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Data-driven soft constrained model predictive control for sludge bulking in wastewater treatment process

Journal of Process Control 2025年 151卷

作者： Han, Hong-Gui Wang, Yan Sun, Hao-Yuan Liu, Zheng Qiao, Jun-Fei School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China Beijing Laboratory of Smart Environmental Protection Beijing100124 China

The complex causes of sludge bulking, strict system constraints, and dynamic operating conditions increase the challenges of controlling wastewater treatment process. To address this issue, a data-driven soft constrained model predictive control (DD-SCMPC) strategy is proposed, which can adaptively adjust the control law in response to the identified fault cause. First, an intelligent diagnosis algorithm is utilized to identify the key cause variable according to the relative reconstruction contribution of process variables. Consequently, the priority control order of the controlled variables can be determined based on the correlation between the cause variable and output variables. Second, a soft constrained MPC strategy is designed to regulate the concentrations of dissolved oxygen and nitrate nitrogen in accordance with the predetermined control order, thereby avoid sludge bulking caused by abnormal process variables. The incorporation of soft constraints alleviates the strict constraints on system outputs, enhancing the adaptability of the controller. Third, a predictive control barrier function is designed to obtain an enlarged attractive domain, ensuring the stability of the system under soft constraints. Then, the feasibility and stability analysis provide theoretical support for the application of DD-SCMPC. Finally, the effectiveness of the proposed DD-SCMPC strategy is verified on the benchmark simulation model 1. © 2025 Elsevier Ltd

关键词： Predictive control systems

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D2SFormer: Dual attention-dynamic bidirectional Transformer for semantic segmentation of urban remote sensing images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Yan, Yi Li, Jiafeng Zhang, Jing Wang, Liuqian Zhuo, Li Beijing University of Technology School of Information Science and Technology Chaoyang Beijing100124 China Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System China

Semantic segmentation of urban remote sensing images (URSIs) is crucial for the development of smart cities and the acceleration of urban information. In view of the characteristics of URSIs with intricate backgrounds, dense distributions of feature objects, and varying object scales, we propose a dual attention-dynamic bidirectional Transformer (D2SFormer) for semantic segmentation of URSIs. First, a dual attention Transformer encoder has a global branch of cross-shaped window self-attention (CWSA) and a local branch of convolutional channel attention block (CCAB), which helps to enhance pixel-level semantic discrimination. Then, CCAB combines the local sensitivity of Conv with the non-local modeling capability of ECA for overlapping region resolution. Finally, the decoder dynamically fuses multiscale features to reconstruct boundaries and suppress artifacts by using a bidirectional feature pyramid network (BiFPN) and dysampling. Experimental results demonstrate that our D2SFormer achieves mIoUs of 87.84%, 81.24%, and 53.19% on the Potsdam, Vaihingen, and LoveDA datasets, respectively, which is highly competitive with existing methods. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2330-2340页

作者： Wei, Chengpu Li, Zhe Hu, Ting Zhao, Mengyang Sun, Zhonghua Jia, Kebin Feng, Jinchao Pogue, Brain W. Paulsen, Keith D. Jiang, Shudong Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Dartmouth College Thayer School of Engineering HanoverNH03755 United States University of Wisconsin-Madison Department of Medical Physics MadisonWI53705 United States

Near-infrared spectral tomography (NIRST) is a non-invasive imaging technique that provides functional information about biological tissues. Due to diffuse light propagation in tissue and limited boundary measurements, NIRST image reconstruction presents an ill-posed and ill-conditioned computational problem that is difficult to solve. To address this challenge, we developed a reconstruction algorithm (Model-CNN) that integrates a diffusion equation model with a convolutional neural network (CNN). The CNN learns a regularization prior to restrict solutions to the space of desirable chromophore concentration images. Efficacy of Model-CNN was evaluated by training on numerical simulation data, and then applying the network to physical phantom and clinical patient NIRST data. Results demonstrated the superiority of Model-CNN over the conventional Tikhonov regularization approach and a deep learning algorithm (FC-CNN) in terms of absolute bias error (ABE) and peak signal-to-noise ratio (PSNR). Specifically, in comparison to Tikhonov regularization, Model-CNN reduced average ABE by 55% for total hemoglobin (HbT) and 70% water (H2O) concentration, while improved PSNR by an average of 5.3 dB both for HbT and H2O images. Meanwhile, image processing time was reduced by 82%, relative to the Tikhonov regularization. As compared to FC-CNN, the Model-CNN achieved a 91% reduction in ABE for HbT and 75% for H2O images, with increases in PSNR by 7.3 dB and 4.7 dB, respectively. Notably, this Model-CNN approach was not trained on patient data;but instead, was trained on simulated phantom data with simpler geometrical shapes and optical source-detector configurations;yet, achieved superior image recovery when faced with real-world data. © 1982-2012 IEEE.

