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Applying the recycled power battery to automatic guided vehicles can greatly reduce system cost and prolong the life cycle of the battery. However, the consistency of recycled power batteries is poor, and the imbalance of battery voltage will occur during the charging or discharging process. Therefore, a double-switch single-transformer integrated equalizer is proposed to balance automatic guided vehicles in both charging and discharging operations. The equalizer's multiwinding transformer shares the dc–dc converter's inductor, and the secondary side generates current by utilizing the current ripple on the primary windings. The battery string is balanced by the multiwinding transformer, without extra switches, when the circuit works in charging and discharging mode. A prototype for four battery cells is built to verify the proposed equalizer and its dynamic balance conditions, and a comparison with existing equalizers is presented. The results show that the system in both modes can realize the automatic balance without affecting the function of the dc–dc converter. The proposed circuit requires fewer components than the existing bidirectional integrated equalizer. [ABSTRACT FROM AUTHOR]

Modular multilevel converter (MMC) is the state-of-the-art converter topology in HVdc transmission applications. The reliability and lifetime of MMCs are strongly dependent on the health of the internal power semiconductors. The operating loss of power semiconductors, which is one of the dominant factors affecting component aging, is not uniformly distributed between the top and bottom switches in the same submodule (SM) due to the dc bias in the arm current. Such loss imbalance leads to differences in lifetime of semiconductors and reduces the reliability of SM. Especially under inverter conditions, the bottom insulated gate bipolar transistor (IGBT) in SM is subjected to a severe thermal stress and has a relatively high failure rate for the thermal vulnerability of IGBT. To extend the lifetime and improve the reliability, this article proposes a component-level loss balancing method. It is achieved by insertion index modification based on a feed-forward gain controller. The proposed method can improve the loss distribution among the top and bottom semiconductors without side effects on ac and dc outputs. Its effectiveness and feasibility are validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this article, the finite-time (FT) deterministic learning control for the hypersonic flight vehicle (HFV) dynamics with model uncertainty is investigated. The design is divided into an offline training phase and an online control phase. First, in the offline training, the radial-basis-function neural networks (RBF NNs) are set along the periodic signals to guarantee the partial PE condition. Meanwhile, the offline FT composite learning laws are constructed driven by the system tracking and learning performance index. Embedding the FT composite learning in the FT command filtered control framework, the FT convergences of the system tracking and learning are guaranteed simultaneously. Moreover, the near-optimal learning knowledge is stored. In the next online process, the stored NNs weights are directly used in the online tracking controller without repeatedly updating the weights. Simulation on HFV dynamics shows that the offline FT learning control can achieve better learning and tracking performance, while recalling the stored knowledge online not only guarantees the control performance but also reduces the computational load. [ABSTRACT FROM AUTHOR]

In the hybrid energy storage system (HESS), the voltages between the battery cell and the supercapacitor cell are different, increasing the circuit and control complexity of the balance system. Therefore, a bidirectional integrated equalizer based on the Sepic–Zeta converter is proposed for the HESS, which realizes both battery string and supercapacitor string balance during the energy exchange. The inductors in the Sepic–Zeta converter are used as the primary side of the balanced multiwinding transformer. The Sepic–Zeta converter generates the current ripples to drive the multiwinding transformer. The secondary side generates currents to balance the HESS, and hence, the equalizer itself is essentially switchless. Therefore, the proposed circuit can reduce the complexity of the HESS. Experimental and comparative results show that the proposed equalizer achieves the balance of the HESS. [ABSTRACT FROM AUTHOR]

Due to the complexity of driving conditions and the increase of the driving range of electric two-wheelers and three-wheelers vehicles, the power battery string is in a long-period unbalanced discharge state, which affects the power output and even causes certain safety risks. A compact-size multi-winding transformer-based discharge equalizer is proposed to solve these problems and minimize the impact on the system volume by sharing the primary inductor with a boost discharger. When the boost converter is powering a load, the multi-winding transformer equalizer is driven by utilizing the square wave voltage generated on the inductor in the boost converter and produces multiple identical voltage levels to achieve voltage balance. Thus, the automatic balance can be achieved with only one winding and one diode for each module. An experimental prototype containing four batteries was established to verify the feasibility of the proposed method. Moreover, a quantitative comparison shows the proposed method obtain a more compact-size equalizer because of no need for a separate transformer, compared with the traditional equalization method based on the transformer. [ABSTRACT FROM AUTHOR]

