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A novel adaptive mesh generation technique for efficient electromagnetic simulation of radio-frequency integrated circuits (RFICs) is herein presented. By exploring the geometrical and physical characteristics of RFICs, some adaptive mesh treatments, such as mesh projection, edge refinement, via polymerization, etc., are utilized to improve the accuracy and efficiency of electromagnetic computations. For strong coupling structures, such as two conductors in close proximity for a relatively large area, a projection-based mesh scheme is introduced to improve the accuracy of numerical integration. Moreover, the current most likely concentrates near the edges of conductors due to the edge effect. To better model the edge effect, an edge refinement scheme is applied. For via arrays that appear common in RFICs, an automatic via aggregation approach is adopted to improve computational efficiency yet still keep good computational accuracy. Finally, some numerical examples are given to validate the computational accuracy and efficiency of the novel adaptive mesh generation technique.

In the transmission and distribution of energy, circuit breaker plays a very important role. In order to fulfill the current market/user demands, they must be capable of promptly identifying the problem and isolating the problematic areas. According to the extinguishing medium used, circuit breakers are divided into the following categories: air blast, SF6, vacuum, and oil. Among others, permanent magnet (PM) based vacuum circuit breaker is also gaining popularity with each passing day and has become a crucial control and protection equipment in the modern power systems. This study proposes the pulse width modulation (PWM) intelligent control strategy for PM vacuum circuit breaker closing/opening capacitance, group charging, and movement process. For the practical implementation of this idea, dual CPUs are utilized for PM vacuum circuit breaker intelligent control components, by taking STM32F103RBT6 as the core and combining peripheral hardware circuit. PWM pulse output drives IGBT to fulfill the closing/opening capacitance group intelligent charging. On the other hand, PM circuit breaker closing/opening motion displacement signals and coil current feedback achieve closed-loop for PWM intelligent control. The experimental results show that the proposed intelligent control component and PWM intelligent control strategy effectively improve the PM circuit breaker intelligent charging and motion dynamic characteristics.

In order to better promote urban development. This paper puts forward the research of smart city planning management system based on multi-source data and multi planning, and discusses the application of data mining in urban data management from three aspects of information management, information management and data management, so as to realize scientific, efficient and comprehensive urban planning management. To promote and improve the planning and management of smart city, it is necessary to establish a unified standard system, integrate all kinds of planning materials, establish a collaborative mechanism for smart city planning and management based on the principle of multi-source data and multi planning, and build a three-dimensional planning platform for public participation in smart city planning and management, so as to make planning standardized, collaborative and open, and ensure the scientific and reasonable planning and management of smart city.

The conventional deep learning model performs well for traffic flow analysis by training with a large number of labeled data using a one-model-for-one-task approach, leading to huge computational complexity in dynamic intelligent transportation system (ITS) applications. To overcome this limitation, this paper propose a Token-based Self-Supervised Network (TSSN), which can learn TF features in a task-agnostic way, and provide a well bootstrapped pre-training model for a variety of tasks. TSSN tokenizes TF data into segments, each of which is named as a token and comprised of numerous consecutive points. Masked Token Prediction (MTP), a pretext task, is designed to understand the TF correlations by forecasting tokens that are randomly masked. MTP enables TSSN to capture the high-level intrinsic semantics of TF, and provide general-purpose token embeddings. Therefore, TSSN can be more generalized while keeping high performance. As a result, by replacing the final fully-connected layer with a set of untrained new layers and fine-tuning with small-scale task- specific data, TSSN can be deployed for a variety of downstream tasks. The simulation results demonstrate that the TSSN can improve overall performance on various downstream tasks when compared to state-of-the-art models.

Aiming at the problem that the traditional urban traffic management system can't effectively integrate the data, which leads to the low efficiency of data processing and the effect of traffic management, a smart city traffic management system based on WEBGIS is designed. Based on the traditional system, the dynamic traffic data acquisition hardware module is designed. According to the weekly similarity characteristics of traffic flow data, the traffic flow in different time periods is predicted and the road signal is controlled. The WEBGIS technology is used to deal with the traffic accidents, and the effective management of the urban traffic is realized. Simulation results show that the designed management system can effectively reduce the congestion of urban trunk roads and improve the efficiency of traffic management.

