The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A high order curvature-compensated CMOS bandgap voltage reference(BGR) is presented in TSMC 0.35μm CMOS technology with low power low temperature-coefficient(TC) and high power supply rejection ratio(PSRR). The design is used in low dropout regulators which is applied in implanted chips. TC is compensated by adjusting resistor ratio which have different temperature characteristics. A PSRR enhance...
We characterize, for the first time, relative intensity noise and timing jitter properties of the OPG output pulses based on the balanced optical cross-correlator technique, which is in a fairly good agreement with numerical simulation.
We demonstrate experimentally a passively relative carrier-envelope phase (CEP)-locking, broadly tunable and robust laser source which is based on narrowband cw injection seeding of a two stage femtosecond optical parametric amplifier (OPA).
Bayesian optimization has been demonstrated as an effective methodology for the global optimization. However, it suffers from a computational bottleneck that the inference time grows cubically with the number of observations. In this paper, a Bayesian optimization based on the data-parallel approach is proposed to alleviate this problem. Firstly, an improved geometry motivated clustering algorithm...
More and more industrial production companies apply computer to process control, operation optimization and performance evaluation, which significantly increases the amount of data collected. Accurate measurement data can provide solid foundation for monitoring, optimization, scheduling, and decision analysis. However, measurement data is inevitably interfered by errors from multiple processes. This...
A multi-objective particle swarm algorithm based on the active learning (MOPSAL) approach is proposed that combines a Multi-Objective particle swarm optimization (MOPSO) with an Pareto Active Learning (PAL) approach. In MOPSAL, the candidate solution set is produced by a sampling method based on mutation operator and preselected by the PAL approach. Then, the best Pareto solution from the candidate...
Parasitic capacitances of silicon carbide (SiC) MOSFET exert an significant influence on the switching performance with direct determination of the switching speed, switching loss and EMI noises, among which the nonlinear gate drain capacitance (Miller capacitance) dominates due to the well-known Miller effect. A precise and comprehensive model of the miller capacitance is proposed according to the...
Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC...
Aiming at the optimal scheduling problem of byproduct gas system in steel industry, a knowledge and mathematical programming-based optimal scheduling method is proposed in this study. On one hand, a fuzzy model is designed to extract the expert scheduling knowledge from the historical data of the industrial process. And then, a great deal of scheduling knowledge is employed to compose a fuzzy rules...
In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added...
This paper considers the problem of global output feedback stabilization by virtue of a reduced-order observer for a class of switched nonlinear systems with time-varying control gains under arbitrary switching. Firstly, a set of coordinate transformations is used to introduce a scaling gain. Then, state feedback controllers are designed for the individual subsystems of transformed switched system,...
In order to solve the problem that the accuracy of flaws identification in ultrasonic testing is not high enough due to the error in sensor information acquisition and the noise interference in the detection environment, a method of flaws identification in ultrasonic testing based on evidential reasoning rule (ER rule) is studied. Firstly, ER rule is proposed to consider the reliability and weights...
The aim of this study is to investigate the dose distribution in the process of X-ray imaging at Shanghai Synchrotron Radiation Facility. We used the Monte Carlo software EGSnrc based on the statistics of the beamline BL13W to simulate the actual irradiation environment and calculate the dose distribution. Three different sizes of sources and two different tomographic models were created to predict...
Current reliability assessment approaches targeted at dynamic systems suffer from the challenge of uncertainties. This paper, we proposed a new online reliability evaluation method which based on evidential reasoning, which can integrate both historic and present status information to online assess the reliability of space relay under uncertainties. Firstly, the space relay is analyzed and the characteristic...
A well understanding of topography effect on the forest reflectance is critical for biophysical parameters retrieval over rugged area. In this paper, a new hybrid bidirectional reflectance distribution function (BRDF) model coupled the geometric optical mutual shadowing (GOMS) and scattering from arbitrarily inclined leaves (SAIL) models with topography consideration (GOSAILT) for sloping forest was...
The blast furnace gas is an important secondary energy for the iron and steel production. Establishing an effective model to describe the state of BFG system is of great significant to maintain the system balance and stability. Considering the strong coupling characteristics of the blast furnace gas system and the high level noises in the industrial data, a simplex unscented Kalman filter-based Wang-Mendel...
Indoor localization technology based on the received signal strength (RSS) of wireless access point (AP) has become very popular in recent years. Considering that the Wi-Fi signal is unstable and uncertain, and the contribution of different APs to the localization are different, a fuzzy indoor localization algorithm based on dynamic weights of APs is proposed in this paper. The Multidimensional-Scaling...
In this paper, the passivity-based stabilization problem of a strict-feedback nonlinear system under a proper state-dependent switching law is investigated. Here, the feedback passification problem of each subsystem does not need to be solvable. First, by using the recursive backstepping technique, a sufficient condition under which the closed-loop switched system with designed state feedback controllers...
In this paper, local strict passivity of a switched discrete-time affine nonlinear system is investigated using the linearization technique. The dwell-time dependent storage function (DTDSF) is employed to analyze passivity and solve feedback passification problem. First, local strict passivity sufficient conditions are established in terms of LMIs, which are all convex in linearized system matrices...
An adaptive fault-tolerant control strategy is proposed for a class of switched nonlinear systems with unknown functions and unavailable states by using the improved average dwell time technique and backstepping method. We use radial basis function neural networks (RBFNNs) to approximate the unknown functions and a switched nonlinear state observer is designed to estimate the unavailable states. In...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.