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In this article, a compact differentially fed ±45° dual-polarized filtering magneto-electric (ME) dipole antenna with improved harmonic suppression is proposed and analyzed. First, the fusion of loop-slot dipole and half-wave vibrator is calculated and mentioned to furnish theoretical guidance. Then, by reasonably placing two pairs of second-order step impedance feedlines and a loop-slot patch, a corresponding two-mode loop-vibrator combination filtering antenna with an upper radiation null is naturally constructed. Moreover, simply by loading four open-circuit stubs at the center of octagonal slot patch edge, another extra resonance mode and radiation null in the upper band can be simultaneously obtained. Later, through hiring the reconstructed third-order stub-loaded-resonator (SLR) feedlines, wideband harmonic suppression along with a third radiation null is unaffectedly achieved. Finally, the optimized antenna is fabricated and tested. The measured results reveal it realizes a wideband impedance bandwidth of 42.0% (2.78–4.26 GHz) and a high port isolation of 35 dB. Also, the length of its harmonic suppression is measured from 4.56 to 7 GHz, while the depth reaches more than 20 dB. In addition, the measured gain and radiation efficiency curves in the operating band are relatively stable and both are at a high average value (8.2 dBi, 85%). [ABSTRACT FROM AUTHOR]

Time-sensitive networking (TSN) can help standardize deterministic Ethernet across industrial automation. The deterministic guarantee of TSN is based on network resource scheduling in the unit of flow. However, the state-of-the-art TSN single flow scheduling scheme cannot meet the coordinated scheduling requirements of multiple data flows in advanced industrial applications (e.g., control and safety applications). In this article, we propose a TSN chain flow abstraction, TC-Flow, for a coordinated multiple-flow scheduling model in industrial control and safety applications. Based on the proposed TC-Flow model, we design an offline TC-Flow scheduling algorithm using integer linear programming and an online heuristic TC-Flow scheduling algorithm to handle network dynamics. To deploy the proposed TC-Flow model and scheduling algorithms in the TSN, we design a CF-TSN network architecture that is compatible with the existing TSN single-flow scheduling scheme. Finally, we implement the proposed CF-TSN architecture and TC-Flow scheduling algorithms in real-world network environments. Experimental results show that the proposed scheduling algorithms can increase the number of schedulable flows by 26 percent compared to the state-of-the-art TSN scheduling benchmark. [ABSTRACT FROM AUTHOR]

A wide axial-ratio beamwidth (ARBW) and half-power beamwidth (HPBW) circularly polarized (CP) antenna is realized by arranging two curved patch dipole pairs orthogonally in a cruciform contour that conforms with a hemispheric shell. First, two orthogonal curved dipole pairs are designed via step-by-step investigation of the evolution of traditional crossed dipoles. Then, an antenna with a wide CP working bandwidth and wide ARBWs and HPBWs is developed by enlarging the diameter of the curved arms, replacing the narrow arms with the wide patches and making these patches conformal with the shell surface; this improves the impedance bandwidth (IBW) and further reduces the antenna’s size. Finally, a prototype with reduced dimensions of $0.52\times 0.52\times0.15$ ($\lambda _{0}^{3}$) is designed, fabricated, and measured. The resulting antenna exhibits a −10 dB IBW of 55.8% (1.06–1.89 GHz) and a 3 dB AR bandwidth of 70.9% (1–2.1 GHz), within which the ARBW and HPBW reach maximum values of 202°/196° and 113°/111° in the two orthogonal principal planes at 1.602 and 1.268 GHz, respectively. Furthermore, a 3 dB ARBW exceeding 100°, an HPBW exceeding 96°, and efficiency exceeding 60% are acquired within the 1.252–1.89 GHz (40.6%) target service coverage band. [ABSTRACT FROM AUTHOR]

Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points that would be beneficial to the model, unlike passive machine learning. The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set. Multiple frameworks for active learning combined with deep learning have been proposed, and the majority of them are dedicated to classification tasks. Herein, we explore active learning for the task of segmentation of medical imaging data sets. We investigate our proposed framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of pancreas and tumors. This work presents a query-by-committee approach for active learning where a joint optimizer is used for the committee. At the same time, we propose three new strategies for active learning: 1.) increasing frequency of uncertain data to bias the training data set; 2.) Using mutual information among the input images as a regularizer for acquisition to ensure diversity in the training dataset; 3.) adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). The results indicate an improvement in terms of data reduction by achieving full accuracy while only using 22.69% and 48.85% of the available data for each dataset, respectively. [ABSTRACT FROM AUTHOR]

Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4 % compared with the deep deterministic policy gradient benchmark. [ABSTRACT FROM AUTHOR]

In this paper, a parameter-dependent multiple discontinuous Lyapunov function (PMDLF) approach is proposed to study the $H_\infty$ refined antidisturbance control problem of switched linear parameter-varying systems. The $H_\infty$ refined antidisturbance means the disturbance appearing in the control channel can be accurately compensated by means of the estimation of the disturbance and the energy bounded external disturbance can be restrained. A key point is to set up a PMDLF framework that provides an effective tool for attenuating the energy bounded disturbances and rejecting the disturbances generated by the exosystem accurately. A parameter-driven and dwell time-dependent switching law is designed, and a solvability condition ensuring the $H_\infty$ refined antidisturbance performance is developed. Then, the $H_\infty$ refined antidisturbance switched parameter-dependent disturbance observers and the disturbance observer-based refined controllers are established to achieve required disturbance attenuation and rejection. Finally, an example of an aero-engine control system is given to verify the availability of the acquired approaches. [ABSTRACT FROM AUTHOR]

This paper investigates dissipativity and related properties for switched linear parameter-varying (LPV) systems by using parameter and dwell time-dependent multiple storage functions, which are different from traditional multiple storage functions because of the introduction of the external parameters into the storage functions construction. First, a novel concept relating to external parameters is proposed to describe the overall dissipativity property of switched parameter-varying systems in the absence of the conventional dissipativity property of the subsystems. Second, a state and parameter-triggered and time-driven switching laws is proposed, which avoids Zeno behavior by guaranteeing a dwell time among the adjacent switching. Third, several classic forms of dissipativity are addressed. For passivity and $ {L_{2}}$ -gain, under certain negative output feedback asymptotic stability is guaranteed with the aid of asymptotic zero state detectability. Finally, the effectiveness of the proposed result is illustrated by its application to a speed adjustment problem of an aero-engine. [ABSTRACT FROM AUTHOR]

To investigate the difference between the sulfur hexafluoride (SF6) decomposition characteristics initiated by two sorts of insulation defects — free metal particles and metal protrusion defects — under negative DC partial discharge (PD), SF6 decomposition data was obtained under different DC voltages on the basis of constructing a SF6 decomposition experimental platform. The differences between the characteristic components and the characteristic ratios of the two defects at the same discharge quantity and voltage were systematically analyzed. Results show that the two defects have significant differences in the type of decomposition components, the yield sizes of the components, and the changes in the component ratios. This paper proposed c(CF4 + CS2)/c(CO2) and c(CF4)/c(SO2F2) as the characteristic ratios to characterize the fault severity of the two defects, and their physical nature were revealed. The combination of the above ratios with the existing c(SO2F2)/c(SOF2 + SO2) can construct a feature quantity for identifying the two defects. [ABSTRACT FROM AUTHOR]

To study the correlation characteristics between SF6 decomposition components and discharge strength under negative DC partial discharge (PD) initiated by free metal particle defect, SF6 decomposition data were obtained under different voltages on the basis of constructing SF6 decomposition experimental platform. The variation law of SF6 decomposition components and characteristic ratios were systematically analyzed. The study shows that SF6 decomposes into CO2, SO2F2, SOF2, SO2, CF4, and a very small amount of CS2 under PD. The SOF2 concentration and its formation rate have the highest correlation with the PD discharge quantity among all components. The yield relationship of decomposition components is different at different discharge quantities. In addition, the fluctuation of each characteristic ratio is small after 72 h and can achieve a certain degree of steady state. c(CF4+CS2)/c(CO2) and the proposed c(SO2)/c(SO2F2) can be used as the fault severity characteristic ratio in free metal particle defect. [ABSTRACT FROM PUBLISHER]

Moving target detection is an important technique in visual surveillance systems. If a camera is freely moving, it becomes more difficult to detect a moving target, especially in the environment of a wide-range background. To compensate for the global motion of a wide-range background, a disparity-based adaptive multi-homography method is proposed. The proposed method comprises four steps: 1) feature point extraction; 2) generation of adaptive multi-homography matrices using motion grouping; 3) generation of background model and detection of background; and 4) target detection. Experimental results show that the proposed method can robustly detect moving targets in sequences taken by a freely moving camera. [ABSTRACT FROM PUBLISHER]

