This page has only limited features, please log in for full access.
Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of −31.27 dB and the deepest null of −101.60 dB. Also, for sparse 20-element LAA, an SLL of −18.85 dB was attained alongside the deepest null of −87.37 dB. MA obtained an SLL of −35.73 dB and −23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.
Eunice Oluwabunmi Owoola; Kewen Xia; Ting Wang; Abubakar Umar; Romoke Grace Akindele. Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm. IEEE Access 2021, 9, 1 -1.
AMA StyleEunice Oluwabunmi Owoola, Kewen Xia, Ting Wang, Abubakar Umar, Romoke Grace Akindele. Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleEunice Oluwabunmi Owoola; Kewen Xia; Ting Wang; Abubakar Umar; Romoke Grace Akindele. 2021. "Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm." IEEE Access 9, no. : 1-1.
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.
Shurui Fan; Dongxia Hao; Yu Feng; Kewen Xia; Wenbiao Yang. A Hybrid Model for Air Quality Prediction Based on Data Decomposition. Information 2021, 12, 210 .
AMA StyleShurui Fan, Dongxia Hao, Yu Feng, Kewen Xia, Wenbiao Yang. A Hybrid Model for Air Quality Prediction Based on Data Decomposition. Information. 2021; 12 (5):210.
Chicago/Turabian StyleShurui Fan; Dongxia Hao; Yu Feng; Kewen Xia; Wenbiao Yang. 2021. "A Hybrid Model for Air Quality Prediction Based on Data Decomposition." Information 12, no. 5: 210.
The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.
Yongke Pan; Kewen Xia; Li Wang; Ziping He. A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM. Symmetry 2021, 13, 757 .
AMA StyleYongke Pan, Kewen Xia, Li Wang, Ziping He. A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM. Symmetry. 2021; 13 (5):757.
Chicago/Turabian StyleYongke Pan; Kewen Xia; Li Wang; Ziping He. 2021. "A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM." Symmetry 13, no. 5: 757.
In general dynamic scenes, blurring is the result of the motion of multiple objects, camera shaking or scene depth variations. As an inverse process, deblurring extracts a sharp video sequence from the information contained in one single blurry image—it is itself an ill-posed computer vision problem. To reconstruct these sharp frames, traditional methods aim to build several convolutional neural networks (CNN) to generate different frames, resulting in expensive computation. To vanquish this problem, an innovative framework which can generate several sharp frames based on one CNN model is proposed. The motion-based image is put into our framework and the spatio-temporal information is encoded via several convolutional and pooling layers, and the output of our model is several sharp frames. Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel nor perceptual loss is suitable for focusing on non-aligned data. To alleviate this problem and model the blurring process, a novel contiguous blurry loss function is proposed which focuses on measuring the loss of non-aligned data. Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods.
Wenjia Niu; Kewen Xia; Yongke Pan. Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring. Symmetry 2021, 13, 630 .
AMA StyleWenjia Niu, Kewen Xia, Yongke Pan. Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring. Symmetry. 2021; 13 (4):630.
Chicago/Turabian StyleWenjia Niu; Kewen Xia; Yongke Pan. 2021. "Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring." Symmetry 13, no. 4: 630.
Traditional ship detection methods for synthetic aperture radar (SAR) mainly utilize the amplitude information to distinguish ship targets from sea clutter, including constant false alarm rate (CFAR), visual attention model, and deep learning methods. The CFAR algorithms adopt the dense sliding window strategy, which is very time-consuming and may generate numerous false alarms. The deep learning methods are supervised and difficult to obtain satisfactory performance when the number of labeled samples is insufficient. The visual attention models can quickly focus on the potential target area, and however, it is still difficult to eliminate the strong clutter, such as radio frequency interference and azimuth ambiguity. In fact, as a coherent imaging system, SAR data itself are complex-valued. Compared with the amplitude information, complex information can essentially reflect the difference between ship target and sea clutter. To improve the accuracy and efficiency of ship detection, in this letter, a novel unsupervised ship detection method based on multiscale saliency and complex signal kurtosis (MSS-CSK) for single-channel SAR images is proposed, which contains the proposal extraction stage and the target discrimination stage. The experimental results based on the Radarsat-2 real SAR data show that the proposed method has high detection accuracy and efficiency.
