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arXiv 2024年

作者： Chen, Junzhou Wen, Xuan Zhang, Ronghui Ren, Bingtao Wu, Di Xu, Zhigang Wang, Danwei The Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing100191 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China The School of Information Engineering Chang’an University Shaanxi Xi’an710064 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning;and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, OfficeHome, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA’s critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation. © 2024, CC BY-NC-SA.

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Reduced Neural Network Based Ensemble Approach for Fault Detection and Diagnosis of Wind Energy Converter Systems

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SSRN 2022年

作者： Mansouri, Majdi Dhibi, Khaled Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Electrical and Computer Engineering Program Texas A&M University Qatar Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

Fault Detection and Diagnosis (FDD) is very important to achieve the best operation and ensure the safe and continuous operation of wind energy conversion (WEC) systems. Over the last decade, artificial neural networks surpass classical machine learning models in a variety of FDD tasks. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. The main idea behind this proposal is to determine variable relationships via a data-reduction framework using Hierarchical K-means (H-K-means) clustering method in order to overcome the problem of computation time and storage cost. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other ensemble and single machine learning methods to illustrate and validate the advantages of the proposed techniques. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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Exploring Channel Estimation and Signal Detection for ODDM-based ISAC Systems

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arXiv 2024年

作者： Wang, Dezhi Huang, Chongwen Liu, Lei Chen, Xiaoming Wang, Wei Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Electrical Engineering and Computer Science the KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication (ISAC) systems based on the orthogonal delay-Doppler multiplexing (ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler (DD) plane. To enhance the communication performance in the ODDM-based ISAC systems, we first propose a low-complexity approximation algorithm for channel estimation, which addresses the challenge of the high complexity from high resolution in the ODDM modulation, and achieves performance close to that of the maximum likelihood estimator scheme. Then, we employ the orthogonal approximate message-passing scheme to detect the symbols in the communication process based on the estimated channel information. Finally, simulation results show that the detection performance of ODDM is better than other multi-carrier modulation schemes. Specifically, the ODDM outperforms the orthogonal time frequency space scheme by 2.3 dB when the bit error ratio is 10−6 Copyright © 2024, The Authors. All rights reserved.

关键词： Signal detection

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Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-29页

作者： Chang, Chein-I Lin, Chien-Yu Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral-spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral-spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods. © 1980-2012 IEEE.

关键词： Iterative methods

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Iterative Gaussian-Laplacian Pyramid Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-22页

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Gaussian pyramid (GP) is a commonly used image coding technique that encodes an image as a pyramid that is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated with GP, a Laplacian pyramid (LP) can be also constructed to represent differential images between images in two consecutive layers of GP. Such resulting Gaussian-Laplacian pyramid (GLP) performs data compression in a lossless and lossy manner. A convolutional neural network (CNN) consists of a series of layers concatenated in a feedforward manner where each layer has a convolutional sublayer (CL) and a pooling sublayer (PL). Interestingly, each layer implemented by CL and PL in a CNN can be realized by a single layer in GP in the sense that CL and PL can be carried out by a low-pass Gaussian filter operated as a Gaussian kernel in a single layer of GP. This article develops a new approach to hyperspectral image classification (HSIC), called the GLP network (GLPN), which uses not only GP to realize CNN but also LP to capture differential information between two consecutive layers that CNN cannot. Furthermore, by incorporating an iterative process into GLPN, we can derive an iterative GLPN (IGLPN) that can be considered a companion of a recently developed iterative random training sampling CNN (IRTS-CNN) by replacing CNN with GLPN. Since GLPN can realize CNN in a better way, it is expected that IGLPN will perform better than IRTS-CNN and also significantly reduce computational efficiency compared to IRTS-CNN. © 1980-2012 IEEE.

关键词： Iterative methods

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Synchronization of Nonlinear Neural Networks with Hybrid Couplings and Uncertain Time-Varying Perturbations: A Novel Distributed-Delay Impulsive Comparison Principle

SSRN

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SSRN 2024年

作者： Fan, Hongguang Shi, Kaibo Zhao, Yi College of Computer Chengdu University Chengdu610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu610106 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China School of Mathematical Sciences Shenzhen University Shenzhen518060 China

In this paper, the synchronization of nonlinear drive-response neural networks with uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling is studied. To address the impact of distributed delay and discrete delay on the system, a novel impulsive comparison principle is established, which can be viewed as an effective continuation of the Halanay inequality. Besides, with the help of Lyapunov stability theory, sufficient conditions guaranteeing the exponential synchronization of concerned neural networks are derived by using a delayed impulsive controller with historical status information, which releases the conventional limitation that impulsive delays should be smaller than impulsive intervals, and generalize the existing synchronization results regarding distributed delay networks. Besides, numerical simulations for chaotic neural networks are presented to demonstrate the validity of theoretical results and the sensitivity of the control gain matrix. © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Escalator passenger posture anomaly detection based on improved SSD model

Escalator passenger posture anomaly detection based on impro...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Teng Xu Jian Chen Zuoyong Li Yuanzheng Cai School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation.

关键词： Euclidean distance Market research Inference algorithms Real-time systems Safety Public healthcare Information technology

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Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

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engineering Applications of Artificial Intelligence 2024年第PartE期133卷

作者： Zhang, Chao Wen, Qifei Li, Deyu Sangaiah, Arun Kumar Lin, Mingwei School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos1102-2801 Lebanon College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). © 2024 Elsevier Ltd

关键词： Sentiment analysis

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SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION

SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATI...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wu, Jiawei Fan, Haoyi Zhang, Xiaoqing Lin, Shouying Li, Zuoyong College of Mecanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665438643

In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures. © 2021 IEEE computer Society. All rights reserved.

关键词： Semantics

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