Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2019"
TL;DR: Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
Abstract: As an effective way to integrate the information contained in multiple medical images with different modalities, medical image fusion has emerged as a powerful technique in various clinical applications such as disease diagnosis and treatment planning. In this paper, a new multimodal medical image fusion method in nonsubsampled shearlet transform (NSST) domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain their multiscale and multidirection representations. The high-frequency bands are fused by a parameter-adaptive pulse-coupled neural network (PA-PCNN) model, in which all the PCNN parameters can be adaptively estimated by the input band. The low-frequency bands are merged by a novel strategy that simultaneously addresses two crucial issues in medical image fusion, namely, energy preservation and detail extraction. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency bands. The effectiveness of the proposed method is verified by four different categories of medical image fusion problems [computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission tomography, and MR and single-photon emission CT] with more than 80 pairs of source images in total. Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
381 citations
TL;DR: A technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA), based on a group of riders, racing toward a target location.
Abstract: Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task. Consequently, a technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA). The development of ROA is based on a group of riders, racing toward a target location. Moreover, a classifier, termed RideNN, is developed by including the proposed algorithm as the training algorithm for the neural network (NN). RideNN, along with the orthogonal transformation and Bhattacharyya coefficient, is applied for the fault diagnosis of analog circuits. The proposed technique is experimented using three basic circuits, such as triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC). The performance is evaluated using two evaluation metrics, namely, accuracy (ACC) and false alarm ratio (FAR). The analysis results show that the proposed technique attains an ACC of 99.9% in TWG, 99.9% in BTA, 99% in DIF, and 95% in SPC without noise.
222 citations
TL;DR: A novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis and some considerations about power consumption of the mobile node have been presented.
Abstract: Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.
194 citations
TL;DR: Experiments of the catenary insulator defect detection along the Hefei–Fuzhou high-speed railway line indicate that the system can achieve high detection accuracy.
Abstract: The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehicles and the environment, defects in the insulator are inevitable. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve the safety of railway operation. However, achieving full automation of insulator defect detection is still very challenging due to the visual complexity of defects and the small number of defective insulators. To overcome these problems, this paper proposes a novel insulator surface defect detection system using a deep convolutional neural network (CNN). The proposed system consists of two stages. First, a Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain the insulators are obtained. Then, the classification score and anomaly score are determined from a deep multitask neural network that is composed of a deep material classifier and a deep denoising autoencoder. The defect state is determined by analyzing the classification score and anomaly score. Experiments of the catenary insulator defect detection along the Hefei–Fuzhou high-speed railway line indicate that the system can achieve high detection accuracy.
176 citations
TL;DR: Two feature extraction methods for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range are proposed: a classification method that utilizes a wavelet packet transform and a deep 1-D convolution neural network that includes a softmax layer.
Abstract: This paper presents an effective diagnosis algorithm for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range. The fault diagnosis is based on a current signature analysis. The complete fault motor diagnosis system requires the extraction of features based on the current method and a subsequent method for adding classifications. In this paper, we propose two feature extraction methods: the first involves a classification method that utilizes a wavelet packet transform and the second is a deep 1-D convolution neural network that includes a softmax layer. The experimental results obtained using real-time motor stator current data demonstrate the effectiveness of the proposed methods for real-time monitoring of motor conditions. The results also demonstrate that the proposed methods can effectively diagnose five different motor states, including two different demagnetization fault states and two bearing fault states.
132 citations
TL;DR: Recent advances in the development of tacholess speed estimation methods for OT with its applications to fault diagnosis are summarized and the shortcuts of these methods are discussed in detail.
