Extended Operating Conditions Research Articles

SAR Target Recognition Based on Joint Representation of Multimode Representations

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests.

A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and noise corruption. To improve the recognition performance, methods based on convolutional neural networks (CNN) have been introduced to solve such problems and have shown outstanding performance. However, most of these methods rely on continuously increasing the width and depth of networks. This adds a large number of parameters and computational overhead, which is not conducive to deployment on edge devices. To solve these problems, a novel lightweight fully convolutional neural network based on Channel-Attention mechanism, Channel-Shuffle mechanism, and Inverted-Residual block, namely the ASIR-Net, is proposed in this paper. Specifically, we deploy Inverted-Residual blocks to extract features in high-dimensional space with fewer parameters and design a Channel-Attention mechanism to distribute different weights to different channels. Then, in order to increase the exchange of information between channels, we introduce the Channel-Shuffle mechanism into the Inverted-Residual block. Finally, to alleviate the matter of the scarcity of SAR images and strengthen the generalization performance of the network, four approaches of data augmentation are proposed. The effect and generalization performance of the proposed ASIR-Net have been proved by a lot of experiments under both SOC and EOCs on the MSTAR dataset. The experimental results indicate that ASIR-Net achieves higher recognition accuracy rates under both SOC and EOCs, which is better than the existing excellent ATR methods.

Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

Decentralized control of autonomous DC microgrids with composite loads: an approach using optimal control

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration.

Extended convolutional capsule network with application on SAR automatic target recognition

Deep learning-based algorithms have a wide range of applications in synthetic aperture radar (SAR) automatic target recognition (ATR). However, most deep learning-based ATR algorithms perform poorly under extended operation conditions (EOCs), such as large depression variation, noise corruption, limited training samples, partial occlusion, and etc. This paper presents a novel deep neural network named extended convolutional capsule network to achieve robust SAR target recognition under EOCs. The proposed method is composed of an encoder network and a decoder network. Specifically, to mitigate the impact of noise on recognition, we first adopt multiple dilated convolutions to extract multi-scale features in the encoder network. Secondly, a feature refinement module is embedded in the multi-scale channels, which aims to extract discriminative and robust features by adaptively highlighting informative features and suppressing useless ones. Finally, we deploy capsule unit-based feature pose preserving layers to retain the spatial relationship among different features in the top-level network layer of the encoder network, which is very useful for improving the recognition performance under limited training samples. The decoder network is formed by stacking transposed convolutional layers to encourage the encoder network to learn discriminative image features. Experiments on the moving and stationary target acquisition and recognition (MSTAR) data demonstrate the effectiveness of the proposed method.

Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available

Synthetic aperture radar (SAR) is an earth observation technology that can obtain high-resolution image in all-weather and all-time conditions, and hence, has been widely used in civil and military applications. SAR target detection and classification are the key processes for the detailed feature information extraction of the interested target. Compared with traditional matched filtering (MF) recovered result, sparse SAR image has lower sidelobes, noise, and clutter. Thus, it will theoretically has better performance in target detection and classification. In this article, we propose a novel sparse SAR image based target detection and classification framework. This novel framework first obtains the sparse SAR image dataset by complex approximate message passing (CAMP), which is an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula>-norm regularization sparse imaging method. Different from other regularization recovery algorithms, CAMP can output not only a sparse solution, but also a nonsparse estimation of considered scene that well preserves the statistical characteristic of the image when protruding the target. Then, we detect and classify the targets by using the convolutional neural network based technologies from the sparse SAR image datasets constructed by the sparse and nonsparse solutions of CAMP, respectively. For clarify, these two kinds of sparse SAR image datasets are named as <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Sp}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula>. Experimental results show that under standard operating conditions, the proposed framework can obtain 92.60&#x0025; and 99.29&#x0025; mAP on Faster RCNN and YOLOv3 by using the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> sparse SAR image dataset. Under extended operating conditions, the mAP value of Faster RCNN and YOLOv3 are 95.69&#x0025; and 89.91&#x0025; mAP, respectively. These values based on the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> dataset are much higher than the classified result based on the corresponding MF dataset.

Combination of multiple decision principles based on sparse representation-based classification for target recognition of SAR image

A synthetic aperture radar (SAR) target recognition method using multiple decision principles in sparse representation-based classification (SRC) was proposed. The traditional SRC generally reconstructed the test sample on the global dictionary and calculated the reconstruction errors of individual training classes. And the decision was reached based on the minimum reconstruction error. However, because of the complexity of SAR target recognition, a single decision principle probably had low adaptivity to the extended operating conditions (EOC). Therefore, this paper employed the global minimum reconstruction, maximum coefficient energy, and local minimum reconstruction error principles to make decisions based on the solved coefficient vector from sparse representation. The global minimum reconstruction error principle directly adopted the traditional one. The maximum coefficient energy principle calculated the coefficient energies of different classes and made decision based on the maximum one. The local minimum reconstruction error principle represented and analyzed the test sample on the local dictionary so the azimuthal sensitivity of SAR imaging could be exploited. For the decision values from the three principles, they were transformed to the same type of probability vectors. Finally, the linear fusion was performed to combine their decisions. Experiments were conducted on the MSTAR dataset under situations including the standard operating condition (SOC), depression angle variance, noise corruption, and target occlusion. The results validate that the combination of multiple decision principles could effectively improve SAR target recognition performance.

