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6. Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

Author

Zhang, Xinzheng, Su, Hang, Zhang, Ce, Gu, Xiaowei, Tan, Xiaoheng, and Atkinson, Peter M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.

Published
2020

8. A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion

Author

Gu, Xiaowei, Khan, Muhammad Aurangzeb, Angelov, Plamen, Tiwary, Bikash, Yourdshah, Elnaz Shafipour, and Yang, Zhao-Xu

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Robotics
Abstract

A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference models, both of which are implemented by a first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The SOF-PID controller self-organizes and self-updates the structures and meta-parameters of both the control and reference models during the control process "on the fly". This gives the SOF-PID control system the capability of quickly adapting to entirely new operating environments without a full re-training. Moreover, the SOF-PID control system is free from user- and problem-specific parameters, and the uniform stability of the SOF-PID control system is theoretically guaranteed. Simulations and real-world experiments with mobile robots demonstrate the effectiveness and validity of the proposed SOF-PID control system., Comment: This paper has been submitted to WCCI2020 for publication

Published
2019

9. A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification

Author

Gu, Xiaowei, Angelov, Plamen P, and Soares, Eduardo Almeida

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50% to 80%) is used for training and the rest for validation. In many problems, however, the data is highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesising feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesise data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesising data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, e.g. support vector machine, k-nearest neighbour, deep networks, rule-based classifiers, decision trees, etc. The results demonstrated that: i) a significantly more balanced (and fair) classification results can be achieved; ii) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning., Comment: This paper has been submitted to International Journal of Intelligent Systems for publication

Published
2019
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24. Self-organising transparent learning system

Author

Gu, Xiaowei and Angelov, Plamen

Subjects
006.3
Abstract

Machine learning, as a subarea of artificial intelligence, is widely believed to reshape the human world in the coming decades. This thesis is focused on both the unsupervised and supervised self-organising transparent machine learning techniques. One particularly interesting aspect is the transparent self-organising deep learning systems. Traditional data analysis approaches and most of the machine learning algorithms are built upon the basis of probability theory and statistics. The solid mathematical foundation of the probability theory and statistics guarantees the good properties of these learning algorithms when the amount of data tends to infinity and all the data comes from the same distribution. However, the prior assumptions of the random nature and same distribution imposed on the data generation model are often too strong and impractical in real applications. Moreover, traditional machine learning algorithms also require a number of free parameters to be predefined. However, without any prior knowledge of the problem, which is often the case in real situations, the performance of the algorithms can be largely influenced by the improper choice. Deep learning-based approaches are currently the state-of-the-art techniques in the fields of machine learning and computer vision. However, they are also suffering from a number of deficiencies including the computational burden of training using huge amount of data, lack of transparency and interpretation, ad hoc decisions about the internal structure, no proven convergence for the adaptive versions that rely on reinforcement learning, limited parallelisation and offline training, etc. These shortcomings largely all hinder the wider applications of the deep learning in real situations. The novel approaches presented in this thesis are developed within the Empirical Data Analytics framework, which is an alternative, but more advanced computational methodology to the traditional approaches based on the ensemble properties and mutual distribution of the empirical discrete observations. The novel self-organising transparent machine learning algorithms presented in this work for clustering, regression, classification and anomaly detection are autonomous, self-organising, data-driven and free from user- and problem- specific parameters. They do not impose any data generation models on the data a priori, but are driven by the empirically observed data and are able to produce the objective results without prior knowledge of the problems. In addition, they are highly efficient and suitable for large-scale static/streaming data processing. The newly proposed self-organising transparent deep learning systems are able to achieve human-level performance comparable to or even better than the deep convolutional neural networks on image classification problems with the merits of being fully transparent, self-evolving, highly efficient, parallelisable and human-interpretable. More importantly, the proposed deep learning systems have the ability of starting classification from the very first image of each class in the same way as humans do. Numerical examples based on numerous challenging benchmark problems and comparisons conducted with the state-of-the-art approaches presented in this thesis demonstrated the validity and effectiveness of the proposed new machine learning algorithms and deep learning systems and show their potential for real applications.

Published
2018
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39. Semi-Supervised Fuzzily Weighted Adaptive Boosting for Classification

Author

Gu, Xiaowei, Angelov, Plamen P, Shen, Qiang, Gu, Xiaowei, Angelov, Plamen P, and Shen, Qiang

Abstract

Fuzzy systems offer a formal and practically popular methodology for modelling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semi-supervised boosting is widely recognised as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labelled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semi-supervised boosting has not been systematically explored yet. In this study, a novel semi-supervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence amongst predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence towards their predictions throughout the process of sample weight updating and ensemble output generation. In so doing, the base classifiers are empowered to gradually focus more on challenging samples that are otherwise hard to generalise, enabling the development of more precise integrated classification boundaries. Numerical evaluations on a range of benchmark problems are carried out, demonstrating the efficacy of the proposed semi-supervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.

