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This paper describes a flow control of the dye in the paper mill with the quasi-Z-source indirect matrix converter (QZSIMC) fed induction motor drive. More than a decade the voltage-source inverter (VSI) and current-source inverter (CSI) have been used to control the speed of the induction motor, which in turns controls the flow of dye. Recently, the matrix converter (MC) has been an excellent competitor for the VSI or CSI for its compactness. The voltage transfer ratio of the VSI, CSI, and MC has been limited to 0.866. Thus, the efficiency of these converters is less. To improve the voltage transfer ratio the quasi-Z-source network (QZSN) is to be used between voltage source and indirect MC (IMC). The modification in the shoot-through duty ratio of the QZSN varies the voltage transfer ratio greater than 0.866. Different voltage transfer ratios are needed for different voltage sag conditions. For a variation of the duty ratio of the QZSN, the fuzzy logic controller has been presented. To control the IMC vector control with space vector modulation has been presented. This paper proposes the implementation of QZSIMC adjustable speed drive for the flow control of dye in paper mill during different voltage sag conditions. A 4-kW prototype has been built and the effectiveness of the proposed system is verified with simulation results and experimental setup. Simulation is done in MATLAB, Simulink platform. Experimental setup is done with the aid of a TMS320F2812 (Texas Instrument) processor. The experimental results validate the maintenance of the speed of an induction motor at the set condition, thus controlling the perfect flow of dye in paper manufacturing technology. [ABSTRACT FROM AUTHOR]

In this paper, we extend the interference channel with degraded message sets model by introducing multiple secondaries. The new model allows for multiple concurrent transmissions in a wireless network by employing dirty paper and superposition coding. We apply the proposed scheme to the communication between nodes in a wireless mesh network (WMN). The cases of different WMN. topologies are analyzed, and numerical results show that considerable capacity gains are achieved when using the proposed model with multiple secondaries. [ABSTRACT FROM AUTHOR]

In this paper, we characterize the capacity of a new class of discrete memoryless multicast networks having a tree topology. For achievability, a novel coding scheme is constructed where some relays employ a combination of decode-and-forward and compress-and-forward and the other relays perform a random binning such that codebook constructions and relay operations are independent for each node and do not depend on the network topology. For converse, a new technique of iteratively manipulating inequalities exploiting the tree topology is used. This class of multicast tree networks includes the class of diamond networks studied by Kang and Ulukus as a special case. [ABSTRACT FROM PUBLISHER]

Negative-sequence current is required to be intentionally injected to support the power grid or compensate the imbalanced load in asymmetrical faults utilizing STATCOM. However, the extreme compensation range of negative-sequence current injection becomes complicated when grid voltage becomes asymmetrical. This article presents a novel off-line strategy for negative-sequence current compensation under two typical kinds of asymmetrical grid faults — single-phase voltage drop and double-phase voltage drop, with the following two benefits simultaneously: maximizing negative current compensation capacity; avoiding iterations and reducing computation burdens. In this paper, the mechanism of DC voltage balancing among three phases in STATCOM is analyzed and a new identification method of negative-sequence current compensation range is explored out. To simplify the calculation, this paper takes the ratio of negative-sequence reactive (Iq-) and active (Id-) current as a variable (s). In this way, the relationship between the extreme compensation range, asymmetrical voltage sag depth of power grid h is drawn with MATLAB. Different from the previous iterative method, the off-line identification method is applied to avoid huge iterations. By fitting the curve, the precise ranges of negative-sequence current compensation under different asymmetrical grid faults are approximated as simple polynomials, thus significantly reducing the computation burdens. Simulation and experimental results verify the practicability of the proposed compensation method. [ABSTRACT FROM AUTHOR]

