DESIGN OF CONTROL SYSTEM FOR MOTOR IMAGERY BASED NEURO-AID APPLICATION

2019 ◽  
Vol 31 (04) ◽  
pp. 1950031
Author(s):  
Gauri Shanker Gupta ◽  
Maanvi Bhatnagar ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Closed Loop ◽  
Open Loop ◽  
Stepper Motor ◽  
Support Vector ◽  
Loop Analysis ◽  
Training Time ◽  
Brain Signals ◽  
And Performance ◽  
Learning Machine

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.

scholarly journals The Modelling, Simulation and FPGA-Based Implementation for Stepper Motor Wide Range Speed Closed-Loop Drive System Design

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 56 ◽  
Author(s):  
Chiu-Keng Lai ◽  
Jhang-Shan Ciou ◽  
Chia-Che Tsai
Keyword(s):  
Embedded System ◽  
High Speed ◽  
Closed Loop ◽  
Open Loop ◽  
Drive System ◽  
Stepper Motor ◽  
Motor Drive ◽  
Digital Controllers ◽  
Loop Control ◽  
Wide Range

Owing to the benefits of programmable and parallel processing of field programmable gate arrays (FPGAs), they have been widely used for the realization of digital controllers and motor drive systems. Furthermore, they can be used to integrate several functions as an embedded system. In this paper, based on Matrix Laboratory (Matlab)/Simulink and the FPGA chip, we design and implement a stepper motor drive. Generally, motion control systems driven by a stepper motor can be in open-loop or closed-loop form, and pulse generators are used to generate a series of pulse commands, according to the desired acceleration/run/deceleration, in order to the drive system to rotate the motor. In this paper, the speed and position are designed in closed-loop control, and a vector control strategy is applied to the obtained rotor angle to regulate the phase current of the stepper motor to achieve the performance of operating it in low, medium, and high speed situations. The results of simulations and practical experiments based on the FPGA implemented control system are given to show the performances for wide range speed control.

Outlook of the Dynamic Behavior of Closed-Loop Control through Open-Loop Analysis for Intensified Separation Processes

2018 ◽  
Vol 57 (49) ◽  
pp. 16795-16808
Author(s):  
Julián Cabrera-Ruiz ◽  
César Ramírez-Márquez ◽  
Shinji Hasebe ◽  
Salvador Hernández ◽  
J. Rafael Alcántara Avila
Keyword(s):  
Dynamic Behavior ◽  
Closed Loop ◽  
Open Loop ◽  
Closed Loop Control ◽  
Loop Analysis ◽  
Separation Processes ◽  
Loop Control

Practical health indicator construction methodology for bearing ensemble remaining useful life prediction with ISOMAP-DE and ELM-WPHM

2021 ◽  
Author(s):  
Yingkui Gu ◽  
Qingpeng Bi ◽  
Guangqi Qiu
Keyword(s):  
Prediction Model ◽  
Weight Coefficient ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health Indicator ◽  
Training Time ◽  
Optimal Weight ◽  
Useful Life ◽  
Learning Machine ◽  
Health Features

Abstract To improve the accuracy of our previous bearing ensemble Remaining Useful Life (RUL) prediction model using the Genetic Algorithm (GA), Support Vector Regression (SVR), and the Weibull Proportional Hazard Model (WPHM) (see reference [1]), we proposed a more practical Health Indicator (HI) construction methodology for bearing ensemble RUL prediction. A weighted coefficient determination method for four prognostic metrics-monotonicity, robustness, trendability, and consistency-was proposed to select sensitive health features accurately using the Analytic Hierarchy Process (AHP). The selected sensitive health features were fused through isometric feature mapping (ISOMAP), and Differential Evolution (DE) was employed to replace GA for computing the optimal weight coefficients of each input fused feature. One-dimensional HI was constructed by multiplying each input fused feature with the corresponding optimal weight coefficient, and RUL prediction was implemented through an extreme learning machine (ELM) and WPHM. The accuracy and effectiveness of the proposed method were validated by a bearing experiment. The results show that HI construction with ISOMAP-DE has achieved the best performance, and the proposed ELM-WPHM model is compared with BP-WPHM, SVM-WPHM, LSTM-WPHM, and DLSTM-WPHM in terms of RMSE criteria. The minimum error and training time appear in ELM-WPHM, indicating the superiority of the proposed bearing ensemble RUL prediction model.