Published
2024

40. Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework

Author

Cui, Hongxing, Tang, Danling, Liu, Huizeng, Liu, Hongbin, Sui, Yi, Lai, Yangchen, Gu, Xiaowei, Cui, Hongxing, Tang, Danling, Liu, Huizeng, Liu, Hongbin, Sui, Yi, Lai, Yangchen, and Gu, Xiaowei

Abstract

Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean–atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. The results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs).

Published
2024

41. Study on the Preparation and Compressive Strength of Boron Mud-Based Basic Magnesium Sulfate Cement.

Author

Li, Jiankun, Gu, Xiaowei, Wang, Shenyu, Hu, Zhihang, Hu, Ziyang, and Li, Xiaqing

Subjects
*MAGNESIUM sulfate, *COMPRESSIVE strength, *BORON, *X-ray diffraction, *HAZARDS
Abstract

The direct discharge of boron mud poses significant environmental hazards to soil and groundwater. Despite extensive research efforts, the reprocessing of boron mud has not yielded significant advancements. Recently, the development of magnesium cement has spurred interest in the reutilization of boron mud. However, the direct treatment of boron mud remains challenging, necessitating pre-treatment in most studies to achieve substantial results. Consequently, research on the direct incorporation of untreated boron mud is scarce. This study explores the feasibility of using uncalcined boron mud as a base material in basic magnesium sulfate cement (BMSC), composed of lightly calcined magnesia and magnesium sulfate heptahydrate. The effects of varying boron mud content on the compressive strength of the BMSC system were investigated. The results indicate that the 5·1·7 phase is the primary strength phase of BMSC. When the boron mud content is 30%, the uncalcined boron mud has a minimal impact on the formation of the 5·1·7 phase. Additionally, the 28 days compressive strength of BMSC-B30 showed a slight difference compared to the control group BMSC-C, registering at 66.7 MPa. TG-DSC analysis revealed that the presence of a small amount of boron mud inhibits the micro-expansion trend of the BMSC structure. Furthermore, XRD and SEM analyses confirmed that the addition of uncalcined boron mud does not significantly alter the phase structure of the 5·1·7 phase in BMSC. This study provides a foundational basis for the long-term development of direct boron mud treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Single‐cell transcriptome sequencing reveals SPP1‐CD44‐mediated macrophage–tumor cell interactions drive chemoresistance in TNBC.

Author

Liu, Fuzhong, Zhang, Junfeng, Gu, Xiaowei, Guo, Qiuyang, and Guo, Wenjia

Subjects
TRIPLE-negative breast cancer, TRANSCRIPTION factors, DRUG resistance in cancer cells, CANCER cells, CELL communication
Abstract

Triple‐negative breast cancer (TNBC) is often considered one of the most aggressive subtypes of breast cancer, characterized by a high recurrence rate and low overall survival (OS). It is notorious for posing challenges related to drug resistance. While there has been progress in TNBC research, the mechanisms underlying chemotherapy resistance in TNBC remain largely elusive. We collect single‐cell RNA sequencing (scRNA‐seq) data from five TNBC patients susceptible to chemotherapy and five resistant cases. Comprehensive analyses involving copy number variation (CNV), pseudotime trajectory, cell–cell interactions, pseudospace analysis, as well as transcription factor and functional enrichment are conducted specifically on macrophages and malignant cells. Furthermore, we performed validation experiments on clinical samples using multiplex immunofluorescence. We identified a subset of SPP1+ macrophages that secrete SPP1 signals interacting with CD44 on malignant cell surfaces, potentially activating the PDE3B pathway within malignant cells via the integrin pathway, leading to chemotherapy resistance. The abnormally enhanced SPP1 signal between macrophages and malignant cells may serve as a factor promoting chemotherapy resistance in TNBC patients. Therefore, SPP1+ macrophages could potentially serve as a therapeutic target to reduce chemotherapy resistance. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Effect of Diethanol-Isopropanolamine and Typical Supplementary Cementitious Materials on the Hydration Mechanism of BOF Slag Cement Pastes.

Author

Wang, Hongyu, Gu, Xiaowei, Xu, Xiaochuan, Liu, Jianping, Zhu, Zhenguo, and Wang, Shenyu

Subjects
SLAG cement, BASIC oxygen furnaces, MORTAR, PORTLAND cement, HYDRATION, CEMENT composites, COMPOSITE materials
Abstract

This study investigated the effects of lithium slag and iron tailings on the hydration mechanism of Basic Oxygen Furnace (BOF) slag cement paste with the addition of 0.06% diethanol-isopropanolamine (DEIPA). This study examined the fluidity, compressive strength, pore solution pH, and hydration products of BOF slag-based composite cementitious materials. The results showed that DEIPA facilitated the conversion from ettringite (AFt) to monosulphate (AFm) and improved the early compressive strength of the BOF slag–cement mortar. Incorporating lithium slag into the DEIPA-containing BOF slag–cement system promoted AFt formation, increased calcium-silicate-hydrate production, and enhanced the microstructure. BOF slag, lithium slag, and iron tailings exhibit synergistic effects in cement pastes. BOF slag and lithium slag provided the reactive components SiO2 and Al2O3. In the early hydration stages, the iron tailings primarily served as fillers, accelerating the system's reactions. [ABSTRACT FROM AUTHOR]

Published
2024
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