Network Telemetry (NT) is a crucial component in today’s networks, as it provides the network managers with important data about the status and behavior of the network elements. NT data are then utilized to get insights and rapidly take actions to improve the network performance or avoid its degradation. Intuitively, the more data are collected, the better for the network managers. However, the gathering and transportation of excessive NT data might produce an adverse effect, leading to a paradox: the data that are supposed to help actually damage the network performance. This is the motivation to introduce a novel NT framework that dynamically adjusts the rate in which the NT data should be transmitted. In this work, we present an NT scheme that is traffic-aware, meaning that the network elements collect and send NT data based on the type of traffic that they forward. The evaluation results of our Machine Learning-based mechanism show that it is possible to reduce by over 75% the network bandwidth overhead that a conventional NT scheme produces.

An advanced method of modeling radio-frequency (RF) devices based on a deep learning technique is proposed for accurate prediction of S parameters. The S parameters of RF devices calculated by full-wave electromagnetic solvers along with the metallic geometry of the structure, permittivity and thickness of the dielectric layers of the RF devices are used partly as the training and partly as testing data for the deep learning structure. To implement the training procedure efficiently, a novel selection method of training data considering critical points is introduced. In order to rapidly and accurately map the geometrical parameters of the RF devices to the S parameters, deep neural networks are used to establish the multiple non-linear transforms. The hidden-layers of the neural networks are adaptively chosen based on the frequency response of the RF devices to guarantee the accuracy of generated model. The Adam optimization algorithm is utilized for the acceleration of training. With the established deep learning model of a parameterized device, the S parameters can efficiently be obtained when the device geometrical parameters change. Comparing with the traditional modeling method that uses shallow neural networks, the proposed method can achieve better accuracy, especially when the training data are non-uniform. Three RF devices, including a rectangular inductor, an interdigital capacitor, and two coupled transmission lines, are used for building and verifying the deep neural network. It is shown that the deep neural network has good robustness and excellent generalization ability. Even for very wide frequency band (0–100 GHz), the maximum relative error of the coupled transmission lines using the proposed method is below 3%.

Frame resolution and physical layer (PHY) protocol type detection are the basis of research and development of intrusion prevention systems for IEEE 802.11 wireless network. Aiming at the problems which cannot be solved by the specifications export, this paper proposed a MAC frame analytical method and a PHY protocol type detection algorithm based on parsing the IEEE 802.11packets captured by the library Libpcap. The packet structure and the length of the frame preamble (18 or 26 bytes) are presented. Then the methods of transforming byte-order and resolving sub-fields are given. A detection algorithm of PHY protocol type is proposed based on the experiments and examples are given to verify these methods. This work can be a reference for the R & D related to link layer frame analysis.

One major difficulty of parameter extraction for an equivalent circuit lies in applying an optimization algorithm to efficiently approach the global minimum. Traditional genetic algorithm (GA) is able to find the global minimum but it has very low convergence rate. On the other hand, direct search method may be easily trapped in local minima. In this work, an improved parameter extraction method which is capable of effectively performing the global minimum search is proposed. This method combines GA and Powell&rsquo, s method to efficiently determine circuit elements of an equivalent circuit model. A spiral inductor and an interdigital capacitor are used as design examples to illustrate the extraction procedure and validate the proposed hybrid method.

Barley stripe mosaic virus (BSMV) Triple Gene Block1 (TGB1) is a multifunctional movement protein with RNA‐binding, ATPase and helicase activities which mainly localizes to the plasmodesmata (PD) in infected cells. Here, we show that TGB1 localizes to the nucleus and the nucleolus, as well as the cytoplasm, and that TGB1 nuclear‐cytoplasmic trafficking is required for BSMV cell‐to‐cell movement. Prediction analyses and laser scanning confocal microscopy (LSCM) experiments verified that TGB1 possesses a nucleolar localization signal (NoLS) (amino acids 95–104) and a nuclear localization signal (NLS) (amino acids 227–238). NoLS mutations reduced BSMV cell‐to‐cell movement significantly, whereas NLS mutations almost completely abolished movement. Furthermore, neither the NoLS nor NLS mutant viruses could infect Nicotiana benthamiana systemically, although the NoLS mutant virus was able to establish systemic infections of barley. Protein interaction experiments demonstrated that TGB1 interacts directly with the glycine–arginine‐rich (GAR) domain of the nucleolar protein fibrillarin (Fib2). Moreover, in BSMV‐infected cells, Fib2 accumulation increased by about 60%–70% and co‐localized with TGB1 in the plasmodesmata. In addition, BSMV cell‐to‐cell movement in fib2 knockdown transgenic plants was reduced to less than one‐third of that of non‐transgenic plants. Fib2 also co‐localized with both TGB1 and BSMV RNA, which are the main components of the ribonucleoprotein (RNP) movement complex. Collectively, these results show that TGB1–Fib2 interactions play a direct role in cell‐to‐cell movement, and we propose that Fib2 is hijacked by BSMV TGB1 to form a BSMV RNP which functions in cell‐to‐cell movement.

