Optimization of bone drilling parameters using grey-based fuzzy algorithm

In this article a modified algorithm (grey based fuzzy algorithm) is used to optimize multiple performance characteristics in drilling of bone. Experiments have been performed with different cutting conditions using full factorial design. The quality parameters considered are temperature, force and surface roughness. Grey relational analysis (GRA) coupled with fuzzy logic is employed to obtain a grey fuzzy reasoning grade (GFRG) combining all the quality characteristics. The highest GFRG is obtained for the feed rate of 40 mm/min and the speed of 500 rpm and is the optimal level. Analysis of variance (ANOVA) carried out to find the significance of parameters on multiple performance characteristics revealed that the feed rate has the highest contribution on GFRG followed by the spindle speed. The optimum level of the process parameters obtained is validated by the confirmation experiment.     

Sensorless control of ship propulsion interior permanent magnet synchronous motor based on a new sliding mode observer

This paper proposes a sensorless speed control strategy for ship propulsion interior permanent magnet synchronous motor (IPMSM) based on a new sliding-mode observer (SMO). In the SMO the low-pass filter and the method of arc-tangent calculation of extended electromotive force (EMF) or phase-locked loop (PLL) technique are not used. The calculation of the rotor speed is deduced from the Lyapunov function stability analysis. In order to reduce system chattering, sigmoid functions with switching gains being adaptively updated by fuzzy logic systems are innovatively incorporated into the SMO. Finally, simulation results for a 4.088 MW ship propulsion IPMSM and experimental results from a 7.5 kW IPMSM drive are provided to verify the effectiveness of the proposed SMO method.     

A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency

The QRS complex detection is very imperative step prior to detecting any other wave component, beat segments, or any other morphological parameters of electrocardiogram (ECG). The paper presents a novel technique for QRS complex detection with minimum pre-processing required. The technique uses two-stage median filter and Savitzky–Golay smoothing filter for pre-processing. The decision rule is based on root-mean-square of the signal. The technique evolves with a novel approach for isoelectric line detection and a kurtosis-based false positive correction method. The robustness of the proposed technique been substantiated on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), MIT-BIH Normal Sinus Rhythm Database (MIT-NSD), and BIDMC Congestive Heart Failure Database (CHFD). The detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Ac) of proposed technique on FTD is Se = 99.90%, +P = 99.91%, DER = 0.19%, and Ac = 99.81%. On MIT-AD the technique has Se = 99.50%, +P = 99.56%, DER = 0.93%, and Ac = 99.08%. The technique shows Se = 99.36%, +P = 99.43%, DER = 1.21%, and Ac = 98.81% on MIT-NSD. Tested on CHFD, the techniques has Se = 99.68%, +P = 99.78%, DER = 0.52%, and Ac = 99.46%. The technique is also tested on self-recorded data-set and has Se = 99.97%, +P = 99.99%, DER = 0.04%, and Ac = 99.96%. The observation of above results shows the high performance and robustness of QRS complex detection, which will lead to highly accurate cardiac health prognosis.     

Nano force sensing using symmetric double stage micro resonator

A new approach is proposed to improve a graphical approach with considering intensity coupling loss coefficients in the analytical derivation of the optical transfer functions for a symmetric double stage vertically coupled microring resonator. An optimum transmission coupling condition is determined with considering terms of couplers intensity loss which leads to low insertion loss of 1.2 dB, finesse of 1525, the out of band rejection ratio of 61.8 dB. The resonating system is used as an optical force sensing system to make the benefit of the accuracy of measurements in micro and nano scales. The sensitivity of proposed force sensor in terms of wavelength-shift is 33 nm/nN and the limit of detection is 1.6 × 10⁻² nN. The proposed sensing system has the advantages of self-calibration and the low power consumption due to the low intensity.     

A force-matching method for quantitative hardness measurements by atomic force microscopy with diamond-tipped sapphire cantilevers

We present a new method to improve the accuracy of force application and hardness measurements in hard surfaces by using low-force (<50 μN) nanoindentation technique with a cube-corner diamond tip mounted on an atomic force microscopy (AFM) sapphire cantilever. A force calibration procedure based on the force-matching method, which explicitly includes the tip geometry and the tip-substrate deformation during calibration, is proposed. A computer algorithm to automate this calibration procedure is also made available. The proposed methodology is verified experimentally by conducting AFM nanoindentations on fused quartz, Si(1 0 0) and a 100-nm-thick film of gold deposited on Si(1 0 0). Comparison of experimental results with finite element simulations and literature data yields excellent agreement. In particular, hardness measurements using AFM nanoindentation in fused quartz show a systematic error less than 2% when applying the force-matching method, as opposed to 37% with the standard protocol. Furthermore, the residual impressions left in the different substrates are examined in detail using non-contact AFM imaging with the same diamond probe. The uncertainty of method to measure the projected area of contact at maximum force due to elastic recovery effects is also discussed.     

