Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and presence.Traditional mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with insecticide.But they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find you.This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time.The speed and active time depend on the repellent cartridge and the number of mosquitoes.The Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous outputs.The input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output variable.The whole FS is designed and simulated using MATLAB Simulink R2016b.The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability.Different simulations of the proposed model are performed in many nonlinear processes.Then,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

Electronic Noses Using Quantitative Artificial Neural Network

The present paper covers a new type of electronic nose (e-nose) with a four-sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error (RE) in changing quantitative analysis into the artificial neural network (ANN). Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, iiquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.

Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR data.The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables.The objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour procedures.In this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator data.Specifically,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between them.The experimental result shows the higher efficiency of k-NN predictor over the kernel predictor.The predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.

AN IMPROVED AGING-PREDICT SCHEME BASED ON SYMMETRICAL NOR GATE

Transistor aging has become the most important factor affecting the integrated circuit reliability.There is detection error caused by stacking effect in the stability checker in the previous respective aging sensors.An improved prediction aging sensor scheme is proposed using symmetrical NOR gate.Simulation experiments show the improved method eliminates prediction error caused by stacking effect.Layout area overhead is 4.65%and 7.06%compared to two respective sensor structures.

Parametric sensitivity analysis of bearing characteristics of single DOF autonomous system of magnetic-liquid double suspension bearing

The mathematical model of single degree of freedom(DOF)nonlinear autonomous bearing system under constant flow supporting model is deduced.The single DOF nonlinear autonomous bearing system is transformed with the method of linear and nonlinear treatment,the mathematical expression and parameters sensitivity of relative error of stiffness and damping are presented.Finally,the main factors of magnetic-liquid double suspension bearing(MLDSB)are analyzed,and the influence on bearing performance indicators of single DOF nonlinear autonomous bearing system of main factors is revealed.The results show that linear stiffness/damping is the first part of equivalent stiffness/damping,and the second and third parts are high order minor term of Tayor series transform.The film thickness,the magnetic-liquid proportionality coefficient,the mass of rotor are the major influence factor of the bearing performance.The research can provide the theoretical reference for the design and nonlinear analysis of MLDSB.

Relation between the Actual and Estimated Maximum Ground Level Concentration of Air Pollutant and Its Downwind Locations

In this work we used the Gaussian plume model to calculate the actual maximum ground level concentration (MGLC) of air pollutant and its downwind location by using different systems of dispersion parameters and for different stack heights. An approximate formula for the prediction of downwind position that produces the MGLC of a pollutant based on the Gaussian formula was derived for different diffusion parameters. The derived formula was used to calculate the approximate MGLC. The actual and estimated values are presented in tables. The comparison between the actual and estimated values was investigated through the calculation of the relative errors. The values of the relative errors between the actual and estimated MGLC lie in the range from: 0 to 70.2 and 0 to 1.6 for Pasquill Gifford system and Klug system respectively. The errors between the actual and estimated location of the MGLC lies in the range from: 0.2 to 227 and 0.7 to 9.4 for Pasquill Gifford system and Klug system respectively.

Quantization error in stereo imaging system with noise distributions

The problem of estimating quantization error in 2D images is an inherent problem in computer vision.The outcome of this problem is directly related to the error in reconstructed 3D position coordinates of an object.Thus estimation of quantization error has its own importance in stereo vision.Although the quantization error cannot be controlled fully,still statistical error analysis helps us to measure the performance of stereo systems that relies on the imaging parameters.Generally,it is assumed that the quantization error in 2D images is distributed uniformly that need not to be true from a practical aspect.In this paper,we have incorporated noise distributions(Triangular and Trapezoidal)for the stochastic error analysis of the quantization error in stereo imaging systems.For the validation of the theoretical analysis,the detailed simulation study is carried out by considering different cases.

Critical current parameterization of high temperature Superconducting Tapes:A novel approach based on fuzzy logic

In this study a novel method was presented to parameterize the critical current of Yttrium Barium Copper Oxide(YBCO)tapes based on their width,thickness,magnetothermal operational conditions,and the applied strain.For this purpose,a fuzzy‐logic‐based model was developed that take tapes structures and their operational conditions as inputs to calculate their critical current,as output.The results of critical current parame-terization by fuzzy‐logic‐based model showed that the relative error of the proposed model is less than 3%comparing to experimentally acquired data.Then,the results of presented model was compared to results of semi‐analytical fitting‐based models and fully‐analytical fitting based models.The comparisons showed the better performance in terms of accuracy and error of fuzzy logic model over fitting‐based methods.At last,the results were also compared with the Artificial Neural Network(ANN)‐based parameterization model and Adaptive Nero‐Fuzzy Interference System(ANFIS)‐based parameterization model.The proposed method had 6%to 8%higher accuracy and about 47%to 54%lower root mean squared error.