Target classification of SAR images using nonlinear correlation information entropy

A synthetic aperture radar (SAR) target classification method is proposed by selecting deep features learned by convolutional neural network (CNN) using the nonlinear correlation information entropy (NCIE). The deep feature maps are learned from original SAR images by CNN. Considering the possible indiscrimination and redundancy, all the deep feature maps from different layers are analyzed by NCIE to select out those discriminative ones. The selected feature maps share high inner correlations, which are jointly classified based on joint sparse representation (JSR). Finally, the target label is determined based on the reconstruction errors from JSR. The famous moving and stationary target acquisition and recognition dataset are employed to setup the standard operating condition and several extended operating conditions, including configuration variants, depression angle differences, noise corruption, and partial occlusion, to comprehensively evaluate the performance of the proposed method. The results confirm the superior effectiveness and robustness of the proposed method in comparison with some state-of-the-art SAR target classification methods.

Two-stage sparse representation of NSCT features with application to SAR target classification

To improve synthetic aperture radar (SAR) target classification performance, both the feature extraction and classification algorithms are considered. The nonsubsampled contourlet transform (NSCT) is employed to decompose SAR images to generate multi-layer components, which provide more discriminative descriptions for the targets. During the classification, the two-stage sparse representations are developed. Each NSCT component is classified using the sparse representation-based classification (SRC). Afterwards, the joint sparse representation (JSR) is adopted to represent the selected NSCT components from the first stage, which are assumed to be highly discriminative. Based on the two decisions, the target label of the test sample is determined. In the experiment, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up scenarios for performance evaluation including the standard operating condition (SOC) and several extended operating conditions (EOCs). The results show the superior performance of the proposed methodover some current methods.

Target recognition of SAR images using fused deep feature by multiset canonical correlations analysis

A target recognition method of synthetic aperture radar (SAR) image is developed based on deep features learned from convolutional neural networks (CNN). The learned feature maps by convolution operations are combined as one unified feature vector through multiset canonical correlations analysis (MCCA). Afterwards, the sparse representation-based classification (SRC) is adopted as the classifier for target recognition. The proposed method could effectively combine the discriminability of different feature maps. In addition, the advantages of CNN and SRC can be integrated simultaneously. In the experiments, several conditions are setup based on the moving and stationary target acquisition and recognition (MSTAR) dataset for the validation. According to the results, the proposed method keeps high effectiveness and robustness under standard operating condition (SOC) and extended operating conditions (EOC).

Experimental and theoretical study on a heat pump driven open-air humidification dehumidification desalination system

This article presents experimental and theoretical investigations of a new configuration of an open-air open-water (OAOW), water-heated (WH), humidification-dehumidification (HDH) desalination system coupled with a vapor compression heat pump (HP) that considers two possibilities of energy recovery. Three different HDH system configurations including the basic system HDH-HP (without brine energy recovery), system A (basic system with brine energy recovery for pre-heating saline water), and system B (basic system with brine energy recovery for pre-heating ambient air) are presented. The performance of the basic system is examined experimentally; while that of systems A and B are assessed theoretically. Furthermore, the theoretical analysis is extended to the basis system to address the boundaries, constraints and limitations of experimental equipment, by analyzing the system performance at extended operation conditions. Results reveal that the developed model shows a good agreement against the experimental findings. Results also show that systems A and B portrayed superior performance over the basic system (especially system A), due to the energy recovery from the rejected brine. A maximum GOR of 2.72, RR of 2.56%, water productivity of 9.23 kg/h and a minimum freshwater cost of 15.14$/m3 are attained experimentally, while theoretical results for system A revealed a maximum GOR of 5.06, RR of 3.98%, water productivity of 11.46 kg/h, and a minimum price of freshwater of 12.38$/m3.

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the k-means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.

On the use of artificial neural networks for condition monitoring of pump-turbines with extended operation

Abstract Because of the addition of stochastic supply to the power grid introduced by new renewable energies, hydropower is required to respond rapidly to the fluctuation between power demand and supply. Consequently, hydropower units must work under extreme off-design conditions, where the machines are more prone to suffer from damages and shorter useful lives. Novel supervision and monitoring techniques which are able to compare the revenues with the remaining useful life of the turbine unit are required to cope with these new scenarios. In this paper, the upgrading of an existing monitoring system to deal with the extended operating range of a pump-turbine is discussed. Previously, the machine operating range was from 50% to 100% power and now it is from 20% to 100% power. For that purpose, the vibration signals collected from the current monitoring system have been used. First, the autoregressive mapping of the overall levels measured in the machine for all the extended operating conditions has been carried out. Back-propagation neural network was applied for the mapping. Second, the complex hydraulic phenomena that may occur in the extended operating range that can produce accelerated wear and tear have been studied. Typical phenomena are excessive turbulence, draft tube vortex rope, cavitation erosion, excessive vibration and excessive stresses in the runner. For each of these abnormal operating conditions, several features (condition indicators) were selected and mapped on the operation hill-chart using neural networks. The consequences of each abnormal operation have been analyzed with physics-based models. With the mapping, the zones where operation is not recommended can be identified and the effects estimated.

Target recognition of SAR images via hierarchical fusion of complementary features

A hierarchical fusion of complementary features is proposed with application to target recognition of synthetic aperture radar (SAR) images. Three features, i.e., principle component analysis (PCA) features, attributed scattering centers (ASCs), and target outline, are used. Sparse representation-based classification (SRC) is adopted to classify the PCA features because of its good effectiveness and robustness. A one-to-one matching algorithm is designed for the ASCs and a partially matching strategy for the target outlines are designed. The test sample is first classified using the PCA features. If the decision is judged to be unreliable, the ASC matching is performed afterwards. Similarly, the outline matching is performed when the ASC matching cannot make a reliable decision on the target label. In this way, the advantages of the complementary features can be combined in a unified framework so the overall target recognition performance can be enhanced. Experiments are implemented based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method achieves a notably high recognition rate of 98.34 % under the standard operating condition (SOC) and superior robustness under the extended operating conditions (EOCs) in comparison with other methods.

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks.