Model Identification and Control of Electromagnetic Actuation in Continuous Casting Process With Improved Quality

This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

Large power analysis of switched reluctance machine system for coal mine

The conventional structures in the Switched Reluctance machines are introduced, such as three-phase 12/8 structure Switched Reluctance machine, three-phase 6/4 structure Switched Reluctance machine, four-phase 16/12 structure Switched Reluctance machine, and four-phase 8/6 structure Switched Reluctance machine. Three-phase 12/8 structure Switched Reluctance machine is the best choice for the large power Switched Reluctance machine system in coal mines. The asymmetric bridge power converter main circuit and the bifilar winding power converter main circuit are also introduced. Three-phase asymmetric bridge power converter main circuit is the best choice for the large power Switched Reluctance machine system in coal mines. The magnetic paths of the designed large power motor are given with one phase excitation and double phases excitation. The phase current waveforms are also given.

Study and optimal simulation of 4H-SiC floating junction Schottky barrier diodes' structures and electric properties

This paper stuides the structures of 4H SiC floating junction Schottky barrier diodes. Some structure parameters of devices are optimized with commercial simulator based on forward and reverse electrical characteristics. Compared with conventional power Schottky barrier diodes, the devices are featured by highly doped drift region and embedded floating junction layers, which can ensure high breakdown voltage while keeping lower specific on-state resistance, and solve the contradiction between forward voltage drop and breakdown voltage. The simulation results show that with optimized structure parameter, the breakdown voltage can reach 4.36 kV and the specific on-resistance is 5.8 mΩ.cm2 when the Baliga figure of merit value of 13.1 GW/cm2 is achieved.

Research on Adaptive Threshold of Received Signal in Communication System

When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.

Research on Motion Attention Fusion Model-Based Video Target Detection and Extraction of Global Motion Scene

For target detection algorithm under global motion scene, this paper suggests a target detection algorithm based on motion attention fusion model. Firstly, the motion vector field is pre-processed by accumulation and median filter;Then, according to the temporal and spatial character of motion vector, the attention fusion model is defined, which is used to detect moving target;Lastly, the edge of video moving target is made exactly by morphologic operation and edge tracking algorithm. The experimental results of different global motion video sequences show the proposed algorithm has a better veracity and speedup than other algorithm.

Characterization of Blue-Green Light Non-Line-of-Sight Transmission in Seawater

The blue-green light in the 450 nm to 550 nm band is usually used in underwater wireless optical communication (UWOC). The blue-green light transmission in seawater is scattered by the seawater effect and can achieve communication in non-line-of-sight (NLOS) transmission mode. Compared to line-of-sight (LOS) transmission, NLOS transmission does not require alignment and can be adapted to various underwater environments. The scattering coefficients of seawater at different depths are different, which makes the scattering of light in different depths of seawater different. In this paper, the received optical power and bit error rate (BER) of the photodetector (PD) were calculated when the scattering coefficients of blue-green light in seawater vary from large to small with increasing depth for NLOS transmission. The results show that blue-green light in different depths of seawater in the same way NLOS communication at the same distance, the received optical power and BER at the receiver are different, and the received optical power of green light is greater than that of blue light. Increasing the forward scattering coverage of the laser will suppress the received optical power of the PD, so when performing NLOS communication, appropriate trade-offs should be made between the forward scattering coverage of the laser and the received optical power.

A novel methodology for constructing a multi-wing chaotic and hyperchaotic system with a unified step function switching control

This paper aims at developing a novel method of constructing a class of multi-wing chaotic and hyperchaotic system by introducing a unified step function. In order to overcome the essential difficulties in iteratively adjusting multiple parameters of conventional multi-parameter control, this paper introduces a unified step function controlled by a single parameter for constructing various multi-wing chaotic and hyperchaotic systems. In particular, to the best of the authors＇ knowledge, this is also the first time to find a non-equilibrium multi-wing hyperchaotic system by means of the unified step function control. According to the heteroclinic loop Shilnikov theorem, some properties for multi-wing attractors and its chaos mechanism are further discussed and analyzed. A circuit for multi-wing systems is designed and implemented for demonstration, which verifies the effectiveness of the proposed approach.

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

DYNAMIC HUNTING EXTREMUM CONTROL METHOD BASED ON MEASURE AND ESTIMATION

Objective To propose a dynamic hunting extremum control method based on comparison of the estimated value and measured value. Methods The output linear group of the system is approximately expressed as an nth order system with large time constant or with time delay. Moreover, the relation between input drive reversal of the logic circuit and dynamic output of the system is analyzed in detail. The calculating formulae for the drive reversal are given for different extremum control systems. Based on principles above, a controller using a micrprocessor and a testing laboratory plant were designed and implemented. Results With this new method, the controller achieves fast optimum point hunting process, good performance in extremum control systems for high order processes, and robust result under sudden change or drift of the extremum characteristics. Conclusion The new control owns the merits of rapid optimizing dynamics, robusticity, and wide applicability.

