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Partial discharge (PD) measurements under repetitive impulse voltages are critical for the qualification of inverter-fed motor insulation systems. Severe electromagnetic interference due to high frequency switching from power electronic devices can cause the traditional PD detection techniques of sinusoidal voltage unfeasible. This article presents the design of an Archimedes spiral antenna that can work effectively for PD detection under fast rise time repetitive impulse voltages. The antenna structure is optimized by a media superstrate with a high dielectric constant over the radiant surface. Through the optimized design, both the gains of the antenna in the 0.5–1.5 GHz frequency range and the signal-to-noise ratio for PD detection are increased substantially. Modeling and experimental results prove that the gain of the antenna can reach 2.5 dB in the frequency range of 500–900 MHz and become higher than 7.0 dB in the frequency range of 900–2.0 GHz, with a voltage standing–wave ratio smaller than 1.4. This seems to be a significant achievement for PD detection under fast rise time impulse voltages. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) is essential to the bidding strategy formulation and market operation. Since EPF is important in the electricity market, lots of forecasting approaches are proposed. However, the new scene caused by the volatility of renewable power generation and other volatility factors has made previous methods inaccurate and inapplicable. To address this problem, we propose an online self-adaptive forecasting method based on random forest, which is different from the traditional batch learning. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different sizes. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy than ordinary approaches, as well as sheds light on possible concept drift in the market. [ABSTRACT FROM AUTHOR]

Semantic scene completion (SSC) is a computer vision task aiming to simultaneously infer the occupancy and semantic labels for each voxel in a scene from partial information consisting of a depth image and/or a RGB image. As a voxel-wise labeling task, the key for SSC is how to effectively model the visual and geometrical variations to complete the scene. To this end, we propose the Anisotropic Network (AIC-Net), with novel convolutional modules that can model varying anisotropic receptive fields voxel-wisely in a computationally efficient manner. The basic idea to achieve such anisotropy is to decompose 3D convolution into three consecutive dimensional convolutions, and determine the dimension-wise kernels on the fly. One module, termed kernel-selection anisotropic (KSA) convolution, adaptively selects the optimal kernel sizes for each dimensional convolution from a set of candidate kernels, and the other module, termed kernel-modulation anisotropic (KMA) convolution, directly modulates a single convolutional kernel for each dimension to derive more flexible receptive field. By stacking multiple such anisotropic modules, the 3D context modeling capability and flexibility can be further enhanced. Moreover, we present a new end-to-end trainable framework to approach the SSC task avoiding the expensive TSDF pre-processing as in many existing methods. Extensive experiments on SSC benchmarks show the advantage of the proposed methods. [ABSTRACT FROM AUTHOR]

Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping. The overall framework is trained end-to-end and is able to spot text of arbitrary shapes. The convolutional features are calculated only once and shared by both the detection and recognition modules. Through multi-task training, the learned features become more discriminative and improve the overall performance. By employing a 2D attention model in word recognition, the issue of text irregularity is robustly addressed. The attention model provides the spatial location for each character, which not only helps local feature extraction in word recognition, but also indicates an orientation angle to refine text localization. Experiments demonstrate that our proposed method can achieve state-of-the-art performance on several commonly used text spotting benchmarks, including both regular and irregular datasets. Extensive ablation experiments are performed to verify the effectiveness of each module design. [ABSTRACT FROM AUTHOR]

In this article, a highly efficient hp -adaptive scheme has been developed for the discontinuous Galerkin time-domain (DGTD) method. In order to achieve the ${h}$ - and ${p}$ -adaptive behaviors, a hierarchical octree-based recursive meshing procedure has been introduced. In the hp -adaptive scheme, the transformation method of the basis function is developed to convert the fields between the parent element and the child elements and between the higher- and the lower-order bases. Instead of the time-consuming field update process according to the governing equations, the error based on modified reference solutions can be rapidly and accurately calculated in the ${h}$ - and ${p}$ -adaptions by using the proposed transformation, thus flexibly manipulating the hp -adaptive procedure. Some 3-D radiation and scattering examples are given to demonstrate the good accuracy and high efficiency of the proposed hp -adaptive DGTD method. [ABSTRACT FROM AUTHOR]