Zhaocheng Wang; Ruonan Wang; Xiaoya Fu; Kewen Xia. Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleZhaocheng Wang, Ruonan Wang, Xiaoya Fu, Kewen Xia. Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleZhaocheng Wang; Ruonan Wang; Xiaoya Fu; Kewen Xia. 2021. "Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost classifier directly to pulmonary nodule detection of labeled and unlabeled lung CT images since there are still some drawbacks to ensemble learning method. Therefore, to solve the labeled and unlabeled data classification problem, the semi-supervised AdaBoost classifier using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, AdaBoost classifier is used to construct a strong semi-supervised classifier using several weak classifiers S4VM (AdaBoost-S4VM). Next, in order to solve the accuracy problem of AdaBoost-S4VM, sparrow search algorithm (SSA) is introduced in the AdaBoost classifier and S4VM. Then, sine cosine algorithm and new labor cooperation structure are adopted to increase the global optimal solution and convergence performance of sparrow search algorithm, respectively. Furthermore, based on the improved sparrow search algorithm and adaptive boosting classifier, the AdaBoost-S4VM classifier is improved. Finally, the effective improved AdaBoost-ISSA-S4VM classification model was developed for actual pulmonary nodule detection based on the publicly available LIDC-IDRI database. The experimental results have proved that the established AdaBoost-ISSA-S4VM classification model has good performance on labeled and unlabeled lung CT images.
Jiangnan Zhang; Kewen Xia; Ziping He; Zhixian Yin; Sijie Wang. Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection. Mathematical Problems in Engineering 2021, 2021, 1 -18.
AMA StyleJiangnan Zhang, Kewen Xia, Ziping He, Zhixian Yin, Sijie Wang. Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection. Mathematical Problems in Engineering. 2021; 2021 ():1-18.
Chicago/Turabian StyleJiangnan Zhang; Kewen Xia; Ziping He; Zhixian Yin; Sijie Wang. 2021. "Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection." Mathematical Problems in Engineering 2021, no. : 1-18.
In many fields, such as oil logging, it is expensive to obtain labeled data, and a large amount of inexpensive unlabeled data are not used. Therefore, it is necessary to use semisupervised learning to obtain accurate classification with limited labeled data and many unlabeled data. The semisupervised support vector machine (S3VM) is the most useful method in semisupervised learning. Nevertheless, S3VM model performance will degrade when the sample number of categories is not even or have lots of unlabeled samples. Thus, a new semisupervised SVM by hybrid whale optimization algorithm (HWOA-S3VM) is proposed in this paper. Firstly, a tradeoff control parameter is added in S3VM to deal with an uneven sample of category which can cause S3VM to degrade. Then, a hybrid whale optimization algorithm (HWOA) is used to optimize the model parameters of S3VM to increase the classification accuracy. For HWOA improvement, an opposition-based cubic mapping is used to initialize the WOA population to improve the convergence speed, and the catfish effect is used to help WOA jump out of the local optimum and obtain the global optimization ability. In the experiments, firstly, the HWOA is tested by 12 classic benchmark functions of CEC2005 and four functions of CEC2014 compared with the other five algorithms. Then, six UCI datasets are used to test the performance of HWOA-S3VM and compared with the other four algorithms. Finally, we applied HWOA-S3VM to perform oil layer recognition of three oil well datasets. These experimental results show that (1) HWOA has a higher convergence speed and better global searchability than other algorithms. (2) HWOA-S3VM model has higher classification accuracy on UCI datasets than other algorithms when combined, labeled, and unlabeled data are used as the training dataset. (3) The recognition accuracy and speed of the HWOA-S3VM model are superior to the other four algorithms when applied in oil layer recognition.
Yong-Ke Pan; Ke-Wen Xia; Wen-Jia Niu; Zi-Ping He. Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition. Mathematical Problems in Engineering 2021, 2021, 1 -19.
AMA StyleYong-Ke Pan, Ke-Wen Xia, Wen-Jia Niu, Zi-Ping He. Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition. Mathematical Problems in Engineering. 2021; 2021 ():1-19.
Chicago/Turabian StyleYong-Ke Pan; Ke-Wen Xia; Wen-Jia Niu; Zi-Ping He. 2021. "Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition." Mathematical Problems in Engineering 2021, no. : 1-19.
A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.
Wenbiao Yang; Kewen Xia; Tiejun Li; Min Xie; Fei Song. A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM. Mathematics 2021, 9, 291 .
AMA StyleWenbiao Yang, Kewen Xia, Tiejun Li, Min Xie, Fei Song. A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM. Mathematics. 2021; 9 (3):291.