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

scholarly journals A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 21
Author(s):  
Xiaokai Guo ◽  
Xianguo Yan ◽  
Zhi Chen ◽  
Zhiyu Meng
Keyword(s):  
Fuel Consumption ◽  
Closed Loop ◽  
Fitness Function ◽  
Critical Role ◽  
Support Vector ◽  
Vehicle Speed ◽  
Prediction System ◽  
Energy Management Strategy ◽  
Training Time ◽  
Speed Prediction

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

A closed-loop analysis of the tubuloglomerular feedback mechanism

1991 ◽  
Vol 261 (5) ◽  
pp. F880-F889 ◽  
Author(s):  
N. H. Holstein-Rathlou
Keyword(s):  
Flow Rate ◽  
Closed Loop ◽  
Feedback Mechanism ◽  
Open Loop ◽  
Tubuloglomerular Feedback ◽  
Operating Point ◽  
Sprague Dawley ◽  
Loop Analysis ◽  
Experimental Conditions ◽  
Tubular Flow

The tubuloglomerular feedback (TGF) mechanism is of importance in the regulation of glomerular filtration rate (GFR). A second mechanism of potential importance is the change in proximal pressure caused by a change, for example, in the rate of proximal fluid reabsorption. The quantitative contributions of these two mechanisms to the regulation of GFR and the late proximal flow rate are not known. To determine the regulatory efficiency of these two mechanisms, the late proximal flow rate was perturbed by microperfusion with artificial tubular fluid in halothane-anesthetized Sprague-Dawley rats. The resulting changes in late proximal flow rate were measured by pulse injection of rhodamine dextran. Fluorescence was excited by means of a He-Ne laser. Bolus velocity was measured by videomicroscopy. Tubular pressure was measured by the servonulling method. The microperfusion rate was varied from -15 to 20 nl/min in steps of 5 nl/min. The open-loop gain (OLG) was 3.1 (range 1.5-9.9, n = 13) at the unperturbed tubular flow rate, and decreased as the tubular flow rate was either increased or decreased. The proximal pressure increased by 0.21 +/- 0.03 mmHg per unit increase in late proximal flow rate (nl/min). By use of a mathematical model of the glomerulus, it is estimated that under the present experimental conditions the pressure increase contributes 8% (range 3-15%) of the OLG. It is concluded that, for small perturbations around the operating point, TGF accounts for most of the regulation of GFR and the late proximal flow rate, with changes in the proximal pressure of lesser importance. Furthermore, under closed-loop conditions the operating point for the TGF mechanism is at or close to the point of maximal sensitivity.

Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes

2020 ◽  
Vol 106 (1) ◽  
pp. 55-63
Author(s):  
Clara Viñals ◽  
Aleix Beneyto ◽  
Juan-Fernando Martín-SanJosé ◽  
Clara Furió-Novejarque ◽  
Arthur Bertachi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Insulin Pump ◽  
Artificial Pancreas ◽  
Cycle Ergometer ◽  
Open Loop ◽  
Target Range ◽  
Exercise Session ◽  
And Performance

Abstract Objective To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). Research Design and Methods A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. Results Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates. Conclusions The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake. Register Clinicaltrials.gov: NCT03577158

scholarly journals Active Gate Driver and Management of the Switching Speed of GaN Transistors during Turn-On and Turn-Off

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 106
Author(s):  
Mamadou Lamine Beye ◽  
Thilini Wickramasinghe ◽  
Jean François Mogniotte ◽  
Luong Viêt Phung ◽  
Nadir Idir ◽  
...  
Keyword(s):  
Gate Voltage ◽  
High Speed ◽  
Closed Loop ◽  
Voltage Control ◽  
Open Loop ◽  
Effective Control ◽  
Loop Analysis ◽  
Switching Speed ◽  
Switching Losses ◽  
Electromagnetic Disturbances

The paper investigates the management of drain voltage and current slew rates (i.e., dv/dt and di/dt) of high-speed GaN-based power switches during the transitions. An active gate voltage control (AGVC) is considered for improving the safe operation of a switching cell. In an application of open-loop AGVC, the switching speeds vary significantly with the operating point of the GaN HEMT on either or both current and temperature. A closed-loop AGVC is proposed to operate the switches at a constant speed over different operating points. In order to evaluate the reduction in the electromagnetic disturbances, the common mode currents in the system were compared using the active and a standard gate voltage control (SGVC). The closed-loop analysis carried out in this paper has shown that discrete component-based design can introduce limitations to fully resolve the problem of high switching speeds. To ensure effective control of the switching operations, a response time fewer than 10 ns is required for this uncomplex closed-loop technique despite an increase in switching losses.

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