In this article, adaptive state feedback stabilization is addressed for a class of delayed uncertain systems with external disturbance, where the time delays and the bound parameters of the delayed states and disturbance are assumed to be unknown. By introducing a new Lyapunov–Krasovskii functional, we present a memoryless control strategy to stabilize nonlinear time-delay systems through newly defined control gain function. The main contribution of this article lies in the construction of control gain function. Multiple gain functions can be revealed in a function set. The asymptotic stability of the closed delayed nonlinear system is ensured. Simulation examples are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes a serial-parallel estimation model (SPEM)-based sliding mode control (SMC) of MEMS gyroscope. For the system nonlinearity, the linear-in-parameterized dynamics are formulated and the updating law of the parameter vector is given. For the system uncertainty, the radial basis function (RBF) neural network (NN) is utilized. To improve the approximation accuracy of the compound nonlinearity, the updating laws of the parameter vector and RBF NN weight are constructed by the tracking error and the filtered modeling error derived from SPEM. Furthermore, the fast terminal (FT) SMC is employed to achieve finite-time convergence. The simulation results show that the proposed controller obtains higher tracking accuracy and faster convergence, while the compound nonlinearity approximation is with higher precision. [ABSTRACT FROM AUTHOR]

The traditional balance circuit is employed to balance the retired battery string. It will occupy some space for the extra switches, inductors, or/and capacitors of the energy storage system. This letter is trying to share the inductor of the dc–dc converter with the balance system by utilizing the current ripple on the inductor of the multiwinding transformer. The balance circuit does not require additional switches and control logic. The results of dynamic charging conditions show that the circuit is appropriate for balancing the battery string, and the system efficiency with the proposed approach reaches 94.1% at rated 28-W input power. The balance system only increases one diode and one inductor for each cell, which reduces the cost by 60% and 66% separately compared with the traditional resistance equalizer and transformer-based equalizer. [ABSTRACT FROM AUTHOR]

Sudden increases in the dc-link voltage, caused by ac grid side voltage drops, can lead to potential damage in the equipment. Therefore, dc choppers are widely used in offshore wind power links to absorb the excess energy and keep the voltage within safe margins. This article investigates the existing chopper topologies and summarizes their advantages and disadvantages. On this basis, a novel dc chopper topology with both centralized and distributed resistances is proposed. It is a combination of two mainstream chopper topologies and gives the flexibility to tradeoff among the pros and cons of them. In this way, the proposed topology achieves great electromagnetic interference performance, precise power control, and very small system disturbance. In addition, the maximum power dissipation of the distributed resistance is reduced by 75%. The switching frequency and the loss of insulated gate bipolar transistors (IGBTs) are also decreased. Finally, the claimed attributes of the proposed topology are verified by test and simulation. [ABSTRACT FROM AUTHOR]

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. To overcome the nonminimum phase behavior, the output redefinition is employed and the attitude subsystem is transformed to the internal subsystem and the input–output subsystem. For the input–output subsystem, the adaptive neural control works together with the robust control to follow the reference command of pitch angle derived from the internal subsystem. Furthermore, the sliding mode control is constructed in a similar way. For the update of the neural weights, the composite learning is constructed using the prediction error. The stability of the closed-loop system is analyzed via the Lyapunov approach and the ultimately uniform boundedness of the tracking errors can be guaranteed. The effectiveness of the methodology is illustrated by the simulation results. [ABSTRACT FROM AUTHOR]