Chicago/Turabian StyleWenbiao Yang; Kewen Xia; Tiejun Li; Min Xie; Fei Song. 2021. "A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM." Mathematics 9, no. 3: 291.
The transient search algorithm (TSO) is a new physics-based metaheuristic algorithm that simulates the transient behavior of switching circuits, such as inductors and capacitors, but the algorithm suffers from slow convergence and has a poor ability to circumvent local optima when solving high-dimensional complex problems. To address these drawbacks, an improved transient search algorithm (ITSO) is proposed. Three strategies are introduced to the TSO. First, a chaotic opposition learning strategy is used to generate high-quality initial populations; second, an adaptive inertia weighting strategy is used to improve the exploration ability, exploitation ability, and convergence speed; finally, a neighborhood dimensional learning strategy is used to maintain population diversity with each iteration of merit seeking. The Friedman test and Wilcoxon’s rank sum test were also used by comparing the experiments with recently popular algorithms on 18 benchmark test functions of various types. Statistical results, nonparametric sign tests, and convergence curves all indicate that ITSO develops, explores, and converges significantly better than other popular algorithms, and is a promising intelligent optimization algorithm for applications.
Wenbiao Yang; Kewen Xia; Tiejun Li; Min Xie; Yaning Zhao. An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems. Symmetry 2021, 13, 244 .
AMA StyleWenbiao Yang, Kewen Xia, Tiejun Li, Min Xie, Yaning Zhao. An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems. Symmetry. 2021; 13 (2):244.
Chicago/Turabian StyleWenbiao Yang; Kewen Xia; Tiejun Li; Min Xie; Yaning Zhao. 2021. "An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems." Symmetry 13, no. 2: 244.
The use of low-dose computed tomography (LDCT) in medical practice can effectively reduce the radiation risk of patients, but it may increase noise and artefacts, which can compromise diagnostic information. The methods based on deep learning can effectively improve image quality, but most of them use a training set of aligned image pairs, which are difficult to obtain in practice. In order to solve this problem, on the basis of the Wasserstein generative adversarial network (GAN) framework, we propose a generative adversarial network combining multi-perceptual loss and fidelity loss. Multi-perceptual loss uses the high-level semantic features of the image to achieve the purpose of noise suppression by minimizing the difference between the LDCT image and the normal-dose computed tomography (NDCT) image in the feature space. In addition, L2 loss is used to calculate the loss between the generated image and the original image to constrain the difference between the denoised image and the original image, so as to ensure that the image generated by the network using the unpaired images is not distorted. Experiments show that the proposed method performs comparably to the current deep learning methods which utilize paired image for image denoising.
Zhixian Yin; Kewen Xia; Ziping He; Jiangnan Zhang; Sijie Wang; Baokai Zu. Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss. Symmetry 2021, 13, 126 .
AMA StyleZhixian Yin, Kewen Xia, Ziping He, Jiangnan Zhang, Sijie Wang, Baokai Zu. Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss. Symmetry. 2021; 13 (1):126.
Chicago/Turabian StyleZhixian Yin; Kewen Xia; Ziping He; Jiangnan Zhang; Sijie Wang; Baokai Zu. 2021. "Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss." Symmetry 13, no. 1: 126.
Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples. First, for the purpose of reducing the generation of label noise, we used an efficient constrained graph construction approach to calculate the affinity matrix, which is capable of constructing a highly correlated similarity relationship between the graph and the samples. Then, we introduced a particle competition and cooperation mechanism into label propagation, which could detect and re-label misclassified samples dynamically, thus stopping the propagation of wrong labels and allowing the overall model to obtain better classification performance by using predicted labeled samples. Finally, we applied the proposed model into hyperspectral image classification. The experiments used three real hyperspectral datasets to verify and evaluate the performance of our proposal. From the obtained results on three public datasets, our proposal shows great hyperspectral image classification performance when compared to traditional graph-based SSL algorithms.
Ziping He; Kewen Xia; Tiejun Li; Baokai Zu; Zhixian Yin; Jiangnan Zhang. A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification. Remote Sensing 2021, 13, 193 .
AMA StyleZiping He, Kewen Xia, Tiejun Li, Baokai Zu, Zhixian Yin, Jiangnan Zhang. A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification. Remote Sensing. 2021; 13 (2):193.