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed competition behavior in multi-robot coordination under variable communication topology and switching one. In terms of multi-robot competition-based coordination, a winner-take-all (WTA) strategy is leveraged to address this issue with inevitable environmental barriers incorporated. Moreover, an innovative control theory stimulated gradient neural network (CTSGNN) algorithm is proposed to realize the WTA with prominent robustness and convergence over the traditional ones. Besides, to adapt to diversified local communication modes among multi-robot systems, fast variable and low switching topologies are constructed to establish two dynamic consensus estimators, accompanied by the proposed distributed control schemes. Traditional algorithms are introduced and served as a contrast. Afterward, the global convergence of the proposed algorithm in dealing with multi-robot competitive coordination, the universality of the application scenario, as well as the weaknesses of traditional methods are substantiated theoretically. The effectiveness and superiority of the proposed CTSGNN algorithm and the resultant distributed control schemes by integrating consensus estimators are further sustained via simulations. Note to Practitioners—The motivation of this paper is the coordination operation of multi-robot systems, but it is also applicable to other fields adopting multi-agent systems. Most of the existing researches on multi-robot coordination only exploit their collaborative behavior, which usually leads to the system redundancy and overflow of control costs. To this end, an innovative control algorithm is proposed for this competitive coordination and remains to be perfect in terms of stability and accuracy. Then, this paper establishes new distributed control schemes for multi-robot systems to compete for optimal dynamic task allocation. In this sense, under the premise of ensuring a successful task execution, only a few individuals with strong abilities and advantages are assigned. It is promising to maximize resource utilization, increase efficiency, and be extended to multi-objective scenarios. Note that the design of this scheme takes into account the environmental constraints and physical constraints of the robot itself. Theoretical analysis and preliminary simulation experiments prove the high efficiency of the control scheme. In ongoing research, the competitive coordination tasks of the mobile robot systems and communication delays or fault in control scheme design are explored to expand the operation scope and system extensibility. [ABSTRACT FROM AUTHOR]

In order to achieve the purpose of extending the speed range and reducing power electronics devices for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied for rail transit applications in this paper. In this topology, compared with the traditional OEW topology, the number of voltage-source-inverters (VSIs) can be reduced from four to three to drive two OEW movers and all these VSIs share a common dc bus voltage. In order to improve the steady-state performances, a four-vector phase model predictive voltage control (FV-PMPVC) is proposed in this paper. Due to the spatial distribution of phase voltage vector (PVV), an orthometric synthesis principle (OSP) is proposed to calculated the duration time for the vector synthesis, which reduces the computation burden and enhances the robustness. Comparing the existing control method, FV-PMPVC can get similar dynamic performances but better steady-state performances. Additionally, the half-centralized topology using FV-PMPVC can have same speed range while less VSIs are required. The effectiveness of FV-PMPVC is verified by experimental results. [ABSTRACT FROM AUTHOR]

Axial flux and radial flux cycloidal permanent magnetic gears are characterized by high gear ratios and high torque densities; however, their rotors experience large unbalanced forces, which stress the bearings used in the gear. This paper presents a new cycloidal magnetic gear topology that combines the radial and axial flux topologies. The paper analyzes the forces and torques contributed by the axial and radial magnets in this topology using 3D finite element analysis. It was found that the perpendicular force is necessary to transfer torque to the high-speed shaft and, thus, cannot be cancelled out, but the eccentric component of the magnetic forces can be largely cancelled out, potentially reducing bearing losses and increasing the lifespan of the bearings used in the gear. A proof-of-concept prototype was constructed. Experimental slip torque results matched 3D finite element analysis (FEA) simulations to within 15%, and testing showed that no-load losses were reduced and the slip torque was increased by combining both axial and radial topologies, compared to using an axial configuration alone. [ABSTRACT FROM AUTHOR]

