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With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter out ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel top-k subscription query called Top-k Ride Subscription (TkRS) query, which continuously returns the best k ride orders that match drivers' trip plans to them. We propose two efficient algorithms to enable the top-k results maintenance. We also design a novel hybrid index and a two-level buffer to efficiently track the top- $k$ results. Finally, extensive experiments on real-life datasets suggest that our algorithms can achieve desirable performance in practical settings.

A 50-year-old man with atrial fibrillation without anticoagulation presented to the hospital after tonic–clonic seizures, followed by pulseless cardiopulmonary arrest with return of spontaneous circulation after 2 min. Imaging demonstrated a left superior cerebellar thromboembolic ischaemic

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles., Comment: The manuscript has been accepted for publication as a regular paper in the IEEE Transactions on Signal Processing

Mission profile emulation for submodules (SMs) in the modular multilevel converter (MMC) is a crucial step before a full-scale system is put into field operation. Existing methods suffer from limitations of the testing accuracy due to the staircase-like arm voltages which result in distorted loading current and require large filter inductance. This is especially the case when limited number of SMs are under test. In this paper, an efficient MPE method is proposed to emulate the loading behaviors of multiple SMs in MMC. The testing circuit allows two arms of MMC with flexible SM number, operating in inverting and rectifying mode under nearest level modulation (NLM), to be tested simultaneously with reduced DC supply voltage. A common H-bridge circuit is introduced in the testing circuit, with each arm of the H-bridge circuit functioning as the voltage compensator by using feed-forward control. In this way, the current distortion can be significantly suppressed and testing accuracy can be guaranteed in a cost-efficient manner even though no large inductor is included in the testing circuit. The proposed method allows SMs under test to be loaded as in the actual back-to-back MMC system. Simulations and experimental measurements are given to validate the proposed method.

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method.

This work studies how to estimate the mean-field density of large-scale systems in a distributed manner. Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density (especially its gradient) is usually used in feedback control design. In the first part, we formulate the density estimation problem as a filtering problem of the associated mean-field partial differential equation (PDE), for which we employ kernel density estimation (KDE) to construct noisy observations and use filtering theory of PDE systems to design an optimal (centralized) density filter. It turns out that the covariance operator of observation noise depends on the unknown density. Hence, we use approximations for the covariance operator to obtain a suboptimal density filter, and prove that both the density estimates and their gradient are convergent and remain close to the optimal one using the notion of input-to-state stability (ISS). In the second part, we continue to study how to decentralize the density filter such that each agent can estimate the mean-field density based on only its own position and local information exchange with neighbors. We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the centralized suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter., arXiv admin note: text overlap with arXiv:2009.05366

With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

Fundamental harmonic analysis (FHA) model is widely used for wireless power transfer (WPT) due to its physical insight and compact form (for example, 8RL/2) of diode rectifier equivalent resistance. Because this compact form is valid for continuous conduction mode (CCM), and resonant tank parameters such as inductance and capacitance are independent of equivalent resistance when designing these parameters based on double side LCC (DS-LCC) FHA model, the selected optimal parameters may not satisfy the CCM prerequisite. This will make actual system characteristics deviate from the FHA model predictions, especially the output voltage and power. Therefore, the original optimal design is invalid and designer needs to execute a circular design, namely designing resonant tank parameters based on FHA model, then judging or simulating whether satisfy CCM operation, redesigning parameters if not satisfy CCM. In order to validate the original optimal design even if CCM isn`t established and make design process straightforward, an effective remedy which is adding a series LC filter in secondary side is proposed, and a computation method about the required minimum filter parameter is provided. Effectiveness of proposed remedy is verified by simulation and experiment. Besides, computation method is packaged as a program to help researchers design rapidly.

This paper proposes an adaptive self-tuned current controller design for an LCL-filtered LC-tuned single-phase shunt hybrid active power filter (HAPF), which offers adaptability and flexibility of the control system to filter parameters changes. System modelling and online identification of parameters are presented. Then, a systematic self-tuned controller (STC) design is proposed to calculate the controller parameters and obtain the final control law adaptively. The exact HAPF parameters design is described and the overall closed-loop stability evaluation with considering digital control delay is discussed in this paper. To confirm the theoretical achievements, simulations and experimental tests on a real prototype system are performed. The results are in good accordance and demonstrate the effectiveness and superior performance of the suggested control technique. Furthermore, a comprehensive comparison between the proposed control technique and other controllers is provided to show the superiority of the proposed system from different aspects.

