<abstract xml:lang="en"><title style='display:none'>Determination of Load Angle for Salient-pole Synchronous Machine</title>This paper presents two methods for load angle determination for salient-pole synchronous generator. The first method uses optical encoder to detect the rotor position. In some cases the end of the rotor shaft is not free to be used and mounting of an encoder is impossible. Therefore, the second method proposes estimation of the load angle based on the measured electrical values that have been already used in excitation control system of the synchronous generator. Estimation method uses corresponding voltage-current vector diagram and parameters of the synchronous generator, transformer and transmission lines. Both methods were experimentally verified on the digital control system and synchronous generator connected to power system. The estimation and measured results were compared. The accuracy of load angle estimation method depends on voltage and current measurement accuracy as well as generator, transformer and transmission line parameter accuracy. The estimation method gives satisfactory accuracy for load angles less than 120° el. Thus, it can be applied in excitation control system to provide stable work of synchronous generator in under-excitation operating area.</abstract>

Determination of Load Angle for Salient-pole Synchronous Machine

<abstract>This paper analyzes the decoupling of a sliding structure six-axis force/torque sensor, which is used to measure the interactive force between surgical tools and soft tissue for the establishment of soft-tissue force model. Because this decoupling structure requires accurate sliding clearance and symmetric grooves, the influence of contact force between the elastic body and the groove sidewall on decoupling is analyzed. The analysis results indicate that the contact force will produce additional coupling error. The robust design method of elastic body size optimization is used to eliminate the influence of contact force. In the calibration test, the expanded uncertainty of the calibration device is evaluated and the calibration results validate the good decoupling.</abstract>

Decoupling Analysis of a Sliding Structure Six-axis Force/Torque Sensor

<abstract>SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In this paper, we first systematically analyze SIFT and its variants. Then, we evaluate their performance in different situations: scale change, rotation change, blur change, illumination change, and affine change. The experimental results show that each has its own advantages. SIFT and CSIFT perform the best under scale and rotation change. CSIFT improves SIFT under blur change and affine change, but not illumination change. GSIFT performs the best under blur change and illumination change. ASIFT performs the best under affine change. PCA-SIFT is always the second in different situations. SURF performs the worst in different situations, but runs the fastest.</abstract>

The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China

Department of Computer Science, University of Central Arkansas, Conway 72035, USA

A Comparative Study of SIFT and its Variants

<abstract xml:lang="en"><title style='display:none'>Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification</title>Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.</abstract>

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

<abstract xml:lang="en"><title style='display:none'>Probing System Characteristics in Coordinate Metrology</title>This paper aims at studying the effect of the dynamic errors on surface measurements using three different types of touch trigger probes attached to a bridge-type-CMM. Unforeseeable dynamic root errors of a ductile touch trigger probing system have been characterized theoretically and experimentally as well. The results were employed in validating a developed analytical two-dimensional-model (2DM) of stylus tip to be developed to demonstrate the capability of such approaches of emphasizing the root error concept, and to evaluate the accuracy of the CMM measurements. A set of experiments was conducted; the results were analyzed in order to investigate the effect of the dynamic root errors in the light of probe scanning speed at different stylus tip radii. Variations in the mass and geometry of the stylus have their consequent effects on its inherent intrinsic dynamic characteristics that in turn would cause relevant systematic root errors in the resulting measurements. 3D bore cylindrical surface form undulations were measured by employing a probe on the trajectory of internal surface diameter for the standard reference test gauge ring. Regression analysis was applied on the results of measurement density distribution; uncertainty of measurement repeatability was then evaluated and graphically presented. The results were investigated and optimum strategic measurement parameters could thus have been derived to ensure foreseeable accurate and precise results.</abstract>

Probing System Characteristics in Coordinate Metrology

<abstract xml:lang="en"><title style='display:none'>Denoising EOG Signal using Stationary Wavelet Transform</title>Eye movements are critical signs of the neurological disorders and they can be acquired by EOG. The EOG signal is electrical signal generated due to eye ball movements and is contaminated with brain signals and power line while recording. As the EOG signal is a non-stationary signal, it can be denoised by wavelet transformation techniques. The present work covers denoising of noisy EOG signal using Stationary Wavelet Transform (SWT), which was done with all suitable wavelets that are morphologically similar to an EOG signal by applying both Soft and Hard Thresholding methods. An EOG signal was simulated and added with noise to obtain noisy EOG signal. The wavelet analysis of the simulated noisy EOG signal reveals that the Biorthogonal 3.3 wavelet is the best wavelet to denoise by using SWT technique, wherein the yield achieved was good with Signal to Noise Ratio of 36.5882 dB and minimum Mean Square Error of 0.383313 for quality diagnosis.</abstract>

