Applicability Of Proposed Method Research Articles

Source Localization of EM Waves in the Near‐Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near‐field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction‐of‐arrival (DoA) estimation technique via the direction‐independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction‐independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction‐independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

Analysis and Calibration of Blade Tip-Timing Vibration Measurement Under Variable Rotating Speeds

Blade tip timing (BTT) vibration measurement is a promising on-line blade monitoring method, but various uncertainties bring great challenge in engineering applications. Most existing works are based on the assumption of constant rotating speeds. However, rotating speed is hardly fixed under variable conditions. In this case, these uncertainties always become more serious. To deal with this problem, this paper proposes to investigate BTT measurement derivations in angular domain, instead of time domain. Firstly, this paper systematically analyzes the effects of variable rotating speed, static angle errors and translational blade motions on the accuracy of BTT vibration measurement. Then the corresponding calibration methods are presented by only using times of arrival (TOAs). In the end, Matlab/Simulink simulations are done to validate the proposed method under linear and quadratic variations of rotating speeds. The results demonstrate that BTT measurement deviation under low rotating speeds is more than those under high rotating speeds and BTT measurement deviation due to static angle errors is independent of rotating speeds. And the proposed TOAs-based calibration method can reduce BTT measurement deviation greatly under variable rotating speeds, compared with traditional methods. BTT measurement deviation due to static angle errors can be calibrated by using low rotating speeds and those due to translational blade motions are difficult to be calibrated under variable rotating speeds. Thus simulation results indicate great potential of the proposed method for practical applications of the BTT method.

Multi-trace post-stack seismic data sparse inversion with nuclear norm constraint

Among many seismic inversion methods, the sparse spike inversion for post-stack seismic data uses the migrated and stacked seismic data which is regarded as zero offset reflection seismic data in the case of normal incidence to extract reflectivity and impedance of underground rocks. The seismic reflectivity and impedance can reflect underground rocks’ lithology, petrophysical property, oil–gas possibility, and so forth. However, the common used post-stack seismic inversion adopts single trace in the process of inversion and completes the whole data cube’s inversion through trace by trace. It cannot use lateral regularization. Hence, the lateral continuity of single trace inversion result is poor. It is difficult to represent the lateral variation features of underground rocks. Based on the conventional sparse spike inversion, the nuclear norm of matrix in the matrix completion theory is introduced in the process of post-stack seismic inversion. At the same time, the strategy of multi-trace seismic data simultaneous inversion is used to carry out lateral regularization constraint. Numerical tests on 2D model indicate that the inversion results obtained from the proposed method can clearly represent not only the vertical variation features but also the lateral variation features of underground rocks. At last, the inversion results of real seismic data further show the feasibility and superiority of the proposed method in practical application.

Computational drug repositioning based on multi-similarities bilinear matrix factorization.

With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug-drug similarities can be measured from target profiles, drug-drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug-disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug-disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug-disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: jxwang@mail.csu.edu.cn Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.

An Improved Irreversible Fractal Scheme for Medical Image Compression

In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.

Robust state of charge estimation based on a sequence-to-sequence mapping model with process information

Unknown loading profiles and variable sampling frequencies are inevitable conditions of the practical usage of lithium-ion batteries, which causes state-of-charge estimation methods to perform poorly. In this paper, a sequence-to-sequence mapping model with process information for state-of-charge estimation is proposed by improving a long short-term memory cell with process information, which allows the modeling of the state information and the process information together. A two-stage pretraining strategy is implemented to enhance the feature-learning capability of the model. In this manner, the dynamic differences within the different loading profiles and variable sampling frequencies can be resolved. Two cases designed with different types of batteries are studied to validate the adaptability and performance of the proposed method. When the sampling frequency is variable, the errors can be highly reduced by means of 21.6% at three ambient temperatures. Moreover, after being trained on a simple profile, the proposed method achieves a mean absolute error of only 1.163%, 1.066% and 0.925% on three unknown but more complicated profiles. A discussion of the results and comparisons shows the advantages of the proposed method over others. The experimental results demonstrate the robustness and generalization ability of the proposed method for practical applications.