With the proliferation of online social networks, the identification or classification of real-life relationship between users has been very useful for many applications such as financial fraud detection. In real life, usually people with different relationships would present gifts with special meanings to each other on different dates. In many Asian cultures, especially in Chinese culture, the red packet is a traditional form of monetary gift. With the rapid development of the Internet, people gradually began to give electronic red packets instead of paper ones as the means of money gifting on social network platforms. As motivated, in this paper we advocate a novel approach that exploits users’ red packet interactions for users relationship identification on WeChat, one of the largest social platforms in China. Specifically, we analyze the WeChat red packets network, identify the real-life relationship types between users through mining the semantic information of the amount and sending time of each red packet. In order to better capture the red packet gifting behaviors between users for relationship identification, on one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet. On the other hand, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the red packets sequence between two users. To validate our approach, we conduct comprehensive experiments on a real-world WeChat Users Red Packets dataset that involves 8 kinds of real-life relationships. The experiments show that our proposed approach performs significantly better than baselines and achieves 81.70 percent prediction accuracy. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing (CMP) planarizes the chip surface through a synergistic effect of chemical etching and abrasive polishing, and has become increasingly essential in modern semiconductor manufacturing. For better production yield, the industry seeks an accurate CMP model to identify, localize and control surface unevenness caused by the layout dependent effect (LDE) prior to fabrication, which provides valuable information for manufacturing-friendly designs, such as accurate hotspot prediction, design-rule optimization, and smart dummy fill. However, existing models are often overly coarse-grained and inaccurate: They are unable to utilize or analyze detailed topology information in a layout design, and can only characterize the post-CMP chip surface into a rough step-height model. The prediction resolution is also constrained by the featuring window size. In this paper, we propose a novel fine-grained and end-to-end feature-scale CMP modeling paradigm based on fully convolutional neural networks. The model directly analyzes the detailed layout topology without an explicit step of featuring parameter extraction, accurately predicts the rich chip surface micro-topography at a feature-scale precision. We designed and taped-out test chips under 28nm and 40nm processes. Experiments on silicon data demonstrated our model’s accuracy improvements of 65% to 76%, as well as its cross-process-node adaptability. [ABSTRACT FROM AUTHOR]

Conventional half bridge modular multilevel converter (MMC) system will suffer great over-current when there is a dc side fault. Therefore, topology study in sub-modules with dc side fault blocking capability have become one of the hotspots in MMC field. However, fault blocking topologies using conventional IGBT devices must employ more semiconductors than half bridge topology. The extra semiconductor devices employed in the topology will lead to the increase of cost and conduction loss. To reduce the conduction loss of the fault blocking topology, a novel sub-module topology is proposed in this paper. The proposed topology employs reverse blocking devices to form the low conduction loss path. Conduction loss analysis is conducted in this paper to show the priority of the proposed topology. Simulation and experiment results verified the dc fault blocking capability of the proposed topology. [ABSTRACT FROM AUTHOR]

DC-DC converters are needed for the future development of high voltage direct current (HVDC) grids, as they allow to interconnect lines with different voltages and topologies. The dc-dc converters can increase the grid controllability adding power flow control, voltage regulation and/or fault blocking capability. The dc modular multilevel converter (dc-MMC) is a non-isolated solution proposed to interconnect HVDC systems with the same line topology. This paper proposes a new dc-MMC with a control strategy, which allows the converter to interconnect different line topologies (e.g., rigid bipole connected to a symmetric monopole). The paper presents the different line topologies in HVDC installations. Then, a mathematical model with a variable transformation is proposed for the new dc-dc converter. A control structure is proposed and implemented in Matlab/Simulink using an average arm model and simplified dc grids. The results validate the control in normal operation, fault blocking capability and post-fault scenario (degraded mode). [ABSTRACT FROM AUTHOR]

This paper proposes a traction unit that combines an interior permanent magnet motor and a PM-assisted synchronous reluctance motor for electric vehicles to improve traction performance, enhance energy efficiency, and reduce cost. The characteristics of three traction units are analyzed and compared, including (a) rear-wheel-drive configuration using one IPM motor; (b) all-wheel-drive configuration using two interior permanent magnet motors; and (c) all-wheel-drive with a combination of one interior permanent magnet motor and one PM-assisted synchronous reluctance as proposed in this paper. The operational characteristics of each motor to achieve a wide constant power speed range are first investigated. Then, the Urban Dynamometer Driving Schedule is utilized to compare and evaluate the operational characteristics, energy consumption, and most importantly, the efficiency of the traction units. Simulation results reveal that the overall efficiency of the proposed traction unit can be improved compared to the other configurations. The advantages and drawbacks of the proposed traction unit are also investigated. Finally, experiments are conducted to validate the analysis. [ABSTRACT FROM AUTHOR]