Different from typical circuit breakers, breakers used to switch AC filters in convertor stations have to operate frequently according to the real-time operation mode and daily load changes. Therefore, the performance of filter breakers decreases very quickly, which leads to frequent accidents. Both preinsertion resistors and controlled switching devices can suppress switching transients. In this paper, a comparative analysis of whether a controlled switching device and preinsertion resistor should be adopted is carried out using PSCAD. The transient is calculated as the combined result of the switching angle and preinsertion resistor value, and the optimal configurations are proposed. After determining the resistor value and whether a controlled switching device should be included, the reference voltage of the arrester should be reevaluated.

Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or environmental states) is utilized by the human driver to guide their behavior throughout the whole merging process. This paper provides quantitative analysis and evaluation of the merging behavior at highway on-ramps with congested traffic in a volume of time and space. Two types of social interaction scenarios are considered based on the social preferences of surrounding vehicles: courteous and rude. The significant levels of environmental states for characterizing the interactive merging process are empirically analyzed based on the real-world INTERACTION dataset. Experimental results reveal two fundamental mechanisms in the merging process: 1) Human drivers select different states to make sequential decisions at different moments of task execution, and 2) the social preference of surrounding vehicles can impact variable selection for making decisions. It implies that efficient decision-making design should filter out irrelevant information while considering social preference to achieve comparable human-level performance. These essential findings shed light on developing new decision-making approaches for AVs., Comment: 12 pages, 8 figures

The three-phase dual active bridge (3p-DAB) is a popular topology suitable for high power DC-DC isolated conversion. This converter features bi-directional power flow, zero-voltage switching capability, and reduced transformer and filter volume. These features meet the requirements of several applications, such as solid-state transformers. But when operating under light load the resulting converter efficiency is highly degraded by the loss of soft-switching capability. This paper introduces the use of a Wye-extended-Delta (Y-e) transformer winding in the 3p-DAB converter, which increases the efficiency under light load, keeping the simplicity of phase-shift control. The framework developed allows a complete analysis of the converter, including the determination of the power and rms current, zero-voltage switching boundaries and the impact of the selected phase-shifting angle in the transformer design. Additionally, this paper presents a negative sequence modulation scheme, allowing maximum efficiency under light loads to be achieved for both direct and reverse power flows. The performance of the 3p-DAB with Y-e transformers is compared with previously reported winding options for 3p-DAB. The experimental results show a converter efficiency increase, up to 10\% for direct power flow and up to 9\% for reverse power flow, when operating at light load (10\% of nominal power).

Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.

The design of electromagnetic interference (EMI) filter with smaller size and lightweight for a high efficiency and high power density 100 kW SiC five-level T-type (5LT2) photovoltaic (PV) inverter becomes more challenging due to the increased EMI noise resulted by high switching frequency, fast switching transients, and the removal of LCL filter. This paper proposes an unbalanced Wheatstone bridge and a two-stage notch filter to attenuate the increased common mode (CM) and differential mode (DM) noise respectively. The conventional Wheatstone bridge will suffer size and weight penalty for 100 kW PV converter due to the high parasitic capacitance of the 100 kW PV panel. An unbalanced Wheatstone bridge with a one-stage LC filter is therefore developed to address this issue and reduce the total CM inductance. The proposed two-stage notch filter is designed to achieve high attenuation, solve its parameter variation issue and help reduce the size and weight of DM filter. The total weight of the designed EMI filter for this 100 kW PV inverter is 3.54 kg. The measured conducted EMI spectrum of the 100 kW SiC PV inverter with and without proposed EMI filter are provided to validate the effectiveness of the EMI filter design.

In this paper, a novel zero-voltage switching (ZVS) full-bridge converter is proposed to overcome the disadvantages of traditional phase-shifted full-bridge (PSFB) converter. In the proposed converter, a half-bridge (HB) converter with center-tap rectifier (CTR) is integrated into the traditional PSFB converter by sharing the lagging-leg switches and output filter. The HB converter operates with full duty cycle to ensure continuous energy transmission and the output voltage is regulated by adjusting the phase-shifted time of FB converter. The proposed converter has many advantages such as reduced filter requirement, wide ZVS range and low primary circulating current. In addition, the rectifier in the proposed converter is composed of a full-bridge rectifier (FBR) section and a CTR section. This feature makes the proposed converter well appropriate for the medium output voltage applications. Key operation principle and characteristics are fully discussed in this paper. The effectiveness of proposed converter is verified using a 1.2-kW prototype circuit with 280 V input voltage and 60-120 V output voltage.

