Average Filtering Method Research Articles

Quick and accurate optical thermometry based on chemometrics model strategy in Na0.5Gd0.5TiO3: Er3+

Current optical temperature measurements are confined to materials with high sensitivity, while probes with fluorescence intensity ratio (FIR) sensitivity above 12% K−1 still confront severe challenges. Therefore, we propose a novel strategy to obtain temperatures in one step from the temperature-dependent emission spectra by establishing a chemometrics model instead of calculating FIR values to improve temperature measurement accuracy of materials with a low FIR sensitivity. Here, Er3+-doped Na0.5Gd0.5TiO3 with low sensitivity and the monotonic variation of FIR is used as a temperature sensing material. The temperature-dependent spectra are preprocessed by the moving average filter method, and the useful information is extracted from the preprocessed spectra by the synergy interval partial least squares (siPLS) algorithm. The extracted spectra are then combined with the partial least squares regression (PLSR), the principal component regression (PCR), the multiple linear regression (MLR), and the support vector machine regression (SVM) respectively to build chemometrics models. The errors between the predicted and the actual temperatures of different models are evaluated to verify the feasibility of the proposed strategy and to select the optimal temperature measurement model for Na0.5Gd0.5TiO3: Er3+. The results show small errors and high correlation coefficients over 0.98 between the predicted and the actual temperatures of all models, indicating the applicability of the chemometrics model strategy in optical thermometry. Finally, the best model (a combination of siPLS-PCR + SVM) and the periodic testing of FIR with temperature may make Na0.5Gd0.5TiO3: Er3+ a candidate for optical thermometer with rapidity, durability and accuracy.

Exploring deep residual network based features for automatic schizophrenia detection from EEG

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.

Synchronous capture method of multi-channel weak signal in long-distance communication network

In order to improve the accuracy, efficiency and network throughput of multi-channel weak signal synchronous acquisition in the network, a multi-channel weak signal synchronous acquisition method in remote communication network is designed. Firstly, by analyzing the multi-channel structure of remote communication network, the interference factors of multi-channel weak signal acquisition are determined. The parameter model method is used to estimate the bispectrum of weak signals, complete the multi-channel weak signal extraction of remote communication network, and preprocess the multi-channel weak signals of remote communication network by average filtering method. On this basis, the characteristics of multi-channel weak signals in the remote communication network are judged, and their characteristics are changed through the short time window function in the time domain, and the multi-channel weak signal synchronous catcher in the remote communication network is constructed to realize the synchronous acquisition of multi-channel weak signals in the remote communication network. The experimental results show that this method has high accuracy, short time-consuming and good network throughput. The acquisition accuracy of this method is always maintained at more than 90%.

Implementasi Moving Average dan Kalman Filter pada Wireless Odometer untuk Informasi Service Kendaraan Bermotor

Regular maintenance is one step to keep your car looking good, but many people still underestimate the importance of regular maintenance. A tool called Wireless Odometer has been created which can notify vehicle owners through the application if the vehicle mileage has entered the recommended criteria for periodic servicing. Based on previous research, Wireless Odometer still has weaknesses. sensor readings on this tool still have an error of 13.2% so it is still less accurate if applied to measure mileage on vehicles. It is necessary to have a digital filter that must be added to the sensor readings so that the sensor is resistant to noise that occurs due to mechanical or electrical. In this research, we implement the Moving Average Filter and Kalman Filter methods for sensor readings on the Wireless Odometer. After several tests were carried out, it was found that the percentage of reading errors when filtered reached 0.80% using the Kalman filter method and 1.81% using the Moving Average Filter method. It can be concluded that the filter that is suitable for use on this Wireless Odometer is the kalman filter.

Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter

An improved Maximum Power Point Tracking (MPPT) method based on a purely mechanical wave energy converter (WEC) of gyroscope precession is proposed. The method adopts dynamic perturbation step adjustment, which improves the stability of power output and reduces steady-state oscillation. The paper introduces the principle of the device, establishes the mathematical model, and obtains the complete expression of power. The effect of wave frequency, pitch amplitude, power take-off (PTO) damping coefficient, and flywheel rotating speed on power output is analyzed. The output regression equation is established, and the extraction conditions of the maximum power are summarized and predicted. Aiming at the time-varying nature of actual ocean waves, a variable step size modified maximum power point (MPP) tracking control algorithm based on perturbation and observation (P&O) method is proposed. The algorithm has a unique technology to dynamically change the perturbation size, which not only improves the dynamic response but also reduces the oscillation. Besides, the boundary conditions ensure that the algorithm will not deviate from the motion trajectory, and the average filtering method and steady-state judgment can further reduce steady-state oscillation. In various ocean conditions, the proposed method has better output stability compared with other variable step size algorithms. Finally, different wave working conditions are given in the experiment, and the results verify the effectiveness of the proposed MPPT control strategy in experimental equipment. The device will be suitable for distributed power sources in small islands and ports.

Subpixel sampling moiré method for in-plane displacement measurement considering the symmetric errors induced by interpolation.

Sampling moiré method (SMM) is a highly accurate vision-based deformation measurement method, whose measurement error is minimized when an integer that is closest to the grating pitch is selected as the sampling pitch. We propose a subpixel SMM by applying subpixel interpolation operation before the downsampling in traditional SMM; the sampling pitch was selected as a noninteger nearest to the grating pitch in the subpixel resolution image. Meanwhile, the average filter method was used to eliminate the symmetric error cause by interpolation. As a result, the period of moiré fringe was enlarged greatly, and the measurement accuracy was also increased. To investigate the efficiency of the subpixel SMM, a computer simulation was applied to analyze the accuracy of the subpixel SMM. Then a simple tensile experiment was conducted to validate the efficiency of this method, and the result of the subpixel SMM accorded well with the fiber Bragg grating. In summary, the proposed novel subpixel SMM is capable of being used for high accuracy in-plane deformation measuring.

Research on land use classification of hyperspectral images based on multiscale superpixels

With the rapid development of remote sensing technology, research on land use classification methods based on hyperspectral remote sensing images has attracted widespread attention. Existing land-use classification studies mostly use the average filtering method at a single scale for spectral image processing. These methods cannot accurately filter the window range, which leads to the neglect of image detail information, and the single kernel matrix cannot characterize multifeature information, resulting in reduced classification accuracy. Therefore, this study intended to use a superpixel segmentation method to perform multiscale superpixel segmentation on the first principal component of a hyperspectral image at multiple scales. By combining the weighted multiscale spatial-spectral kernel and the original spatial-spectral kernel to form a synthetic kernel for land use classification, the hyperspectral image of the National Mall in Washington DC was used as experimental data to test and analyze this method. The experimental results showed that the classification accuracy of this method on the experimental test set was 98.53%, which is compared with the classification results obtained by the single-scale spatial spectral synthetic nuclear method, the original spatial spectral synthetic nuclear method and the wavelength segmented synthetic nuclear method, the effective classification accuracy with this method was increased by 7.56%. The results prove that this method can effectively solve the problems of the lack of adaptability of the image spectrum and the lack of comprehensive spectral information and can significantly improve the accuracy of land use classification.

Method Based on Floating Car Data and Gradient-Boosted Decision Tree Classification for the Detection of Auxiliary Through Lanes at Intersections

The rapid detection of information on continuously changing intersection auxiliary through lane is a major task of lane-level navigation data updates. However, existing lane number detection methods possess long update cycles and high computational costs. Therefore, this study proposes a novel method based on floating car data (FCD) for the detection of auxiliary through lane changes at road intersections. First, roads near intersections are divided into three sections and the spatial distribution characteristics of the FCD of each section are analyzed. Second, the FCD is preprocessed to obtain a standardized FCD dataset by removing redundant data through an improved amplitude-limiting average filtering method. Third, a basic classifier for the number of lanes is constructed. Fourth, the final number of lanes of the road section is determined by combining the basic classifier and the gradient-boosted decision tree model. Finally, the presence of an auxiliary through lane at the intersection is determined in accordance with the change in the number of intersection lanes. The method was tested using data for a road in Wuchang District, Wuhan City. Experimental results show that this method can rapidly obtain auxiliary through lane information from the FCD and is superior to other classification methods.