This paper presents the design method of a permanent-magnet (PM) actuator and an intelligent control module for the associated PM contactor. The original design parameters of the PM actuator are obtained using magnetic circuit method. The optimization model for the PM actuator is solved by genetic algorithm and simulated annealing algorithm. A monostable cylindrical 140-A PM contactor is designed, and the corresponding intelligent control module of the actuator is developed. A dedicated control technique based on voltage feedback is proposed to realize the intelligent dynamic adjustment of the making process for the PM contactor. The electromagnetic force calculation model, the dynamic making model of the iron core and contact, the buffering model for the iron core, and the contact bounce model are all built using MATLAB. Experimental results are reported to showcase the correctness of the simulation method and the effectiveness of the feedback control technique to decrease the closing velocities of both the movable contact and the moving iron core that, in turn, will reduce the first and the second contact bounces significantly to lengthen the life span of the device.

An air circuit breaker (ACB) with novel double-breaker contact and permanent magnet actuator (PMA) is presented. Three-dimensional (3-D) finite element method (FEM) is employed to compute the electro-dynamic repulsion forces, including the Holm force and Lorentz force, which are acting on the static and movable contacts. The electro-dynamic repulsion forces of different contact pieces are computed, illustrating there is an optimal number of contact pieces for the ACB being studied. The electro-dynamic repulsion force of each contact, which varies from the outer position to the inner position, is also computed. Finally, the contacts of the double-breaker are manufactured according to the analyzed results to validate the simulations.

This paper reports a new application of permanent magnet actuator for vacuum contactors. A novel permanent magnet (PM) actuator to drive a vacuum contactor with intrinsically safe and low supply voltage is proposed. PM is mounted on the static iron of the contactor to enhance its holding force and reliability. While only one coil is used to break the vacuum contactor, two series coils are deployed to yield a high magnetomotive force to realize the making operation. The electrical circuit equation, mechanical equation and magnetic field are all coupled in the simulation. A prototype is manufactured and tested to validate and showcase the performance of the contactor which is equipped with the proposed PM actuator.

This paper presents a method for analytically analyzing the magnetic field and predicting the cogging force and torque of a linear and rotary permanent magnet actuator (LRPMA). The tubular mover of the LRPMA is transferred into a planar one by using a proposed magnetic field curvature factor and a relative permeance function for the stator slotting so as to simplify the magnetic field calculation. Magnetic field distributions of the LRPMA when the stator slotting is neglected are analytically analyzed using the magnetic scalar potential, and validated by 3-D finite-element method. The linear cogging force and rotary cogging torque of slotted LRPMA are subsequently predicted by the Maxwell stress tensor method and verified by the experimental results on the prototype.

A new displacement profile control strategy based on pulse-width-modulation (PWM) for improving the dynamic characteristics and reducing the contact bounce of permanent magnet (PM) contactor during the making process is reported. Sensorless method of real time displacement signal detection is realized using magnetic circuit model (MCM) and electric circuit model (ECM). Finite element method (FEM) is used to obtain the nonlinear relationships among the flux linkage, displacement and current off-line. The information is then used for subsequent fast simulation of the dynamic characteristics of the contactor. A set of periodically inter-transferred dynamic characteristic equations are built and the complete making dynamic process of the PM contactor is simulated using 4th-order Runge-Kutta method. Experiments are carried out to validate the proposed control method on a prototype.

In this paper, a new method is proposed to control the permanent magnet actuator (PMA) of air circuit breaker (ACB). The characteristics such as magnetic field, making and breaking processes are analyzed using finite-element method. The inductance and permanent magnetic parameters are computed in advance for subsequent incorporation into a cosimulation system. The simulation model is setup in Matlab and data are shared with the automatic dynamic analysis of mechanical systems (ADAMS) software. The making and breaking processes are integrated into a simulation system to allow simple observation of the simulation procedure. Experiments are carried out on an ACB prototype to verify the proposed control method.