A vast amount of activation values of DNNs are zeros due to ReLU (Rectified Linear Unit), which is one of the most common activation functions used in modern neural networks. Since ReLU outputs zero for all negative inputs, the inputs to ReLU do not need to be determined exactly as long as they are negative. However, many accelerators usually do not consider such aspects of DNNs, losing a huge amount of opportunities for speedups and energy savings. To exploit such opportunities, we propose early negative detection (END), a computation pruning technique that detects the negative results at an early stage. The key to the early negative detection is the adoption of inverted twos complement representation for filter parameters. This ensures that as soon as the intermediate results become negative, the final results are guaranteed to be negative. Upon detection, the remaining computation can be skipped and the following ReLU output can be simply set to zero. We also propose a DNN accelerator architecture (ComPreEND) that takes advantage of such skipping. ComPreEND with END significantly improves both the energy efficiency and the performance according to the evaluation.

We consider a robust state space filtering problem in the case that the transition probability density is unknown and possibly degenerate. The resulting robust filter has a Kalman-like structure and solves a minimax game: the nature selects the least favorable model in a prescribed ambiguity set which also contains non-Gaussian probability densities, while the other player designs the optimum filter for the least favorable model. It turns out that the resulting robust filter is characterized by a Riccati-like iteration evolving on the cone of the positive semidefinite matrices. Moreover, we study the convergence of such iteration in the case that the nominal model is with constant parameters on the basis of the contraction analysis in the same spirit of Bougerol. Finally, some numerical examples show that the proposed filter outperforms the standard Kalman filter.

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

This work presents three single-phase isolated ac-ac converters based on the dual active bridge (DAB) converter and interleaved transformers. The structures allow obtaining filter elements with reduced dimensions, as well as bidirectional power flow and high efficiency over a wide load range. Multilevel voltage waveforms exist in the input and output sides of the symmetrical converters, resulting in reduced harmonic content. The possible arrangements include a symmetrical half-bridge converter (SHBC), a symmetrical full-bridge converter (SFBC), and a symmetrical interleaved full-bridge converter (SIFBC). The introduced topologies can be regarded as prominent solutions for applications involving solid-state transformers (SSTs), dynamic voltage restorers (DVRs), and uninterruptible power supplies (UPSs). A thorough mathematical analysis of the SHBC is carried out and experimental results are presented to investigate the converter performance in steady-state and transient conditions.

This study investigates the long-term instability problem of periodic feedback loop-based control schemes such as repetitive control (RC), periodic adaptive disturbance observer (PADOB), and iterative learning control (ILC). The long-term instability problem is one wherein a closed-loop system becomes unstable because a tracking error diverges with the increase in the number of iterations. A novel robust periodic adaptive disturbance observer is proposed in this paper to guarantee the long-term stability of the closed-loop system. The overall system eventually becomes stable because the proposed control scheme using an IIR filter with an optimized lead time can stabilize the error propagation in the iteration domain. Compared to other methods such as zero-phase and IIR filters, the proposed control scheme is very practical because it allows selecting the cut-off frequency of the filter intuitively to stabilize the control system. The optimized lead time in the IIR filter is calculated considering the phase response of the IIR filter; it can improve adaptation speed using a high adaptation gain of the control system. Various simulation and experimental tests based on high-load motion systems that require high-precision positioning are performed to verify the theoretical analysis of the long-term instability problem and the effectiveness of the proposed control scheme.

This paper presents a systematic and an efficient synthesis technique for designing single-band and dual-band waveguide filters with transmission zeros. The proposed EM-based synthesis technique directly results in a filter design with an RF performance that is in excellent agreement with the ideal filter performance, thus significantly reducing the post fine optimization effort. Furthermore, the proposed technique also reduces the EM simulation resources required for the filter synthesis. The synthesis technique is lucidly explained by designing a symmetrical 6th order waveguide filter with 4 transmission zeros, a 9th order filter with 3 asymmetric transmission zeros and a 6th order dual-band filter. To the best of author’s knowledge, this is the first filter synthesis technique, which can be adopted to design waveguide filters including transmission zeros with minimum or no post fine optimization.