Periodical validation and calibration to microgrid models are necessary for investigating the behaviors of microgrids and evaluating the stability of connected power grids. However, instead of calibrating the problematic parameters that vary from their actual values, the commonly used methods normally adjust the selected sensitive parameters. The performance of the calibrated models is still in doubt. Therefore, this article proposes a generic approach for microgrid model calibration, which aims to accurately locate the indeed “ill-conditioned” problematic parameters, as well as address the multiple solutions issue. With the application of sensitivity and correlation analysis, the potentially problematic parameters (PPPs), which are sensitive and independent, are selected to ensure the accuracy and uniqueness of parameter calibration results. Then, the problematic parameters are further screened out by comparing the energy distribution of PPPs and model errors in the frequency domain. Also, hybrid dynamic simulation based parameter estimation is employed to adjust the problematic parameters, by which each device in microgrids can be calibrated independently. Finally, the simulation results demonstrate the outstanding performance in parameter calibration of a real microgrid. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus heavily depending on large-scale labeled data, which could be infeasible when labels are prohibitively expensive. In this paper, we propose a novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. It consists of three parts: Sequence Reconstructor (SER), Contrastive Motion Learner (CML), and Information Fuser (INF). SER learns representation from skeleton coordinate sequence via reconstruction. However the learned representation tends to focus on trivial postural coordinates and be hesitant in motion learning. To enhance the learning of motions, CML performs contrastive learning between the representation learned from coordinate sequences and additional velocity sequences, respectively. Finally, in the INF module, we explore varied strategies to combine SER and CML, and propose to couple postures and motions via a knowledge-distillation based fusion strategy which transfers the motion learning from CML to SER. Experimental results on several benchmarks, i.e., NTU RGB+D 60/120, PKU-MMD, CMU, and NW-UCLA, demonstrate the promise of the our method by outperforming state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The improvements in signal-to-noise ratio (SNR) are indispensable to obtain the desired output signal of partial discharge (PD) under square wave pulsewidth modulation (PWM) voltage. In this article, the effect of complex PWM voltage parameters (rise time, fall time, frequency, duty cycle) on the SNR of PD tests is investigated. The results revealed that the sensor's high-frequency gain should be increased to improve the SNR under faster switching. PD impulses are more likely to be submerged in the residual interference under high-frequency voltage. The SNR can be improved by only detecting the PD at the rising front because the PD signal overlaps with commutation noise at the falling front of short duty cycle voltage, such as 0.03% duty cycle. The above results can reference the PWM parameters and improve the SNR in PD tests for rotating machines controlled by power electronics. [ABSTRACT FROM AUTHOR]

Accurate and reliable machine performance degradation tracking and remaining useful life (RUL) prognosis establish the foundation for predictive maintenance scheduling toward improved safety and productivity of machine operations. In general, machine performance degradation exhibits a nonlinear and nonhomogeneous pattern that arises from time-varying degradation rate and abrupt performance changes. To address this challenge and improve the generalizability of degradation modeling, in this article, we present a stochastic modeling technique based on the Lévy process, which generalizes system variations as the accumulations of successive and jump increments. The developed Lévy process model consists of two terms: a linear Brownian motion term for capturing the gradual degradation with time-varying rates and a nonhomogenous compound Poisson process term for capturing transient performance changes. By calculating the moments of the characteristic function of the Lévy model, explicit expressions for the probability distributions of predicted performance degradation and RUL are derived. To obtain the time-varying parameters in the Lévy model, Markov chain Monte Carlo is investigated. The developed technique is evaluated through simulation and run-to-failure tests of roller and ball bearings, and the good performance of the developed Lévy model is confirmed. [ABSTRACT FROM AUTHOR]

The square-wave voltage signal injection method with the proportional-integral (PI) observer is commonly adopted to estimate the rotor position of interior permanent magnet synchronous motor (IPMSM). However, the saliency estimation method with PI observer is a continuous injection method and the polarity identification method is a discrete one, which causes extra transition time and complexity. To solve this problem, a fast unipolar sequence-pulse injection method is proposed to unify the process of saliency and polarity identification and the complexity of the algorithm as well as the identification time are reduced. At the same time, a four-stage search algorithm is proposed to ensure the fast convergence and improve the accuracy by reducing the number of the pulse voltage signals. The experiment on motor drive platform verifies the effectiveness of the proposed method to IPMSMs with different saliency ratios. [ABSTRACT FROM AUTHOR]

We propose and demonstrate, with hardware, the first experimental technique to measure a vertical component of FinFET source resistance. Forward bias is applied to the well-to-source p-n junction, and the forward voltage at constant current density, ${V}_{{\text {fb}}}$ , is measured as a function of source-current, ${I}_{\text {ds}}$. The source-current, ${I}_{\text {ds}}$ , is varied by variation of ${V}_{\text {gs}}$ , and an effective vertical resistance is calculated, ${R}_{\text {ve}} =$ dVfb/dIds. Significant self-heating effects of the p-n junction are observed, and we demonstrate a technique to correct ${V}_{\text {fb}}$ for the local rise in temperature. We find that our technique gives well-behaved results for vertical source resistance. Quantitative ${R}_{\text {ve}}$ values are found to be consistent with technology characterization of (linear region) total device resistance. [ABSTRACT FROM AUTHOR]