Smoothing analysis of HLSII storage ring magnets

Hefei Light Source(HLS) has been upgraded to improve the quality and stability of the synchrotron light, and the new facility is named HLSII. However, a final accurate adjustment is required to smooth the beam orbit after the initial instalment and alignment of the magnets. We implement a reliable smoothing method for the beam orbit of the HLSII storage ring. In addition to greatly smoothing and stabilizing the beam orbit, this method also doubles the work efficiency and significantly reduces the number of magnets adjusted and the range of the adjustments.

Iteration framework for solving mixed lubrication computation problems

The general discrete scheme of time-varying Reynolds equation loses the information of the previous step,which makes it unreasonable.A discretization formula of the Reynolds equation,which is based on the Crank-Nicolson method,is proposed considering the physical message of the previous step.Gauss-Seidel relaxation and distribution relaxation are adopted for the linear operators of pressure during the numerical solution procedure.In addition to the convergent criteria of pressure distribution and load,an estimation framework is developed to investigate the relative error of the most important term in the Reynolds equation.Smooth surface with frill contacts and mixed elastohydrodynamic lubrication is tested for validation.The asperity contact and sinusoidal wavy surface are examined by the proposed discrete scheme.Results show the precipitous decline in the boundary of the contact area.The relative error suggests that the pressure distribution is reliable and reflects the accuracy and effectiveness of the developed method.

CFD simulation and experiment on the flow field of air-assisted ultra-low-volume sprayers in facilities

In order to explore the performance of the B-ULV-616A knapsack sprayer,computational fluid dynamics(CFD)was used to simulate the B-ULV-616A knapsack air-assisted device,which features an ultra-low-volume electric sprayer.Field experiments were carried out to test the spraying effects,and the KANOMAX anemometer was used to verify the simulated results.First,the internal and external flow fields and droplet deposition distribution of the ultra-low-volume sprayer were established.The results showed that the air-assisted spray device can change the airflow speed and direction and produce a high-speed swirling airflow at the outlet of the air-assisted spray device.The high-speed airflow(maximum of 83.5 m/s)generates negative pressure(minimum of 0.099 MPa)and causes a rapid increase in the droplet velocity and a secondary droplets spray,allowing droplets to reach a longer distance.Then,the maximum relative error was 20.14%,and its average value was 9.59%,indicating that the CFD method is suitable for the flow field analysis of the air-assisted spray device.Finally,based on the greenhouse experiment,the knapsack air-assisted ultra-low-volume electric sprayer was found to effectively improve the deposition on the rear of the crop,increase the droplet density(maximum of 81/cm2;droplet density of conventional electric sprayer is 64/cm2),and reduce the deposition amount and coefficient of variation(below 20%)within and between regions.Further,it managed to reduce pesticide use(by 69.85%)and rural non-point source pollution.

On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities

A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same time.In this paper,we give a unified proof on theO(1/t)iteration complexity,together with the linear convergence rate for this kind of proximal-based decomposition methods.Besides theε-optimal iteration complexity result defined by variational inequality,the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same order.Further,the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties,without necessary strong monotone conditions.

Localization Algorithm of Wireless Sensor Network Based on Concentric Circle Distance Calculation

Node Localization is one of the key technology in the field of wireless sensor network(WSN)that has become a challenging research topic under the lack of distance measurement.In order to solve this problem,a localization algorithm based on concentric circle distance calculation(LACCDC)is proposed.The LA-CCDC takes the beacon as the center of the concentric circle,then divides the task area into concentric circles with the k communication radius of sensor,which forms concentric rings.The node located in the k hops ring intersects the concentric circle with(k−1)r radius that forms an intersection area.This area is used to calculate the distance from the beacon to the unknown node,hyperbola is then adopted to locate the unknown node.In the application scenario with node random distribution,the simulation results show that the LA-CCDC algorithm gets the node location with low error under different node number,different beacons and different communication radius of sensor.