We show theoretically and experimentally how a flat-top second-order response can be achieved with a self-coupled single add-drop ring resonator based on two couplers with different splitting ratios. The resulting device is a 1x1 filter, reflecting light back in the input waveguide at resonating wavelengths in the passbands, and transmitting light in the output waveguide at all other non-resonating wavelengths. Different implementations of the filter have been designed and fabricated on a micron-scale silicon photonics platform. They are based on compact Euler bends - either U-bends or L-bends - and Multi-Mode Interferometers as splitters for the ring resonators. Different finesse values have been achieved by using either 50:50 MMIs in conjunction with 85:15 MMIs or 85:15 MMIs in conjunction with 95:05 double MMIs. Unlike ordinary lowest order directional couplers, the MMIs couple most of the power in the cross-port which make them particularly suitable for the topology of the self-coupled ring, which would otherwise require a waveguide crossing. Experimental results are presented, showing good agreement with simulations. The proposed devices can find applications as wavelength-selective reflectors for relatively broad-band lasers or used as 2x2 add-drop filters when two exact replicas of the device are placed on the arms of a Mach-Zehnder interferometer., 9 pages, 17 references, 12 figures

To enable chemical speciation, monitoring networks collect particulate matter (PM) on different filter media, each subjected to one or more analytical techniques to quantify PM composition present in the atmosphere. In this work, we propose an alternate approach that uses one filter type (teflon or polytetrafluoroethylene, PTFE, commonly used for aerosol sampling) and one analytical method, Fourier transform infrared (FT-IR) spectroscopy to measure almost all of the major constituents in the aerosol. In the proposed method, measurements using the typical multi-filter, multi-analytical techniques are retained at a limited number of sites and used as calibration standards. At all remaining sites, only sampling on PTFE and analysis by FT-IR is performed. This method takes advantage of the sensitivity of the mid-IR domain to various organic and inorganic functional groups and offers a fast and inexpensive way of exploring sample composition. As a proof of concept, multiple years of samples collected within the Interagency Monitoring of PROtected Visual Environment network (IMPROVE) are explored with the aim of retaining high quality predictions for a broad range of atmospheric compounds including mass, organic (OC), elemental (EC), and total (TC) carbon, sulfate, nitrate, and crustal elements. Findings suggest that models based on only 21 sites, covering spatial and seasonal trends in atmospheric composition, are stable over a 3 year period within the IMPROVE network with acceptable prediction accuracy (R2 > 0.9, median bias less than 3 %) for most constituents. The major limitation is measuring nitrate as it is known to volatilize off of PTFE filters. Incorporating additional sites at low cost, partially replacing existing, more time- and cost-intensive techniques, or using the FT-IR data for quality control or substitute for missing data, are among the potential benefits of the one-filter, one-method approach.

Introduction The aim of this study was to evaluate mean glandular dose (MGD) and image quality in low energy imaging from contrast-enhanced spectral mammography (CESM) when using double-layer filtration. Methodology A dedicated phantom was used to quantitatively estimate the MGD and image quality. The target slab of the phantom consisted of three iodine coins having a concentration of 1.0 mgI/cm3, 2.0 mgI/cm3, 4.0 mgI/cm3, a 100% adipose equivalent coin and a 100% glandular equivalent coin. The phantom was exposed using a semiautomated function at 28 k, 30 kV and 32 kV. MGD. Contrast to noise ratio (CNR) and figure of merit (FOM) were estimated for Mo/Rh, Mo/Rh + Cu, Mo/Rh + Al and Mo/Rh + Cd combinations using three breast equivalent compositions. Results MGD was reduced up to a maximum of 1.03 mGy from 1.17 mGy for 100% adipose tissue. 1.18 mGy from 1.34 mGy for 50% glandular tissue and 1.39 mGy from 1.72 mGy for the 100% glandular phantom when using double-layer filtration. All of the above-mentioned results were obtained for the 50 mm phantom using 32 kV. CNR and FOM values were not significantly reduced with a double-layer filter when compared to a single-layer filter. Conclusion The present study concluded that Mo/Rh + Cu is the best combination to reduce the MGD significantly when compared to Mo/Rh + Al or Mo/Rh + Cd. Mo/Rh + Cu also achieved optimal image quality when compared to the Mo/Rh single filter combination. Implications of practice The use of a double-layer filter in low energy imaging of CESM results in a significant reduction in MGD without degrading the quality of the image.