Topology identification and parameter estimation form the basis for the operation and control of the distribution network with little or no observability. This paper proposes a method to estimate the admittance matrix for open-loop distribution networks based only on active/reactive power injection and voltage magnitude data. We derive the linear relationship between the admittance matrix and the measurements based on decoupled linear power flow (DLPF) equations. A total least squares regression method is proposed based on analysis of the characteristics of the open-loop grid admittance matrix. Case studies on the IEEE 33-bus system demonstrate the proposed method's effectiveness, accuracy, and efficiency. [ABSTRACT FROM AUTHOR]

This article presents a novel reduced order average model for dual-active-bridge converter which can be applied to all modulation methods, such as single-phase-shift modulation, dual-phase-shift modulation, extended-phase-shift modulation and triple-phase-shift modulation. This model considers conduction, inductance, and transformer power losses. Furthermore, the input–output filters are suitable for system performance analysis. Based on the large-signal average model, the small-signal model and the output transfer function are derived. The detailed model for predicting the large-signal transient is established and the small signal analysis in frequency domain is carried out. This model can accurately predict the steady-state and transient-state response of the system. The experimental results agreed well with the simulation results which proved the accuracy and correctness of the model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a set of new sounding reference signals (SRSs) for time-division duplexing (TDD) massive MIMO systems with channel aging. It is analytically shown that the proposed set of SRSs exhibit zero correlation zone (ZCZ) property, which guarantees sparse and marginal interference between the SRSs. Therefore, all SRSs in the set can be transmitted concurrently in a cell with a short SRS period per user to alleviate the channel aging effect. We also analytically derive the peak-to-average power ratio (PAPR) and show how the SRS should be constructed to achieve low PAPR. Finally, simulation results are provided to show that the proposed SRSs result in larger downlink throughput than the 5G New Radio SRSs. [ABSTRACT FROM AUTHOR]

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., “how to describe”) and localization (i.e., “where to look”) information, and their interactions. To this end, we propose a biologically inspired framework for image-based fashion product retrieval, which mimics the hypothesized two-stream visual processing system of human brain. The proposed attentional heterogeneous bilinear network (AHBN) consists of two branches: a deep CNN branch to extract fine-grained appearance attributes and a fully convolutional branch to extract landmark localization information. A joint channel-wise attention mechanism is further applied to the extracted heterogeneous features to focus on important channels, followed by a compact bilinear pooling layer to model the interaction of the two streams. Our proposed framework achieves satisfactory performance on three image-based fashion product retrieval benchmarks. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals. [ABSTRACT FROM AUTHOR]

Accurate equivalent model can be efficient to analyze the dynamic properties of active distribution network (ADN) as well as assess their impacts on stabilities of interconnected power system. However, due to the stochastic nature of renewable resources and time-varying configurations of load conditions, traditional ADN equivalent model may not be robust enough to different operation conditions. To overcome the limitations, this article proposed a robustness-improved method for dynamic equivalent modeling of ADN. To sketch out the most representative operation conditions of ADN, two-step clustering method with fisher discriminant analysis are used for grouping of operation conditions featured characteristic data sets. With the key parameter based identification technique applied, the multiple solution issue in parameter identification process could be effectively avoided. To further enhance the robustness of equivalent model, long short-term memory neural network is adopted to generalize the identified parameters. The performance of proposed modeling method is comprehensively evaluated by an actual ADN based verification cases. [ABSTRACT FROM AUTHOR]

A novel single-phase pulsewidth modulation (PWM) direct ac–ac converter based on two-level nondifferential dual-buck ac chopper legs with inverting and noninverting operations is first proposed in this article. It has the ability to resolve both voltage sag and swell problems at the same time when used as distributed flexible voltage conditioner. Compared to the traditional ac–ac converter, it has much enhanced system reliability thanks to no shoot-through problems even when all switches of each ac chopper legs are turned on, and therefore, the PWM dead time is not needed leading to improve the utilization of the duty cycles. Only half of the switches in the proposed converter is switched at high frequency during a switching period at most, which significantly reduces the total switching loss. In particularly, the converter has two greatest advantages that it retains the common sharing ground of the input and output and has the same buck/boost operation process for noninverting and inverting modes. In order to fully testify the performance of the proposed converter, a 500-W experimental prototype is built and tested at different conditions. [ABSTRACT FROM AUTHOR]