Department of Physics, Jawaharlal Nehru Technological University Hyderabad, Hyderabad-3, India

Department of Electrical Engineering, IIT Bombay, India

Department of Biomedical Engineering, Osmania University, Hyderabad-7

Denoising EOG Signal using Stationary Wavelet Transform

<abstract>
<title style='display:none'>Abstract</title>
In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.
</abstract>

Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems

<abstract>
<title style='display:none'>Abstract</title>
Millions of people worldwide suffer from diabetes, a medical condition that is spreading at an accelerating pace. Numerous studies show that risk factors that may arise from diabetes can be avoided if the disease is detected early. The health-care monitoring system has benefited greatly from early diabetes prediction made possible by the integration of Deep Learning (DL) and Machine Learning (ML) algorithms. The objective of many early studies was to increase the prediction model accuracy. However, DL algorithms often cannot fully exploit the potential of the available datasets because they are too small. This study includes a very accurate DL model as well as a novel system that integrates cloud services and allows users to directly enhance an existing data set, which can increase the accuracy of DL techniques. Therefore, the Long Short-Term Memory (LSTM) model with controller is chosen for efficient type-1 diabetes prediction. Experimental validation of the proposed Nonlinear Model Predictive Control (NMPC)_LSTM algorithm method is compared with other conventional DL algorithms. The proposed controller method achieves excellent blood glucose set point tracking and the proposed algorithms achieves 98.95% accuracy for the obtained data. It outperforms other existing methods with an increase in percentage accuracy compared to the Benchmark Pima Indian Diabetes Datasets (PIDD).
</abstract>

A Cloud-Connected Digital System for Type-1 Diabetes Prediction using Time Series LSTM Model

<abstract>
<title style='display:none'>Abstract</title>
Emotion recognition systems from speech signals are realized with the help of acoustic or spectral features. Acoustic analysis is the extraction of digital features from speech files using digital signal processing methods. Another method is the analysis of time-frequency images of speech using image processing. The size of the features obtained by acoustic analysis is in the thousands. Therefore, classification complexity increases and causes variation in classification accuracy. In feature selection, features unrelated to emotions are extracted from the feature space and are expected to contribute to the classifier performance. Traditional feature selection methods are mostly based on statistical analysis. Another feature selection method is the use of metaheuristic algorithms to detect and remove irrelevant features from the feature set. In this study, we compare the performance of metaheuristic feature selection algorithms for speech emotion recognition. For this purpose, a comparative analysis was performed on four different datasets, eight metaheuristics and three different classifiers. The results of the analysis show that the classification accuracy increases when the feature size is reduced. For all datasets, the highest accuracy was achieved with the support vector machine. The highest accuracy for the EMO-DB, EMOVA, eNTERFACE’05 and SAVEE datasets is 88.1%, 73.8%, 73.3% and 75.7%, respectively.
</abstract>

Comparative Performance Analysis of Metaheuristic Feature Selection Methods for Speech Emotion Recognition

<abstract>
<title style='display:none'>Abstract</title>
Based on the well logging requirements of horizontal stripper wells, the flow characteristics of the oil-water two-phase flow in the invisible horizontal tri-electrode capacitive sensor (HTCS) measurement pipeline are studied. First, an experimental device and a numerical validation model of a horizontal 20 mm glass pipeline are established to study the flow characteristics of the oil-water two-phase flow. Then, the flow characteristics of the horizontal oil-water two-phase flow in the measurement pipeline under different horizontal inclination angles are studied and the flow patterns and inclination angles suitable for the new tri-electrode capacitive sensor are discussed. Finally, using the horizontal oil-water two-phase flow loop platform of the largest oil and gas testing center in China, the dynamic response of the new capacitive sensor is studied under different inclination angles, flow rates, and water-cut conditions, and the dynamic response law is analyzed based on the simulation results.
</abstract>