Abstract: Order tracking (OT), which is realized by signal sampling in equal-angle increment according to the measured rotating speed, is a powerful technique for rotating machine fault diagnosis under variable-speed condition. However, if the tachometer cannot be installed on the rotating machine or the speed signal is not available for some reasons, OT is difficult to be realized. This review summarizes recent advances in the development of tacholess speed estimation methods for OT with its applications to fault diagnosis. First, the basis of rotating speed estimation and OT is revisited. Then, the methods are categorized into three groups including vibration or sound signal, electrical motor current signal, and video stream according to the signal source from which the speed is estimated. The principle, implementation procedures, key techniques, along with the merits, and shortcuts of these methods are summarized and discussed in detail. Afterward, a contrastive case study using three kinds of methods is provided to intuitively illustrate the performances of OT along with the applications in motor bearing fault diagnosis. A bibliography of the recent publications related to this topic is also provided to facilitate the selection and improvement of the tacholess OT methods in fault diagnosis applications. Finally, the research prospects are discussed.
124 citations
TL;DR: The biosensor exhibits the advantages of small size, ease of fabrication, high sensitivity, label-free, and rapid response, and provides a new solution for detecting low concentration of biological solution, presenting great application potential in the biochemistry field.
Abstract: A highly sensitive optical fiber surface plasmon resonance (SPR) biosensor based on graphene oxide (GO) and staphylococcal protein A (SPA) co-modified tilted fiber Bragg grating (TFBG) is proposed and demonstrated for the detection of human immunoglobulin G (IgG) for the first time. The gold film on the surface of the sensor was first fixed with GO and then modified with an SPA to improve the sensitivity of the sensor. Large specific surface area and abundant functional groups of GO can adsorb more antibodies. The combination of SPA and the antibody molecule Fc region makes the Fab area with antigen-binding sites extend outward, resulting in highly oriented antibody immobilization on the sensor surface and high antigen–antibody binding efficiency. The experimental results show that the sensitivity as well as the limit of detection of GO-SPA-modified TFBG-SPR biosensor is around 0.096 dB/( $\mu \text{g}$ /mL) and $0.5~\mu \text{g}$ /mL, showing better responses to human IgG solutions with a concentration range of 30– $100~\mu \text{g}$ /mL compared with the TFBG-SPR biosensors modified singly with GO or SPA. The biosensor exhibits the advantages of small size, ease of fabrication, high sensitivity, label-free, and rapid response, and provides a new solution for detecting low concentration of biological solution, presenting great application potential in the biochemistry field.
121 citations
TL;DR: A three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++, which is superior to others in accuracy, and has a considerable speed.
Abstract: Split pins (SPs) play an important role in fixing joint components on catenary support devices (CSDs) of high-speed railway. The occurrence of loose and missing defects of SPs could make the structure of CSDs unstable. In this paper, we present a three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++. First, SPs are localized by PVANET++ and the Hough transform & Chan–Vese model, and then, three proposed criteria are applied to detect defects of SPs. In PVANET++, a new anchor mechanism is applied to produce suitable candidate boxes for objects, and multiple hidden layer features are combined to construct discriminative hyperfeatures. The performance of PVANET++ and several recent state-of-the-art deep CNNs is compared in a data set that is collected from a 60-km rail line. The results show that our model is superior to others in accuracy, and has a considerable speed.
121 citations
TL;DR: A joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis and has better performance than those methods based on conventional TF analysis and resampling.
Abstract: High-resolution time-frequency representation (TFR) method is effective for signal analysis and feature detection. However, for variable speed bearing vibration signal, conventional TFR method is prone to blur and affect the accuracy of the instantaneous frequency estimation. Moreover, the traditional order tracking, relying on equi-angular resampling, usually suffers from interpolation error. To solve such problems, we propose a joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis. The method can represent the time-varying fault characteristic frequency precisely and be free from resampling. First, using fast spectral kurtosis to select the optimal-frequency band which is sensitive to rolling bearing fault, and extracting envelope by Hilbert transform within the selected optimal frequency band. Next, a high-quality TF clustering method based on short-time Fourier transform is applied to the TF analysis of the envelope to get a clear TFR, from which the frequency information for GD is obtained. Finally, processing the basic demodulator via the peak search through the TF analysis results in the TFR for GD to gain a resampling-free-order spectrum. Based on the more precise TF information from the clearer TFR, the bearing fault can be diagnosed via GD without tachometer or any resampling involved, avoiding the amplitude error and low computational efficiency of resampling. Simulation study and experimental signal analysis validate that the proposed method has better performance than those methods based on conventional TF analysis and resampling.