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial–spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback–Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, a wide-angle space filter (SF) with absorption function based on 1-D magnetized plasma photonic crystals is proposed, which has a good rectangularity of greater than 0.95. Since the angle range (AR) of the TM polarization is larger than that of the TE, they can be split at specific angles, possessing the potential to be a polarization splitter (PS). The effects of plasma density ne and magnetic induction intensity B on the AR of the SF are discussed based on the tunability of the magnetized plasma. The increase in ne shrinks the AR of TE and TM polarizations at the same time, but that of the TM decreases faster, resulting in a narrower AR of PS. If B is augmented, only the AR of the TM wave will be reduced, which will also lead to the reduction in the AR of the PS. In addition, the impedance matching theory is used to explain the cause of the generation of SF. Compared with the traditional angular filter only with the transmission function, the SF we proposed with the absorption function is relatively rare, and it may have important application possibilities in the antenna field. [ABSTRACT FROM AUTHOR]

Terahertz (THz) transmission technologies constitute a promising candidate for supporting ultra-broadband short-range next generation communications. Hence, we analyse the performance of unmanned aerial vehicle (UAV) in the THz networks. The coverage probability is derived as well as the area spectral efficiency (ASE) and a pair of Line-of-sight (LoS)/non-line-of-sight (NLoS) probability models, namely the macrocell LoS/NLoS probability model and picocell LoS/NLoS probability model are adopted. Furthermore, the lower-bound of the network performance are derived via homogeneous Poisson point process (HPPP) analysis, as well as the upper-bound. The simulation results match the analytical results well, which show that the coverage probability of the network first increases upon increasing the THz UAV BS density, and then decreases beyond the maximum. Given the severe path loss experienced by THz signals, a higher UAV density is required for a certain coverage probability than at lower carrier frequencies. [ABSTRACT FROM AUTHOR]

In this article, penalty factor threshold and time step bound in discontinuous Galerkin time method based on vector wave equation (DGTD-WE) method are well estimated. Based on the semidiscrete form of the DGTD-WE method, properties of the system matrices are studied and the stability condition related to the penalty factor is derived. By decomposing the global system matrices of the DGTD-WE method into the local ones and developing an efficient iteration procedure, the lower bound threshold of the penalty factor is well estimated to guarantee the positive semidefinite property of the global system matrices. With the calculated penalty factor, the maximum time step is analytically determined by approximating spectral radius of the local system matrix. Both the penalty factor bound and the maximal time step are computed element-wise instead of a global system matrix operation, and thus, the proposed method can be efficiently applied into the large-scale meshes with different types of the basis functions and boundary conditions. Numerical examples are presented to demonstrate the validity and good performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The linear transformer driver (LTD) is widely used in the field of pulsed power, especially for generating intense electron beam. The design of an LTD with quasi-rectangular voltage output is presented. The Blumlein pulse-forming network (BPFN) with L-type configuration replaced traditional capacitors to produce a quasi-rectangular voltage waveform; the adding voltage is achieved through a linear transformer (LT) that couples the voltage from multiple primaries to a single secondary. Since the output waveforms of BPFN and LT determine the output waveform of LTD, the effects of the configuration of the BPFN and the parameters of the LT on the output voltage waveform are studied. At the same time, according to the results, the output voltage waveform is improved by optimizing the inductance of the BPFN and selecting the appropriate parameters of the LT. After that, an electron beam generator based on six-stage LTD cavities is developed, and a well-shaped quasi-rectangular waveform is obtained at a planar diode load. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]

Energy systems, e.g., power systems and natural gas systems, are increasingly interdependent, which brings benefits under normal conditions and introduces risks under extreme weather events, e.g., hurricanes. A comprehensive method is proposed to reflect the spatial-temporal hurricane impacts on interdependent power and natural gas systems. Mechanism analysis based and data based component failure models are used to map hurricane effects on exposed component failure probabilities based on which system states are generated using Monte Carlo simulation. For system states with failed components, the integrated energy flow is developed to determine minimum joint load shedding considering inherent properties of each system and characteristics of interdependence coordinately. Operational metrics and infrastructure metrics are employed to quantify the service adequacy and network resistance of the interdependent energy systems, respectively. The modified IEEE 24-reliability test system and the 12-node natural gas system under a simulated hurricane are used to validate the effectiveness of the proposed method. Simulation results show the method can identify weak parts and quantify the performance of interdependent power and natural gas systems under hurricanes to provide information for effective and coordinative preparedness. [ABSTRACT FROM AUTHOR]

The uncertainty of power system is intensified by the integration of large-scale renewable energy resources such as wind farm (WF). Considering the impact of system uncertainties, the probabilistic stability analysis methods have been used in the stability assessments of power system with the WF integration. However, in traditional probabilistic analysis methods, WF is normally considered as PQ bus or aggregated as one single-machine equivalent model regardless of wake effect, which might reduce the accuracy of probabilistic stability studies. In this article, a dynamic equivalent modeling method of WF for probabilistic stability assessments is proposed. The wake effect is considered in the modeling process and a practical four-machine clustering method is used in wind turbines clustering. Besides, the fisher discriminant analysis (FDA) is adopt to combine the similar WTs clustering results reasonably. Then, the WF is aggregated to a multi-machine equivalent model by capacity weighted method. Also, the established WF probabilistic model can be directly used in time-domain simulation based on Monte Carlo simulation and FDA. Finally, the efficiency of the proposed method is verified in an actual WF in China. [ABSTRACT FROM AUTHOR]