Existing Global Data Computation (GDC) protocols for asynchronous systems are round-based algorithms designed for fully connected networks. In this paper, we discuss GDC in asynchronous chordal rings, a non-fully connected network. The virtual links approach to solve the consensus problem may be applied to GDC for non-fully connected networks, but it incurs high message overhead. To reduce the overhead, we propose a new non-round-based GDC protocol for asynchronous chordal rings with perfect failure detectors. The main advantage of the protocol is that there is no notion of rounds. Every process creates two messages initially, with one message traversing in a clockwise direction and visiting each and every process in the chordal ring. The second message traverses in a counterclockwise direction. When there is direct connection between two processes, a message is sent directly. Otherwise, the message is sent via virtual links. When the two messages return, the process decides according to the information maintained by the two messages. The perfect failure detector of a process need only detect the crash of neighboring processes, and the crash information is disseminated to all other processes. Analysis and comparison with two virtual links approaches show that our protocol reduces message complexity significantly.

For a synchronous distributed system of n processes with up to t potential and f actual crash failures, where (t

If we can accurately compute the leaving-water radiance and atmospheric diffuse transmittance, we will accurately retrieve the atmosphere optical properties over the case II water by MODIS image. According to the paper by Wang [Wang, Menghua, 1999. Atmospheric correction of ocean color sensors: computing atmospheric diffuse transmittance. Appl. Opt. 38, 451–455] to using the reciprocal equation derived by Yang and Gordon [Yang, H., Gordon, H.R., 1997. Remote sensing of ocean color: assesment of water-leaving radiance bidirectional effects on atmospheric diffuse transmittance. Appl. Opt. 36, 7887–7897] for atmospheric diffuse transmittance of the ocean–atmosphere system, we examined the accuracy of an analytical equation proposed by Gordon et al. [Gordon, H.R., Clark, D.K., Brown, J.W., Brown, O.B., Evans, R.H., Broenkow, W.W., 1983. Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of ship determinations and CZCS estimates. Appl. Opt. 22, 20–36] in computing the atmospheric diffuse transmittance for wavelengths from MODIS band 1 central wavelength to MODIS band 7 for central wavelength both a pure Rayleigh and a two-layer Rayleigh-aerosol atmosphere overlying a rough ocean surface. It was found that for viewing angles up to approximately 408, the analytical formula produces errors between 3% and 4% for nonabsorbing and weakly absorbing aerosols and for aerosol optical thicknesses saV0.4. The error increases with an increase in aerosol absorption, aerosol optical thicknesses, and viewing angle, and with the decrease of wavelength. By a simple numerical fit to modify the analytical formula, the atmospheric diffuse transmittance can be accurately computed usually to within ~1% for a variety of aerosol models

Bivalency argument is a widely-used technique that employs forward induction to show impossibility results and lower bounds related to consensus. However, for a synchronous distributed system of n processes with up to t potential and f actual crash failures, applying bivalency argument to prove the lower bound for reaching uniform consensus is still an open problem. In this paper, we address this problem by presenting a bivalency proof that the lower bound for reaching uniform consensus is (f + 2)-rounds where 0 ≤ f ≤ t - 2.

Motivation: Grid computing is used to solve large-scale bioinformatics problems with gigabytes database by distributing the computation across multiple platforms. Untill now in developing bioinformatics grid applications, it is extremely tedious to design and implement the component algorithms and parallelization techniques for different classes of problems, and to access remotely located sequence database files of varying formats across the grid. In this study, we propose a grid programming toolkit, GLAD (Grid Life sciences Applications Developer), which facilitates the development and deployment of bioinformatics applications on a grid. Results: GLAD has been developed using ALiCE (Adaptive scaLable Internet-based Computing Engine), a Java-based grid middleware, which exploits the task-based parallelism. Two bioinformatics benchmark applications, such as distributed sequence comparison and distributed progressive multiple sequence alignment, have been developed using GLAD. Availability: GLAD and ALiCE are available at http://www.comp.nus.edu.sg/~teoym/alice.htm Contact: teoym@comp.nus.edu.sg