A highly accurate ultrasonic ranging method based on onset extraction and phase shift detection

The opposite-type ultrasonic ranging application is widely used in spatial 3D coordinates measurement systems. The traditional phase shift (PS) estimation based on multi-frequency ultrasonic pulse is a highly accurate ranging method but has high requirement to the transducers and signal processing algorithm. This paper proposes a novel opposite-type ultrasonic ranging method with single frequency pulse. It can estimate the time of flight (TOF) roughly through extracting the signal’s onset based on self-correlation and correct the TOF accurately through detecting the phase shift between the transmission and reception signals. In order to reduce the noise disturbance to this algorithm, a new de-noising method based on wavelet decomposition is presented, and the de-noising effect is analyzed by MATLAB simulation. Finally, three separate ultrasonic ranging experiments were designed to validate the effects of wavelet de-noising, PS detection and temperature field compensation. The accuracy of distance measurement can achieve 0.5 mm for the distance up to 5000 mm.     

Application of the continuous wavelet transform in periodic error compensation

This paper introduces a new discrete time continuous wavelet transform (DTCWT)-based algorithm, which can be implemented in real time to quantify and compensate periodic error for constant and non-constant velocity motion in heterodyne displacement measuring interferometry. It identifies the periodic error by measuring the phase and amplitude information at different orders (the periodic error is modeled as a summation of pure sine signals), reconstructs the periodic error by combining the magnitudes for all orders, and compensates the periodic error by subtracting the reconstructed error from the displacement signal measured by the interferometer. The algorithm is validated by comparing the compensated results with a traditional frequency domain approach for constant velocity motion. The algorithm demonstrates successful reduction of the first order periodic error amplitude from 4 nm to 0.24 nm (a 94% decrease) and a reduction of the second order periodic error from 2.5 nm to 0.3 nm (an 88% decrease). The algorithm also reduces periodic errors for non-constant velocity motion overcoming limitations of existing methods.     

A three-dimensional triangular vision-based contouring error detection system and method for machine tools

Contouring error detection for machine tools can be used to effectively evaluate their dynamic performances. A triangular vision-based contouring error detection system and method is proposed in this paper, realizing the three-dimensional error measurement of an arbitrary trajectory in conditions of a high feed rate and wide motion range. First, a high-precision measurement fixture, which consists of high-precision circular coded markers and a highly uniform light source, is designed to accurately characterize the motion trajectory of a machine tool and realize the high-quality collection of an image sequence. Then, to improve the contouring error detection accuracy, a coded marker decoding and center location method for the automatic recognition and high-precision center positioning of the circular coded markers are applied. Using image preprocessing and matching, the markers’ three-dimensional coordinates in the camera coordinate system can be constructed. Moreover a data transformation method induced by the orthogonal motion of machine tools is proposed to obtain the three-dimensional trajectory in the machine tool coordinate frame and the contouring error can be calculated. Finally, a three-dimensional contouring error detection study of an equiangular spiral interpolation at different feed rates is performed in the laboratory. It is shown that the average contouring error for a feed rate of 1000 mm/min is about 3 µm, which verifies the vision measurement accuracy and feasibility.     

Three-dimensional profile stitching measurement for large aspheric surface during grinding process with sub-micron accuracy

A novel measurement method is proposed to realize three-dimensional (3D) profile stitching for large aspheric surface. The proposed method is based on the multiple sub-regions stitching technology applying a four-axis fixture and a commercial small-range profiler. The partition of sub-regions is due to the effective profiler’s range and the characteristic parameters of aspheric surface, and the measurement for each sub-region within the profiler’s range is achieved through the fixture to translate and rotate the aspheric surface. Then a stitching algorithm including the multi-body theory, the invariability of curvature radiuses and the least square principle is established to reconstruct the full 3D profile. Simulations of multiple sub-regions stitching for different aspheric surfaces are performed to predict the stitching accuracy of proposed method and analyze the influence of alignment errors in Y direction caused by the rotation error along Z direction (Δβ_w,g). The stitching accuracy of proposed method is verified by measuring the 3D profile of an off-axis parabolic surface and an axisymmetric aspheric surface. The experimental standard deviations of stitching errors are 0.16 μm and 0.42 μm, which are less than the form errors of aspheric surface during grinding process. The results show that the proposed method achieves 3D profile stitching for large aspheric surface with sub-micron accuracy.     