Managing the distributed energy resources in cybersecure smart grids will involve placing trust in organizations that act between operators and proactive consumers. We present a Stackelberg game in a three-party neighborhood area network in conjunction with open access and preservation of private data. [ABSTRACT FROM AUTHOR]

In this article, an electrical doping method to realize the high n-type doping for a germanium (Ge) layer in a low-doped n-type Ge metal-oxide-semiconductor (MOS) structure in the accumulation condition is proposed, which maintains the initial crystal quality of Ge. The direct-bandgap electroluminescence (EL) from Ge light-emitting diode (LED) has been demonstrated with 1.5– 1.6μm infrared emission using the metal/graphene/high-k/ interfacial layer (IL)/n-Ge MOS structure fabricated on bulk-Ge substrate. An onset current density of 5 A cm−2 is observed, which is, up to now, the lowest onset current density compared with the Ge LEDs fabricated on unstrained Ge substrates. These results indicate the superiority of the electrical doping method and the interface passivation technique on yielding the high-performance Ge-based LEDs and lasers. [ABSTRACT FROM AUTHOR]

The performance of the long-wavelength infrared (LWIR) HgCdTe photodetectors with nBn structure is susceptible to the thickness of barrier layer and the huge mismatch of the energy band between the barrier layer and the absorption layer. Herein, a numerical simulation analysis for LWIR HgCdTe nBn device is carried out to present an enhanced performance with optimized structural and physical parameters (including thickness, doping concentration, and composition). The results provide that the valence band-offset (VBO) could be reduced by structure optimization, and could greatly improve the transport properties of photogenerated carriers. In addition, the effect of composition diffusion caused by the annealing process has also been studied to demonstrate that larger composition diffusion leads to a much higher dark current. The model and calculation results established in this article can provide important theoretical support and guidance for further researches of LWIR HgCdTe nBn devices. [ABSTRACT FROM AUTHOR]

Objective: The interaction between superparamagnetic iron oxide (SPIO) nanoparticles and mesenchymal stem cells (MSCs) in the presence of pulsed magnetic field (PMF) has become an important area of research in recent years. Methods: A parameter-adjustable pulsed magnetic field was developed based on the principle of insulated gate bipolar translator transistor-controlled discharge of large capacitances. The internalizations of SPIO nanoparticles by MSCs were investigated under the treatment of PMF in both intermittent stimulation mode and continuous stimulation mode. Results: The intensities and frequencies of pulsed magnetic field can be adjustable in the range of 1.9–4.6 mT and 3–5 kHz, respectively. This PMF was safe to the MSCs. However, the uptake of SPIO nanoparticles by MSCs was significantly prohibited under the treatment of kHz-ranged PMF while the 10 Hz PMF enhanced the cellular uptake of nanoparticles. This phenomenon was relative with the magnetic effect of the PMF with different frequency. Conclusion: The PMF can be used to effectively regulate the cellular uptake of SPIO nanoparticles and the mechanism lies in the magnetic effect. Significance: The interaction between SPIO nanoparticles and the MSCs is a fundamental and important issue for nanomedicine and stem cell research. Our results demonstrate that the external magnetic field can be used to regulate their interaction. We believe that this safe, facile, and flexible method will greatly promote the development and clinical translation of regenerative medicine and nanomedicine. [ABSTRACT FROM AUTHOR]

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China. [ABSTRACT FROM AUTHOR]

In arc flash hazards calculations, protection boundary can be used to gain insight in personnel protection against thermal injuries. Although IEEE 1584-2002 has provided the calculation guide based on tests with the arcing electrodes in a vertical plane, applications where the traditional vertical arrangement is not directly applicable have been drawing more attention over the past few years. Due to the varied thermal behaviors of arc under different equipment configuration and the varied bus connection mode, for the purpose to achieve the adequate protection on arc flash thermal injury, it is necessary to analyze the effect of different electrode geometries on arc flash protection boundary (AFPB). According to the prior work on the development of IEEE 1584-2018, this article fully discusses the impact of electrode geometry on incident energy exposure level and AFPB. With the multiple electrode geometry designed tests, thermal behaviors of arc under different electrode orientation, gap distance, and equipment configurations have been analyzed. According to test results, correction equations were derived under each typical electrode geometry to adjust the AFPB calculation model of IEEE 1584-2002. Through the comparisons, the corrected model can better reflect the actual effects of electrode geometry, meanwhile showing a good ability to provide reasonable AFPB for practical uses. [ABSTRACT FROM AUTHOR]

