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Flexible traction power supply system (FTPSS) can eliminate neutral sections of all catenaries and improve the poor power quality of railway substations. The static power converter (SPC) is the key equipment of FTPSS and its internal faults must be quickly identified and cleared with minimal impact on catenaries to ensure the safety and reliability. This study investigates the two-stage fault characteristics in FTPSS. Before blocking, overcurrent and voltage distortion may result in a wide range of abnormal power supply. After blocking, capacitor overvoltage becomes the primary problem threatening the safety of faulty SPC because of the energy feeding from other SPCs. Equivalent circuits are established and applied to various fault conditions. The most serious fault case can be found through the comparison, so the complicated large-scale simulation scanning can be avoided. A T-shaped grounding method is proposed to solve the problem of fault current and capacitor overvoltage. Based on the calculation and simulation results of several typical fault cases, the dimensioning criterion of grounding circuit is provided for reference. Moreover, a comprehensive scheme of grounding protection is designed to detect and isolate serious faults. The theoretical fault analysis and the proposed grounding method are verified by simulations and experiments.

The parallel three-level neutral-point clamp (3L-NPC) inverters are widely solicited today to satisfy the higher power demand by using low-rated switching devices in industrial applications such as renewable power generation. With the 3L-NPC in parallel, the zero-sequence circulating current (ZSCC) occurs in the closed circuit and decreases the performances of the current response if the two 3L-NPCs share the same dc bus with no galvanic isolation at the ac side. From the analysis of the ZSCC equivalent circuit, the virtual voltage vectors are proposed and exploited by the finite control set model predictive control (FCS-MPC) for mitigating the ZSCC and improving the current accuracy. In order to keep the neutral-point balance and reduce the switching loss, the differential capacitor voltage and the average switching frequency are considered in the proposed MPC. An experimental platform based on the Simulink-Real-Time system is presented for further verification of the effectiveness of the proposed method. The simulation and the experimental results obtained under the proposed strategy show that the ZSCC is reduced to near zero, and the current response provided has lower THD.

Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.

We consider the task of grasping a target object based on a natural language command query. Previous work primarily focused on localizing the object given the query, which requires a separate grasp detection module to grasp it. The cascaded application of two pipelines incurs errors in overlapping multi-object cases due to ambiguity in the individual outputs. This work proposes a model named Command Grasping Network(CGNet) to directly output command satisficing grasps from RGB image and textual command inputs. A dataset with ground truth (image, command, grasps) tuple is generated based on the VMRD dataset to train the proposed network. Experimental results on the generated test set show that CGNet outperforms a cascaded object-retrieval and grasp detection baseline by a large margin. Three physical experiments demonstrate the functionality and performance of CGNet., 7 pages, 2 figures, Accepted to the ICRA2021

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.

Energy Router(ER) is the core of metro energy Internet construction. The paper proposes an ER based on dual DC bus. First, with the high voltage 1500V DC bus and the low voltage 600V DC bus as the core, 35kV AC grid, catenary, station load, Photovoltaic (PV) and energy storage are interconnected through multiple ports. Second, to analyze the working mode of the ER based on the traction characteristics of the metro. Then combined with the control method of each port, a coordinated control strategy is proposed to realize the flexible and autonomous distribution of electric energy. Finally, the metro power supply network based on the ER is built in Simulink. The results show that the combination of ER and metro power supply network solves the problems of serious regenerative braking energy loss, uncontrollability of electric energy, and low new energy consumption capacity.

Wireless power transfer (WPT) systems usually adopt traditional single-phase-shift (SPS) control and connect auxiliary converter to maintain a constant voltage or current outputs. Besides, efficiency optimization is highly crucial under the wide range of load variation. In this paper, a dual-phaseshift (DPS) control using finite control set model predictive control (FCS-MPC) is proposed to achieve voltage regulating while achieving maximum efficiency point tracking (MEPT). The discrete predictive mathematical model of double-sided LCC compensation network has been established and analyzed. The operating principle of proposed control strategy is designed and implemented. To optimize the efficiency, the phase shift degree of receiver side is introduced as an extra degree of control freedom. With the proposed method and its corresponding operation principle, fast dynamic response and accurately steady-state voltage trace can be achieved. The simulation results validate the feasibility of proposed control method.