104 citations
TL;DR: A timely, systematic survey on such video-based remote HR measurement approaches, with a focus on recent advancements that overcome dominating technical challenges arising from illumination variations and motion artifacts.
Abstract: Heart rate (HR) estimation and monitoring is of great importance to determine a person’s physiological and mental status. Recently, it has been demonstrated that HR can be remotely retrieved from facial video-based photoplethysmographic signals captured using professional or consumer-level cameras. Many efforts have been made to improve the detection accuracy of this noncontact technique. This paper presents a timely, systematic survey on such video-based remote HR measurement approaches, with a focus on recent advancements that overcome dominating technical challenges arising from illumination variations and motion artifacts. Representative methods up to date are comparatively summarized with respect to their principles, pros, and cons under different conditions. Future prospects of this promising technique are discussed and potential research directions are described. We believe that such a remote HR measurement technique, taking advantages of unobtrusiveness while providing comfort and convenience, will be beneficial for many healthcare applications.
104 citations
TL;DR: The experimental results show that CTFM outperforms state-of-the-art methods according to both the pixel-level index and the defect- level index.
Abstract: Computer vision systems have attracted much attention in recent years for use in detecting surface defects on rails; however, accurate and efficient recognition of possible defects remains challenging due to the variations shown by defects and also noise. This paper proposes a coarse-to-fine model (CTFM) to identify defects at different scales. The model works on three scales from coarse to fine: subimage level, region level, and pixel level. At the subimage level, the background subtraction model exploits row consistency in the longitudinal direction, and strongly filters the defect-free range, leaving roughly identified subimages within which defects may exist. At the next level, the region extraction model, inspired by visual saliency models, locates definite defect regions using phase-only Fourier transforms. At the finest level, the pixel subtraction model uses pixel consistency to refine the shape of each defect. The proposed method is evaluated using Type-I and Type-II rail surface defect detection data sets and an actual rail line. The experimental results show that CTFM outperforms state-of-the-art methods according to both the pixel-level index and the defect-level index.
TL;DR: The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel, and a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs.
Abstract: Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.
TL;DR: Compared with other optical fiber magnetic field sensors, the advantages of the proposed sensor in this paper are simple structure, small in size, easy to make, low cost, high sensitivity, and anti-interference.
Abstract: A novel magnetic field sensing system based on surface plasma resonance (SPR) optical fiber sensor and filled with magnetic fluid (MF) is proposed and demonstrated for the first time. In the magnetic field SPR optical fiber sensor, SPR is excited by Ag as metallic material and MF is filled into the capillary sealed with epoxy glue, which utilizes the tunable refractive index (RI) of MF, and the transmission spectrum will change with different magnetic field intensities. The magnetic-optic effect of MF and the high RI sensitivity of optical fiber SPR sensor are utilized to enhance the sensitivity of the novel magnetic field sensor significantly. In the experiment, the performances of the magnetic field sensing system are tested by applying different measured magnetic fields. The final results indicated that a sensitivity of 303 pm/Gs is achieved. Compared with other optical fiber magnetic field sensors, the advantages of the proposed sensor in this paper are simple structure, small in size, easy to make, low cost, high sensitivity, and anti-interference.
TL;DR: This work proposes a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints, and shows that the proposed method outperforms existing popular steplength estimation methods.
Abstract: Pedestrian dead reckoning (PDR) is a popular indoor localization method due to its independence of additional infrastructures and the wide availability of smart devices. Step length estimation is a key component of PDR, which has an important influence on the performance of PDR. Existing step length estimation models suffer from various limitations such as requiring knowledge of user’s height, lack of consideration of varying phone carrying ways, and dependence on spatial constraints. To solve these problems, we propose a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints. Experimental results show that the proposed method outperforms existing popular step length estimation methods.
TL;DR: A new technique of signal processing for ICA-based feature extraction in a 3-D feature space for IM fault diagnosis using as input the current signal measured from one of the three motor phases is presented.