Surface flaw inspection is of great importance for quality control in the field of manufacture. In this paper, a novel surface flaw inspection algorithm is proposed based on adaptive multiscale image collection (AMIC) using convolutional neural networks. First, the inspection networks are pretrained with ImageNet data set. Second, the AMIC is established, which consists of adaptive multiscale image extraction and with-contour local extraction from training images. Through the AMIC, the training data set is greatly augmented, and labels of images can be accomplished automatically without artificial consumption. Then, transfer learning is performed with the AMIC established from training data set. Finally, an automatic surface flaw inspection instrument for large-volume metal components embedded with the proposed inspection algorithm is designed. Experiments with small metal components are performed to analyze the influence of parameters, and comparative experiments are carried out. The inspecting precisions for indentation, scratch, and pitted surface of the proposed method are 97.3%, 99.5%, and 100%, respectively. The experimental results demonstrate the effectiveness of the proposed method in the detection of various surface flaws. [ABSTRACT FROM AUTHOR]

A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS-cGAN) is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on the improved U-Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for the generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real–fake segmented images. Based on the c-GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ground-fault location method in resonant grounded systems based on distributed modulation and compensation adjustment. After the permanent single-phase ground fault arc has been suppressed, the Petersen coil will be adjusted to make the zero-sequence currents at the upstream monitoring points of the fault point exceed a certain threshold and be modulated in situ. At this time, the corresponding modulated signals can be extracted from the neutral-to-earth voltage on the busbar for measuring these zero-sequence currents. Taking the carrier-frequency of every monitoring point as its identification tags and the variation characteristic of the zero-sequence currents along feeders as another detection criterion, the fault point can be determined online accurately without a high-performance real-time monitoring network. In this paper, the carrier-frequencies allocation and signal conditioning scheme are proposed for a good detection accuracy of the zero-sequence currents, then the signal detection principle with the control of Petersen coil is described in detail. Simulations and experiments verified the feasibility of the proposed fault location method. [ABSTRACT FROM AUTHOR]

This paper proposes a modulation reference compensation method to completely eliminate the intercoupling between the output active and reactive powers of modular multilevel converters (MMCs). Different from the existing methods, it is realized not based on an accurate control model, but by directly amending the initial arm modulation references of MMCs. Therefore, the proposed method is essentially a compensation algorithm, which is extremely easy to implement in a microcontroller with low calculation burden. In addition, it does not need any additional closed-loop controllers or voltage/current sensors, which is very beneficial to practical projects. The proposed method finally realizes accurate and fully decoupled active and reactive power regulation, avoids overload operation and protection malfunction, and significantly improves the dynamic performance of MMCs. The effectiveness and accuracy of the proposed analysis and compensation method are carefully verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Direct modulation principle is widely used in modular multilevel converters (MMCs) for its simplicity and natural stability. However, it results in strong active and reactive power coupling. This paper carefully analyzes and identifies four power coupling paths and two power influence factors with the direct modulation principle. Full power decoupling of the MMC can be realized by cutting off the derived coupling paths and compensating the influence factors. However, this will further deteriorate the already poor damping of the MMC and significantly increase dc-bus current fluctuations. To solve this problem, the sources resulting in poor damping are identified and then a new closed-loop dc-bus current controller is incorporated into the control system. Finally, the proposed power decoupling strategy with the newly designed current controller realizes completely decoupled power regulation, avoids overload operation, and obviously improves the dynamic performance of the MMC. Furthermore, it increases the stability of the MMC system under all possible operation conditions. The proposed control strategy is confirmed by experimental results. [ABSTRACT FROM AUTHOR]

To improve the interception performance in missile guidance applications, this paper proposes a novel control strategy for the integrated guidance and control design subject to input constraints. The system constraints are translated into a restriction in an invariant set, which is then enforced by manipulating the derivative of the applied reference. For the integrated system in the presence of disturbances and parametric uncertainties, disturbance observers are applied to estimate the lumped disturbances existing in each loop. The quadratic stability via dynamic surface control is guaranteed, and an ellipsoidal approximation of the invariant set is obtained, which can be enlarged to allow some degree of the input saturation. The effectiveness of the proposed scheme is illustrated using numerical simulations against a maneuvering target. [ABSTRACT FROM AUTHOR]