Nonlinear adaptive noise-induced algorithm and its application in penetration signal

A nonlinear adaptive noise induced algorithm with nonlinear weights was proposed to extract rigid body deceleration during penetration events; it has 3rd-order nonlinear weight, which ensures deceleration curve is smooth everywhere (not only continuous) and avoids sharp points (crucial for targets detection). In addition, an autocorrelation algorithm was improved by applying moving window method to be compared with the proposed nonlinear adaptive algorithm. By calculating penetration depth and Power Spectrum Density (PSD) of 4 deceleration time series, we show that the nonlinear adaptive algorithm more effectively reduces noise in deceleration for striking velocities between 538 and 800 m/s compared with Adaptive Paйta Criterion, moving window autocorrelation and wavelet algorithms. It is further shown that the proposed adaptive algorithm is of the same order as the other 3 methods in terms of computational complexity.     

Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

In this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the Mean Shift Clustering-Based Attribute Weighting (MSCBAW) and is based primarily on mean shift clustering algorithm finding the number of the sets automatically. In this study, we have used two different datasets including lumbar disc diseases (with two classes-our database) and vertebral column disorders datasets (with two or three classes) taken from UCI (University of California at Irvine) machine learning database to test the proposed approach. The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed. And then, the mean values of each property in dataset are calculated. The weighted datasets by multiplying these mean values by each property value in the dataset that have been obtained by dividing the above mentioned mean values by the centres of the sets belonging to the relevant property are achieved. After the data weighting stage, three different classification algorithms that included the k-NN (k-Nearest Neighbour), RBF–NN (Radial Basis Function–Neural Network) and SVM (Support Vector Machine) classifying algorithms have been used to classify the datasets. In the classification of vertebral column disorders dataset with two classes (normal or abnormal), while the obtained classification accuracies and kappa values were 78.70% ± 0.455 (the classification accuracy ± standard deviation), 81.93% ± 0.899, and 80.32% ± 0.56 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.03% ± 0.977, 99.67% ± 0.992, and 99.35% ± 0.9852, respectively. In the classification of second dataset named vertebral column disorders dataset with three classes (Normal, Disk Hernia, and Spondylolisthesis), while the obtained classification accuracies and kappa values were 74.51% ± 0.581, 78.70% ± 0.659, and 83.22% ± 0.728 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.35% ± 0.989, 96.77% ± 0.948, and 99.67% ± 0.994, respectively. As for the lumbar disc dataset, while the obtained classification accuracies and kappa values were 94.54% ± 0.974, 94.54% ± 0.877, and 93.45% ± 0.856 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 100% ± 1.00, 99.63% ± 0.991, and 99.63% ± 0.991, respectively. The best hybrid models in the classification of vertebral column disorders dataset with two classes, vertebral column disorders dataset with three classes, and lumbar disc dataset were the combination of MSCBAW and k-NN classifier, the combination of MSCBAW and RBF–NN classifier, and the combination of MSCBAW and SVM classifier, respectively.     

Measurement of highly reflective surface shape using wavelength tuning Fizeau interferometer and polynomial window function

In this study, a 5N − 4 phase shifting algorithm comprising a polynomial window function and a discrete Fourier transform is developed to measure interferometrically the surface shape of a silicon wafer, with suppression of the coupling errors between the higher harmonics and the phase shift error. A new polynomial window function is derived on the basis of the characteristic polynomial theory by locating five multiple roots on the characteristic diagram. The characteristics of the 5N − 4 algorithm are estimated with respect to the Fourier representation in the frequency domain. The phase error of the measurements performed using the 5N − 4 algorithm is discussed and compared with those of measurements obtained using other conventional phase shifting algorithms. Finally, the surface shape of a 4-in. silicon wafer is measured using the 5N − 4 algorithm and a wavelength tuning Fizeau interferometer. The accuracy of the measurement is discussed by comparing the amplitudes of the crosstalk noise calculated by other algorithms. The uncertainty of the entire measurement was 34 nm, better than that of any other conventional phase shifting algorithms.     

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0–60 m/s wind speed with resolution of 0.1 m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.     