Solid-state transformers (SSTs) have emerged as an interesting solution for replacing the traditional low-frequency transformers in power distribution systems owing to the capacity of performing ancillary services and providing the integration of multiple energy sources. This work proposes a four-port, single-phase, multi-directional ac-ac converter for SST applications. Considering the integration of conversion stages, it is possible to achieve higher efficiency and lower component count when compared with the typical three-stage topologies reported in the literature. The introduced structure is based on the interleaving concept associated with the well-known dual active bridge (DAB) converter. The medium-voltage (MV) side employs a multilevel neutral-point-clamped (NPC) converter. The voltages across the ac ports present five levels, whereas the operating frequency of the filter elements is twice the switching frequency. Experimental results are presented and thoroughly discussed to validate the theoretical assumptions.

Most DC power distribution architectures are organized as a typical cascaded converter system that is composed of source and load converters, and may experience a system oscillation due to the negative impedance effect of constant power loads. The load-side virtual impedance method is a possible solution to such stability problem, which however makes the dynamic response of the load converter slow, and thus increases the requirement of the filter capacitance. In this paper, an auxiliary circuit that is connected to the output port of the load converter is proposed to enhance the transient performance of the power converter system, while the system can be stabilized by the virtual impedance method. The control and parameter design of the proposed auxiliary circuit is introduced. The capacitance requirement of the system for an expected transient deviation of the output voltage is significantly reduced by using the proposed auxiliary circuit. An example design is given to verify the feasibility of the proposed method with simulation analysis and experimental results.

In this article, the fault detection (FD) filter design problem is addressed for discrete-time memristive neural networks with time delays. When constructing the system model, an event-triggered communication mechanism is investigated to reduce the communication burden and a fault weighting matrix function is adopted to improve the accuracy of the FD filter. Then, based on the Lyapunov functional theory, an augmented Lyapunov functional is constructed. By utilizing the summation inequality approach and the improved reciprocally convex combination method, an FD filter that guarantees the asymptotic stability and the prescribed H∞ performance level of the residual system is designed. Finally, numerical simulations are provided to illustrate the effectiveness of the presented results.

The paper deals with the performance checking of an optimally designed LCL-filter connected along the grid-side converter (GSC) in a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) in attenuating harmonics. A repetitive approach is required for designing the parameters of the LCL-filter. The reasons being the explicitness of design parameters and design requirements of the proposed filter like IEEE-519 Std. for harmonic current attenuation, maximum voltage difference allowed in between two sides of the filter to limit switching losses, and limit of reactive power compensation. The proposed filter is compared with the conventional LCL-filter. Based on the proposed method, a minimal inductance and optimal capacitance-based filter is obtained in this paper which is validated using a 2.2 kW DFIG based WECS. Comparative stability analysis of DFIG system with and without proposed filter is performed using small signal stability analysis. The system under study is simulated using MATLAB/Simulink and OPAL-RT 4510, simultaneously. Simulation results validates the effective use of proposed filter design method into DFIG system in terms of harmonic attenuation, total harmonic distortion and reactive power compensation.

When suffering the grid voltage distortion, the phase-locked loop (PLL) should equip with the fast response filter to precisely extract the fundamental voltage information for proper grid integration. To shorten the time delay induced by filters, this paper proposes a new three-samples filter (TSF) to fast eliminate the harmonics of sampled grid voltage. Theoretically, it can filter out arbitrary individual harmonic with short time delay which could be as small as only two sampling periods. Moreover, TSF can reject a group of harmonics simultaneously within the specifically set time interval. The implementation issues of TSF as in-loop filter or prefilter for both three- and single-phase applications are demonstrated in SRF-PLL. Simulation and experimental results verified its advanced performance.