Chicago/Turabian StyleZiping He; Kewen Xia; Tiejun Li; Baokai Zu; Zhixian Yin; Jiangnan Zhang. 2021. "A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification." Remote Sensing 13, no. 2: 193.
An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user’s experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and nontextual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.
Naila Aslam; Waheed Yousuf Ramay; Kewen Xia; Nadeem Sarwar. Convolutional Neural Network Based Classification of App Reviews. IEEE Access 2020, 8, 185619 -185628.
AMA StyleNaila Aslam, Waheed Yousuf Ramay, Kewen Xia, Nadeem Sarwar. Convolutional Neural Network Based Classification of App Reviews. IEEE Access. 2020; 8 (99):185619-185628.
Chicago/Turabian StyleNaila Aslam; Waheed Yousuf Ramay; Kewen Xia; Nadeem Sarwar. 2020. "Convolutional Neural Network Based Classification of App Reviews." IEEE Access 8, no. 99: 185619-185628.
Synthetic aperture radar (SAR) images have limited labeled samples, and thus, it is difficult to learn a perfect convolutional neural network (CNN) model for target classification. The commonly used single-channel SAR images have much less information than those of the three-channel natural images. Transfer learning (TL) is an effective way to improve the generalization ability of the CNN model. The existing TL methods for SAR images usually transfer the knowledge from the three-channel natural images to the single-channel SAR images, where the SAR images are simply duplicated from one channel to three channels. This is obviously not reasonable. Indeed, the single-channel SAR image is complex valued, which can be divided into multiple channels (e.g., three channels) via the subaperture decomposition (SD) algorithm. In order to fully utilize the complex-valued data of the single-channel SAR images, in this letter, we propose a novel TL method with SD (TL-SD), where the SD can generate pseudocolor SAR images to realize TL with the large-scale natural image data sets. The experimental results based on the MSTAR real data set show that the proposed TL-SD method achieves an average accuracy of 99.88% on classification of ten-class targets and is superior to the other compared target classification methods, which verify the effectiveness of the proposed method.
Zhaocheng Wang; Xiaoya Fu; Kewen Xia. Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleZhaocheng Wang, Xiaoya Fu, Kewen Xia. Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleZhaocheng Wang; Xiaoya Fu; Kewen Xia. 2020. "Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.
Jiangnan Zhang; Kewen Xia; Ziping He; Shurui Fan. Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model. Computational Intelligence and Neuroscience 2020, 2020, 1 -24.
AMA StyleJiangnan Zhang, Kewen Xia, Ziping He, Shurui Fan. Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model. Computational Intelligence and Neuroscience. 2020; 2020 ():1-24.
Chicago/Turabian StyleJiangnan Zhang; Kewen Xia; Ziping He; Shurui Fan. 2020. "Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model." Computational Intelligence and Neuroscience 2020, no. : 1-24.
Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.
Hong Liu; Kewen Xia; Tiejun Li; Jie Ma; Eunice Owoola. Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding. Sensors 2020, 20, 4413 .
AMA StyleHong Liu, Kewen Xia, Tiejun Li, Jie Ma, Eunice Owoola. Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding. Sensors. 2020; 20 (16):4413.
Chicago/Turabian StyleHong Liu; Kewen Xia; Tiejun Li; Jie Ma; Eunice Owoola. 2020. "Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding." Sensors 20, no. 16: 4413.
Miniaturization design of the universal circuit breaker is very necessary, but it is not enough to consider only the miniaturization in the design but also consider the energy consumption and breaking capacity of the universal circuit breaker. To this end, a comprehensive optimization design method in this paper is proposed and studied. Firstly, based on the analysis of the universal circuit breaker miniaturization model, combines with the universal circuit breaker’s low energy consumption model and high-segmentation model, a comprehensive optimization model for designing universal circuit breakers is constructed. Secondly, for the comprehensive model solution, an improved gray wolf optimization (GWO) algorithm is proposed, that is, a “cloud model” is introduced to balance the local search and global search capabilities to improve the convergence speed; also, a weight strategy is introduced to avoid falling into the local minimum, and simulations of typical test functions show that the improved algorithm is superior to other algorithms. Finally, the improved gray wolf optimization algorithm is applied to the comprehensive optimization design of universal circuit breakers. The experimental results show that the proposed comprehensive design method is feasible and improves the design accuracy and efficiency of the universal circuit breaker.