Monocular 3D object detection has the merit of low cost and can be served as an auxiliary module for autonomous driving system, becoming a growing concern in recent years. In this paper, we present a monocular 3D object detection method with feature enhancement networks, which we call MonoFENet. Specifically, with the estimated disparity from the input monocular image, the features of both the 2D and 3D streams can be enhanced and utilized for accurate 3D localization. For the 2D stream, the input image is used to generate 2D region proposals as well as to extract appearance features. For the 3D stream, the estimated disparity is transformed into 3D dense point cloud, which is then enhanced by the associated front view maps. With the RoI Mean Pooling layer, 3D geometric features of RoI point clouds are further enhanced by the proposed point feature enhancement (PointFE) network. The region-wise features of image and point cloud are fused for the final 2D and 3D bounding boxes regression. The experimental results on the KITTI benchmark reveal that our method can achieve state-of-the-art performance for monocular 3D object detection. [ABSTRACT FROM AUTHOR]

This paper investigates the singular perturbation (SP) theory-based composite learning control of a flexible-link manipulator using neural networks (NNs) and disturbance observer (DOB). For the dynamics, the system states are separated into fast and slow variables in terms of time scale. For the multi-input–multi-output slow dynamics, the intelligent control is designed where NNs are used for system uncertainty approximation and the DOB is used for compound disturbance estimation. The main contribution is that a novel controller using NN and DOB is constructed to deal with unknown dynamics and time-varying disturbances while the composite learning algorithm is proposed with prediction error. For the fast dynamics, sliding mode control is employed. The boundedness of the tracking error is proved via Lyapunov approach. The simulation results show that the DOB-based composite neural control can greatly improve the tracking precision. [ABSTRACT FROM AUTHOR]

This paper addresses two composite learning controller designs of quadrotor dynamics with unknown dynamics and time-varying disturbances using the terminal sliding mode. For unknown system dynamics, the single-hidden-layer feedforward network is employed for approximation which provides the information for the disturbance observer. Based on composite learning using neural approximation and disturbance estimation, the terminal sliding mode control (TSMC) is synthesized to obtain the finite-time convergence performance. To overcome the singularity problem, nonsingular TSMC is proposed. The closed-loop system stability under the two proposed controllers is presented via Lyapunov approach and the system trajectory will converge to the region caused by approximation error and disturbance estimation error. Simulation results demonstrate that the composite learning can efficiently estimate the system uncertainty and the tracking performance under the proposed controllers can be enhanced. [ABSTRACT FROM AUTHOR]

Considering the unknown dynamics of the multiple-input-multiple-output strict-feedback nonlinear systems, this paper proposed the neural composite learning control using the online recorded data. The control structure follows the back-stepping scheme, while neural networks (NNs) are employed for uncertainty approximation. Through the error dynamics analysis, the critical prediction error is constructed to indicate the performance of intelligent learning over the interval. Furthermore, the novel composite learning law is proposed for NN weights update. The stability of the closed-loop system is analyzed via the Lyapunov approach and the signals are guaranteed to be bounded. Through simulation test of two-input and two-output systems, the proposed controller can achieve better tracking performance, while with the composite learning algorithm, the NNs can efficiently approximate nonlinear functions. Similar conclusions are obtained on the control of the hypersonic reentry vehicle. [ABSTRACT FROM PUBLISHER]

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained. [ABSTRACT FROM PUBLISHER]

This paper investigates the dynamic surface control of nonlinear transport aircraft model during the process of continuous heavy cargo airdrop in case of disturbance and actuator saturation. For the continuous airdrop process, the effects of moving process parameters include cargo mass and moving items upon the flight and the aircraft dynamics is with dramatic change. The dynamic surface related technique is designed for the attitude subsystem with unmatched disturbance and unknown dynamics so that in each step the virtual control is carefully considered using disturbance observer while the auxiliary signal is constructed in case of actuator saturation. The closed-loop stability is established via Lyapunov approach. In simulation test, it is interesting to find out that continuous airdrop process will deteriorate the system stability if additional control effort is not exerted. The proposed disturbance observer-based control can effectively enhance the reliability and robustness of the system, and can make sure of the smooth system performance at the instant of the heavy cargo separating from the aircraft. [ABSTRACT FROM AUTHOR]