Helical coil electromagnetic launchers (HEMLs) using brush-commutation strategy, solving the problem of synchronization control perfectly, have a promising prospect in accelerating big mass to high speed. One of its current technical bottlenecks is the arc erosion caused by the brush commutation, which damages the current-fed rails, besides reduces the energy conversion efficiency of HEMLs. During the commutation process, the leading, trailing edges of the commutating brush act as the closing, opening switches, respectively. When the brush-controlled circuit turns ON and OFF, the commutation-induced voltage (CIV) is a key to research the arc erosion of brushes. It can be concluded that the CIV is proportional to the current, the commutation inductance gradient (CIG), and the projectile velocity by a theoretical analysis and numerical simulation. The CIG is a critical structural parameter of HEMLs, and the formulas of the CIG for single and multiple turn commutation are deduced. Compared with single turn commutation, the current in the commutating turns changes moderately, and the CIV is lower in the case of multiple turn commutation. Two armatures of 35 turns and 135 turns were tested in an HEML of coil-unit barrel; the experimental results are in good agreement with the theoretical calculations. [ABSTRACT FROM AUTHOR]

We introduce potential capacities of quantum channels in an operational way and provide upper bounds for these quantities, which quantify the ultimate limit of usefulness of a channel for a given task in the best possible context. Unfortunately, except for a few isolated cases, potential capacities seem to be as hard to compute as their plain analogues. We thus study upper bounds on some potential capacities. For the classical capacity, we give an upper bound in terms of the entanglement of formation. To establish a bound for the quantum and private capacity, we first lift the channel to a Hadamard channel and then prove that the quantum and private capacity of a Hadamard channel is strongly additive, implying that for these channels, potential and plain capacity are equal. Employing these upper bounds, we show that if a channel is noisy, however close it is to the noiseless channel, then it cannot be activated into the noiseless channel by any other contextual channel; this conclusion holds for all the three capacities. We also discuss the so-called environment-assisted quantum capacity, because we are able to characterize its potential version. [ABSTRACT FROM AUTHOR]

Industrial wireless sensor networks (IWSNs) have the potential to contribute significantly in areas such as cable replacement, mobility, flexibility, and cost reduction. Nevertheless, the industrial environment that the IWSNs operate in is very challenging because of dust, heat, water, electromagnetic interference, and interference from other wireless devices, which make it difficult for current IWSNs to guarantee reliable real-time communication. In this paper, we present a novel method based on the segmented slot assignment, fast slot competition, and free node concept that will improve the reliability and real-time communication significantly so that more advanced applications can be enabled. The main purpose of the algorithms is to improve the retransmission efficiency for time-division-multiple-access-based multihop IWSNs by using limited shared slot resources more efficiently. More importantly, the proposed algorithms support efficient slot rescheduling caused by link or node failure. We evaluate the proposed methods by using simulations and a real implementation targeting monitoring of welder machines. Our obtained results show that the proposed method outperforms the first published and most widely used IWSN standard called WirelessHART. [ABSTRACT FROM PUBLISHER]

This paper reports empirical and numerical analyses on the electrical behavior of no-insulation (NI) coil. The spiral and radial current flows of NI coil in the case of the nominal operation and over-current condition are quantitatively identified using numerical analysis. Moreover, the origins of charging/discharging delays and magnetic field saturation are discussed in detail. Experimental results are also compared with the simulations to validate the numerical approach. [ABSTRACT FROM PUBLISHER]

This paper proposes a numerical approach to calculate the characteristic resistance (Rc) of partial-insulation (PI) and no-insulation (NI) high-temperature superconductor pancake coils with the nonuniform current path in such coils taken into consideration. Recently, an analytic approach has been proposed to estimate (R \rm c) of an NI coil, where the coil current is assumed to be “uniform” over the entire coil. This model, however, is not effective to explain the increase of (Rc) when a coil is modified from NI to PI. In this paper, we first introduce our numerical approach based on a finite element analysis. Then, the charging characteristics of selected PI and NI coils that we had previously reported are analyzed by the proposed approach. Reasonable agreement between the measured and calculated data validates the proposed approach to estimate (Rc) of a PI as well as an NI coil. [ABSTRACT FROM PUBLISHER]