Abstract: This paper presents a novel approach on motor current signature analysis (MCSA) for broken bar fault detection of induction motors (IMs), using as input the current signal measured from one of the three motor phases. Independent component analysis (ICA) is used over the Fourier-domain spectral signals obtained from the input and its autocorrelation function. The standard deviation of spectral components within a region of interest (ROI) of an ICA signal output was found to exhibit substantial differences between damaged and healthy motors. Separation of the ROI in one, two, and three sectors leads to an improved extraction of feature vectors, which are further fed into a neural network for classification purposes. The assessment of the proposed method is carried out through several experiments using two damage levels (broken bar and half broken bar) and two load motor conditions (50% and 75%), with a classification accuracy ranging from 90% to 99%. The contribution of this paper lies in a new technique of signal processing for ICA-based feature extraction in a 3-D feature space for IM fault diagnosis.
TL;DR: Good sensitivities have been obtained for the proposed sensors, which are coherent with previous works by other authors, and the main novelty is the use of $Q$ factor and the maximum S21 of the resonance as sensing magnitudes.
Abstract: A glucose concentration sensor for microliter-volume water–glucose solutions is presented. The proposed sensor is composed of an open-loop microstrip resonator with a dielectric liquid holder (5–25- $\mu \text{L}$ volume) glued onto the gap between the line ends. The resonator is coupled to two microstrip lines forming a two-port network whose S-parameter response provide information about the dielectric properties of the liquid under study. Three versions of the sensor at resonant frequencies between 2 and 7 GHz are presented. The sensors are assessed by measuring the complex permittivity of standard liquids reported in the scientific literature. Models of the sensors are presented, which properly match the experimental results. This paper presents an experimental study of the sensors as glucose concentration retrievers. The main novelty is the use of $Q$ factor and the maximum S21 of the resonance as sensing magnitudes. The dependence of these parameters on the glucose concentration of the solutions obeys almost linear relationships. Good sensitivities have been obtained for the proposed sensors, which are coherent with previous works by other authors.
TL;DR: It is shown that, by using only an additional 16% of LUTs, the proposed PRNG obtains a much better performance in terms of randomness, increasing the NIST passing rate from 0.252 to 0.989.
Abstract: In this paper, a new pseudorandom number generator (PRNG) based on the logistic map has been proposed. To prevent the system to fall into short period orbits as well as increasing the randomness of the generated sequences, the proposed algorithm dynamically changes the parameters of the chaotic system. This PRNG has been implemented in a Virtex 7 field-programmable gate array (FPGA) with a 32-bit fixed point precision, using a total of 510 lookup tables (LUTs) and 120 registers. The sequences generated by the proposed algorithm have been subjected to the National Institute of Standards and Technology (NIST) randomness tests, passing all of them. By comparing the randomness with the sequences generated by a raw 32-bit logistic map, it is shown that, by using only an additional 16% of LUTs, the proposed PRNG obtains a much better performance in terms of randomness, increasing the NIST passing rate from 0.252 to 0.989. Finally, the proposed bitwise dynamical PRNG is compared with other chaos-based realizations previously proposed, showing great improvement in terms of resources and randomness.
TL;DR: Experimental results show that square chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric.
Abstract: This paper reports the development of a practical visible light positioning (VLP) system using received signal strength. The indoor localization system is accurate and easy to train and calibrate despite using fingerprinting technique. The VLP system consists of cheap photodiode-based receiver and consumer grade LED luminaires. The impact of distance metrics used to compute the weights of the weighted $K$ -nearest neighbor (WKNN) algorithm on the localization accuracy of the VLP is investigated. Experimental results show that square chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric. A room-scale implementation shows that a mean error of 2.2 cm and a 90-percentile error of 4.9 cm within a 3.3 $\text {m} \times 2.1$ m 2-D floor space are achievable. However, the high localization accuracy comes at the cost of requiring 187 offline measurements to construct the fingerprint database. A method for estimating an optical propagation model using only a handful of measurements is developed to address this problem. This leads to the creation of a dense and accurate fingerprinting database through fabricated data. The performance of the VLP system does not degrade noticeably when the localization is performed with the fabricated data. A mean error of 2.7 cm and a 90-percentile error of 5.7 cm are achievable with only 12 offline measurements.
