI/. [ABSTRACT FROM AUTHOR]

This study deals with a new approach to tackle the challenges encountered in the filters-based reactive power calculation techniques utilized in the control system of static VAr compensators. In this approach, step and time-varying responses are improved by a combination of differential and filter-based methods. Then, optimum values of the main parameters of the low-pass and band-stop filters are determined considering three criteria: accuracy, speed, and capability for following the time-varying loads. To assess the performance of the proposed method, a new objective function is introduced, calculated for two sets of signals, captured from a real field in Mobarakeh Steel Company in Isfahan/Iran. Then, the proposed and the other reactive power calculation methods are assessed in practice and simulation. Also, the algorithm is implemented using a TMDSCNCD28335 as the processor to assess the real-time feasibility of the method. To do so, the reactive power variation of an electrical arc furnace is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [ABSTRACT FROM AUTHOR]

Integrated sensing and communication (ISAC) can provide efficient usage for both spectrum and hardware resources. A critical challenge, however, is to design the dual-functional waveform for simultaneous radar sensing and communication. In this paper, we propose a sparse vector coding-based ISAC (SVC-ISAC) waveform to simultaneously provide low sidelobes for radar sensing and ultra reliability for communication transmission. The key idea of the proposed waveform is to embed the communication information into the support of one sparse vector and transmit a low-dimensional signal via the spreading codebook. We derive a closed-form expression of the ambiguity function for the proposed SVC-ISAC waveform, and prove that it exhibits low sidelobes in the delay and Doppler domains, regardless of the distribution of the transmitted bit stream. In addition, the information decoding at the communication receiver is solved through the support identification and sparse demapping. Simulation results demonstrate that the proposed waveform improves the reliability while consistently suppressing the sidelobe levels. [ABSTRACT FROM AUTHOR]

Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adaptive modulation and coding (AMC) scheme to accommodate both spatial diversity and temporal variation on users’ channel conditions for QoS provisioning in wireless scalable video multicast. The QoS-guaranteed AMC policy optimization is formulated as constrained stochastic optimization based on Markov decision processes. An adaptive policy iteration algorithm is developed to find the optimal AMC policy online without any prior knowledge of users’ channel statistics or intensive calculations. This algorithm is attractive due to adaptability to unknown environments and less run-time computation expenditure. Simulations results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents improved indices to evaluate the contribution of reactive power compensators at HVdc converter terminals. The presence of such compensators impacts converter performance, such as the maximum available power (MAP) limit, susceptance to commutation failure, fault recovery time and temporary over voltage. Traditionally, the Effective Short Circuit Ratio (ESCR) has been used to indicate system strength. However, this index cannot be directly applied when power-electronics based converters are used to provide the reactive power support. The approach of this paper is to use an improved form of the ‘Apparent increase in Short Circuit Ratio’ (AISCR) index to quantify the impact of the compensator on the HVdc system. This index is evaluated by comparing the performance with the reactive power device with a system without the device, but having the same performance. The paper shows that a single AISCR index cannot quantify all behaviors and suggest modifications to AISCR for each of several HVdc phenomena. In this paper two types of compensators are considered – the Synchronous Compensator and the Static Synchronous Compensator (STATCOM). [ABSTRACT FROM AUTHOR]

This paper proposes the design of two coordinated wide-area damping controllers (CWADCs) for damping low frequency oscillations (LFOs), while accounting for the uncertainties present in the power system. The controllers based on Deep Neural Network (DNN) and Deep Reinforcement Learning (DRL), respectively, coordinate the operation of different local damping controls such as power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers for DC lines (DC-SDCs). The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of linear matrix inequality (LMI)-based mixed $H_2/H_\infty$ optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as bounded exploratory control-based DDPG (BEC-DDPG). The studies performed on a 33 machine, 127 bus equivalent model of the Western Electricity Coordinating Council (WECC) system-embedded with different types of damping controls demonstrate the effectiveness of the proposed CWADCs. [ABSTRACT FROM AUTHOR]

This article presents a new collaborative control method based on expert decision, proposed for the combined system of an ac grid with a low THD thyristorized static var compensator (SVC), interconnected to a dc microgrid through a distribution static compensator (D-STATCOM) that also works as a bidirectional power converter. The combination of large rating SVC and a low rating D-STATCOM functions as a hybrid compensator for the ac distribution grid. In the combined mode of operation, the D-STATCOM mitigates voltage fluctuations and compensates initial var demands, while the SVC caters to the base var requirements. The D-STATCOM has also been used for integration of a dc microgrid to the utility grid, permitting exchange of power between the grids and thereby reducing voltage fluctuations of both the systems. The proposed collaborative control method based on expert decision can extract the full advantages of the respective subsystems as per demand of the operating condition. This combined system complies with the relevant harmonic standards and can effectively correct system power factor, stabilize bus voltage, and mitigate voltage flicker under fluctuating load conditions. Theoretical analyses with simulation results as well as experimental results show that the proposed system can meet the requirement of continuous dynamic reactive power compensation and voltage stabilization with low cost, high reliability, and large capacity. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-automatic Fontan surgery planning method for designing and manufacturing hemodynamically optimized patient-specific grafts. Fontan surgery is a palliative procedure for patients with a single ventricle heart defect by creating a new path using a vascular graft for the deoxygenated blood to be directed to the lungs, bypassing the heart. However, designing patient-specific grafts with optimized hemodynamic performance is a complex task due to the variety of patient-specific anatomies, confined surgical planning space, and the requirement of simultaneously considering multiple design criteria for vascular graft optimization. To address these challenges, we used parameterized Fontan pathways to explore patient-specific vascular graft design spaces and search for optimal solutions by formulating a nonlinear constrained optimization problem, which minimizes indexed power loss (iPL) of the Fontan model by constraining hepatic flow distribution (HFD), percentage of abnormal wall shear stress (%WSS) and geometric interference between Fontan pathways and the heart models (InDep) within clinically acceptable thresholds. Gaussian process regression was employed to build surrogate models of the hemodynamic parameters as well as InDep and $\text{N}_\text{v}$ (conduit model smoothness indicator) for optimization by pattern search. We tested the proposed method on two patient-specific models (n=2). The results showed the automatically optimized (AutoOpt) Fontan models hemodynamically outperformed or at least are comparable to manually optimized Fontan models with significantly reduced surgical planning time (15 hours versus over 2 weeks). We also demonstrated feasibility of manufacturing the AutoOpt Fontan conduits by using electrospun nanofibers. [ABSTRACT FROM AUTHOR]