As a minimally invasive surgery to treat atrial fibrillation (AF), catheter based ablation uses high radio-frequency energy to eliminate potential sources of abnormal electrical events, especially around the ostia of pulmonary veins (PV). Fusing a patient-specific left atrium (LA) model (including LA chamber, appendage, and PVs) with electro-anatomical maps or overlaying the model onto 2-D real-time fluoroscopic images provides valuable visual guidance during the intervention. In this work, we present a fully automatic LA segmentation system on nongated C-arm computed tomography (C-arm CT) data, where thin boundaries between the LA and surrounding tissues are often blurred due to the cardiac motion artifacts. To avoid segmentation leakage, the shape prior should be exploited to guide the segmentation. A single holistic shape model is often not accurate enough to represent the whole LA shape population under anatomical variations, e.g., the left common PVs vs. separate left PVs. Instead, a part based LA model is proposed, which includes the chamber, appendage, four major PVs, and right middle PVs. Each part is a much simpler anatomical structure compared to the holistic one and can be segmented using a model-based approach (except the right middle PVs). After segmenting the LA parts, the gaps and overlaps among the parts are resolved and segmentation of the ostia region is further refined. As a common anatomical variation, some patients may contain extra right middle PVs, which are segmented using a graph cuts algorithm under the constraints from the already extracted major right PVs. Our approach is computationally efficient, taking about 2.6 s to process a volume with 256\,\times\,256\,\times\,245 voxels. Experiments on 687 C-arm CT datasets demonstrate its robustness and state-of-the-art segmentation accuracy. [ABSTRACT FROM AUTHOR]

In this communication, two compact co-polarized decoupled microstrip patch array antennas (MPAAs) based on TM02/TM03 modes cancellation are proposed. It is only necessary to introduce shorting pins and etch slots on the patch antenna to precisely adjust the frequency and impedance of TM02 and TM03 modes to make them consistent, which can significantly reduce the strong coupling between the tightly spaced patch antennas. In addition, the L-shaped open-stub coupling feeding is used to eliminate the parasitic inductance caused by the shorting pins to achieve better impedance matching. Finally, the proposed two-/eight-port MPAAs are fabricated and measured. The simulation and measurement results show that the coupling of adjacent ports is decreased lower than −30 dB within the impedance matching bandwidth (3.42–3.58 GHz), while the edge-to-edge spacing is only $0.023 \lambda _{0}$ between the antenna elements. With the advantages of simple design, significant decoupling effect, and good radiation performance, the proposed decoupling scheme has broad application prospects in the fifth-generation (5G) array antennas. [ABSTRACT FROM AUTHOR]

Although ship detection methods based on deep learning have achieved remarkable progress, the design of the universal ship detection (USD) system is rarely studied. The main challenge of USD lies in the notorious domain bias and shift problem across multiple domains. This article implements USD based on domain-invariant representations to alleviate this issue. Specifically, the proposed method integrates a multilevel domain classification network (MDCN) and a domain-centric cut-paste module (DCM). First, the backbone network is facilitated to learn domain-independent image features from multiple domains through MDCN, thereby reducing the disturbance of domain-specific features to universal detector. Furthermore, the proposed method combines the domain-related synthetic samples generated by DCM to provide MDCN with strong supervision information, which further motivates the network to be more attentive to the domain-invariant representations at the instance level. Finally, we conduct experiments on multiple ship datasets in the synthetic aperture radar (SAR) and optical domains to verify the effectiveness of the method. The results show that the proposed method outperforms baseline by around 2.95% average precision (AP50), which achieves an effective USD system by complementing the information between domain-invariant representations. [ABSTRACT FROM AUTHOR]

Helical coil electromagnetic launchers (HEMLs) using rail-fed strategy, local excitation and single-turn commutation, solve the problem of synchronization control perfectly. Through the simulation, a novel helical coil launcher is designed using the basic HEML's structure. A D-shaped cross section armature is proposed to achieve the maximum projectile velocity. The energizing length of the driving coil, as well as, the armature position of the maximum force is determined. The maximum driving force on the armature increases with the increasing coil axial length and the armature acceleration reaches its maximum value when the ratio of axial to radial length of the coil is 1:2. The force distribution of the armature and driving coils is analyzed. [ABSTRACT FROM PUBLISHER]

A new hybrid self-adaptive training approach-based radial basis function (RBF) neural network for power transformer fault diagnosis is presented in this paper. The proposed method is able to generate RBF neural network models based on fuzzy c-means (FCM) and quantum-inspired particle swarm optimization (QPSO), which can automatically configure network structure and obtain model parameters. With these methods, the number of neuron, centers and radii of hidden layer activated functions, as well as output connection weights can be automatically calculated. This learning method is proved to be effective by applying the RBF neural network in the classification of five benchmark testing data sets, and power transformer fault data set. The results clearly demonstrated the improved classification accuracy compared with other alternatives and showed that it can be used as a reliable tool for power transformer fault analysis. [ABSTRACT FROM AUTHOR]

Deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult. More flexible transmission expansion plans are needed, to cope with the increased risks. In this paper, a novel planning approach is proposed to meet the above challenge. In our approach, the planning process is modeled as a mixed integer nonlinear programming (MINLP) problem, so that conflicting objectives can be optimized simultaneously. To minimize planning risks, our method identifies several scenarios based on statistics and expert knowledge; the most flexible expansion plan is selected as the plan that has least adaptation cost. The proposed method is tested with the IEEE 14-bus system. Promising results are obtained to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The application of evolutionary computation methods in search and optimization has been growing over the past few decades. As a promising approach in metaheuristic optimization algorithms, differential evolution (DE) has been attracting increasing attention for wide engineering applications including power engineering. Different from conventional evolutionary algorithms using predefined probability distribution function for mutation process, differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective functions topographical information toward the optimization process, and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. In this paper, detailed numerical studies are carried out to propose the characterization of the performance of several DE mutation methods with and without fitness sharing scheme. All the approaches using the proposed modified DE are presented on an example in power system planning. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models. [ABSTRACT FROM AUTHOR]

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining-based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms—support vector machine and probability classifier—are chosen to be the spike occurrence predictors and are discussed in detail. Realistic market data are used to test the proposed model with promising results. [ABSTRACT FROM AUTHOR]

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis. [ABSTRACT FROM AUTHOR]

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using a multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open-access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of a deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system, and relevant analyses and discussions have been presented. [ABSTRACT FROM AUTHOR]

This paper explores how to promote business model innovation through intraindustry search and extraindustry search with the assistance of fit dynamic capabilities (internal coordination capability and stakeholder engagement capability). Based on evolutional learning and business model literatures, six hypotheses are proposed and examined with data from 204 firms in China. The results show that intraindustry search has an inverted U-shaped effect on business model innovation, whereas extraindustry search has a positive effect. Internal coordination capability strengthens the effect of intraindustry search but weakens that of extraindustry search. Stakeholder engagement capability strengthens the effect of extraindustry search but weakens that of intraindustry search. Our findings enrich the business model literature by identifying different roles of intraindustry search and extraindustry search as the antecedents of business model innovation and their fitness with different types of dynamic capabilities. [ABSTRACT FROM AUTHOR]

Power flow feasibility boundaries are constraints limiting the operating transmission capability and the available stability (security) margin of the system. The convexity properties of this boundary are important to the analytical description of its shape (including the needs of approximation) and for the calculation of security margins. These analyses are significantly simplified if the boundary is convex. The conjecture that the boundary is convex in the space of system parameters has been used over a long time. However, some alarming experimental counterexamples already exist which show that this boundary is not always convex. In this letter, an analytical approach is presented to analyze the convexity properties of the power flow feasibility boundary. In particular, the approach helps to identify nonconvex parts of the boundary. Numerical case studies illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of ${n}$ stacked transformations are applied to each image during network training. The underlying assumption is that the “expected” domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented “big” data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with ${n}={9}$ transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than “shallower” stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment. [ABSTRACT FROM AUTHOR]

This paper investigated quench initiation and propagation characteristics of grease insulation coils by conducting thermal-quench tests for two GdBCO single-pancake coils, namely a coil co-wound with silicon grease (termed as the SiG coil) and the other coil co-wound with Kapton tape (termed as the INS coil), as turn-to-turn insulation. The test results confirmed that the SiG coil exhibited better thermal and electrical stabilities when compared with the INS coil because the operating current could be bypassed in the radial direction through the turn-to-turn contacts when a local hot spot was generated. The SiG coil had superior thermal and electrical stabilities. However, a nonrecovering resistive zone could be generated because excessive Joule heat energy could be induced by radial current flow due to the existence of radial resistance that was mainly generated by silicon grease and nonsuperconducting materials including the substrate, stabilizer, and buffer layers within the high-temperature superconductor (HTS). Therefore, it is essential to consider critical Joule heat energy that is influenced by operating current and stored magnetic energy as well as the radial resistance to achieve self-protective 2G HTS coils. [ABSTRACT FROM PUBLISHER]

Convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) target detection. Typical methods based on CNN have obtained favorable detection accuracy at the cost of high model complexity, and thus are difficult to be directly applied to real-time satellites on board as well as maritime rescue. To deal with this problem, this article proposes an efficient and lightweight target detection network incorporating soft quantization. First, to compensate for the lack of accuracy caused by lightweight networks, a feature fusion module called split bidirectional feature pyramid network is proposed to alleviate the interference of complex background on SAR images. Meanwhile, to adapt the lightweight network and the feature fusion module, a linear transformation module is presented to enhance the linear representation of the model via learnable parameters. Eventually, to make the model size smaller, a soft quantization algorithm is proposed to reduce the accuracy degradation caused by quantization errors. We validate the robustness of the model in several publicly available datasets. Experimental results show that our model achieves 97.0% detection accuracy on SAR ship detection dataset, with a 0.9% accuracy improvement compared to mainstream methods using less than $15\times $ the number of parameters and less than $6\times $ the number of flops. [ABSTRACT FROM AUTHOR]

As a kind of future weapon, an electric launcher uses high current to drive projectiles reaching velocities on the order of several kilometers per second. As is known, high and rapidly changing currents through electric launcher can lead to strong radiation. A PSPICE model is used to analyze the frequency content of currents in the helical electromagnetic launcher (HEML) by nonuniform fast Fourier transform. The frequency spectrum is mainly determined by conducting duration and intensity of entering current. The main frequency of HEML is low, but high-frequency components can produce more radiation. The total radiation can be considerable. [ABSTRACT FROM PUBLISHER]

This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme. [ABSTRACT FROM AUTHOR]

Several excellent researches have been proposed to improve the performance of solid-state drives (SSDs) by exploiting I/O parallelism of multi-channel architecture. However, these researches do not fully explore the internal parallelism and do not take wear leveling into consideration. In this paper, I/O performance is further improved by interleaving requests in channel level and striping sub-requests in plane level. A wear-leveling-aware distributed garbage collector is proposed to improve SSD lifetime and reclamation efficiency. To balance the utilization of user space among all channels, data migration is performed implicitly during channel selection and explicitly during garbage collection. To the best of our knowledge, this is the first paper on the design of distributed garbage collector for multi-channel flash-memory storage system. The experimental results showed that the proposed scheme can achieve good wear leveling and improve the overall performance by 34% for the Windows workload, 56.5% for the Linux workload, 88.4% for the multimedia workload, and 9.3% for the on-line transaction processing (OLTP) workload under the two-die-two-plane architecture, compared with the related work. [ABSTRACT FROM AUTHOR]

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples. [ABSTRACT FROM AUTHOR]

In general, the stabilizer of a ReBCO-coated conductor (CC) plays a very important role in bypassing and transferring the excessive heat when quench occurs. For an optimal design, selecting the appropriate materials and stabilizer thickness is an important issue for enhancing the thermal/electrical stability of superconducting power devices. This study examined the electrical and thermal behavior of GdBCO CC tape in terms of the stabilizer thickness as well as the effects of variations of the Ag stabilizer thickness on the thermal/electrical stability through an over-current test. An electroplating technique was employed to obtain a Ag stabilizer with various thicknesses without serious degradation of the superconducting properties. [ABSTRACT FROM AUTHOR]

During the operation of gas insulated switchgear (GIS) and gas insulated line (GIL), the conducting bar generates joule heat, and a non-uniform distribution of temperature forms in the bulk of the insulators. As the load varies, the temperature distribution changes, and it has an influence on the bulk conductivity of the insulators. Moreover, the surface charge accumulation can be affected. In order to clarify this, a surface charge measurement system which could achieve temperature control was constructed, and a model insulator with truncated cone type was employed, as well as two types of high-voltage electrodes, i.e. plate and needle electrodes. It is found that both polarities of charges existed on the insulator surface when a dc high voltage was applied to the plate or needle electrode, and homo-charge density was much higher than that of hetero-one. As for the plate electrode, homo-charges resulted from micro-discharges, while generated by corona in the case with needle electrode. As temperature increased, homo-charge density decreased, kept unchanged and increased for three cases, respectively, i.e. when dc voltage was applied to the plate electrode, and when voltage of 3 and 20 kV was applied to the needle electrode. Moreover, a simulation model involving multi-physics was established, which included heat conduction in solid and transient field changing from the initial capacitive to stationary resistive field distribution. It is proved that the electric conduction of the insulator bulk contributed to the accumulation of hetero-charges, and temperature could enhance this. Besides, when temperature increased, the corona inception voltage reduced, and hence the homo-charge density increased. Due to the effect of neutralization and the differences in the sensitivity of corona with different intensity to temperature, the tendency of homo-charge density changing with temperature for the three cases showed distinct in the experiments. [ABSTRACT FROM PUBLISHER]