This paper proposes a system configuration of the half-bridge submodule based modular multilevel converter (HBSM-MMC) combined with SCOTT transformer, which can be applied in the 50Hz three-phase to 50Hz single-phase conversion for railway supply. Three AC terminals of MMC are respectively connected to the public- and railway- grids. The railway and SCOTT transformer are grounded together to provide circuit path for the catenary current. A control strategy based on fundamental-frequency zero-sequence voltage injection is proposed, to implement separate control of public-and railway- grids. Simulation results verify the validation of the proposed system and control strategy. The proposed system can eliminate the imbalance of public grid, and MMC arm output voltages are fully and evenly used. It is economical advantageous due to the reduction in converter capacity, capacitor requirement, and converter loss. In addition, the bulky transformers and filters can be avoided.

The power converter based co-phase traction power system has the advantages of canceling neural sections and providing high power quality for both the external grid and the traction system. Electromagnetic transient simulation is a powerful tool for the transient phenomena study, control and protection system design and testing of the co-phase system. However, the number of the converters and its relatively complex converter topology both impose the challenge for the simulation speed, which affects the productivity for the co-phase system study. This work proposes an accelerated model on PSCAD/EMTDC for the static power converter (SPC) based on cascaded AC/DC/AC sub-modules (SMs), which is applied in Beijing Daxing Airport Express co-phase project. The accelerated model can obtain 19.8 times speedup with the accurate and detailed simulation results compared with the complete model using discrete switches in the co-phase system containing 4 SPCs with 16 SMs in each of them.

Continuous co-phase traction power supply system is an effective method to eliminate neutral sections and provide high power quality for both the public grid and the catenary. Modular multilevel converter (MMC) can be applied in co-phase power supply system to perform three-phase/single-phase conversion. However, the existing schemes of MMC have some problems including high cost, bulky size and coupling between public grid and catenary. This paper proposed a new main circuit scheme based on four-port MMC (4P-MMC) and three-phase Y/D transformer, which significantly reduces the cost and realizes the decoupling of three-phase and single-phase sides. Simulation results are presented to verify the validation of the proposed scheme.

With the advance of urban railway transportation and wireless communication technology, an increasing concern is placed on the reliability and low latency of urban mass transit communication system. Nowadays, both of WLAN and LTEM systems, known as two mainstream subway communication systems, are deployed redundantly with single communication network in case of unexpected failure. However, using a single communication system has irresistibility to frequency interference and traditional redundant cold backup manual has an intolerable delay in switching. In this paper, we adopt a multipath transmission network architecture with heterogeneous communication systems to solve the problems mentioned above. To realize the hot backup networks, the Redundant Forward Gateway (RFG) device is designed to connect terminals to the two networks. Information forwarded by source RFGs can be transmitted redundantly and simultaneously via both WLAN and LTE-M communication systems. At the other side, the redundant data packets will be removed before being forwarded to the destination terminals. Besides, tests were performed in various communication scenarios to evaluate the availability and effectiveness of the RFG and the network architecture. The results indicate that the RFG has a negligible processing delay and improves the robustness and performance compared to the traditional LTE-M network according to the transmission delay and packet loss tests. In conclusion, it is considered that the RFG and the heterogeneous LTE-M and WLAN networks are of vital importance in enhancing reliability in the metro communication system.

Cellular vehicle-to-everything (C-V2X) communication, as a part of 5G wireless communication, has been considered one of the most significant techniques for Smart City. Vehicles platooning is an application of Smart City that improves traffic capacity and safety by C-V2X. However, different from vehicles platooning travelling on highways, C-V2X could be more easily eavesdropped and the spectrum resource could be limited when they converge at an intersection. Satisfying the secrecy rate of C-V2X, how to increase the spectrum efficiency (SE) and energy efficiency (EE) in the platooning network is a big challenge. In this paper, to solve this problem, we propose a Security-Aware Approach to Enhancing SE and EE Based on Deep Reinforcement Learning, named SEED. The SEED formulates an objective optimization function considering both SE and EE, and the secrecy rate of C-V2X is treated as a critical constraint of this function. The optimization problem is transformed into the spectrum and transmission power selections of V2V and V2I links using deep Q network (DQN). The heuristic result of SE and EE is obtained by the DQN policy based on rewards. Finally, we simulate the traffic and communication environments using Python. The evaluation results demonstrate that the SEED outperforms the DQN-wopa algorithm and the baseline algorithm by 31.83 % and 68.40 % in efficiency. Source code for the SEED is available at https://github.com/BandaidZ/OptimizationofSEandEEBasedonDRL.