Study on Oil-Water Two-phase Flow in the Invisible Measuring Pipeline of the Horizontal Tri-electrode Capacitive Sensor

<abstract>
<title style='display:none'>Abstract</title>
The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.
</abstract>

Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification

<abstract>
<title style='display:none'>Abstract</title>
This article presents a study carried out on a 2 kN·m torque transducer at the Central Office of Measures (GUM) and the Physikalisch-Technische Bundesanstalt (PTB). The weighted least squares method was used to generate the linear regression equations for this torque transducer. The Monte Carlo method and the law of uncertainty propagation were used to calculate the expanded uncertainty. In addition, a creep study was carried out at eight measurement points ranging from 200 N·m to 2000 N·m. The investigations showed that the highest readings of the torque transducer, expressed in electrical units as mV/V, occur within the initial few seconds of the test after the removal of the maximum reference torque.
</abstract>

Study of a 2 kN·m Torque Transducer Tested at GUM and PTB, Including Creep Behaviour

<abstract><title style='display:none'>Abstract</title>Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.</abstract>

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

<abstract>This study presents the variations of photoplethysmogram (PPG) morphology with age. PPG measurement is done noninvasively at the index finger on both right and left hands for a sample of erectile dysfunction (ED) subjects. Some parameters are derived from the analysis of PPG contour showed in association with age. The age is found to be an important factor that affects the contour of PPG signals which accelerates the disappearance of PPG’s dicrotic notch and PPG’s inflection point as well. Arterial compliance is found to be degraded with age due to the fall of arterial elasticity. This study approaches the establishment of usefulness of PPG’s contour analysis as an investigator to the changes in the elastic properties of the vascular system, and as a detector of early sub-clinical atherosclerosis.</abstract>

College of computer science and engineering Taibah University, Madinah-Saudi Arabia

Department of Electrical, Electronic and Systems Engineering, University Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

The Analysis of PPG Morphology: Investigating the Effects of Aging on Arterial Compliance

<abstract><title style='display:none'>Abstract</title> In this paper, the selected results of measurements and analysis of the active surfaces of a new generation of coated abrasive tools obtained by the use of focus-variation microscopy (FVM) are presented and discussed. The origin of this technique, as well as its general metrological characteristics is briefly described. Additionally, information regarding the focus variation microscope used in the experiments - InfiniteFocus® IF G4 produced by Alicona Imaging, is also given. The measurements were carried out on microfinishing films (IMFF), abrasive portable belts with Cubitron™ II grains, and single-layer abrasive discs with Trizact™ grains. The obtained results were processed and analyzed employing TalyMap 4.0 software in the form of maps and profiles, surface microtopographies, Abbott- Firestone curves, and calculated values of selected areal parameters. This allowed us to describe the active surfaces of the coated abrasive tools, as well as to assess the possibility of applying the FVM technique in such kinds of measurements.</abstract>

Subject Group of Metrology and Quality, Department of Production Engineering, Faculty of Mechanical Engineering, Koszalin University of Technology, Racławicka 15-17, 75-620 Koszalin, Poland

Department of Production Engineering, Faculty of Mechanical Engineering, Koszalin University of Technology, Racławicka 15-17, 75-620 Koszalin, Poland

Faculty of Mechanical Engineering, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland

The Use of Focus-Variation Microscopy for the Assessment of Active Surfaces of a New Generation of Coated Abrasive Tools

<abstract xml:lang="en"><title style='display:none'>Multi-Wavelength Interferometry for Length Measurements Using Diode Lasers</title>This paper describes the principle of precise length measurements using multi-wavelength interferometry. Three different examples are given in order to demonstrate PTB's ability in this field: 1) Measurements of absolute distances up to 10 m performed by combination of the variable wavelength interferometry with fixed wavelength interferometry using two wavelengths simultaneously, 2) a three-wavelength diode laser interferometer for precision measurements of surface profiles (&lt; 100 μm) and 3) PTB's Precision Interferometer for length measurements of prismatic bodies, e.g. gauge blocks, using three wavelengths. The setup and principle of measurements and some results are presented and discussed in each case.</abstract>

Multi-Wavelength Interferometry for Length Measurements Using Diode Lasers

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