In this paper, a transform-domain filtering method is proposed for polarimetric synthetic aperture radar (POLSAR) images via patch ordering and simultaneous sparse coding (SSC). First of all, we establish a signal-dependent additive noise model for the POLSAR covariance matrix and derive the noise variance for each element of the matrix based on the complex Wishart distribution. Next, we propose an extended patch ordering algorithm for POLSAR images by extracting sliding patches and organizing them in a regular way. Then, the ordered patches are filtered by SSC, for the purpose of which we develop a new weighted simultaneous orthogonal matching pursuit algorithm by embedding the signal-dependent noise model of the POLSAR data. Finally, the filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. Experimental results with both simulated and real POLSAR images demonstrate that the proposed method can achieve state-of-the-art filtering performance. [ABSTRACT FROM AUTHOR]

Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model - the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional $k$ -nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications. [ABSTRACT FROM PUBLISHER]

An adaptive robot control approach that can achieve an accurate approximation of the complex robot dynamics without knowledge unlearning in the case of multiple sequential tasks is proposed in this article. A locally weighted learning (LWL) model with automatic structure growth according to the size of the learning domain is introduced to approximate unknown robot dynamics, and a composite learning technique with regressor extension is applied to improve parameter convergence. The LWL ensures that learning in one area of the learning domain does not cause unlearning in another area, and the composite learning theoretically guarantees the identification performance of the LWL model. By the exploitation of both stored data in memory and instantaneous data, parameter convergence in the LWL model is achieved under a more achievable interval-excitation condition than the stringent persistent-excitation condition. This further enhances the trajectory tracking performance for multiple sequential tasks, which is generally not achievable by existing approximation-based adaptive robot control approaches. Experimental studies have verified the superiority of the proposed approach over prevalent approaches. [ABSTRACT FROM AUTHOR]

Localization is of great importance in mobile and wireless network applications. Time Difference of Arrival (TDOA) is one of the widely used localization schemes, in which the target (source) emits a signal and a number of anchors (receivers) record the arriving time of the source signal. By calculating the time difference of different receivers, the location of the target is estimated. In such a scheme, receivers must be precisely time synchronized. But time synchronization adds computational cost, and brings errors which may lower localization accuracy. Previous studies have shown that existing time synchronization approaches using low-cost devices are insufficiently accurate, or even infeasible under high requirement for accuracy. In our scheme (called Whistle), several asynchronous receivers record a target signal and a successive signal that is generated artificially. By two-signal sensing and sample counting techniques, time synchronization requirement can be removed, while high time resolution can be achieved. This design fundamentally changes TDOA in the sense of releasing the synchronization requirement and avoiding many sources of errors caused by time synchronization. We implement Whistle on commercial off-the-shelf (COTS) cell phones with acoustic signal and perform simulations with UWB signal. Especially we use Whistle to localize nodes of large-scale wireless networks, and also achieve desirable results. The extensive real-world experiments and simulations show that Whistle can be widely used with good accuracy. [ABSTRACT FROM PUBLISHER]

The output power of a thin-film thermoelectric generator consisting of a Cu–20- \mu\m nanowire (NW) array (NWA)–Si bulk–Cu sandwich with Si or \Si0.8\Ge0.2 NWs is measured and compared to Cu–Si bulk–Cu for small temperature differences around room temperature. The array of NWs is made by metal-assisted chemical etching that retains the Ge concentration in the wires. The conversion of one surface of Si bulk into an array of short—relative to the remaining bulk—Si NWs improves the maximum output power by a factor of two. Using an array of Si NWs in combination with \Si0.8\Ge0.2 increases the maximum output power by a factor of 20. The increased output power, under the same heating power, is due to the lower thermal conductivity and contact resistance of the NWA and SiGe compared to Si bulk. A matrix model is developed to solve the coupled thermal transport equations for an arbitrary number of layers. Fits to the measurement allow the extraction of the electrical contact resistance and the effective internal temperature drop across the semiconductor composite. [ABSTRACT FROM AUTHOR]