An Effective Method to Improve the Accuracy of a Vernier-Type Absolute Magnetic Encoder

This article proposes a method to improve the accuracy of a vernier absolute magnetic encoder. The encoder consists of a master and a nonius multipolar magnetic track. Sinusoidal signals from the master and nonius tracks are used to infer the absolute information. Unfortunately, these signals are contaminated by nonideal factors such as different amplitudes, dc-offsets, phase shifts, and random noise. Moreover, harmonics existing in the encoder signals distort the vernier principle and significantly affect the accuracy of the encoder. To address these problems, the present article proposes an efficient method with three main parts. The first is an observer phase-locked loop (OPLL), which is used to estimate the phase and eliminate the nonideal factors. The second is nonlinear phase compensation, which is used to correct the vernier principle that deviated due to the existing harmonics. Finally, a pole pitch compensation method is introduced to modulate the master phase angle from the OPLL to eliminate the harmonic distortion. The proposed method can eliminate the nonideal factors, harmonic distortion and improve the accuracy of the encoder. All the proposed methods were implemented on an ARM STM32F407ZG. The experimental results confirm the validity of the proposed method for practical applications.

Polityka utrzymania ruchu układów smarowanych olejem w oparciu o dane z analizy oleju

Maintenance of oil-lubricated systems plays a significant role in reducing unexpected system failures and improving machine availability. This paper deals with the oil-lubricated systems subject to gradual degradation that revealed by metal wear debris monitored using oil analysis. Oil-lubricated systems usually undertake several preventive maintenances during operation, after each maintenance, the system typical restores to an intermediate state between good-as-new state and bad-as-old state due to system aging such as cumulative wear. Furthermore, oil-lubricated systems often operate continuously in mission execution with availability constraints. However, existing literature still lacks a method to integrate the availability constraints with the system aging into the cause of optimizing the maintenance policy. To fill this gap, this paper develops a maintenance policy optimization method to determine the optimal maintenance threshold joint considering the availability constraints and the system aging. A case study of the power-shift steering transmission systems modelled by a wiener process is presented to illustrate the proposed method in practical application.

An Efficient Calibration Method of Line Structured Light Vision Sensor in Robotic Eye-in-Hand System

For line structured light vision sensor in robotic eye-in-hand system, the calibration of camera parameters, robot hand eye parameters and structured light plane parameters is an indispensable procedure before conducting 3-D measurement. Traditionally, these three sets of parameters are calibrated separately, making the calibration process complicated and inefficient. To address this, an efficient calibration method is proposed in this paper. By changing the poses of robot and calibration target, a set of images of the calibration target is captured, upon which the camera calibration is conducted. The extrinsic parameters obtained from camera calibration are utilized in the calibration of the structured light plane parameters and hand eye parameters, using the same set of images. Therefore, the three sets of parameters can be simultaneously calibrated with simple on-site operations. Moreover, the calibration target used in this method is only a planar checkerboard, which can be easily acquired at low cost. It is verified in the experiment that the proposed method is efficient and accurate, with the calibration duration within 10 minutes and the measurement accuracy within 0.40mm. Additionally, object pose measurement experiment is conducted to validate the effectiveness of the proposed method in practical application.

Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data

Eliciting individual travelers’ Origin-Destination (OD) information is critical for enabling public transit system policy-makers and operators to serve travelers in a calculated way. Accurate estimation of route choice model parameters is also important, in that it can help assess or predict the service levels that such a system can be expected to achieve. The knowledge of both the OD links and route choice logic is especially in demand for emerging mobility services, where providers work to accommodate individualized services and also offer incentives to travelers for specific trips. We show that all this information can be distilled from a particular type of data – the Automated Fare Collection (AFC) system data – in a fast, low-cost way. This paper presents a two-step methodological framework to identify individual travelers’ true ODs (beyond stop-level ODs), as well as infer their travel preferences. The key to our work is the ability to identify and process the observations of travelers’ routing choices between the same ODs under different travel environment conditions. A presented specially-crafted case study validates the proposed method in application with a real-world AFC data of Seoul, Korea, confirming the method’s high inferential ability, under a basic route choice model.

Inkjet printing assisted fabrication of polyphenol-based coating membranes for oil/water separation

While polyphenol-based coating has been regarded as a promising alternative to functionalize membrane surface, it usually suffers from problems of low-efficient procedure and low utilization rate of the polyphenolic compounds, hindering its large-scale implementations. To solve these problems, this study provided a first report on inkjet printing of polyphenols (catechol (CA) or tannic acid (TA)) and sodium periodate (SP) on a polyvinylidene fluoride (PVDF) membrane to improve membrane performance. A series of analyses showed the efficient formation of homogenous films on the PVDF membrane surface and the improvement of hydrophilicity by the inkjet printing technique. The PVDF membranes decorated with the optimized polyphenolic coating exhibited a promising oil/water separation efficiency (higher than 99%) with a high average water permeation flux of 5.2 times higher than that of the pristine membrane. Meanwhile, the modified membranes illustrated a good stability under acidic conditions (pH = 2–7). The novel method proposed in this study is facile, cost-saving and environment-friendly. The advantages of the proposed method and the modified membranes demonstrated the great significance of the proposed method in practical applications.