Histogram equalization smoothing for determining threshold accuracy on ancient document image binarization

Ancient documents are inheritance that must be preserved. The documents contain historical, scientific, social, religious information, etc. Converting ancient documents into digital image formats is one of ways to preserve the inheritance and can be stored into a computer. However, images of ancientdocuments have many blemishes caused by age, moisture, flood, etc. Therefore, special techniques are needed for those images to be restored and can improve the legibility of the ancient documents’ images. In this study, the image restoration process uses separation of background and foreground/text on histogram equalization such as research conducted by Fitri Arnia in 2008. Through histogram equalizationimages can be seen the distribution of pixels from the intensity of black color "0" to white "1". The distribution of pixels on histogram equalization describes the curves of foreground/text and curves of background. Among the histogram curves, the determination of thresholdvalues can be done so as to clarify the foreground/text and background areas on images of ancient documents. The lowest point between the two curves is the lowest pixel (local minima) which is used as the threshold value. However, the selection of such threshold values in some cases is very difficult to determine because there are still many fluctuations in the curve at the lowest curve. Therefore, this study proposesa histogram smoothing method in the ancient documents’ images to minimize curvature fluctuations and to determine more accurate threshold values. In this research, average filtering method is used for smoothing the histogram image. This filter successfully refines the histogram and makes the image of the restoration or binary image display the value of the ancient document image readability increases.

Implementation of an estimator-based adaptive sliding mode control strategy for a boost converter based battery/supercapacitor hybrid energy storage system in electric vehicles

For the energy management of the boost converter based battery/supercapacitor (SC) hybrid energy storage system (HESS) in electric vehicles, a robust current control should be achieved for the battery such that the battery safety can be guaranteed. In this paper, an estimator-based adaptive sliding-mode control (estimator-based ASMC) strategy is proposed for the current tracking control of the boost converter based battery/SC HESS. By considering the unmodeled dynamics of the SC and the unknown disturbances, the equivalent circuit and the state-space average model are established for the boost converter based battery/SC HESS. To achieve the current tracking control, a sliding surface is defined based on the estimated tracking current error. The average control factor is designed according to the sliding-mode control (SMC) strategy. Furthermore, the adaptation laws are designed based on the state observers and the Lyapunov function, which can be used to estimate the load variations and unknown external input voltage. Finally, the adaptive control factor is recalculated based on the estimator-based ASMC strategy. In practical application, a hysteresis control strategy and an average filter method are developed to guarantee the smooth SMC for the boost converter based battery/SC HESS. Simulations and experiments are established to verify the estimator-based ASMC strategy. Compared to the conventional total SMC (TSMC) and PI strategies, the estimator-based ASMC strategy has over 37% and 50% settling-time improvement of current adjustment to deal with the load variation, respectively. The estimator-based ASMC strategy can improve the operating stability of the boost converter based battery/SC HESS under different operating modes. The battery can provide a constant or optimal current to the load. Therefore, the boost converter based battery/SC HESS with the proposed estimator-based ASMC strategy can effectively ensure the system stability and extend the battery life, simultaneously.

Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization

This paper presents a novel approach for aggressive and agitated behavior recognition using accelerometer data. Our approach applies first a noise reduction technique using the moving average filter method. Then, multiple features such as mean, variance, entropy, correlation and covariance are extracted from the filtered acceleration data using a sliding window. Non-negative matrix factorization is then used in order to project the data into a new reduced space that captures the significant structure of the data. The recognition is performed using the rotation forest ensemble method. The proposed approach is validated using extensive experiments on a real dataset collected at Toronto Rehabilitation Institute. We empirically demonstrate that our proposed approach accurately discriminates between behaviors and performs better than several state-of-the-art approaches.