The search for new approaches for output feedback control of uncertain nonlinear systems with unknown input and output hysteresis is an interesting problem in control theory. One challenging issue obstructs the development of output feedback control design is that both the genuine system input and output are unknown signals and unable to be employed in the observer and controller design. To obviate such obstruction, a new control design framework for adaptively compensating the input and output hysteresis is proposed with two adaptive hysteresis inverse operators, which are also utilized to develop a novel adaptive hysteresis operator-based filter. It is proved that with the proposed control scheme, all the closed-loop signals are bounded and the tracking error ultimately converges to a tunable residual around zero. Simulation studies demonstrate the methods developed.

High-performance multicast-enabled packet forwarding engines (PFEs), as an essential component of high-end switches, use a polynomial-time membership query algorithm to determine which port(s) the data packet should be forwarded. The currently widely used query algorithm is Bloom Filter (BF), which has been proven to have many fatal flaws. Another error-free membership query algorithm includes Scalar-pair Vectors Routing Forwarding (SVRF), Fractional- N Scalar-pair Vectors Routing Forwarding (Frac- N SVRF), and the Per-Port Prime Filter Array (P3FA) also have some shortcomings in space and time efficiencies. In this paper, we proposed a hybrid strategy: Divaricate Heterogeneous SVRF (DH-SVRF) scheme, which based on the P3FA and Frac- N SVRF, which randomly divides all member ships into N groups, and each group has the same structure and is independent of each other to obtain higher time efficiency and space utilization. Finally, we also discussed the selection of the optimal egress-diversity threshold. Through mathematical modeling and simulation, we validate that the proposed DH-SVRF scheme is superior to the SVRF/Frac -N SVRF and traditional BF in terms of scalability, space utilization, and time efficiency in specific conditions such as appropriate egress-diversity thresholds.

In this article, a common-mode noise filter with a wideband suppressing and dual-band high absorbing common mode filter, which is called absorptive common-mode filter, is proposed. The filter is constructed by a reflective-type common-mode filter, a phase matching, and an absorber. It is composed of three layers with 14.98 mm × 9.585 mm. The broadband common-mode noise suppression can be achieved by positioning multiple transmission zeros. By using dual-band phase matching and an absorber, the dual-band absorption can be realized. The frequencies are in the range of 2.4 to 2.5 GHz and 5.17 to 5.33 GHz, respectively. The positioning of transmission zeros and absorption points at those frequencies leads to a high absorption efficiency. The simulation result shows that the common-mode suppression, which is below −10 dB, is from 2 to 11 GHz. The fractional bandwidth is 138%. The differential-mode signal keeps integrity in the range from DC to 12 GHz. The absorption efficiencies in the two frequency sections are 97% and 96%, respectively. The experiment results show only slight differences from the simulation results.

The nonlinear filtering problem is a hot spot in robot navigation research. As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy, which becomes the purpose of this paper. This paper formulates the processing of trajectory tracking measurement noise problem as a Kalman filtering procedure and the measurement noise covariance matrix in controller, is jointly estimated based on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced. Then we proposed an adaptive fuzzy and backpropagation neural network controller based on PGAFVS (AFNPGA-VS) that can improve the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. The simulation results show that the proposed algorithm outperforms the state-of-the-art methods.

This brief considers Kalman filter for linear systems with unknown structural parameters. We design a Bayesian parameter identification algorithm based on maximum likelihood (ML) criterion and expectation maximization (EM). Under the identification of structural parameter, we perform the Kalman filter to estimate the states. The Kalman filter and the parameter identification algorithm are interactive to estimate the states and the structural parameters. More specifically, first, we use EM algorithm together with Kalman smoother to estimate the structural parameters. Then, on the basis of results provided by the first step, we employ the classical Kalman filter to predict and update the states, which formulates the final proposed Kalman filter. Performance analysis and simulation results verify the presented filter.

Experiments are conducted to quantify leakage through a circular geomembrane (GMB) hole using two subgrades (underliners) not meeting modern filter criteria. Test results show that a 1.9- and 5.0-fold increase in hole diameter causes a 1.8- and 6.2-fold increase in leakage, respectively, whereas a 2-fold increase in GMB thickness results in an approximate 10% decrease in leakage. For a constant hydrostatic effective stress, a 2- and 3-fold increase in water head above the GMB leads to a 1.8- and 2.5-fold increase in leakage, respectively. Higher leakage arising from the intrusion of subgrade materials through the hole is quantified. A numerical model, considering the localized concentration of head loss around the hole and the consequent heterogeneity in hydraulic conductivity due to the seepage force, is presented. The percent of head loss within the hole and from the hole to any position above the GMB is independent of loading conditions and only influenced by the GMB thickness and hole diameter. Finally, empirical equations for predicting leakage through a circular GMB hole in tailings storage applications are proposed. Compared with published equations that do not consider consolidation induced heterogeneity in hydraulic conductivity, the proposed equation (which does consider these factors) gives a more accurate prediction for the cases examined.

