In the process of geological exploration, the development of automatic control is insufficient. The paper presents an improved strategy of attitude control for directional drilling tools used in the geological environ...
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In the process of geological exploration, the development of automatic control is insufficient. The paper presents an improved strategy of attitude control for directional drilling tools used in the geological environment. The drilling kinematics model is linearized with the Taylor series, and the model linearization solves the nonlinear term of the azimuth *** PI controllers are used to control attitude inclination and azimuth, respectively. The results of the transient simulation are presented, and the control effect of improved performance are certificated.
Accurate and timely assessment of drilling system is key for achieving safety and efficiency in deep drilling. In this paper, an online assessment model is proposed by applying online sequential extreme learning mach...
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Accurate and timely assessment of drilling system is key for achieving safety and efficiency in deep drilling. In this paper, an online assessment model is proposed by applying online sequential extreme learning machine(OS-ELM). The model has been tested through the actual drilling data for drilling system safety assessment and accidents early warning. By analyzing the mechanism characteristics of accidents, well logging parameters are chosen as the input and accident types are chosen as the output. Owing to the OS-ELM is capable of updating network parameters based on new arriving data without retraining historical data, the model can be updated online for specific formation accidents information to make it more adaptable to a particular environment. The numerical test results show that, comparing with other widely used assessment techniques like support vector machines(SVM) and back propagation(BP), the proposed model has a higher accuracy and shorter recognition time.
The microphone array speech enhancement algorithm(MASEA), the minimum mean square error algorithm for short-time logarithmic spectrum estimation based on voice activity detection(VAD-LSA-MMSE), and the Wiener filt...
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The microphone array speech enhancement algorithm(MASEA), the minimum mean square error algorithm for short-time logarithmic spectrum estimation based on voice activity detection(VAD-LSA-MMSE), and the Wiener filtering algorithm based on voice activity detection(VAD-Wiener) are currently the three most commonly used speech enhancement algorithms. Among them, the MASEA algorithm has some disadvantages such as poor noise reduction effect. VAD-LSA-MMSE algorithm has some disadvantages of relying on high SNR and introducing music noise, thereby reducing the intelligibility. The VAD-Wiener algorithm has some disadvantages such as higher SNR requirements. Aiming at the shortcomings of these three speech enhancement algorithms, based on the VAD algorithm and the MASEA algorithm, this paper proposed a new speech enhancement algorithm by combining the characteristics of Wiener filtering algorithm and LSA-MMSE algorithm. The new speech enhancement algorithm is a VAD-based microphone array speech enhancement algorithm(VAD-MASEA). VAD-MASEA is better than the other three algorithms in noise reduction, speech enhancement and voice intelligibility, and has the characteristics of adapting to a lower SNR environment. This paper used MATLAB to carry out experimental research, including the new algorithm and the three existing algorithms were simulated and compared the signal waveforms of the four algorithms. Experimental results show that the proposed VAD-MASEA algorithm overcomes the high SNR requirement and can be used in low SNR environments and obtain highly intelligible enhanced signals.
A piecewise control strategy is proposed to realize the position-posture control of a planar four-link Active-PassiveActive-Active(APAA) underactuated manipulator(UM). Specially, the particle swarm optimization(...
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A piecewise control strategy is proposed to realize the position-posture control of a planar four-link Active-PassiveActive-Active(APAA) underactuated manipulator(UM). Specially, the particle swarm optimization(PSO) algorithm is used to obtain the target angle of all links based on the constraints of control objects of the system. The overall control process of the system is divided into two stages: Firstly, we design a fuzzy-PI controller for the first link to realize the control target of the passive link, and the error between the current angle and the desired angle of the passive link is converged to zero by adjusting the velocity of the first link. Secondly, the position mode of the servo controller is adopted to control the active links move to their target angles, respectively. Finally, the experimental results of the real system verify the effectiveness of the proposed control strategy.
The residual vibration(RV) problem of the flexible link manipulators(FLMs) is very difficult to solve due to the low stiffness and the underactuated feature of these *** paper presents a control strategy with zero...