This paper reports the two-photon absorption (TPA) and the spectral-narrowed light emission (SNLE) from the crystals of 9, 10-distyrylanthracene (DSA) derivatives. The results obtained by the Z-scan method show that the tested molecules possess high TPA cross sections. Upon near-IR excitation, strong two-photon-excited fluorescence could be observed in the crystals of four types of materials. Furthermore, SNLE with low threshold occurs in three of the four crystals under pumping with the second harmonic generated in the amplifier. Single crystals of the DSA derivatives exhibit unique photonic properties, including strong solid-state fluorescence, large TPA cross section, and stimulated emission. The results demonstrate that these are potential candidates for compact and practical solid-state laser applications. [ABSTRACT FROM PUBLISHER]

Although wind power has gained tremendous development in recent years, how to achieve mechanical loads optimization to extend service life-time of wind turbines is still a hot and challenging topic. In this study, artificial intelligence and advanced control techniques are combined to approach this objective for variable-speed wind turbine systems operating in high-speed region. First, the real-time information of effective wind speed is extracted and predicted via support vector regression (SVR) by exploiting data stream acquired online. Optimization of the support vector regression’s parameters is completed by the particle swarm optimization algorithm. Subsequently, the predicted wind speed is routed to a novel feedforward mechanism designed to build a nonlinear relationship between wind speed and pitch angle. Additionally, an uncertainty and disturbance estimator (UDE) based feedback controller is implemented to deal with the model uncertainties and external disturbances. Both loads optimization and rotor speed/generator power regulation are achieved via strict math analysis. Finally, extensive comparison studies between the proposed scheme and traditional pitch angle controllers are conducted on GH bladed platform to verify the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a fuzzy adaptive tracking control algorithm for hypersonic flight vehicles with input quantization and faults. Different from the usual stabilization results, we present a method to achieve asymptotic tracking with predefined-time performance. An error transformation is established and a modified Lyapunov function (MLF) is constructed. The predefined-time asymptotic tracking control goal can be achieved by simply guaranteeing the boundness of the proposed MLF which is related to the transformed tracking errors. The proposed control strategy has three advantages. First, the predefined-time tracking performance can be ensured even when the initial tracking conditions are completely unknown. Second, no compensation is required for the fuzzy reconstruction errors to achieve accurate tracking control such that the control design is significantly simplified. Third, the derived controller is fault-tolerant, and since the system is controlled by quantized signals, the communication burden can be considerably reduced. Theoretical proof and numerical simulation are presented to demonstrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

The traditional pure switched-capacitor equalizer suffers from a large inrush current and low balance speed. An automatic parallel resonant switched-capacitor equalizer (PReSCE) for series-connected battery strings is proposed, which utilizes resonant switched-capacitor to eliminate the inrush current. The parallel ReSC converters not only minimizes output impedance at the low switching frequency, but delivers the excess energy to the low-voltage battery directly from the high-voltage battery in one cycle. Both of the two functions increase the balance speed. All of the switches are controlled by a pair of complementary pulsewidth modulation signals at a fixed operational frequency. Both simulation and experiment are used to verify the theoretical analysis and system feasibility of the proposed circuit. The results show that the PReSCE circuit eliminates the inrush current and increases the balance speed three times than the parallel pure switched-capacitor equalizer. [ABSTRACT FROM AUTHOR]