Single Borehole Radar for Well Logging in a Limestone Formation: Experiments and Simulations

A borehole radar (BHR) prototype system was developed for the exploration of complicated oil and gas reservoirs. To verify the performance of the system, single-hole reflection imaging experiments were carried out in an abandoned limestone mine. In the physical experiments, the cliff wall and a metal plate were selected as the imaging targets to evaluate the detection performance of the prototype system. The average filter method was used to remove the background noise, then the frequency–wave number (F-K) imaging algorithm was adopted for radar imaging. The unknown fractures surrounding the borehole produced complex reflections that were not beneficial to effectively extract the target echo when the down-hole sensor was shifted along the borehole. However, by fixing the down-hole sensor and shifting the target, the detection range of the radar system extended up to about 10 m in the limestone formation. A 2-D finite-difference time-domain (FDTD) modeling method was also implemented to simulate the experimental procedure, and demonstrated that the prototype system can provide enough accuracy to predict the echo signal characteristics and reproduce the radar response in the formation. The combination of field experiment, theoretical analysis, and numerical simulation not only objectively validated the fundamental performance of the radar prototype, but also generated some new concepts for further improvement on the radar system design.

Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold

Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system. As a result, denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation. A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface. Based on the distribution characteristics of cloud point data, the noisy points were divided into three types: The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data, which could be deleted directly by a grid method; the third type of the noisy points was mixed with the main body of point cloud data, which were most difficult to distinguish. The point cloud data with those noisy points were projected into a horizontal plane. An image denoising method, discrete wavelet threshold (DWT) method, was applied to delete the third type of the noisy points. Three kinds of denoising methods including average filtering method, median filtering method and DWT method were applied respectively and compared for denoising the point cloud data. Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE (Root Mean Square Error, 0.219) as well as the lowest relative error of grain volume (0.086%) compared with the other two methods. Furthermore, the proposed denoising method could not only achieve the aim of removing noisy points, but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface. The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume. Keywords: point cloud data, denoising, grid method, discrete wavelet threshold (DWT) method, 3-D laser scanning, stored grain DOI: 10.3965/j.ijabe.20160904.2333 Citation: Shao Q, Xu T, Yoshino T, Song N, Zhu H. Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold. Int J Agric & Biol Eng, 2016; 9(4): 123－131.

Automatic Measurement Of Shape Parameters For Hydraulic Torque Converter

Abstract In this paper, the automatic measurement method of the Hydraulic Torque Converter shape parameters is studied. Aiming at the defects of the traditional manual measurement method, the solution for measuring the shape parameters of Hydraulic Torque Converter with the automatic measurement system is proposed on the basis of in-depth analysis for the Hydraulic Torque Converter shape parameters’ characteristics. Automatic measurement system mainly contains two parts, the Hardware Measurement System and the Software Measurement System. The working principle of the automatic measurement system are as follows: firstly, as system starting, the arm upper Measuring Fixture put the Hydraulic Torque Converter work piece into the Measuring Fixture, and then the Measuring Fixture make the Pump Hub Axle and Cover Hub Axle of the measured work piece fixed; secondly, the measured work piece start to rotate, at the same times, s the work piece is measured by MARPOSS displacement sensor. The measuring stop as soon as the measured work piece completes rotating 360 degrees. Lastly, measurement data is collects by the Software Measurement System based on LabVIEW development platform using NI acquisition card. The Moving Average Filter and Butterworth approximation method is mainly used during processing the measurement data. The tolerance measuring value of each shape parameter can be obtained, after the measurement data processed. The external dimensions of the measured work piece have been qualified by the data obtained. Under practical test, this method fit the industrial production demand very well; it can help to improve the production efficiency greatly used in the industrial production

Study on Wheel Run-Out Detection System Based on Harmonic-Analysis Principle

In order to reduce error of the wheel run-out detection system, a harmonic-analysis-based detection method was proposed to enhance the precision of online detection. The moving average filtering method was used for digital filtering between the axial and radial run-out errors so that to decrease the effect of outside noise on the measured data. Practical application shows that this system works stably and reliably on the wheel detection line and it realizes 100% online detection on the axial and radial run-out of work pieces, with the measurement error lower than 0.1mm.