In this paper, a new lumped-element Class-F3/E and transmission-line (TL) Class-F3/E2 power amplifiers are presented. It is shown that employing the series output filter (inverse Class-E) combined with a third harmonic control circuit (Class-F) leads to the new Class-F/E power amplifier. Compared with other switch-mode Class-E, Class-F/E, and Class-E/F power amplifiers, the proposed circuit has a higher maximum operating frequency (fmax), higher output power capability (Cp), and lower peak drain voltage (Vmax). The capability of the Class-F/E mode with a series filter has been demonstrated by two design examples, one with lumped elements at 100 MHz and the other with transmission lines at 2.21 GHz. Two test boards of power amplifiers were measured, and high-performance results have been obtained. The simulated and measured drain efficiency (DE) of 86.4/85%, output power of 41/40.6 dBm, and gain of 15.9/15.6 dB for the lumped-element Class-F3/E power amplifier have been obtained. On the other hand, the simulated/measured DE of 83/81%, power added efficiency of 79/78%, and output power of about 40/39.8 dBm for Class-F3/E2 power amplifier at an input power of 27 dBm were obtained.

This brief investigates the quantized consensus in discrete-time multi-agent systems over directed networks with limited bandwidths of communication channels and limited capacities of physical memories. A dynamic consensus protocol based on the first-order recursive channel filter is proposed. Several conditions for preserving the asymptotic consensus are obtained, which are dependent on the filter parameters, the sampling period and the topologies of the communication networks. Then, by describing the closed-loop multi-agent system via a higher-order state-space model, the effects of the probabilistic quantization are analyzed. We prove that the quantized consensus is reached under the proposed protocol with quantized communications. Moreover, the distributed PI based protocol is discussed as a special case of the proposed dynamic protocol.

This paper presents an analog baseband (ABB) circuit in a 0.13 μm SiGe technology for transmitter leakage cancellation and close-in blocker suppressions in fully duplex (FD) frequency-modulated continuous-wave (FMCW) radar. This ABB comprises a programmable gain amplifier (PGA) and a cascaded LPF/Notch hybrid, which incorporates a closed-loop (CL) reconfigurable low-pass filter (LPF) and an open-loop (OL) tunable notch filter. The adopted key topologies include active-RC bi-quads and Gm-C ones. In an FD FMCW transceiver, Tx leakage and close-in blockers are difficult to be eliminated in the RF domain, especially when leakage/blockers are very close to desired signals or even in-band in the frequency domain. This LPF/notch hybrid is proposed to solve this issue. The LPF and PGA provide bandwidth (BW)/gain programmability, while the Gm-C bi-quad provides adaptable center frequency for a notch filter. With this adaption, the notch filter could be adjusted to match the leakage/blocker offset frequency. Thus, digitally discrete programmability and analog continuous tuning capability are combined in this solution for improving the overall front-end interference robustness without de-sensitizing the Rx. Furthermore, the order of LPF/notch hybrid is programmable from 2 to 10 with a step of 2 for different rejection levels of interferences. The measured chip achieves a -3dB bandwidth of 6 21 MHz with 4-bit digital control and 1 MHz/step programmability, and a voltage gain of 0 70 dB with 9-bit digital control (3-bit from pre-amplifier, and 6-bit from PGA with 1 dB/step). With the condition of 15 dB gain, output P-1dB is 11.8 dBm@3MHz, and the output IP3 is 20.8 dBm@3MHz.