In renewable energy micro-source applications, a wide range of voltage conversion of the step-up converter is an essential part to equalize the typical low voltage of micro-source with DC-bus voltage of Inverter or motor drives. In order to meet this purpose, an ultra-voltage gain step-up DC-DC converter is proposed in this paper. The proposed converter has made an arrangement of the regenerative-boost fed switched inductor and capacitor configurations. Thereby, due to such an organization of structure, the proposed converter can develop an extremely high voltage-gain even at lower duty ratios. In addition, it has the advantage of lower switching stress across all power semi-conductive diodes. Furthermore, this paper describes the steady-state analysis and comparative features of the converter with existing recent literature. Finally, to validate theoretical analysis and test the feasibility and suitability of the proposed ultra-gain converter, the experimental results were observed by a 500 W fabricated prototype. [ABSTRACT FROM AUTHOR]

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems. [ABSTRACT FROM AUTHOR]

This paper proposes a scalable framework for the real-time detection and localization of power line outages in transmission networks. While localizing outages is pivotal for ensuring grid reliability, forming such decisions faces an inherent combinatorial complexity that grows with the grid size and becomes prohibitive even for moderate grid sizes. Hence, designing outage detection and localization algorithms that are amenable to real-time implementation critically hinges on circumventing the computational complexity. This paper proposes a graph-guided quickest change detection (GG-QCD) approach that leverages the grid topology and performs quickest change detection in the spectral domain of the graph underlying grid's topology. The GG-QCD algorithm's key features are that (i) it uses a one-dimensional metric that tests the data's conformity to the grid topology, and (ii) it decouples the detection and localization processes to avoid testing all the lines at all times. Specifically, a lack of such conformity of the data to the system model will be alarming the potential existence of an outage. Once an outage is deemed to exist, an active graph clustering approach will be used to localize the line in outage. The clustering approach will also be relying on the same one-dimensional conformity metric. Overall, this approach will be performing only one test over time when the system is outage-free. Once an outage is detected, it will require $\mathcal {O}(\log (L))$ additional tests to identify the line in outage. This paper presents the theory for GG-QCD and algorithms for outage detection and localization. To evaluate these algorithms’ efficiency and complexity, they are examined in the standard IEEE 30- and 118-bus systems. [ABSTRACT FROM AUTHOR]

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology. [ABSTRACT FROM AUTHOR]

This paper presents a dual active bridge (DAB) converter with reactive power control capability (Q-capability) for a battery energy storage system (BESS) or an electric vehicle (EV) to deliver vehicle-to-home (V2H) and vehicle-to-grid (V2G) services. The DAB topology is preferable for EV charging systems due to its galvanic isolation and bidirectional power flow capabilities. However, this topology increases reactive current peak and loses zero voltage switching (ZVS) operation at light loads if it is not controlled with improved modulation strategies and proper Q control. This paper proposes a mixed modulation method and a reactive power optimization (RPO) algorithm based on power system load flow concepts. Due to the complexities associated with the analytical solution using time domain analysis, this paper develops detailed universal expressions of a DAB converter using the harmonic analysis method. Analytical identification of ZVS boundaries and DSP implementation of the RPO algorithm become easier with these universal expressions. Multiple modes analysis for different operational states is no longer required. Simulation results have been shown for different modulation strategies of the DAB converters. The theoretical analysis of modulation strategies, RPO algorithm and ZVS operation are validated by experimental results on a DAB prototype. Results show that the proposed control strategy reduces the reactive power and provide ZVS operation over a wide ranges of voltages. [ABSTRACT FROM AUTHOR]