Collaborative representation (CR), known as an effective way to address the signal representation (regression) problem, has achieved remarkable success in visual classification. According to our theoretical analysis, the subspace learning issue can also be deemed as a signal representation problem. Therefore, we extend the graph embedding (GE) framework as a CR model to improve the discriminating power of the subspace learning algorithm. The new GE framework, which is named collaborative GE (CGE) framework, enjoys many desirable properties of CR. From theoretical analysis, CGE is robust to the noise and has the same computational complexity as GE. From experimental analysis, CGE can generally enhance the subspace learning algorithms and a reasonable regularization parameter can be inferred from its intrinsic graph. Several state-of-the-art subspace learning algorithms are plugged into our framework to produce their collaborative versions. Meanwhile, by exploring the intrinsic relation among GE methods, we present a new collaborative method named collaborative class-scattering locality preserving projections (CCSLPPs). The results of extensive experiments on ORL, AR, Scene15, Caltech256, LFW-A, and OU-ISIR-A databases demonstrate that the collaborative versions consistently outperform their original algorithms with a remarkable improvement and CCSLPP gets the best performance compared with all used methods. [ABSTRACT FROM AUTHOR]

We initiate the study of passive environment-assisted communication via a quantum channel, modeled as a unitary interaction between the information carrying system and an environment. In this model, the environment is controlled by a benevolent helper, who can set its initial state such as to assist sender and receiver of the communication link (the case of a malicious environment, also known as jammer, or arbitrarily varying channel, is essentially well-understood and comprehensively reviewed). Here, after setting out precise definitions, focusing on the problem of quantum communication, we show that entanglement plays a crucial role in this problem: indeed, the environment-assisted capacity where the helper is restricted to product states between the channel uses is different from the one with unrestricted helper. Furthermore, prior shared entanglement between the helper and the receiver makes a difference, too. [ABSTRACT FROM AUTHOR]

We have discovered that there exist dynamic scaling behaviors for the nucleation and the saturation field in hysteresis loop of Co/Pt multilayer thin films. The dynamic hysteresis measurement was carried out with variation of sweeping rate of cycling fields, which reveals that not only the coercivity and the loop area, well known as the Steinmetz law, but also the nucleation and the saturation field exhibit similar scaling behaviors. We have systematically analyzed the scaling factor of nucleation, coercivity, and saturation phase with respect to the Co sublayer thickness. Local structural configuration depending on the Co sublayer thickness were examined by extended X-ray absorption fine structure, implying that damping parameter and interface effect modifies the coercivity and nucleation scaling factor but does not significantly induce the change in saturation scaling factors. [ABSTRACT FROM AUTHOR]

A model is developed to investigate the commutation process of a helical coil electromagnetic launcher (HCEML) structure. When the armature moves, the motion of the projectile coils’ magnetic energy would supply reactive power to move the leakage into and out of the coil windings of the launcher to accomplish the commutation. The energy conversion of the coil electromagnetic launcher can be considered as the process of energy transfer from the back commutation coils to the excited coils and then from the excited turns to the front commutation coils. In this paper, governing equations of the commutation were deduced theoretically, and finite-element model of HCEML was established in ANSOFT MAXWELL. Then a deeper circuit investigation into the overall performance of commutation was carried out by means of PSPICE software. The validity of the theoretical analysis and that of the circuit network model were finally verified by experiments. The results show that during the commutation process, the current of back commutation coils drops to zero with no energy remaining in the internal; the current of front commutation coils and drive–projectile coil system gradually changes to a stable value, which is related to the geometric parameters of the launcher; the drive–projectile coil system releases energy overall that is mainly used to increase the kinetic energy of the projectile; the power supply system has little effect on energy conversion and transformation at the commutating moment and just keeps the voltage and current from decaying due to the impedance and inductance of the launcher system. [ABSTRACT FROM PUBLISHER]

Based on understanding the characteristics of the impulse force generated by pulsed current of the armature, it is valuable to explore effective ways to improve the armature structure to enhance the stability and prevent from the transition and erosion during launching. This paper first lists a series of conversional armature and the characteristics of these armatures are illuminated. A C-shaped armature is simulated based on the electrical–magnetic–mechanical coupled analysis method. The simulation shows that the stress is mainly distributed in the area between the cross member and arm, it may lead to local deformation. A novel concave armature is proposed and the simulation results indicate that the stress is mainly distributed in the inner side of arm and the front of armature. It is beneficial to push heavy load and avoid deformation and fracture. A device is built to validate the feasibility of armature. The armature is staying still to simulate pushing the infinite heavy load. The experimental results show that the concave armature endures higher current and has the stronger capability of pushing load compared with the C-shaped armature. This type of armature provides a valuable reference for future armature design. [ABSTRACT FROM PUBLISHER]