Compared with optical images, polarimetric synthetic aperture radar (PolSAR) images usually maintain lower resolution. Ship targets in PolSAR images have fewer pixels than those in optical images. Therefore, architectures such as Faster R-CNN and its variations that focus more on target pixels may fail for PolSAR images. Due to limited data and the simple geometric structures of small ship targets, the R-CNN framework with a large neural network as its backbone easily overfits the training set. In this article, we propose a novel lightweight patch-to-pixel convolutional neural network (P2P-CNN) for ship detection via PolSAR images. P2P-CNN focuses on both the target and its surroundings. A patch of proper size that contains both a target and its surroundings is used as the input of the neural network to determine whether the pixel in the center of the patch belongs to the target. To utilize contextual semantic information at all scales, all feature maps from the top down are combined to improve the final result. Instead of conventional convolution, dilated convolutions are used in the proposed neural network to exponentially expand receptive fields without adding any model parameters. The proposed approach and comparative methods are tested and compared on PolSAR images in various environments under varying image resolution, target size, sea condition, sensor type, etc. The experimental results demonstrate that the proposed approach outperforms all the compared methods. [ABSTRACT FROM AUTHOR]

Parameter uncertainty and unmodeled dynamics are inevitable for an actual nonlinear system, and are hard to deal with in control design, especially when the internal dynamics of this nonlinear system are unstable. The control design for a multiinput multioutput nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics is discussed in this paper. The internal dynamics of nonminimum phase system are unstable, the control of this kind of system is challenging. Ideal internal dynamics (IID) based controller design method are utilized here, and a partially linearized model is constructed. A state tracking model is constructed on account of the partially linearized model and IID. Then the robust fuzzy controller design problem is discussed and a fuzzy logical system is utilized to identify the parameter uncertainty and unmodeled dynamics. A robust adaptive fuzzy controller is proposed for the stability of the nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics. Finally, by simulations on vertical takeoff and landing aircraft, the availability of the presented adaptive fuzzy controller is validated. [ABSTRACT FROM AUTHOR]

The development of magnetic properties extends application fields of Fe–Mn–Si-based shape memory alloys (SMAs) into the field of electromagnetic sensors and actuators. In the previous studies, magnetic Fe–Mn–Si-based shape memory ribbons have been prepared by the melt-spinning technique. However, the melt-spinning technique could only obtain 2-D thin materials. In this paper, a ferrite-coated Fe–18.81Mn–5.61Si–9.31Cr–5.36Ni bulk shape memory material, exhibiting both magnetic and shape memory functions, was fabricated by means of the oxidation at 1173 K for different times. The ferritic layer was depleted in the Mn element due to the selective oxidation of the Mn element, and its growth behavior followed the parabolic law. Furthermore, we prepared not only ferrite/austenite/ferrite sandwich sheets but also matrix at ferrite core–shell wires in the case of ferrite-coated Fe–18.81Mn–5.61Si–9.31Cr–5.36Ni alloy. The shape recovery ratio of sandwich sheets decreased to 74.1% from 78.3% of the ferrite-free state while their saturation magnetization increased to 7.8 emu/g. The shape recovery ratio of core–shell wires decreased to 54.8% from 78.3% of the ferrite-free state with increasing the saturation magnetization to 36.2 emu/g. For both sandwich sheets and core–shell wires, there is a tradeoff relationship between shape memory effect (SME) and saturation magnetization. Accordingly, it is necessary to strike a balance between their shape memory and magnetic properties according to engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved attributed scattering model to mathematically unify the scattering models of several canonical primitives. These primitives include not only point- and line-segment-scatterers, such as trihedral, cylinder, dihedral, and rectangular plane, but also arc scatterers, such as sphere and top-hat. The estimation of the model parameters can be posed as an ill-posed linear inverse problem. To overcome the ill-posedness, we employ the incremental sparse Bayesian learning method to realize the sparsity-driven continuous parameter estimation. Inverse scattering experiments demonstrate that the proposed methodology not only provides desirable sparse representations of the target scattering response but is also able to capture richer geometrical information than the existing models. [ABSTRACT FROM AUTHOR]

Explicit fault analysis is the basis of protection for the bipolar HVDC Line. The characteristics of the initial values of traveling waves under various internal fault conditions are investigated on the basis of the symmetrical component analysis. The criteria of fault classification and faulty-pole selection are put forward based on the zero- and positive-sequence backward traveling waves, and an integrated traveling wave-based protection scheme is proposed. The simulations based on real-time digital simulation show that the proposed scheme can detect faults rapidly, determine the fault type effectively, and select the faulty pole correctly. [ABSTRACT FROM AUTHOR]