The stochastic block model (SBM) is a random graph model in which the edges are generated according to the underlying cluster structure on the vertices. The (ferromagnetic) Ising model, on the other hand, assigns ±1 labels to vertices according to an underlying graph structure in a way that if two vertices are connected in the graph then they are more likely to be assigned the same label. In SBM, one aims to recover the underlying clusters from the graph structure while in Ising model, an extensively-studied problem is to recover the underlying graph structure based on i.i.d. samples (labelings of the vertices). In this paper, we propose a natural composition of SBM and the Ising model, which we call the Stochastic Ising Block Model (SIBM). In SIBM, we take SBM in its simplest form, where $n$ vertices are divided into two equal-sized clusters and the edges are connected independently with probability $p$ within clusters and $q$ across clusters. Then we use the graph $G$ generated by the SBM as the underlying graph of the Ising model and draw $m$ i.i.d. samples from it. The objective is to exactly recover the two clusters in SBM from the samples generated by the Ising model, without observing the graph $G$. As the main result of this paper, we establish a sharp threshold $m^\ast $ on the sample complexity of this exact recovery problem in a properly chosen regime, where $m^\ast $ can be calculated from the parameters of SIBM. We show that when $m\ge m^\ast $ , one can recover the clusters from $m$ samples in $O(n)$ time as the number of vertices $n$ goes to infinity. When $m < m^\ast $ , we further show that for almost all choices of parameters of SIBM, the success probability of any recovery algorithms approaches 0 as $n\to \infty $. [ABSTRACT FROM AUTHOR]

This paper analyzes high-frequency interactions between a Modular Multilevel Converter (MMC) and High Voltage Direct Current (HVDC) grid resonances by studying their effect on the system stability. The recent appearance of converter-related instabilities due to high-frequency oscillations at the converter's ac side raises concerns about whether similar interactions can also take place at its dc side. To determine the risks imposed by such interactions within an HVDC grid, this paper assesses the impact of the MMC internal dynamics and dc system resonances on the stability using an analytical impedance-based method. The effect of fault current-limiting inductors, grid topology changes and transmission line length is investigated, indicating that these parameters considerably influence the electromagnetic characteristics of the HVDC grid and consequently the system stability. Furthermore, a sensitivity analysis of the MMC internal controller dynamics on the converter's non-passivity, causing the instability, is performed. [ABSTRACT FROM AUTHOR]

Battery energy storage system (BESS) plays an important role in solving problems in which the intermittency has to be considered while operating distribution network (DN) penetrated with renewable energy. Aiming at this problem, this paper proposes a global centralized dispatch model that applies BESS technology to DN with renewable energy source (RES). The method proposed in this paper aims to minimize the power purchase cost considering network active loss cost as well as voltage deviation penalty cost. Later, second-order cone programming (SOCP) method as well as big M method are applied to make the problem tractable. Last, the method illustrated in this paper is applied and validated on a modified IEEE 33-bus benchmark system to verify the effectiveness of the proposed scheduling model. [ABSTRACT FROM AUTHOR]

Heterogeneous graph embedding aims at learning low-dimensional representations from a graph featuring nodes and edges of diverse natures, and meanwhile preserving the underlying topology. Existing approaches along this line have largely relied on meta-paths, which are by nature hand-crafted and pre-defined transition rules, so as to explore the semantics of a graph. Despite the promising results, defining meta-paths requires domain knowledge, and thus when the test distribution deviates from the priors, such methods are prone to errors. In this paper, we propose a self-learning scheme for heterogeneous graph embedding, termed as self-guided walk (SILK), that bypasses meta-paths and learns adaptive attentions for node walking. SILK assumes no prior knowledge or annotation is provided, and conducts a customized random walk to encode the contexts of the heterogeneous graph of interest. Specifically, this is achieved via maintaining a dynamically-updated guidance matrix that records the node-conditioned transition potentials. Experimental results on four real-world datasets demonstrate that SILK significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents the DC fault transient study of a meshed DC grid with high-temperature superconducting (HTS) HVDC cables to integrate offshore wind power. A four-terminal meshed DC grid model with a ±100 kV DC voltage rating is developed. DC circuit breakers (DCCBs) are implemented in the DC grid to deal with DC cable faults. To conduct the fault transient study, the fault scenarios with different fault locations and protection delays are studied. The sensitivity study versus different DC fault locations reveals that the faults on the faulted cable will not cause the quenching of healthy cables. The sensitivity study considering different DC fault protection delays demonstrates that the HTS cables have a current-limiting effect and the long delay of DC fault protection does not result in large fault current and the quenching of healthy cables. Extensive simulations in PSCAD/EMTDC validate the founding. [ABSTRACT FROM AUTHOR]

