Article
Computer Science, Artificial Intelligence
Pei-Yi Hao, Jung-Hsien Chiang, Yu-De Chen
Summary: This paper proposes a novel possibilistic classification algorithm using support vector machines (SVMs) to effectively handle uncertain information and improve classification performance. The algorithm aims at finding a maximal-margin fuzzy hyperplane based on possibility theory and solves a fuzzy mathematical optimization problem. The proposed algorithm retains the advantages of fuzzy set theory and SVM theory, and it is more robust for handling outliers. Experimental results demonstrate the satisfactory generalization accuracy and ability to describe inherent vagueness in the given dataset.
Article
Computer Science, Information Systems
Xiangwei Chen, Weixing Sheng
Summary: Two improved ESP-based RAB methods are proposed in this study, which solve the problems of poor performance and source enumeration in traditional ESP method by using sequenced steering vector estimation.
Article
Computer Science, Artificial Intelligence
Imran Razzak, Mohamed Reda Bouadjenek, Raghib Abu Saris, Weiping Ding
Summary: Traditional support vector machines (SVMs) are sensitive to outliers and corrupted data, leading to a deterioration in classification performance. This article proposes an efficient Support Matrix Machine that performs matrix recovery and feature selection simultaneously. It can handle high-dimensional data with corrupted columns and recover an intrinsic matrix of higher rank under incoherence and ambiguity conditions. The objective function combines matrix recovery, low rank, and joint sparsity, and the method leverages structural information and intrinsic data structure. Experimental results show significant improvements in BCI, face recognition, and person identification datasets, especially in the presence of outliers.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Guanjin Wang, Kup-Sze Choi, Jeremy Yuen-Chun Teoh, Jie Lu
Summary: This article introduces a new approach called DCOT-LS-SVMs, which is based on least-squares support vector machines and utilizes deep cross-output knowledge transfer. The approach improves the generalizability of LS-SVMs and simplifies the parameter tuning process. Experimental results on UCI datasets and a prostate cancer diagnosis case study demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Geochemistry & Geophysics
Bing Tu, Chengle Zhou, Xiaolong Liao, Guoyun Zhang, Yishu Peng
Summary: The study introduces a novel spatial-spectral classification method for hyperspectral images based on structural-kernel collaborative representation (SKCR), which considers a weak assumption of spatial neighborhood. The method utilizes superpixel segmentation and dual kernels to achieve excellent classification performance even with relatively small training samples.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Mehrnaz Ahmadi, Mehdi Khashei
Summary: Support vector machines (SVMs) are widely used in modeling, but traditional SVMs and fuzzy SVMs may not be sufficient for modeling both certain and uncertain patterns simultaneously. This paper proposes a generalized SVM that can effectively model both kinds of patterns to achieve more accurate wind speed forecasting results.
Article
Engineering, Industrial
Nick Pepper, Luis Crespo, Francesco Montomoli
Summary: This work demonstrates how to approximate the failure probability of an expensive computational model with reliability requirements using Support Vector Machines. An algorithm is proposed to select informative parameter points to improve the approximation accuracy iteratively. Additionally, a method is provided to quantify the uncertainty in the Limit State Function and estimate an upper bound to the failure probability using geometrical arguments.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Pedro Ribeiro Mendes Junior, Terrance E. Boult, Jacques Wainer, Anderson Rocha
Summary: When dealing with real-world recognition problems, it is often necessary to have classification methods that can handle unknown classes and reject samples not seen during training. Existing classifiers are mainly designed for closed-set scenarios, where all test samples are assumed to belong to known classes. However, in open-set scenarios, a test sample may not belong to any known class and must be properly rejected as unknown.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Agronomy
Chen-Feng Long, Zhi-Dong Wen, Yang-Jun Deng, Tian Hu, Jin-Ling Liu, Xing-Hui Zhu
Summary: This study proposes a rice variety identification method based on leaf hyperspectral characteristics, which shows excellent performance in distinguishing different rice varieties and achieves higher identification rates compared to other methods.