In this communication, we report the scanning manipulation on radiation beams with an active metamaterial (MTM) antenna using p-i-n diode switches. Thanks to the MTM antenna possessing periodical configuration with isolated units, p-i-n diodes in each unit are capable of being actuated individually with OFF/ON-states (coding sequences 0/1), equivalently tuning the phase constant $\beta $ of each cell either in state $\beta _{0}$ or state $\beta _{1}$. Therefore, for MTM array with a certain number of units, we can obtain plenty of tunable states presented via digitally coding sequence combinations, thereby manipulating beam scanning in a fixed frequency band. Especially, for the first time, we propose the digitally modulated array factor (DMAF) method for the MTM array: introducing unit-cell dispersion curves of both 0/1 states to the array factor, thus, map the digitally coding sequences linearly to the scanning beams at a fixed frequency, resulting in scanning functionalities. Experiments are implemented with applying micro-control-unit (MCU) to the active MTM antenna for driving p-i-n diodes, and measurements demonstrate beam scanning continuously from −30° to +30° at fixed frequencies around 5 GHz, validating the proposed method. [ABSTRACT FROM AUTHOR]

Accurate modeling is an important method for dynamic response analysis and control strategy verification of high photovoltaic (PV) penetration distribution networks. This paper proposes a precise dynamic modeling framework for the two-staged PV station cluster, namely as deep learning clustering hybrid modeling framework. It includes clustering-based equivalent model and error correction model (ECM). A long short-term memory network is used to form the ECM, which models the dynamic response error between the existing equivalent model and the detailed model. The competence of this framework is validated by numerous case studies based on a practical PV cluster construction. The simulation results reveal that the proposed method is featured of low complexity and fast response speed as the equivalent model but has much higher accuracy. [ABSTRACT FROM AUTHOR]

An experimental method of producing dusty plasma with high dusty particle density is proposed and implemented. Microdusty particles are immersed into an ac glow discharge plasma with large scale by applying electronically controlled vibratory screen mesh and continuous equidistributional dusty environment is formed. The impact of dusty particles on microwave propagation in a laboratory plasma device can be observed. The gigahertz microwave transmittance through the dusty plasma region is measured with vector network analyzer and horn antenna placed in the sides of plasma region. The results show that the dusty particles can synergize the influence on propagation of microwave in plasma. [ABSTRACT FROM AUTHOR]

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton–Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot. [ABSTRACT FROM AUTHOR]

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non Gaussian signal processing and machine learning. In this paper, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important properties. We apply the KRSL to adaptive filtering and investigate the robustness, and then develop the MKRSL algorithm and analyze the mean square convergence performance. Compared with correntropy, the KRSL can offer a more efficient performance surface, thereby enabling a gradient-based method to achieve faster convergence speed and higher accuracy while still maintaining the robustness to outliers. Theoretical analysis results and superior performance of the new algorithm are confirmed by simulation. [ABSTRACT FROM PUBLISHER]

Tunneling magnetoresistive (TMR) reading heads at an areal density of 80-100 Gbit/in² in a longitudinal magnetic recording mode have for the first time been commercialized for both laptop and desktop Seagate hard disk drive products. The first generation TMR products utilized a bottom TMR stack and an abutted hard bias design. These TMR heads have demonstrated three times the amplitude of comparable giant magnetoresistive (GMR) devices, resulting in a 0.6 decade bit error rate gain over GMR. This has enabled high component and drive yields. Due to the improved thermal dissipation of current-perpendicular-to-plane geometry, TMR runs cooler and has better lifetime performance, and has demonstrated the similar electrical static discharge robustness as GMR. TMR has demonstrated equivalent or better process and wafer yields compared to GMR. The TMR heads is proven to be a mature and capable reader technology. Using the same TMR head design in conjunction with perpendicular recording, 274 Gbitfin2 has been demonstrated. Advanced design can reach 311 Gbit/in² [ABSTRACT FROM AUTHOR]