A novel single-stage doubly grounded transformerless inverter configuration which can buck-boost the output voltage is proposed in this paper. In this transformerless inverter configuration grid neutral is directly connected to PV panel negative rail which suppresses/eliminates the PV leakage currents. In addition, the tracking of maximum power point is possible for wide variation of weather conditions due to buck-boost voltage capability of inverter. Moreover, the proposed inverter topology has symmetrical operation in both half cycles hence injects negligible DC offset current in to the grid. Here the symmetrical operation of inverter is achieved due to one inductor is used for both half cycles of the grid. The proposed topology is realized using five switches, two of which work at 50 Hz and remaining three switches at high frequency which reduces overall semiconductor losses. Further, the proposed inverter topology is controlled through a newly developed modulation technique and possible working modes are analyzed and described thoroughly. For performance validation, the experimental analysis of grid connected proposed inverter is performed on a 1 kVA system and results show that proposed inverter feeds power in to the grid with lesser THD while achieving greater efficiency. [ABSTRACT FROM AUTHOR]

The use of power converters as solid state transformers is an attractive solution to modernize the power network, but this solution has not been fully addressed for MV and HV substations. This paper presents a customized and simple control for the Modular Multilevel Matrix Converter (M3C), specially conceived for its operation on synchronous ports, which is the case of AC substations. The control allows bidirectional power transfer, generation/absorption of reactive power and provisions of ancillary services. The converter is compared to the back-to-back Modular Multilevel Converter (B2B-MMC) where the key performance indicators to carry out the comparison are power efficiency, number of semiconductor devices, passive components required, footprint, voltage cell balance, fault blocking capability and stress of components. The simulation results show the features, performance and attractiveness of the M3C topology in a 33/11 kV, 16 MW substation under different operating conditions, including grid faults and dynamic operation. The M3C presents similar efficiency and performance than the B2B-MMC, but it uses fewer semiconductor devices, passive components and total cell capacitor energy than the B2B-MMC, reducing cost and footprint. The experimental results show the performance of the M3C under less ideal conditions including a substation transformer saturation and power step response. [ABSTRACT FROM AUTHOR]

Cloud providers tend to support dynamic computing resources reallocation (e.g., Autoscaling) to handle the bursty workload for web applications (e.g., e-commerce) in the cloud environment. Nevertheless, we demonstrate that directly scaling a bottleneck server without quickly adjusting its soft resources (e.g., server threads and database connections) can cause significant response time fluctuations of the target web application. Since soft resources determine the request processing concurrency of each server in the system, simply scaling out/in the bottleneck service can unintentionally change the concurrency level of related services, inducing either under- or over-utilization of the critical hardware resource. In this paper, we propose the Scatter-Concurrency-Throughput (SCT) model, which can rapidly identify the near-optimal soft resource allocation of each server in the system using the measurement of each server’s real-time throughput and concurrency. Furthermore, we implement a Concurrency-aware autoScaling (ConScale) framework that integrates the SCT model to quickly reallocate the soft resources of the key servers in the system to best utilize the new hardware resource capacity after the system scaling. Based on extensive experimental comparisons with two widely used hardware-only scaling mechanisms for web applications: EC2-AutoScaling (VM-based autoscaler) and Kubernetes HPA (container-based autoscaler), we show that ConScale can successfully mitigate the response time fluctuations over the system scaling phase in both VM-based and container-based environments. [ABSTRACT FROM AUTHOR]