A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is trained on synthetic data with 6 classes of primitive shapes generated by a simulation engine. Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region. The grasps are priority ordered via proposed ranking algorithm, with the first feasible one chosen for execution. On task-free grasping of individual objects, the method achieves a 94% success rate. On task-oriented grasping, it achieves a 76% success rate. Overall, the method supports the hypothesis that shape primitives can support task-free and task-relevant grasp prediction., 8 pages

This paper proposes a new variational model for deblurring low-resolution images, a.k.a. single image nonparametric blind super-resolution. In specific, a type of new adaptive heavy-tailed image priors are presented incorporating both the model discriminativeness and effectiveness of salient edge pursuit for accurate and reliable blur kernel estimation. With the assistance of appropriate non-blind super-resolution approaches, nonparametric blind super-resolution can be cast as a regularized functional minimization problem. An efficient numerical algorithm is derived by harnessing the alternating direction method of multipliers as well as the conjugate gradient method, with which alternatingly iterative estimations for kernel and image are finally implemented in a multi-scale manner. Numerous experiments are conducted along with comparisons made among the proposed approach and two recent state-of-the-art ones, demonstrating that the proposed approach is able to better deal with low-resolution images which are blurred by various possible kernels, e.g., Gaussianshaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories.

Recent advances in single-cell technologies, in particular intracellular cytokine staining (ICS), have enabled multidimensional functional measurements of naturally occurring or vaccine-induced T-cell responses in clinical studies. Analysis of such increasingly multidimensional datasets presents a great challenge to statisticians. Currently, multidimensional functional cell measures are largely analyzed, either by univariate analysis of all combinations of functions individually, or by summarizing a few particular groups of functions separately. Such simple analyses do not reflect comprehensively the polyfunctional profile of the T-cell responses, nor do they allow more sophisticated statistical analysis and inference. In this paper, we introduce a new approach to statistical inference for multidimensional ICS data. We propose to reduce the dimensionality by using a weighted sum, followed by computing the minimum and maximum of the test statistic over all eligible assignments of weights which satisfy the underlying partial ordering of the data. The computation technique is presented. Statistical inference is then based on the minimum and maximum of the test statistic. We illustrate, through an example, that the technique can be useful in reducing the complexity of the multidimensional response data and providing insightful reporting of the results.

Responder analysis is in common use in clinical trials, and has been described and endorsed in regulatory guidance documents, especially in trials where “soft” clinical endpoints such as rating scales are used. The procedure is useful, because responder rates can be understood more intuitively than a difference in means of rating scales. However, two major issues arise: 1) such dichotomized outcomes are inefficient in terms of using the information available and can seriously reduce the power of the study; and 2) the results of clinical trials depend considerably on the response cutoff chosen, yet in many disease areas there is no consensus as to what is the most appropriate cutoff. This article addresses these two issues, offering a novel approach for responder analysis that could both improve the power of responder analysis and explore different responder cutoffs if an agreed-upon common cutoff is not present. Specifically, we propose a statistically rigorous clinical trial design that pre-specifies multiple tests of responder rates between treatment groups based on a range of pre-specified responder cutoffs, and uses the minimum of the p-values for formal inference. The critical value for hypothesis testing comes from permutation distributions. Simulation studies are carried out to examine the finite sample performance of the proposed method. We demonstrate that the new method substantially improves the power of responder analysis, and in certain cases, yields power that is approaching the analysis using the original continuous (or ordinal) measure.