Article
Geochemistry & Geophysics
Chao Pan, Xiuping Jia, Jie Li, Xinbo Gao
Summary: This article introduces a novel approach for addressing the over-smoothing issue in MRF by class-by-class refinement and adaptive edge preservation. Experimental results demonstrate the superiority of aEPMs in evaluation metrics and detail preservation compared to traditional methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Shaoguang Huang, Hongyan Zhang, Aleksandra Pizurica
Summary: The authors propose a scalable subspace clustering method in this article, which reduces significantly the size of optimization problems by learning a concise dictionary and robust subspace representation. The method also introduces an adaptive spatial regularization to improve the robustness to noise.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Jayson P. Van Marter, Anand G. Dabak, Naofal Al-Dhahir, Murat Torlak
Summary: Ranging solutions for IoT localization applications aim to achieve high accuracy at low cost using Bluetooth low energy (BLE) technology. However, accurately measuring the distance with BLE poses challenges due to multipath components and model imperfections. To address this, we propose a data-driven SVR method that achieves decimeter-level accuracy with single antenna devices, outperforming the model-based MUSIC method which requires multiple antennas. Our method also proves robust in various multipath environments and offers computational complexity reduction compared to MUSIC.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Environmental Sciences
Shuhan Jia, Yu Li, Quanhua Zhao, Changqiang Wang
Summary: This paper addresses the problem of unstable classification results caused by randomly generated random projection matrix. To solve this problem, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. The algorithm selects a random projection matrix based on the separable information of a single class, and measures the projection result using the degree of separability to obtain the low-dimensional image with optimal separability. The Minimum Distance classifier is then used to calculate the distance matrix, and the weight of the distance matrix is considered in ensemble classification using information entropy.
Article
Computer Science, Artificial Intelligence
Andreas Artemiou, Yuexiao Dong, Seung Jun Shin
Summary: The proposed real-time approach for sufficient dimension reduction, namely principal least squares support vector machines, provides better estimation of the central subspace compared to existing methods such as sliced inverse regression and principal support vector machines. This new proposal is also capable of quick real-time updates in the presence of streamed data, outperforming existing algorithms in terms of performance and speed.
PATTERN RECOGNITION
(2021)
Article
Geochemistry & Geophysics
Qiang Zhang, Yushuai Dong, Qiangqiang Yuan, Meiping Song, Haoyang Yu
Summary: Mixed noise pollution greatly affects hyperspectral image processing and applications. We propose a method combining deep denoising priors with low-rank tensor factorization to restore the image, by leveraging the intrinsic low-rank property of the image and the feature extraction ability of deep learning.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Editorial Material
Engineering, Electrical & Electronic
Jon Atli Benediktsson, Melba Crawford, John Kerekes, Jie Shan
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Xu Yang, Zhiyong Lv, Jon Atli Benediktsson, Fengrui Chen
Summary: A novel spatial-spectral channel attention neural network (SSCAN) is proposed to improve the performance of land cover change detection (LCCD) with remote-sensed images. The SSCAN employs spatial channel attention module and convolution block attention module to process pre- and post-event images, amplifying the change magnitude among the changed areas and minimizing the change magnitude among the unchanged areas. Comparative experiments show that the proposed network outperforms other methods in terms of accelerating network convergence speed, reinforcing learning efficiency, and improving LCCD performance.
Article
Chemistry, Analytical
Xianghui Zhang, Haoyang Yu, Chengchao Li, Zhanjiang Yu, Jinkai Xu, Yiquan Li, Huadong Yu
Summary: In this paper, an in situ monitoring system based on machine vision is designed to monitor tool wear behavior in micro end milling of titanium alloy Ti6Al4V. Image processing algorithms are proposed to measure indicators for evaluating wear behavior, and the accuracy and reliability of these algorithms are verified. The relationship between the level of wear and cutting time is analyzed, and the main influencing reasons for the change in each wear evaluation indicator are identified.
Article
Chemistry, Analytical
Haoyang Yu, Junwei Zhang, Zixiao Xiang, Biao Liu, Huamin Feng
Summary: In this study, the Pixel Value Order (PVO) technology is applied to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. A novel scheme called PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI) is proposed, which satisfies the (k,n) threshold property. The image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. Empirical tests show that PCSRDH-EI outperforms the state-of-the-art and achieves a maximum embedding rate of 5.706 bpp, demonstrating superior encryption effects.
Article
Environmental Sciences
Linghong Meng, Danfeng Liu, Liguo Wang, Jon Atli Benediktsson, Xiaohan Yue, Yuetao Pan
Summary: Spectral unmixing (SU) is an important preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the scene. To address that issue, we investigate the effects of SV on SU and propose an augmented generalized bilinear model to address spectral variability (AGBM-SV).