Shuidong Dai; Kewen Xia; Lili Shi; Min Xie. Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm. Mathematical Problems in Engineering 2020, 2020, 1 -12.
AMA StyleShuidong Dai, Kewen Xia, Lili Shi, Min Xie. Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm. Mathematical Problems in Engineering. 2020; 2020 ():1-12.
Chicago/Turabian StyleShuidong Dai; Kewen Xia; Lili Shi; Min Xie. 2020. "Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm." Mathematical Problems in Engineering 2020, no. : 1-12.
A nonlinear hysteresis model of magneto-mechanical-thermo coupling for Terfenol-D materials is presented according to Wiss ferromagnetic theory, thermodynamics relations and Jiles–Atherton model. Numerical calculation and experimental results show that the mode well reflects the magnetostrictive characteristics of Terfenol-D rod under the coupling of stress, temperature and magnetic field. A fiber Bragg grating current transformer based on Terfenol-D material is designed according to the strain sensing mechanism of fiber Bragg grating and the demodulation principle of unbalanced M–Z interferometer. The theoretical analysis and research on the working characteristics of the fiber current transformer under the influence of different prestressing force and bias current are carried out. The results are important for the design and application of the current transformer with the Terfenol-D material.
Wang Li; Xia Kewen; Weng Ling. Model and Experimental Study on Optical Fiber CT Based on Terfenol-D. Sensors 2020, 20, 2255 .
AMA StyleWang Li, Xia Kewen, Weng Ling. Model and Experimental Study on Optical Fiber CT Based on Terfenol-D. Sensors. 2020; 20 (8):2255.
Chicago/Turabian StyleWang Li; Xia Kewen; Weng Ling. 2020. "Model and Experimental Study on Optical Fiber CT Based on Terfenol-D." Sensors 20, no. 8: 2255.
The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.
Ting Wang; Ke-Wen Xia; Hai-Lin Tang; Su-Wei Zhang; Mukase Sandrine. A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis. International Journal of Antennas and Propagation 2020, 2020, 1 -12.
AMA StyleTing Wang, Ke-Wen Xia, Hai-Lin Tang, Su-Wei Zhang, Mukase Sandrine. A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis. International Journal of Antennas and Propagation. 2020; 2020 ():1-12.
Chicago/Turabian StyleTing Wang; Ke-Wen Xia; Hai-Lin Tang; Su-Wei Zhang; Mukase Sandrine. 2020. "A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis." International Journal of Antennas and Propagation 2020, no. : 1-12.
One of the key technologies of compressed sensing is the signal reconstruction. And the two important indicators of signal reconstruction are the reconstruction probability and the time consumed. The Stagewise Weak Orthogonal Matching Pursuit (SWOMP) is widely used because the sparsity does not need to be a priori condition. The use of fixed threshold parameter in the iterative process can easily lead to overestimation and underestimation. Inspired by the idea of “the initial stage is approaching quickly and the final stage is approaching gradually,” that is, the search rule of “firstly fast and then slow,” an improved algorithm replacing the fixed threshold selection with S-shaped function value in each iteration is proposed to overcome the shortcoming that the fixed threshold parameter is selected in every iteration of SWOMP algorithm. Through compared experiment of six different S-shaped functions, the results show that the influence of different S-shaped functions on the SWOMP algorithm is different, and the improved SWOMP algorithm with the sixth S-shaped function has the best reconstruction effect.
Lan Pu; Zhang Jiangtao; Xia Kewen; Zhou Qiao; He Ziping. Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm. Pattern Recognition and Image Analysis 2019, 29, 613 -620.
AMA StyleLan Pu, Zhang Jiangtao, Xia Kewen, Zhou Qiao, He Ziping. Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm. Pattern Recognition and Image Analysis. 2019; 29 (4):613-620.
Chicago/Turabian StyleLan Pu; Zhang Jiangtao; Xia Kewen; Zhou Qiao; He Ziping. 2019. "Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm." Pattern Recognition and Image Analysis 29, no. 4: 613-620.
The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.
Shurui Fan; Zirui Li; Kewen Xia; Dongxia Hao. Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array. Sensors 2019, 19, 3917 .
AMA StyleShurui Fan, Zirui Li, Kewen Xia, Dongxia Hao. Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array. Sensors. 2019; 19 (18):3917.
Chicago/Turabian StyleShurui Fan; Zirui Li; Kewen Xia; Dongxia Hao. 2019. "Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array." Sensors 19, no. 18: 3917.