TL;DR: Motivated by spectral kurtosis (SK) and extreme learning machine (ELM), a novel intelligent diagnosis method for fault classification of rotating machines is proposed and the significance of SK as a feature set is examined and improved ELM in comparison with traditional methods.
Abstract: The condition monitoring of rotating machinery systems based on effective and intelligent fault diagnosis has been widely accepted. Traditional signal processing (SP) methods are less effective due to noises and interferences from different sources and incipient faults which remain active for a short time with a particular frequency. In recent times, SP techniques along with artificial intelligence methods are being used for fault classification. Various complex approaches in SP domain have used for feature extraction of the vibration data to design a feature set. A challenging task is to select dominant features from the available feature set for improving the accuracy of fault classification. Thus, motivated by spectral kurtosis (SK) and extreme learning machine (ELM), we propose a novel intelligent diagnosis method for fault classification of rotating machines. In this paper, SK is used as an input feature set to avoid the task of finding the dominant feature set. The extracted features are fed to ELM for fault identification. However, ELM performance primarily depends upon the hidden node parameters and the number of hidden nodes. The selection of optimum ELM parameters for good performance is an open issue. Therefore, modified bidirectional search with local search method is proposed to determine the optimum set of ELM parameters. The developed method is tested on two vibration data sets of rolling element bearings. We examined the significance of SK as a feature set and improved ELM in comparison with traditional methods. The experimental results demonstrate that the proposed method efficiently optimizes the ELM parameters to provide a compact ELM architecture and also enhances the fault classification accuracy.
TL;DR: The proposed network, one channel of end-to-end network, denoted as EtoE-Net, is designed to realize unsupervised learning, obtaining representative and global fused features with fewer noises, by building pixel-by-pixel mapping between the two source data.
Abstract: To solve the problem of supervised convolutional neural network (CNN) models suffering from limited samples, a two-channel CNN is developed for medical hyperspectral images (MHSI) classification tasks. In the proposed network, one channel of end-to-end network, denoted as EtoE-Net, is designed to realize unsupervised learning, obtaining representative and global fused features with fewer noises, by building pixel-by-pixel mapping between the two source data, i.e., the original MHSI data and its principal component. On the other hand, a simple but efficient CNN is employed to supply local detailed information. The features extracted from different underlying layers of two channels (i.e., EtoE-Net and typical CNN) are concatenated into a vector, which is expected to preserve global and local informations simultaneously. Furthermore, the two-channel deep fusion network, named as EtoE-Fusion, is designed, where the full connection is employed for feature dimensionality reduction. To evaluate the effectiveness of the proposed framework, experiments on two MHSI data sets are implemented, and results confirm the potentiality of the proposed method in MHSI classification.
TL;DR: This paper presents a novel automated detection system to distinguish between intracranial EEG time courses with seizures and those that are seizure-free based on complexity measures, and outperformed the existing state-of-the-art models.
Abstract: Electroencephalogram (EEG) signals are widely used to detect epileptic seizures in a patient’s neuronal activity. Since visual inspection and interpretation of EEG signal are time-consuming and prone to errors, various computer-aided diagnostic (CAD) tools have been proposed. In this paper, we present a novel automated detection system to distinguish between intracranial EEG time courses with seizures and those that are seizure-free based on complexity measures. Specifically, the features used to characterize the EEG signals are estimates of multiscaling properties over a large spectrum measured by using the generalized Hurst exponent. We tested the capacity of these estimates to correctly classify seizure intervals using a publicly available data set. Using the k-nearest neighbor classifier and testing with tenfold cross validation, we achieved 100% accurate classification. Our proposed CAD system outperformed the existing state-of-the-art models. Moreover, our CAD system is not only accurate but also fast and simple to implement. Therefore, it can be used as an expert system to support a decision in clinical applications.