Partial discharge inception voltage measurements under repetitive impulsive voltages play an essential role in evaluating the reliability of inverter-fed low voltage rotating machines. Using impulsive voltages, partial discharge (PD) signals and the disturbance induced by power electronics switches have often similar energy distribution in the frequency domain. Thus, PD test technologies used with sinusoidal voltages are no longer effective. Under repetitive impulsive voltages, ultra-high frequency detection is recommended to couple high-frequency spectral components from the PD and remove the low-frequency disturbance coming from power electronics. However, to obtain high signal to noise (S/N) ratio, the parameters of the antenna used for PD detection should be designed considering the PD and the disturbance energy distributions in the frequency domain. In this paper, we discuss the design and show the properties of an antenna with Archimedes plane spiral structure intended for PD detection under repetitive impulse voltages. The simulation shows that the antenna has low voltage standing wave ratio (<2) and improved directivity in the frequency range from 0.5 to 2 GHz. Moreover, PD tests performed using repetitive square waves with different rise times demonstrate that the designed antenna can receive PD signals efficiently with high S/N. [ABSTRACT FROM AUTHOR]

Data center networks demand high-performance, robust, and practical data plane load balancing protocols. Despite progress, existing work falls short of meeting these requirements. We design, analyze, and evaluate Luopan, a novel sampling based load balancing protocol that overcomes these challenges. Luopan operates at flowcell granularity similar to Presto. It periodically samples a few paths for each destination switch and directs flowcells to the least congested one. By being congestion-aware, Luopan improves flow completion time (FCT), and is more robust to topological asymmetries compared to Presto. The sampling approach simplifies the protocol and makes it much more scalable for implementation in large-scale networks compared to existing congestion-aware schemes. We provide analysis to show that Luopan's periodic sampling has the same asymptotic behavior as instantaneous sampling: taking 2 random samples provides exponential improvements over 1 sample. We conduct comprehensive packet-level simulations with production workloads. The results show that Luopan consistently outperforms state-of-the-art schemes in large-scale topologies. Compared to Presto, Luopan with 2 samples improves the 99.9%ile FCT of mice flows by up to 35 percent, and average FCT of medium and elephant flows by up to 30 percent. Luopan also performs significantly better than Local Sampling with large asymmetry. [ABSTRACT FROM AUTHOR]

The existing power decoupling control method of modular multilevel converter (MMC) was developed from that of conventional two-level voltage source converters. Therefore, it cannot fully realize power decoupling due to neglecting the coupling effects from submodule capacitor voltage, dc current, circulating current as well as control strategies of circulating current, which inevitably results in inaccurate regulation of active and reactive power, deteriorated system dynamic performance, possible overload operation and eventually threatens the safety and stability of MMC. This paper analyzes the intercoupling among all electrical quantities and corresponding control strategies in MMC. An accurate control model of active and reactive power/current with four coupling paths and influence factors in the rotating frame is also established. A full power decoupling control method has been proposed based on the developed model. The proposed control method can realize accurate and completely decoupled active and reactive power regulation, avoid overload operation, and significantly improve the dynamic performance of MMC. Moreover, it facilitates the parameters selection of MMC controller with no extra cost, which is of great significance for practical projects. The effectiveness and accuracy of the proposed analysis and control methods were verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of exploiting parallelism in a hard real-time streaming application modeled as an acyclic synchronous data flow (SDF) graph and scheduled on a heterogeneous multiprocessor system-on-chip platform to alleviate the capacity fragmentation due to partitioned scheduling algorithms and reduce the number of required processors when a throughput requirement is satisfied. As the main contribution in this paper, we propose a method to determine a replication factor for each task in an acyclic SDF graph such that by distributing the workloads among more parallel tasks with lower utilization in the obtained transformed graph, the left capacity on the processors can be efficiently exploited, hence reducing the number of required processors. The experimental results, on a set of real-life streaming applications, demonstrate that our approach can reduce the minimum number of processors required to schedule an application and considerably improve the memory requirements and application latency compared to related approaches while meeting the same throughput constraint. [ABSTRACT FROM AUTHOR]

Nowadays, with the highly penetrated wind generations, the accurate wind farm (WF) model is required for power system stability analysis. Due to the complexity on detailed WF model, the aggregated model, with a reasonable reduction of the detailed model meanwhile retaining the required level of accuracy is essential to be developed. In this paper, an improved WF aggregated modeling method for large-scale power system stability studies is proposed. To overcome the limitations of the traditional methods, a geometric template matching based time series wind turbines clustering method is developed. Moreover, a multiobjective optimization algorithm, which fully considered the wind speed disturbance and system fault together, is designed to identify both the generator and control parameters. Additionally, to shrink the size of the identified parameters and increase the modeling accuracy, a sensitivity and correlation analysis based key parameters selection scheme is also adopted. To verify the effectiveness of the proposed method, dynamic responses of the proposed aggregated model are compared against the responses of the traditional equivalent model for various wind scenarios through an actual case. [ABSTRACT FROM AUTHOR]

Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA (Fact-based VQA), a VQA dataset which requires, and supports, much deeper reasoning. FVQA primarily contains questions that require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answer triplets, through additional image-question-answer-supporting fact tuples. Each supporting-fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting-facts. [ABSTRACT FROM AUTHOR]

Power allocation in hybrid energy storage systems (HESSs) is an important issue for dc microgrids. In this paper, an integral droop (ID), inspired by the electrical characteristics of capacitor charging/discharging process, is proposed and applied to a cluster of energy storages (ESs) with high ramp rates. Through the coordination of the ID and conventional V-P droop, the transient power allocation in HESSs can be intrinsically realized in a decentralized manner. The high-frequency components of power demand can be compensated by the ESs with ID, whereas the ESs with V-P droop respond to the smooth change of load power. Additionally, the ID coefficient can be designed according to the nominal ramp rate of the ESs with slow response, which helps to extend the lifespan of the HESS. On the other hand, to easily assess the stability of the system feeding constant power loads, a minimum relative impedance criterion (MRIC) is developed. Based on MRIC, it is revealed that the proposed ID can shape the output impedance of the HESS and stabilize the entire system. The feasibility and effectiveness of ID are verified by both simulations and experimental results. [ABSTRACT FROM PUBLISHER]

The dc offset and scaling errors in the voltage and current measurements cause the injection of undesired dc and unbalanced currents into the three-phase input currents and subsequently lead to voltage ripple at the dc output voltage of the ac/dc converter. This paper proposes a compensation scheme for current measurement error where the dc offset and scaling errors in the current measurement are estimated from the voltage ripple characteristics of the dc output voltage ripple combining with simple band-pass and low-pass filters. Meanwhile, the dc offset and scaling errors in the voltage measurement can be rejected by using an advanced current controller developed with a proportional resonant plus a repetitive controller designed in the synchronous (d–q) reference frame. The proposed control algorithm is able to effectively reject the impact of both voltage and current measurement error so that the three-phase input currents are regulated to be balanced and sinusoidal with ultralow dc current component. Meanwhile, the dc output voltage is also well regulated to be pure dc with a negligibly small voltage ripple. The proposed compensation method is developed without the need of extra hardware circuit, sensor, or precise information of system parameters so that it can be considered as more cost effective and robust solution. The effectiveness of the proposed solution is verified by experimental results. [ABSTRACT FROM AUTHOR]

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set. [ABSTRACT FROM AUTHOR]

The dc offset and scaling errors in the voltage measurements cause the injection of undesired dc and harmonic currents into the three-phase output currents of the grid-connected inverter. This study proposes an enhanced current control scheme to eliminate the dc and harmonic currents caused by voltage distortion and voltage measurement error in three-phase grid-connected inverters. The proposed current controller is designed in the synchronous (d-q ) reference frame and composed of a proportional resonant (PR) controller and a repetitive controller (RC). The role of the RC is to regulate the grid current follow the reference value as well as compensate all 6\rmn\omega {\rm{s}} and (6\rmn\pm 2)\omega{\rm{s}}\,(n= 1,\,2,\,3,\ldots) harmonic components caused by distorted grid voltage and scaling errors. Meanwhile, the PR controller helps to compensate the dc current generated by dc offset measurement errors to guarantee that the three-phase grid currents are balanced and sinusoidal with extremely low dc component. Since the RC employed in the proposed current controller reduces to time delay by four times compared to the conventional RC, the proposed control system ensures good steady-state performance of the grid current without deteriorating its fast dynamic response. The effectiveness of the suggested solution is verified by various experimental tests. [ABSTRACT FROM AUTHOR]

This paper proposes a fault location method based on the adaptive control of neutral-to-earth complex impedance in a resonant grounded system, whose neutral point grounds are via an electromagnetic hybrid Petersen coil (EHPC). The EHPC can be equivalent to a parallel circuit of a negative resistance and an inductance, both of which can be adjusted. When a permanent single line-to-earth fault occurs, the ground-fault current will be compensated to almost zero, and the capacitance to earth and leakage resistance of system can be measured accurately for subsequent fault location. Then, the complex impedance of EHPC is adjusted, and the faulty feeder and its faulted section can be identified online based on the characteristics that the measurements of zero-sequence admittances of points along the faulty feeder change with the system's neutral-to-ground complex impedance. The detection criterion is certain and unique, without comparing all of the feeders. This paper also proposes an adaptive jitter adjustment and decoupling control of neutral-ground complex impedance by EHPC, which is adaptive to the grounding transient resistance and is repeatable, and can ensure that the magnitude of the zero-sequence parameter is large enough to be measured while without arc re-ignition, including in high impedance fault states. Simulations and experiments verified the feasibility of this fault location method and its control strategy. [ABSTRACT FROM PUBLISHER]