Design and modeling of an intelligent temperature to frequency converter

This paper presents the development of an intelligent temperature to frequency converter to measure the temperature using a negative temperature coefficient (NTC) thermistor. The signal conditioning circuit (SCC) of the NTC thermistor is a modified timer circuit whose control voltage is generated by a difference amplifier. The thermistor SCC acts as a temperature to frequency converter and exhibits a moderate linear temperature-frequency characteristic over a range of 0–100 °C with a linearity error of ±3.5%. A multilayer perceptron (MLP) neural network with Levenberg–Marquardt (LM) algorithm is used for modeling and nonlinearity estimation of the converter. The LM algorithm effectively reduces the linearity error as compared to the Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the scaled conjugate gradient (SCG) algorithms. Mean square error (MSE), regression coefficients, linearity, accuracy and dispersion spread are used to evaluate the performance of the proposed ANN-based modeling. The intelligence of the ANN-based modeling is embedded in a low cost microcontroller unit and the performance is experimentally verified on a prototype unit. The linearity error and sensitivity of the proposed unit are approximately ±0.35% and 5 kHz/°C respectively.     

Small-capacity deadweight force standard machine compensating loading frame

The Korea Research Institute of Standards and Science (KRISS) has developed a 20 N deadweight force standard machine. The machine consists of a weight stack, a loading frame, a taring system, a main body and a control system. The taring system has the role of compensating the initial force generated by the loading frame. With two motors, a displacement sensor, several limit switches, and a synthetic control system consisting of a programmable logic controller and an operating PC, the machine can be operated almost fully automatically. The machine can generate a compressive force in the range of 0.5–22 N with a relative expanded uncertainty of 1.0 × 10^–4. The machine was compared with a 200 N deadweight force standard machine. In the comparison, the relative deviation was 5.8 × 10^–5, less than the declared expanded uncertainty of the force standard machines, therefore confirming the machine’s accuracy.     

基于快速DBSCAN聚类的铁路异物侵限检测算法

在铁路建设及运营阶段,侵入限界的异物对既有线的安全行车构成极大威胁。将复杂的空间侵限检测转换为简单的平面内异物检测,研究利用二维激光测距传感器构建三维幕墙的侵限检测方法。在异物检测过程中,正常通过的列车不能认作异物,必须设计合理的算法检测并予以剔除。由于列车主要体现在轨道平面的检测结果中,重点研究通过分析扫描点云的分布特征对轨道平面内侵限物体进行分类的算法,提出了利用测量序列极值点作为核心对象的快速DBSCAN(density-based spatial clustering of applications with noise)聚类方法,并利用点簇的运动及分布特征判断是否为正常通过列车。现场试验表明,该方法能够有效区分侵限异物和正常通过的列车。     

Large-scale accelerator alignment using an inclinometer

A precise inclinometer (Talyvel 4) was adopted for evaluating aligning straightness of the first 71 m of the KEK electron/positron injector linear accelerator (linac). The straightness could be evaluated with a standard deviation of less than 49 μm. It is in good agreement with those obtained using a conventional alignment telescope and our laser-based alignment system.Error estimation based on the rules of error propagation shows that shape evaluation with a standard deviation of 0.3 mm for a distance of 500 m can be achieved using the proposed method. It indicates that this method is suitable for evaluating straightness of several hundred meters of linacs with sub-millimeter of accuracy.     

A precision CNC turn-mill machining center with gear hobbing capability

With ever increasing demand for small parts with complex shapes and high dimensional accuracy, many traditional machine tools have become ineffective for machining these miniature components. Typical examples include dental implants, parts used in mechanical watch movements, and parts used in medical endoscopes. This paper introduces our PC-controlled CNC turn-mill machining center. It has 5 axes, an automatic bar feeder, an automatic part collection tray, and a tool changer. It features a special control algorithm for the synchronization of its axes that produces not only higher accuracy but also makes the machine easier to use. In addition, a volumetric error compensation algorithm is implemented to improve accuracy. Based on experiments, the machining error is ±3 μm for turning, ±7 μm for milling and the maximum profile error is less than ±7.5 μm for gear hobbing.     

一种时间约束的多机器人WSNs节能数据收集方法

为了在不同数据收集时间约束下最小化无线传感器网络(WSNs)的能量消耗,提出了一种时间约束的多机器人WSNs数据收集方法。首先,给出网络模型假设,并在此基础上,利用最优化理论,构建时间约束的多机器人WSNs数据收集模型;然后,基于图论和分簇技术,设计一种基于最优子路由树的多机器人WSNs数据收集算法;最后,通过与现有方法进行对比实验,实验结果表明,所提出的方法能够在满足应用时间约束的同时,有效地降低WSNs的能量消耗,延长网络生命周期。