TL;DR: This paper presents and characterizes a measurement method for positioning of passive tags, by a drone equipped with a UHF-RFID reader, based on a synthetic aperture radar approach and exploits the knowledge of the reader/drone trajectory, which is achieved with a differential Global Navigation Satellite System.
Abstract: This paper presents and characterizes a measurement method for positioning of passive tags, by a drone equipped with a UHF-RFID reader. The method is based on a synthetic aperture radar approach and exploits the knowledge of the reader/drone trajectory, which is achieved with a differential Global Navigation Satellite System. Different sources of measurement uncertainty are analyzed by means of numerical simulations and experimental results. The method capabilities are discussed versus the length and shape of the reader trajectory. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a microclass unmanned aerial vehicle.
TL;DR: A new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented, and exploits the simultaneous measurements given by PMUs on different nodes and branches of the network.
Abstract: Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible.
TL;DR: This paper presents a novel open-sourced method to extract light detection and ranging point clouds with ground truth annotations from a simulator automatically and shows that using additional synthetic data for training can achieve a visible performance boost in accuracy.
Abstract: The recent success of deep learning in 3-D data analysis relies upon the availability of large annotated data sets. However, creating 3-D data sets with point-level labels are extremely challenging and require a huge amount of human efforts. This paper presents a novel open-sourced method to extract light detection and ranging point clouds with ground truth annotations from a simulator automatically. The virtual sensor can be configured to simulate various real devices, from 2-D laser scanners to 3-D real-time sensors. Experiments are conducted to show that using additional synthetic data for training can: 1) achieve a visible performance boost in accuracy; 2) reduce the amount of manually labeled real-world data; and 3) help to improve the generalization performance across data sets.
TL;DR: Wang et al. as mentioned in this paper proposed a temperature measurement and compensation method of molten iron based on infrared computer vision, which can measure the MIT at the taphole continuously and accurately and provide reliable MIT data for operators to control BF.
Abstract: The temperature of molten iron at the taphole of blast furnace (BF) is an important parameter that reflects molten iron quality and BF conditions. It is not easy to measure the temperature of molten iron at the taphole in real time. To achieve continuous and accurate detection of molten iron temperature (MIT) at taphole, this paper proposes a temperature measurement and compensation method of molten iron based on infrared computer vision. First, an infrared computer vision system is designed and installed to capture the infrared thermal images of molten iron flow at the taphole. Then, the molten iron flow area is determined by using image processing. Afterward, the temperature information of slag region is obtained to calculate the MIT based on threshold segmentation. Furthermore, considering the measurement error caused by dust, the texture features influenced by dust are extracted based on the defined temperature-level co-occurrence matrix and the neighboring temperature-level-dependence matrix, and a compensation model is established to compensate the measurement error based on ensemble neural network and support vector regression. Finally, considering that the MIT and the slag temperature are approximately the same at the taphole, the MIT at the taphole is acquired according to the slag temperature. Industrial experiments and applications demonstrate that the proposed method can measure the MIT at the taphole continuously and accurately and provide reliable MIT data for operators to control BF.
TL;DR: The results suggest that the proposed novel relative wavelet entropy complex network (RWECN) allows obtaining intrinsic and effective features from fatigue EEG signals and enables to improve the classification accuracy of EEG-based fatigue driving.
Abstract: Detecting fatigue driving from electroencephalogram (EEG) signals constitutes a challenging problem of continuing interest since fatigue driving has caused the majority of traffic accidents. We carry out a simulated driving experiment for EEG data acquisition. Then, we calculate the wavelet entropy under the alert and fatigue state, respectively, and find that the wavelet entropy gets an acceptable performance on classification. Despite that the traditional entropy-based methods have been successfully applied to detect EEG-based fatigue driving, how to improve the classification remains to be investigated. To solve this problem, we in this paper propose a novel relative wavelet entropy complex network (RWECN) for improving the classification accuracy. In particular, we infer the complex network by regarding each EEG channel as a node and determining the connections of nodes in terms of the relative wavelet entropy between the EEG signals. Then, we extract a series of network statistical measures to characterize the topological structure of the brain networks. We combine the wavelet entropy and RWECN statistical measures to form a feature vector for realizing the classification of different states through the Fisher linear discriminant analysis. The results suggest that our method allows obtaining intrinsic and effective features from fatigue EEG signals and enables to improve the classification accuracy of EEG-based fatigue driving.