This brief proposes a novel perspective for solving single input and multiple output (SIMO) nonlinear systems with multi-cost function adaptive algorithm. Based on Euler polynomials, the Euler filter with finite memory is proposed to construct a filter bank, which realizes the accurate identification of high-dimensional nonlinear systems. To improve the algorithm performance, a multi-cost function is proposed. Moreover, the mean-square stability and steady-state performance of the proposed method are rigorously analyzed. The typical FitzHugh-Nagumo (FHN) system, which fiercely exhibits chaotic dynamics, is considered for justifying the performance of this new algorithm. Numerical results highlight the solution accuracy via comparison with existing approaches.

In this paper, a new 5-level inverter is developed by inserting a switched-capacitor (SC) unit into the traditional 3-level neutral-point-clamped (NPC) inverter phase-leg. The SC unit consists of two capacitors and one bidirectional switch, all of which withstand a quarter of the dc input voltage. Whilst increasing the output levels, the performance is also improved in terms of power loss, common-mode voltage, switching stress dv/dt and output filter. Compared to other 5-level inverters like the conventional NPC and active NPC 5-level topologies, the new solution not only reduces the number of components and simplifies the design, but also has the advantage of self-balanced capacitor voltages. The analysis, simulation and experiment indicate that the proposed inverter is suitable for a wide range of applications like renewable source grid-connected interfaces and motor drivers. Simulation and experimental results of grid-connected operation verify that the new inverter is capable of providing both active and reactive power to the grid. Its excellent performance is also experimentally evaluated by a 1.2 kW prototype and the measured efficiency is above 97% for a wide range of load.

In this paper, a tuneable low-power active bandpass filter (BPF) is designed for channel selection in TSMC’s 65-nm CMOS technology. The proposed filter consists of two identical BPFs which are based on a gm-C topology, and can be controlled with a constant quality factor. The center frequency of this design can be changed to acquire a specific passband channel from 60 kHz to 2.5 MHz by adjusting the bias voltage in discrete increments, with an almost constant quality factor equal to 7.2. To validate the design, the integrated circuit and a test board are fabricated. Five passband channels are measured from the proposed design with a low power consumption equal to 68 ‘W. The measured dynamic range is equal to 51.6 dB when the total harmonic distortion (THD) is equal to 1%. Hence, the proposed controllable channel selection filter shows an excellent performance as compared to existing work and offers a great potential for use applications with heterogeneous sensor nodes.

In this paper, by combining substrate integrated waveguide (SIW) and microstrip technologies, a novel dual-band filter is proposed to achieve a quasi-elliptic response for both passbands. The dual-mode characteristics of the SIW cavities, namely TE101 and TE201 modes, are used to create two passbands. Different with the conventional inductive windows, two pairs of uniform impedance resonators (UIRs) with electric mutual coupling, which are etched between the two rectangular SIW cavities, are used to couple the TE101 and TE201 modes, respectively. Good merits are achieved in this way, including: 1) Independent coupling paths are created for TE101 and TE201 modes, which would reduce the complexity of numerical optimization; 2) Since the electric couplings are generated between each pair of the UIRs, it would be easy to realize a cross coupling by only constructing inductive windows between the SIW cavities. A filter prototype with central frequencies operated at 8 GHz and 10 GHz is then designed and fabricated for validation, which shows a compact size and high selectivity with controllable transmission zeros.

In this paper, an active electromagnetic interference (EMI) filter is designed to reduce the DC side common mode (CM) EMI in motor drive system. Different from the traditional passive EMI filter which is added in the main circuit, the designed AEF in this paper is added in the grounding circuit which is connected between motor frame and the ground and it can be decoupled from the main circuit. By properly setting the components value, the DC side CM EMI can be suppressed while maintaining a low motor frame voltage. The structure of the AEF is simple and the volume of the AEF can be kept small. The operation principle of the AEF is introduced first and the performance of this AEF is evaluated through simulation. Experiment results verified the effect of this AEF that it can suppress the DC side CM EMI and the motor frame voltage can be kept low.

This paper deals with fault detection problem of delayed semi-Markov jump systems with disturbances. More specifically, the asynchronous phenomenon is taken into account between modes of linear and nonlinear dynamics. Accordingly, an efficient solution of fault detection is developed based on asynchronous mode-dependent fault filter with conditional probability. On the basis of constructing appropriate Lyapunov-Krasovskii functions, sufficient criteria are established such that H ∞ performance can be satisfied for filter error with residual signal. Furthermore, the devised filter gains are computed in terms of convex optimization. Eventually, the applicability of our proposed design is verified by an illustrative example.