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis.

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

Microstructure topology optimization by targeting prescribed nonlinear stress-strain relationships

In this paper, we proposed a new material design method by microstructure topology optimization. Novelty of the proposed method is to target the whole nonlinear volume-averaged effective stress-strain curve of microstructure representative volume element (RVE) rather than aiming specific values such as strength, stiffness or Poisson ratio. J2 plasticity model with a linear isotropic hardening model was chosen for local residuals. Global residuals are computed within nonlinear finite element framework for the topology optimization. Sensitivities of the objective function augmented with the residuals and adjoint response vectors with respect to design variables are derived with details and their numerical computational procedures were also presented. Microstructure topologies showing two different targeted stress-strain curves under uniaxial and biaxial loadings were obtained by using the method of moving asymptotes (MMA) optimization algorithm. Accuracy of the sensitivity computations was verified and numerical examples demonstrated a potential of the proposed method in applications to multiscale topology optimization.

Shrinking the eigenvalues of M-estimators of covariance matrix

A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward the grand mean of the eigenvalues of the SCM. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data adaptive method to compute the optimal shrinkage parameter with minimum mean squared error is proposed. Our approach permits the use of any weight function such as Gaussian, Huber's, Tyler's, or t weight functions, all of which are commonly used in M-estimation framework. Our simulation examples illustrate that shrinkage M-estimators based on the proposed optimal tuning combined with robust weight function do not loose in performance to shrinkage SCM estimator when the data is Gaussian, but provide significantly improved performance when the data is sampled from an unspecified heavy-tailed elliptically symmetric distribution. Also, real-world and synthetic stock market data validate the performance of the proposed method in practical applications.

A Novel Method of Emergency Situation Evaluation for Deep-Sea Based on Bayesian Network

In order to make effective emergency decisions timely, this paper proposes an intelligent emergency situation evaluation method based on the Bayesian network for deep-sea emergency response, which is used to evaluate the deep-sea emergency situation quantitatively by calculating the occurrence probability of emergency situation risk at each risk level. In this research, a Bayesian network is constructed to analyze the deep-sea emergency situation firstly. Aiming at the bottleneck problem of insufficient case data for the Bayesian network's parameter learning, an automatic parameter allocation algorithm is presented based on the domain knowledge. Then, in order to obtain the quantitative analysis results of the overall emergency situation, a comprehensive situation evaluation model is presented based on the Bayesian network reasoning results, and the fuzzy comprehensive evaluation theory. Finally, a case study is given to illustrate this proposed method's application, which can provide a scientific basis for decision-making in the deep-sea emergency practice.

Nuclear norm subspace identification of continuous time state–space models with missing outputs

Subspace identification methods using Generalized Poisson Moment Functionals (GPMF) have been proposed previously to tackle the problem of derivative estimation in continuous time (CT) systems. In this paper, a convergence result underpinning the GPMF methods for continuous time identification is detailed. Based on this, a CT-MOESP method is proposed to estimate the system matrices in state–space models. Since these results hold in the asymptotic case where the number of data points tend to infinity, Nuclear Norm Minimization (NNM) is used to integrate the low rank approximation step in subspace identification with a goodness of fit criterion. This paper extends these existing discrete time methods to continuous time by formulating the NNM optimization into the framework of the Alternating Direction Method of Multipliers (ADMM) algorithm. On the numerical front, the accuracy of the proposed method is demonstrated with the help of simulations on two systems frequently cited in literature. An industrial dryer application is considered in order to demonstrate the practical applicability of proposed method.

An EWMA and region growing based control chart for monitoring image data

ABSTRACT The widespread application of machine vision system promotes the development of image-based statistical process control methodologies to improve the utilization of image data. Previous research has focused on either identification of fault size and/or location or detection of fault occurrence. There is limited research on both fault detection and identification. In this paper, an EWMA and region growing based control charting method is proposed for monitoring images of industrial products of which quality is either characterized by uniformity (e.g. LCD monitors) or a specific pattern (e.g. manufactured tiles). Simulation results show that the proposed method is not only effective in quick detection of the fault but also accurate in estimating the fault size and location. An experimental study is also provided to highlight how practitioners can implement the proposed method in applications.

Lysin cell-binding domain-functionalized magnetic beads for detection of Staphylococcus aureus via inhibition of fluorescence of Amplex Red/hydrogen peroxide assay by intracellular catalase.