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. To this end, we treat the annotations as active contour models that can deform themselves while preserving their topology. This enables us to jointly train the network and correct potential errors in the original annotations. The result is an approach that boosts performance of deep networks trained with potentially inaccurate annotations. [ABSTRACT FROM AUTHOR]

Voltage source converter (VSC) based multi -terminal HVDC (VSC-MTDC) systems have emerged as flexible collector systems for multi-area interconnected AC/DC hybrid grids with high share of volatile renewables energy. However, it remains an open question as how to exploit the flexible and cooperative regulation of multiple VSC stations and enhance secure operation on the premise of information privacy protection of multiple independent dispatch areas. This paper proposed an Analytical Target Cascading (ATC)-based three-layer decentralized optimization approach on flexible operation of VSC-MTDC hybrid systems. The low layer is a chance-constrained stochastic optimal power flow (SOPF) problem for AC grids. To guard against the secure operation risk by a VSC's random unipolar atresia fault, a modified loss of load probability index (MLOLP) is proposed to improve the day-ahead generation-reserve scheme. The middle layer and the high layer are a multiperiod OPF problem for VSC stations and the MTDC grid, respectively, where a detailed power regulation model is built to fully exert the flexible cooperation of multiple VSC stations. Two case systems are tested to verify the effectiveness and superiority of the proposed flexible operation hierarchy. [ABSTRACT FROM AUTHOR]

An $(m,n,a,b)$ -tensor code consists of $m\times n$ matrices whose columns satisfy ‘ $a$ ’ parity checks and rows satisfy ‘ $b$ ’ parity checks (i.e., a tensor code is the tensor product of a column code and row code). Tensor codes are useful in distributed storage because a single erasure can be corrected quickly either by reading its row or column. Maximally Recoverable (MR) Tensor Codes, introduced by Gopalan et al., are tensor codes which can correct every erasure pattern that is information theoretically possible to correct. The main questions about MR Tensor Codes are characterizing which erasure patterns are correctable and obtaining explicit constructions over small fields. In this paper, we study the important special case when $a=1$ , i.e., the columns satisfy a single parity check equation. We introduce the notion of higher order MDS codes ($\mathrm {MDS}(\ell)$ codes) which is an interesting generalization of the well-known MDS codes, where $\ell $ captures the order of genericity of points in a low-dimensional space. We then prove that a tensor code with $a=1$ is MR if the row code is an $\mathrm {MDS}(m)$ code. We then show that $\mathrm {MDS}(m)$ codes satisfy some weak duality. Using this characterization and duality, we prove that $(m,n,a=1,b)$ -MR tensor codes require fields of size $q=\Omega _{m,b}(n^{\min \{b,m\}-1})$. Our lower bound also extends to the setting of $a>1$. We also give a deterministic polynomial time algorithm to check if a given erasure pattern is correctable by the MR tensor code (when $a=1$). [ABSTRACT FROM AUTHOR]

The topological interference management (TIM) problem refers to the study of the $K$ -user partially connected interference networks with no channel state information at the transmitters (CSIT), except for the knowledge of network topology. In this paper, we study the TIM problem with confidential messages (TIM-CM), where message confidentiality must be satisfied in addition to reliability constraints. In particular, each transmitted message must be decodable at its intended receiver and remain confidential at the remaining $(K-1)$ receivers. Our main contribution is to present a comprehensive set of results for the TIM-CM problem by studying the symmetric secure degrees of freedom (SDoF). To this end, we first characterize necessary and sufficient conditions for feasibility of positive symmetric SDoF for any arbitrary topology. We next present two achievable schemes for the TIM-CM problem: For the first scheme, we use the concept of secure partition and, for the second one, we use the concept of secure independent sets. We also present outer bounds on symmetric SDoF for any arbitrary network topology. Using these bounds, we characterize the optimal symmetric SDoF of all $K=2$ -user and $K=3$ -user network topologies. [ABSTRACT FROM AUTHOR]

In this paper, the interior permanent-magnet vernier (PMV) machines and the conventional PMV machine are quantitatively compared regarding the requirements for electric automatic guided vehicles (AGV). The key feature of the interior PMV machines is to place the middle iron pole sandwiched by PMs in the slots between the salient rotor poles. As a result, three types of interior PMV machines with different PM arrangements, namely two V-type PMV (V-PMV) and one U-type PMV (U-PMV) machines, are developed. Due to the improved flux modulation effect, the V-PMV and U-PMV machines can respectively provide over 20% and 30% higher torque density than the conventional PMV counterpart, under a high power factor level of 0.86. The working principle is illustrated based on the field modulation theory. A comparative study among the conventional PMV and interior PMV machines is conducted in terms of electromagnetic performances and dynamic responses, after performing global optimization of design parameters. Finally, a prototype is manufactured and tested for experimental validations. [ABSTRACT FROM AUTHOR]

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions. [ABSTRACT FROM AUTHOR]

Point clouds have attracted attention as a representation of an object’s surface. Deep generative models have typically used a continuous map from a dense set in a latent space to express their variations. However, a continuous map cannot adequately express the varying numbers of holes. That is, previous approaches disregarded the topological structure of point clouds. Furthermore, a point cloud comprises several subparts, making it difficult to express it using a continuous map. This paper proposes ChartPointFlow, a flow-based deep generative model that forms a map conditioned on a label. Similar to a manifold chart, a map conditioned on a label is assigned to a continuous subset of a point cloud. Thus, ChartPointFlow is able to maintain the topological structure with clear boundaries and holes, whereas previous approaches generated blurry point clouds with fuzzy holes. The experimental results show that ChartPointFlow achieves state-of-the-art performance in various tasks, including generation, reconstruction, upsampling, and segmentation. [ABSTRACT FROM AUTHOR]

A human contact network (HCN) consists of individuals moving around and interacting with each other. In HCN, it is essential to detect malicious users who break the data delivery through terminating the data delivery or tampering with the data. Since malicious users will pay more but gain less when breaking the data delivery of opportunistic contacts, we focus on the non-opportunistic contacts that occur more frequently and stably. It is observed that people contact with each other more frequently if they have more social features in common. In this paper, we build up topology structure for HCN based on social features, and propose a graph theoretical comparison detection model to perform malicious users detection. Then we present an adaptive detection scheme based on Hamiltonian cycle decomposition. Also, we define comparison-0-string and comparison-1-string to improve the detection efficiency. Moreover, we perform scenario simulations on real data to realize the detected process of malicious users. Experiments show that, when the number of malicious users is bounded by the dimension of HCN, our scheme has a detection rate of 100% with both false positive rate and false negative rate being 0%, and the running cost is also very low when compared to baseline approaches. When the number of malicious users exceeds the bound, the detection rate of our scheme decreases slowly, while the false positive rate and false negative rate increase slowly, but they are still better than the baseline approaches. [ABSTRACT FROM AUTHOR]

The dc-dc converters with high step-up DC-voltage gain play a vital role in integrating low voltage DC sources. Though several converter topologies are reported in the recent past, attempts have been made to reduce the components, especially the switching devices, passive elements, converter losses, etc., of the converter. A novel DC-DC converter topology, viz., single switched impedance network (SSIN)-based converter with n-stage is proposed in this paper. The operation of the SSIN based converter in continuous and discontinuous conduction modes are discussed. The effect of parasitic elements on DC-voltage gain and efficiency of the SSIN is carried out. The small-signal model of the SSIN converter is derived. The performance of the SSIN converter is compared with the similar converter topologies. The 500 W prototypes of the SSIN with n = 1 and n = 2 are fabricated, and the experimental results are presented in this paper. To improve the converter performance, SiC-based semiconductor devices are used. The SSIN converter with two-stage has low switch loss at 500 W load, improved efficiency during high power and high output voltage operation, the switch stress of Vo/2, etc. [ABSTRACT FROM AUTHOR]