Article
Pharmacology & Pharmacy
Xiuping Guo, Rui Li, Jinjin Cui, Chujuan Hu, Haoyang Yu, Ling Ren, Yangyang Cheng, Jiandong Jiang, Xiao Ding, Lulu Wang
Summary: This study aims to explore the anti-colorectal cancer (CRC) effect of Erigeron breviscapus injection (EBI) and its underlying mechanism. The results demonstrate that EBI significantly inhibits the proliferation of CRC cells, suppresses the migration and invasion of SW620 cells, and exerts its antitumor effect through inducing necroptosis of tumor cells. Furthermore, EBI activates the RIPK3/MLKL signaling pathway and promotes intracellular reactive oxygen species (ROS) generation, enhancing its anticancer effect.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Zhiyong Lv, Haitao Huang, Weiwei Sun, Meng Jia, Jon Atli Benediktsson, Fengrui Chen
Summary: This article proposes an iterative training sample augmentation (ITSA) strategy to improve the performance of deep learning neural networks in land cover change detection (LCCD) tasks with remote sensing images. The strategy is verified with experiments on seven pairs of real remote sensing images, showing excellent visual performance and quantitative improvement in detection accuracies of LCCD. The results indicate that the deep learning network coupled with ITSA can effectively improve LCCD performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Zhiyong Lv, Pengfei Zhang, Weiwei Sun, Jon Atli Benediktsson, Junhuai Li, Wei Wang
Summary: In this article, two novel features, the Gaussian-weighting spectral (GWS) feature and the area shape index (ASI) feature, are proposed to improve land cover classification with high spatial resolution remotely sensed imagery. Experimental results show that the proposed features can enhance classification accuracies and complement each other to improve classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xiao
Summary: This technical review examines the problem of mixed noise pollution in hyperspectral imaging (HSI), providing analysis of noise in different noisy HSIs and discussing crucial points for programming HSI denoising algorithms. It presents a general HSI restoration model for optimization and comprehensively reviews existing HSI denoising methods, including model-driven, data-driven, and model-data-driven strategies. The advantages and disadvantages of each strategy are summarized and contrasted, and evaluation of denoising methods is provided using simulated and real experiments. The review also presents prospects for future HSI denoising methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Zhiyong Lv, Pingdong Zhong, Wei Wang, Zhenzhen You, Jon Atli Benediktsson, Cheng Shi
Summary: This article proposes a novel sparse key-point distance (SKPD) method based on adaptive region key-points extraction for measuring the change magnitude between bitemporal VHR_RSIs, aiming at land cover change detection (LCCD). The proposed approach includes three steps: exploring spatial-contextual information using an adaptive region generation algorithm, sparsely representing the adaptive region with the box-whisker plot theory, and defining a piecewise distance to measure the change magnitude between the bitemporal images. Experimental results based on four pairs of real VHR_RSIs and four state-of-the-art methods effectively demonstrate the superiority of the proposed approach in achieving LCCD with VHR_RSIs, with improvements in overall accuracy ranging from 5.25% to 22.24%.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Chemistry, Physical
Jin-Jin Cui, Rui Li, Xiao-Lei Ma, Hao-Yang Yu, Zhi-Gang Luo, Peng Du, Ling Ren, Xiao Ding, Xiu-Ping Guo, Wen-Sheng Zheng, Jian-Dong Jiang, Yongsheng Che, Lu-Lu Wang
Summary: The pathogenesis of nonalcoholic fatty liver disease (NAFLD) is multifactorial and composite, with lipid metabolism-induced lipotoxicity being one of the main risk factors. Atorvastatin (AT), the most widely prescribed lipid-lowering drug, has beneficial effects on NAFLD treatment. However, its low absorption rate in the gut and potential disruption of gut flora limit its applications. In this study, a prebiotic-based AT nanoamorphous (PANA) was developed to enhance AT efficacy against NAFLD by improving liver and gut health. Oral administration of PANA resulted in increased drug accumulation in the liver tissue and restored gut health, as indicated by reconstructed gut flora, improved intestinal immunity, barrier integrity, and inflammation. Compared to AT, PANA treatment showed significant inhibition of weight gain and fat deposition, decreased plasma lipid levels, and alleviated hepatic steatosis and liver inflammation. Transcriptome analysis suggested improved immunity and inflammation as potential mechanisms. This study presents a promising strategy for NAFLD treatment using nanotechnology and functional biomaterials in synergy.
Article
Geochemistry & Geophysics
Zhiyong Lv, Haitao Huang, Weiwei Sun, Tao Lei, Jon Atli Benediktsson, Junhuai Li
Summary: This paper proposes a novel approach, E-UNet, for land cover change detection with multimodal remote sensing images (MRSIs). Experimental results demonstrate the feasibility and advantages of the proposed method in terms of visual observations and quantitative evaluations.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Zhiyong Lv, Pengfei Zhang, Weiwei Sun, Jon Atli Benediktsson, Tao Lei
Summary: In this article, a novel land-cover classification method with nonparametric sample augmentation is proposed to improve the performance of HRSI classification. The method iteratively explores reliable samples and exhibits advantages in improving the visual performance and quantitative accuracies of HRSI classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Zhiyong Lv, Fengjun Wang, Weiwei Sun, Zhenzhen You, Nicola Falco, Jon Atli Benediktsson
Summary: In this article, a novel change detection approach based on adaptive region shape similarity (ARSS) is proposed for Landslide inventory mapping (LIM) with very high-resolution (VHR) remote sensing images to improve detection performance. The proposed approach achieved higher accuracies and better performance compared to ten state-of-the-art methods when applied to three pairs of landslide site images acquired with aerial plane and one land use change dataset acquired by Quick Bird Satellite.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