TL;DR: This paper investigates the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS), and validates the actual feasibility of PMU-based UFLS in a real-time simulated scenario.
Abstract: In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection, and Control (WAMPAC) applications. However, a standard approach toward ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques, and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and interharmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model loses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme, and we show their impact on the successful grid restoration.
TL;DR: Evaluations on three public datasets from different domains show that the proposed binary volumetric CNNs can achieve a comparable recognition performance as their floating-point counterparts but consume less computational and memory resources.
Abstract: To address the high computational and memory cost in 3-D volumetric convolutional neural networks (CNNs), we propose an approach to train binary volumetric CNNs for 3-D object recognition. Our method is specifically designed for 3-D data, in which it transforms the inputs and weights in convolutional/fully connected layers to binary values, which can potentially accelerate the networks by efficient bitwise operations. Two loss calculation methods are designed to solve the accuracy decrease problem when the weights in the last layer are binarized. Four binary volumetric CNNs are obtained from their corresponding floating-point networks using our approach. Evaluations on three public datasets from different domains (Computer Aided Design (CAD), light detection and ranging (LiDAR), and RGB-D) show that our binary volumetric CNNs can achieve a comparable recognition performance as their floating-point counterparts but consume less computational and memory resources.
TL;DR: The experimental results showed that the SINDICOMP technique assures a significant improvement of CT and VT metrological performances in harmonic measurements.
Abstract: This paper aims at characterizing and improving the metrological performances of current and voltage instrument transformers (CTs and VTs) in harmonic measurements in the power system. A theoretical analysis is carried out to demonstrate that, due to the iron core nonlinearity, CT and VT output signal is distorted even when the input signal is a pure sine wave. Starting from this analysis, a new method for CT and VT characterization and compensation is proposed. In a first step, they are characterized in sinusoidal conditions and the harmonic phasors of the distorted output are measured; in the second step, these phasors are used to compensate the harmonic phasors measured in normal operating conditions, which are typically distorted. The proposed characterization and compensation techniques are called SINusoidal characterization for DIstortion COMPensation (SINDICOMP). Several experimental tests, using high-accuracy calibration setups, have been performed to verify the proposed methods. The experimental results showed that the SINDICOMP technique assures a significant improvement of CT and VT metrological performances in harmonic measurements.
TL;DR: Test results show that the developed EMPF technique can capture a system’s dynamic behavior and track system characteristics effectively and is implemented for the remaining useful life prediction of lithium-ion batteries.
Abstract: The particle filter (PF) has been used for the analysis of nonlinear, non-Gaussian dynamical systems with hidden state variables. However, PF has some limitations in real-world applications, for instant, the sample degeneracy and the impoverishment, which are considered as long-standing challenges in this research and development field. Although several techniques have been proposed in the literature for this purpose, they have some limitations: for example, they cannot represent the entire probability density function (pdf) effectively and are usually problem dependent. In this paper, an enhanced mutated PF (EMPF) technique is proposed to improve the performance of PFs. In the EMPF technique, first, a novel enhanced mutation approach is proposed to actively explore the posterior pdf to locate the high-likelihood area. Second, a new selection scheme is suggested to process low-weight particles for optimizing the posterior distribution and tackling sample degeneracy. Third, an outlier assessment method is adopted to monitor the overall pattern of the posterior distribution based on the interquartile range statistical analysis. The effectiveness of the proposed EMPF technique is verified by simulation tests. It is also implemented for the remaining useful life prediction of lithium-ion batteries. Test results show that the developed EMPF technique can capture a system’s dynamic behavior and track system characteristics effectively.