Software Anti-Interference Design of SCM Control System

SCM control system requires high reliability and security, the entire control system to operate safely and reliably only when a strong anti-interference capability. In switch input and output, the use of the instruction redundancy methods to achieve the switch signal input and output filtering; in data collection, based on the hardware anti-interference taken arithmetic average filtering method, in order to strengthen its anti-jamming capability. In the system status monitoring, watchdog feature is enabled with the MSP430F449 microcontroller, when the watchdog timer overflows, the system automatically reset, the experiment proved that this method can save hardware resources, flexible design, low cost, single-chip control, it can guarantee the smooth operation of the system.

Shifting Window Average Method for Phasor Measurement at Offnominal Frequencies

A shifting window average method (SWAM) is proposed for phasor measurement at offnominal frequencies. The proposed SWAM is not only applicable for fundamental frequency phasor measurement, but also for harmonic phasor measurement as well. SWAM is based on a mathematical analysis of the errors induced in the commonly used DFT method at offnominal frequency inputs. First, a comprehensive derivation of magnitude and phase-angle measurement errors of discrete Fourier transform (DFT) is proposed. It is indicated that the errors are caused by modulations between different exponential components in the signal, and can be modeled by quasisinusoids with frequencies equal to the frequency differences between modulated components. SWAM is then introduced to eliminate the errors by examining the analytic form of the errors. Simulation tests have been performed on several benchmark signals. Simulation cases have validated that SWAM achieves higher accuracy compared with the DFT-based method and the three-sample average filter method. Experimental tests were also performed to validate the accuracy of the proposed method. Due to its high accuracy and reasonably low processing effort, SWAM is a valuable candidate for online phasor measurement in power systems.

The Research of Power plant Operating Data Based on Real-time Digital filtration technology

Real-time monitoring of the data of the thermal power plant is the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise is inevitable, we need real-time monitoring data filtering to get accurate information of units and equipment in the operating data of thermal power plant. Real-time filtering algorithm can’t be used to correct the current data with future data. Compared with traditional filtering algorithm, there are a lot of constraints. First-order lag filtering method and weighted recursive average filtering method can be used for real-time filtering. This paper analyzes the characteristics of the two filtering methods and applications for real-time processing of the positive spin simulation data, and the thermal power plant operating data. The analysis revealed that the weighted recursive average filtering method applied to the simulation and real-time plant data filtering achieved very good results. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3468

Study on Industrial Automation with SCARA Robot Vision System Design

To realize scara robot in industrial automation work environment identifying target objects independently, this paper puts forward a kind of machine vision solution based on opencv . First the k neighbor average filtering method and otsu is used to the initial image filtering and segmentation, and given a method based on pixel area, using the target object itself geometric characteristics and center of mass calibration to identify, locate purpose. The experimental results show that the system can achieve good object identification orientation effect in more complex industrial automation environment, so as to provide the necessary information for scara robot to grab target objects.This kind of robot vision system play an important role in industrial automation.

Multifunction GPS/GPRS Automobile Security System

Based on ARM,multifuncttion GPS/GPRS automobile security system was designed,which focused on security and detectiong.The system combines with detection technology, sensor technology, GPS technology, GPRS technology and digital filtering technology.This system was discussed from software and hardware. Related math model was established. Corresponding test was been brought forward to improve system reliability. Average filtering method and median filtering algorithm have been used to inhibiting interference signals with variety of frequency in software. In this paper,the advantages of this system have been discussed.The embeded system realizes the function with characteristics of low cost, high performance, real-time and reliability ,it has high practical value.