Blind image deblurring or deconvolution is a very hot topic towards which numerous methods have been put forward in the past decade, demonstrating successful deblurring results on one or another benchmark natural image dataset. However, most of existing algorithms are found not robust enough as dealing with images in specific scenarios, such as images with text, saturated area or face. In this paper, a robust blind deblurring approach is presented using relative total variation as a regularization penalty. To the best of our knowledge, none of previous studies have followed this modeling principle. The underlying idea is to pursue more accurate intermediate image for more reliable kernel estimation by harnessing relative total variation which could extract salient structures from textures and suppress ringing artifacts and noise as well. An iterative estimating algorithm is finally deduced for alternatively update of sharp image and blur kernel by operator splitting and augmented Lagrangian. Extensive experiments on a challenging synthetic dataset and real-world images as well are conducted to validate the new method with comparison against the state-of-the-art approaches from 2006 to 2014. Based on both PSNR and SSIM and a non-reference evaluation metric, the results demonstrate that our approach is comparatively more effective to process blurred images in more challenging scenarios.

This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total variation. On the second, a plug-and-play algorithm is deduced to alternatively estimate the intermediate sharp image and the nonparametric blur kernel. With the numerical scheme, image estimation is simplified to an image smoothing problem. Lastly, a great many experiments are performed accompanied with comparisons with state-of-the-art approaches on synthetic benchmark datasets and real blurry images in various scenarios. The experimental results show well the effectiveness and robustness of the proposed method.

The biased coin randomization approach is frequently adopted in randomized clinical trials to control the balance in overall treatment assignments. In this article, an algorithm is developed to theoretically determine the exact allocation ratios for all the patients that will be enrolled in a randomized clinical trial with biased coin randomization based on the order in which they are randomized. Our results show that the exact allocation ratios can significantly deviate from the ratio as specified for the trial, which poses challenges to the enrollment of the trial and to the interpretation of the results. In order to maintain a constant allocation ratio throughout the trial, a modification of the widely adopted permuted block randomization is proposed, which is shown to achieve better balance for not only the overall treatment assignments but also the baseline stratification variables that are desired to be balanced at the end of the trial.

For ethical reasons, group sequential trials were introduced to allow trials to stop early in the event of extreme results. Endpoints in such trials are usually mortality or irreversible morbidity. For a given endpoint, the norm is to use a single test statistic and to use that same statistic for each analysis. This approach is risky because the test statistic has to be specified before the study is unblinded, and there is loss in power if the assumptions that ensure optimality for each analysis are not met. To minimize the risk of moderate to substantial loss in power due to a suboptimal choice of a statistic, a robust method was developed for nonsequential trials. The concept is analogous to diversification of financial investments to minimize risk. The method is based on combining P values from multiple test statistics for formal inference while controlling the type I error rate at its designated value.This article evaluates the performance of 2 P value combining methods for group sequential trials. The emphasis is on time to event trials although results from less complex trials are also included. The gain or loss in power with the combination method relative to a single statistic is asymmetric in its favor. Depending on the power of each individual test, the combination method can give more power than any single test or give power that is closer to the test with the most power. The versatility of the method is that it can combine P values from different test statistics for analysis at different times. The robustness of results suggests that inference from group sequential trials can be strengthened with the use of combined tests.

Various methods exist in the literature for achieving marginal balance for baseline stratification variables in sequential clinical trials. One major limitation with balancing on the margins of the stratification variables is that there is an efficiency loss when the primary analysis is stratified. To preserve the efficiency of a stratified analysis one recently proposed approach balances on the crossing of the stratification variables included in the analysis, which achieves conditional balance for the variables. A hybrid approach to achieving both marginal and conditional balances in sequential clinical trials is proposed, which is applicable to both continuous and categorical stratification variables. Numerical results based on extensive simulation studies and a real dataset show that the proposed approach outperforms the existing ones and is particularly useful when both additive and stratified models are planned for a trial.

Covariate adaptive allocation is often adopted in sequential clinical trials to maintain the balance of baseline covariates that could potentially confound the outcome of a trial. Several allocation methods exist in the literature that can handle both continuous and categorical covariates. We propose a minimization approach to maintaining the balance of multiple continuous and categorical covariates in sequential clinical trials, which uses the area between the empirical cumulative distribution functions of the observed covariate values as the imbalance metric. Numerical results based on extensive simulation studies and a real dataset show that the proposed approach produces more accurate estimates of the treatment effect and leads to more powerful trials than the existing approaches for trials with binary, continuous, and time-to-event outcomes.