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In remote areas, the distribution networks are usually underdeveloped; thus, three-phase medium-voltage grids may provide a low-cost power supply solution for residential buildings. A new centralized approach topology of three-port power hub converter, which consists of a V/V transformer, a back-to-back inverter, and a static volt-ampere reactive (VAR) compensator with adaptive impedance, is proposed for remote area power supply. The proposed three-port converter interfaces two bidirectional power ports connecting three-phase ac grid and dc grid, respectively, and one unidirectional power port connects ac loads. The functions of the proposed converter are the bidirectional power flow control between three-phase ac grid and dc grid and power quality compensation to the three-phase ac grid. Finally, to verify the validity of the proposed three-port converter and its control performance, different simulation and experimental cases are implemented under different operation conditions. [ABSTRACT FROM AUTHOR]

Induction generators (IG) are widely used in renewable energy systems, such as wind power, and micro/mini hydro-turbines. In applications, such as urban wind-energy systems, electric vehicles and more electric aircrafts, induction machines can be used to supply electrical power to auxiliary loads, deriving power from main engines. In this article, an IG system with open-end stator windings is proposed for simultaneous supply of regulated power to frequency insensitive ac-loads and dc-link loads for the first time. One end of the stator winding is connected to auxiliary loads while the other end is connected to a capacitor-fed voltage source inverter (VSI). In the proposed system, VSI is controlled to supply reactive power required by the IG while simultaneously regulating the dc-link voltage and ac load voltage at required level. The proposed method is based on scalar-control of the induction machine, thus making it a simple and robust system to supply active power demanded by ac and dc loads. The proposed control scheme has been simulated in MATLAB-Simulink and experimentally verified for steady state and transients with various ac loads including linear, nonlinear, and unbalanced loads. [ABSTRACT FROM AUTHOR]

The respective advantages of ac and dc microgrids lead to the blooming development of the hybrid ac/dc microgrid, which consists of ac and dc microgrid tied by an interlinking converter. A new structure of hybrid coupled interlinking converter (HCIC), which composed of a converter in series with a static VAR compensator (SVC), is proposed for hybrid ac and low voltage dc (LVdc) microgrids. Moreover, a new droop control method is correspondingly developed for the proposed HCIC. Its fundamental active and reactive power flow in the hybrid ac/dc microgrid is controlled by P-δ and Q-P-V droop control for converter part of the HCIC, and P-Q-α droop control is proposed for SVC part of the HCIC. Furthermore, a fast harmonic control is given based on a decoupled control strategy to improve the filtering characteristics of SVC part of the HCIC, suppress the harmonic power, and/or inject active power. Finally, simulation and experimental results are provided to verify that the proposed HCIC for hybrid ac/LVdc microgrids has the ability of good power flow control and power quality compensation, which not only meets the power flow requirements with low rating system, but also greatly promotes the development of more robust hybrid ac/dc microgrids. [ABSTRACT FROM AUTHOR]

The increasing integration of utility-scale renewable energy sources (RESs) brings emerging challenges to the classical problems of voltage control in transmission grids, including increased potential for voltage violations. To address this challenge, we present a novel measurement-based coordinated voltage control scheme that can enable fast participation of RESs. In this approach, RESs are coordinated with the traditional voltage control devices such as synchronous generators (SGs) and static var compensators (SVCs) to maintain all bus voltages within operational limits while respecting device power limits. The control scheme allows different priorities to be assigned to different control resources, and ensures that both voltage and reactive power constraints are met in steady-state whenever it is possible to do so. The controller design requires only an approximate model of the steady-state relationships between voltage and reactive power in the system, and in online operation, processes voltage and reactive power measurements to produce set-point updates for RESs, SVCs, and SGs; this feedback provides robustness against both model uncertainty and unmeasured disturbances. The feasibility and effectiveness of the controller is demonstrated via simulation case studies on a detailed power system model. [ABSTRACT FROM AUTHOR]

Fast reactive power variation in wind farms creates flicker. Static VAR compensators (SVCs) are the most commonly used devices for flicker mitigation. The reactive power calculation unit is the main component in the control system of SVCs. This article deals with two new approaches for reactive power calculation. In both methods, the main parameters of a filter-based technique are optimized using a novel optimization approach. The first method has a non monotonic step response which is converted to a desirable monotonic response by the second method. This results in a better response to a time-varying reactive power. The problem formulation and definition of a new objective function are presented. A data pool is gathered from Manjil wind farm, Iran, for different conditions. This data pool is used in the objective function to derive the optimal setting. The performance of the proposed methods is assessed using this dataset and is compared with other techniques. Also, real-time implementation of the algorithm is evaluated using TMDSCNCD28335 as the processor. To do so, the reactive power variation in a wind generation unit is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [ABSTRACT FROM AUTHOR]

This paper deals with the use of utility scale solar PV systems as PV-STATCOM in high voltage transmission systems. In this case a 75 MW solar PV system is considered in a 138 kV transmission system. [ABSTRACT FROM AUTHOR]