Close-Loop System Identification Using Over-sampling Scheme and Its Estimate Accuracy Analysis

A new identification method for a linear discrete-time closed-loop system is proposed based on an output over-sampling scheme. When the system outputs are over-sampled the new output sequences would contain more information about the plant structure. Using general least squares method （GLS） the plant over-sampled model should be recognized. Then the original plant model should be obtained by its relationship with the over-sampled model. Compared with conventional approaches the advantage of the new method is that even if the ordinary identifiability conditions are not satisfied, a close-loop system can be identified by using the oversampled output without utilizing any external test signal. Accuracy analysis shows the relationship between the estimation error and the over-sampling rate. Numerical simulation illnstrates its effectiveness.

An Initial Approach for a NFC M-Ticketing Urban Transport System

Public transport plays an important role in the daily lives of hundreds of millions of people. The limited national and local finances starve the public transport networks of funding, although greater usage would imply important environmental and financial benefits. The recent advances in mobile technologies and services could be harnessed to increase the attractiveness and efficiency of various public transport systems. Increasingly flexible electronic ticketing, smart cards and real-time information feeds via smartphones are making transportation networks faster, cheaper and more efficient for both the passengers and operators. In this paper, we present our approach in designing and implementing an M-ticketing system for urban transport based on NFC technology. We make initial design decisions based on a study of two similar systems Oyster and U-Go. We make use of Microsoft technologies for the prototype implementation and we propose a test suite to validate the system. We conclude on presenting the experience gained from this project and propose a set of further developments in order for the prototype to become more realistic.

Simplified Optimization Routine for Tuning Robust Fractional Order Controllers

Fractional order controllers have been used intensively over the last decades in controlling different types of processes. The main methods for tuning such controllers are based on a frequency domain approach followed by optimization routine, generally in the form of the Matlab fminsearch, but also evolving to more complex routines, such as the genetic algorithms. An alternative to these time consuming optimization routines, a simple graphical method has been proposed. However, these graphical methods are not suitable for all combinations of the imposed performance specifications. To preserve their simplicity, but also to make these graphical methods generally applicable, a modified graphical method using a very straightforward and simple optimization routine is proposed within the paper. Two case studies are presented, for tuning fractional order PI and PD controllers.

Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems

To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence,multiple-input multiple-output(MIMO)technology is a valid way.A wireless ultraviolet(UV)MIMO channel estimation approach based on deep learning is provided in this paper.The deep learning is used to convert the channel estimation into the image processing.By combining convolutional neural network(CNN)and attention mechanism(AM),the learning model is designed to extract the depth features of channel state information(CSI).The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.

Changes in the power spectrum of electrical signals in maize leaf induced by osmotic stress

To quantify the characteristics of the power spectrum of plant electrical signals, we defined the following concepts:spectral edge frequency (SEF), spectral center frequency (SCF), power index (PI) and power spectral entropy (PSE). These parameters were used to examine and quantify changes in the power spectrum of electrical signals in maize leaves under osmotic stress. In the absence of osmotic stress, the SEF of the electrical signal in maize leaves was approx. 0.2 Hz and the SCF was approx. 0.1 Hz. The electrical signal in maize leaves was mainly a slow wave signal with a frequency of 0-0.1 Hz. After 2 h osmotic stress, the SEF and SCF of the electrical signal increased to higher frequencies. The proportion of the fast wave frequency also increased to 0.1-0.2 Hz, resulting in a dramatic increase in PSE. We also found that the changes in PSE and SCF were significantly correlated during osmotic stress. We propose that the changes in the PSE and SCF in maize leaves can be used as a sensitive signal indicating water deficit in leaf cells under osmotic stress. Thus, measurement of SCF or PSE of electrical signals in maize leaves could be used to develop early warning and rapid diagnosis techniques for the water demands of plants.

Adaptive Feedback Stabilization with Quantized State Measurements for a Class of Chaotic Systems

We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed at the input terminal of the quantizer,and another zoomer is located at the output terminal of the quantizer.The zoomers possess a common adjustable time-varying parameter.By using the adaptive laws for the time-varying parameter and estimating boundary error of values of quantization,the stabilization feedback controller with the quantized state measurements is proposed for a class of chaotic systems.Finally,some numerical examples are given to demonstrate the validity of the proposed methods.

Research on electrical pulse of 20-kV/30-Hz GaAs photoconductive switches

Photoconductive semiconductor switches （PCSSs） are widely used in high power ultra-wideband source applications and precise synchronization control due to their high power low-jitter high-repetition-frequency. In this letter, a 14-mm gap semi-insulating GaAs PCSS biased under 20 kV is triggered by a 1064-nm laser with a repetition frequency of 30 Hz. Although the trigger condition is greater than the threshold of the lock-on effect, the high gain mode is not observed. The results indicate that the high gain mode of the PCSS is quenched by decreasing the remnant voltage of pulsed energy storage capacitor.