This paper presents a study on the effects of winding tension on the characteristic resistance of a no-insulation (NI) coil. Two ReBCO NI test pancake coils, having the same winding i.d. (60 mm), o.d. (67.6 mm), and number of turns (60), were sequentially prepared in a way that the first test coil was wound with a winding tension of 12-N, tested, and then rewound with a new winding tension of 20-N for the same tests. In each test, the test coil was energized at a target current, the power supply was “suddenly” disconnected, and then the temporal decay of the coil center field was measured, from which the time constant of the test coil and the consequent characteristic resistance were obtained. To check the reproducibility of experimental data, each test was repeated four times and each time the test coil was unwound and rewound with a given winding tension. The experimental results were analyzed with equivalent circuit analyses. Correlation between the winding tension and the characteristic resistance was discussed in detail. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of detecting a signal in partially homogeneous and homogeneous environments. In partially homogeneous environments, i.e., both the test data and training data share the same noise covariance matrix structure up to an unknown scaling factor, a persymmetric adaptive coherence estimator (Per-ACE) detector is proposed. By exploiting the persymmetric structure of the covariance matrix, the Per-ACE can reduce training data requirements. Furthermore, the expressions for the probabilities of false alarm and detection are derived along with the distribution of the loss factor \beta. In homogeneous environments, a persymmetric adaptive matched filter (Per-AMF) detector has been presented. However, its probability of detection has not been obtained yet. Thus, we derive the expression for the probability of detection. For both the Per-ACE and Per-AMF, numerical results of these proposed expressions are confirmed with those of Monte Carlo trials. In addition, simulation results show that the proposed Per-ACE outperforms the conventional ACE in training-limited scenarios. [ABSTRACT FROM AUTHOR]

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits.This paper aims to analyze these potential profit impacts and the possible compensation to affected generation compaflies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emissionbased and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are -used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price. [ABSTRACT FROM AUTHOR]

Load model is one of the most important elements in power system simulation and control. Based on more than 20 years of practice using load characteristic recorders in the measurement-based load modeling, this paper proposes the Expectation Composite Load Model to predict unseen data. The methods for load bus classification and parameter identification are also provided. The generalization capability of the proposed load modeling is validated by the measured load dynamics and system dynamics during two three-phase short circuit tests of the NE power grid in China. In order to evaluate the measurement-based load model, the Probabilistic Collocation Method (PCM) is applied to quantitatively analyze uncertainties of the simulation responses raised by the parameters. [ABSTRACT FROM AUTHOR]

A load model is one of the most important elements in power system simulation and control. Recently, the constant impedance, constant current, and constant power load in combination with the induction motor load have been widely used as the composite load model, whose parameters are all identified from the field measurements in measurement-based load modeling practices so far. However, there is virtually no research conducted on whether all these parameters could really be identified. This paper investigates the possibility on reducing the number of composite load model parameters to be identified from field measurements. This paper first shows that direct application of the IEEE load motor parameters in the composite load model may be inadequate on describing the load dynamics over different operating status. Then the perturbation method is used to derive the trajectory sensitivities of the equivalent motor parameters, based on which the reduction on the identified parameters of the composite load model is presented. Two cases of measurement-based load modeling in North China and Northeast China are studied to illustrate the validity of the reduction method. It is shown that the reduction does not lose the model's capability on describing the load dynamics. The reduction on the number of identified parameters not only provides a possible way to solve the multi-valued load model problem based on the current practices on measurement-based load modeling, but it also facilitates building of the load model with more components included in it. Meanwhile, reducing the identified parameters reduces the identification time; thus, the proposed strategy significantly enhances the efficiency of the load modeling work. [ABSTRACT FROM AUTHOR]

Research effort has been given to locate the optimal locations of thyristor-controlled series capacitor (TCSC) and their initial compensation levels using mixed-integer programming (MIP). As a useful technique for combinatorial optimisation over integer and continuous variables, the MIP approach can provide robust performance as well as high computational efficiency while solving complex optimal problems. Previous work using MIP employed dc load flow model ignoring reactive power balance, power loss and transformer tap ratios. In this paper, a new planning method is developed based on recently reported line flow equations and basic linearisation of binary-continuous products. The objectives of the planning strategy are to improve system loadability, voltage profile in the network, as well as to minimise the investment cost by choosing proper locations and settings of devices. Simulation results are presented and discussed for IEEE 9-, 57-, 118-, and 300-bus systems. [ABSTRACT FROM AUTHOR]