Accurate and rapid identification of Staphylococcus aureus (S. aureus) is of great significance for controlling the food poisoning and infectious diseases caused by S. aureus. In this study, a novel strategy that combines lysin cell-binding domain (CBD)-based magnetic separation with fluorescence detection was developed for the specific and sensitive quantification of S. aureus in authentic samples. The S. aureus cells were separated from the sample matrix by lysin CBD-functionalized magnetic beads. Following lysis by lysostaphin, intracellular catalase was released from S. aureus cells and detected by a fluorometric system composed of horseradish peroxidase (HRP), hydrogen peroxide (H2O2), and Amplex Red. S. aureus was quantified via the inhibitory effect of the released intracellular catalase on the fluorometric system since the catalase could decompose the H2O2. Optimized conditions afforded a calibration curve for S. aureus ranging from 1.0 × 102 to 1.0 × 107CFUmL-1. The detection limit was as low as 78CFUmL-1 in phosphate-buffered saline (PBS), and the total detection process could be completed in less than 50min. Other bacteria associated with common food-borne and nosocomial infections negligibly interfered with S. aureus detection, except for Staphylococcus epidermidis, which may have slightly interfered. Moreover, the potential of this proposed method for practical applications has been demonstrated by detection assays of sterilized milk and human serum. Graphical abstract.

Risk-Limiting Real-Time Economic Dispatch in a Power System with Flexibility Resources

The integration of numerous intermittent renewable energy sources (IRESs) poses challenges to the power supply-demand balance due to the inherent intermittent and uncertain power outputs of IRESs, which requires higher operational flexibility of the power system. The deployment of flexible ramping products (FRPs) provides a new alternative to accommodate the high penetration of IRESs. Given this background, a bi-level risk-limiting real-time unit commitment/real-time economic dispatch model considering FRPs provided by different flexibility resources is proposed. In the proposed model, the objective is to maximize the social surplus while minimizing the operational risk, quantified using the concept of conditional value-at-risk (CVaR). Energy and ramping capabilities of conventional generating units during the start-up or shut-down processes are considered, while meeting the constraints including unit start-up/shut-down trajectories and ramping up/down rates in consecutive time periods. The Karush–Kuhn–Tucker (KKT) optimality conditions are then used to convert the bi-level programming problem into a single-level one, which can be directly solved after linearization. The modified IEEE 14-bus power system is employed to demonstrate the proposed method, and the role of FRPs in enhancing the system flexibility and improving the accommodation capability for IRESs is illustrated in some operation scenarios of the sample system. The impact of the confidence level in CVaR on the system operational flexibility is also investigated through case studies. Finally, a case study is conducted on a regional power system in Guangdong Province, China to demonstrate the potential of the proposed method for practical applications.

Antiswing Cargo Transportation of Underactuated Tower Crane Systems by a Nonlinear Controller Embedded With an Integral Term

A tower crane is a nonlinear mechatronic system with complicated underactuated characteristics, which is widely used in modern construction sites. At present, most existing methods for tower cranes are proposed by linearizing the original nonlinear dynamics near equilibrium points, which are, thus, prone to suffering from unexpected steady errors due to such factors as unmodeled dynamics, imperfect friction compensation, etc., since they have not included integral terms in either controller design or stability analysis. Therefore, in this paper, an improved feedback controller with an elaborately constructed integral term is proposed for 3-D tower cranes without linearization, which can achieve both antiswing and positioning control while being able to effectively reduce steady errors in the presence of, e.g., inaccurate friction compensation. Furthermore, asymptotic stability results are proven through rigorous theoretical analysis. Owing to no linearization, the proposed controller is applicable when state variables (e.g., cargo swing angles) are not close enough to the equilibrium points, which makes it suitable for complicated working conditions. Hardware experimental results are included to verify the effectiveness of the proposed controller. Note to Practitioners —This paper is motivated by the requirement of effective control methods for tower cranes. Tower cranes are widely applied in modern construction sites to fulfill cargo transportation tasks. For such systems, the jib slew motion not only enlarges the workspace but also brings more difficulties to suppress unexpected cargo swing during the transportation process. Up until now, most existing methods use simplified system models or need exact model knowledge, which are difficult to reflect real dynamics in many practical situations. To handle these existing problems, in this paper, a novel feedback control approach embedded with an elaborately constructed integral term is presented without model linearization. By introducing the integral term, even when frictions are inaccurately compensated, steady errors can be reduced effectively and, hence, the positioning accuracy can be improved. Also, the proposed controller can handle parametric uncertainties. By applying the proposed controller, the closed-loop system achieves asymptotic results, which is rigorously proven theoretically. Finally, the effectiveness of the proposed controller is verified by implementing several groups of hardware experiments. In the future studies, we will apply the proposed method in practical applications.