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The residual vibration(RV) problem of the flexible link manipulators(FLMs) is very difficult to solve due to the low stiffness and the underactuated feature of these *** paper presents a control strategy with zero RV for a planar singlelink flexible manipulator(PSLFM).The stable control objective of the system is to stabilize the PSLFM at a target equilibrium point with zero ***,the dynamic model of the PSLFM is built by using the assumed mode method(AMM).Then,we transform the control to the trajectory tracking control.A forward trajectory and a reverse trajectory are obtained by using a bidirectional trajectories planning ***,these two trajectories are connected by using the genetic algorithm(GA).After doing this,we get a trajectory of the system from the initial equilibrium point to the target equilibrium ***,we design a trajectory tracking controller based on the sliding mode variable structure control method to control the PSLFM track this *** simulation results show that the PSLFM arrives the target equilibrium point with zero RV,which demonstrates the effectiveness of this control strategy.
To realize drilling visualization, an effective interpolation method is essential to construct three-dimensional *** is an effective interpolation method commonly used by geologists. However, the variogram model param...
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To realize drilling visualization, an effective interpolation method is essential to construct three-dimensional *** is an effective interpolation method commonly used by geologists. However, the variogram model parameters in traditional Kriging have a certain subjectivity, which will influence the accuracy of interpolation. Quantum Genetic Algorithm(QGA)is introduced to optimize the variogram model parameters selection in this paper. Elevation values of boreholes are used as data sets for simulation. The results show that the proposed improved Kriging has a better prediction accuracy.
To improve the accuracy of Electroencephalogram(EEG) emotion recognition,a stacking emotion classification model is proposed,in which different classification models such as XGBoost,LightGBM and Random Forest are inte...
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To improve the accuracy of Electroencephalogram(EEG) emotion recognition,a stacking emotion classification model is proposed,in which different classification models such as XGBoost,LightGBM and Random Forest are integrated to learn the *** addition,the Renyi entropy of 32 channels' EEG signals are extracted as the feature and Linear discriminant analysis(LDA) is employed to reduce the dimension of the feature *** proposal is tested on the DEAP dataset,and the EEG emotional states are accessed in Arousal-Valence emotion space,in which HA/LA and HV/LV are classified,*** result shows that the average recognition accuracies of 77.19%for HA/LA and 79.06%for HV/LV are obtained,which demonstrates that the proposal is feasible in EEG emotion recognition.
The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production *** find ways of reducing the energy consumption, it is ne...
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The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production *** find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.
With the application of magnetic thin films becoming more and more widespread,people pay more and more attention to the performance *** order to obtain a magnetic film with a specific performance,it is very important ...
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With the application of magnetic thin films becoming more and more widespread,people pay more and more attention to the performance *** order to obtain a magnetic film with a specific performance,it is very important to judge the quality of the magnetic film and measure the magnetic properties of the ***,with the increase of the film preparation process,the thickness of the prepared film is getting thinner and the magnetic moment signal contained therein is also *** brings a certain degree of difficulty to the traditional measurement *** example,the VSM system that obtains the hysteresis loop by measuring the magnetic moment signal has become somewhat inadequate for the measurement of ultra-thin *** order to solve this issue,a new method based on anomalous Hall effect is introduced in this *** test system of this system adopts the four-probe measuring method,a constant current is applied across the surface of the film sample,and the abnormal Hall voltage is measured at the other two *** R-H curve of the sample can be obtained through *** compared to VSM measurement,this method is simpler and stable,more accurate,which can greatly reduce the anomalous Hall-effect device R-H characteristic measurement cost.
This paper investigates the problem of finite-time H∞ state estimation for discrete-time stochastic switched genetic regulatory networks(GRNs) with time-varying delays and exogenous disturbances. A new discrete tim...
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This paper investigates the problem of finite-time H∞ state estimation for discrete-time stochastic switched genetic regulatory networks(GRNs) with time-varying delays and exogenous disturbances. A new discrete time-delayed stochastic switched GRN model with uncertain sojourn probabilities is devised, which is more general than the switched GRNs model with completely known sojourn probabilities. The sufficient conditions which guarantee the stochastic finite-time boundedness of the estimation error dynamics with a prescribed H∞ disturbance attenuation level are derived. By solving several matrix inequalities,the state estimator parameters can be obtained. A numerical example is given to illustrate the effectiveness of our results.
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