关键词： Photointerpretation

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CacheAlarm: Monitoring Sensitive Behaviors of Android Apps Using Cache Side Channel

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Tian, Jianwen Ma, Haoyu Gao, Debin Kuang, Xiaohui Academy of Military Sciences Institute of System Engineering China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100045 China Singapore Management University School of Computing and Information Systems Singapore

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android's own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this paper, we argue that a more traditional side-channel attack strategy, namely the CPU-cache-based side channel, could be exploited in the benign scenario of app behavior surveillance. Specifically, we propose CacheAlarm, a sensitive app behavior monitor and foreground app identification system, which works by measuring cache side-channel readings of selected methods within the Android framework, and conducted in-lab and in-the-wild user studies to compare the effectiveness of our scheme against SideNet, a previous Android app behavior surveillance scheme using API-based side channels. Results of the studies suggested that CacheAlarm outperforms SideNet on the accuracy of detecting sensitive behaviors in addition to gaining the capability of detecting apps running at foreground of the user device. © 2025 IEEE. All rights reserved.

关键词： Android malware

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CESFusion: Cross-Frequency Enhanced Spatial-Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China Engineering Research Center of Learning- Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou310018 China

The fusion of hyperspectral and multispectral images involves integrating high spectral resolution hyperspectral image (HSI) and high spatial resolution multispectral image (MSI) to generate a HSI with high spatial and spectral resolution (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this paper, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency domain fusion module (CFFM) to perform global analysis through the Fourier transform and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 2025 IEEE.

关键词： Image fusion

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VFLMonitor: Defending One-Party Hijacking Attacks in Vertical Federated Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4828-4843页

作者： Xiangrui Xu Yiwen Zhao Yufei Han Yongsheng Zhu Zhen Han Guangquan Xu Bin Wang Shouling Ji Wei Wang School of Information Science and Technology Qingdao University of Science and Technology Qingdao China Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA INRIA Rennes Brittany France Institute of Computing Technologies China Academy of Railway Sciences Corporation Ltd. Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University Tianjin China Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Zhejiang China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experiments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries.

关键词： Training Servers Electronic mail Artificial intelligence Reliability theory Predictive models Federated learning Data models Computational modeling Collaboration

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Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-Identification

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IEEE Internet of Things Journal 2025年

作者： Pei, Jiangbo Jiang, Zhuqing Men, Aidong Wang, Haiying Luo, Haiyong Wen, Shiping Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing Key Laboratory of Network System and Network Culture China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Technology Australian AI Institute Faculty of Engineering and Information Technology SydneyNSW2007 Australia

Single-camera-training person re-identification (SCT re-ID) aims to train a re-ID model using SCT datasets where each person appears in only one camera. The main challenge of SCT re-ID is to learn camera-invariant feature representations without cross-camera same-person (CCSP) data as supervision. Previous methods address it by assuming that the most similar person should be found in another camera. However, this assumption is not guaranteed to be correct. In this paper, we propose a novel solution: the Camera-Invariant Meta-Learning Network (CIMN) for SCT re-ID. CIMN operates under the premise that camera-invariant feature representations should remain robust despite changes in camera settings. To achieve this, we partition the training data into a meta-train set and a meta-test set based on camera IDs. We then conduct a cross-camera simulation using a meta-learning strategy, aiming to enforce the feature representations learned from the meta-train set to be robust when applied to the meta-test set. We further introduce three specific loss functions to leverage potential identity relations between the meta-train set and the meta-test set. Through the cross-camera simulation and the introduced loss functions, CIMN can extract feature representations that are both camera-invariant and identity-discriminative even in the absence of CCSP data. Our experimental results demonstrate that CIMN can extract feature representations that are both camera-invariant and identity-discriminative, even in the absence of CCSP data. our method achieves comparable performance with and without the use of CCSP data, and outperforms state-of-the-art methods on three SCT re-ID benchmarks. © 2014 IEEE.

关键词： Contrastive Learning

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Behavioral Unicity: On the Limits of Anonymized Social Behavior Metadata 1st

Behavioral Unicity: On the Limits of Anonymized Social Beh...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Tang, Hao Wang, Cheng Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200030 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9789819794119

Social behavior metadata has altered business models and human lifestyles. However, the inclusion of personal information in social behavior metadata poses risks of identity exposure for users. Under the requirements of modern data management laws, service providers now employ anonymization techniques when collecting user behavior metadata, which aims to break the link between personal identifiers and behavioral attributes for privacy protection. This paper conducts experiments on an open-source dataset of anonymized social behavior metadata and finds that even with privacy protection techniques such as data generalization, there remains a high risk of reidentification. Specifically, four social behavior metadata records are sufficient to reidentify users from the anonymized social behavior metadata, achieving a unicity of 90.10%. This indicates that the current widespread collection of social behavior metadata brings a high risk of privacy breaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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Burst-Sensitive Traffic Forecast Via Multi-Property Personalized Fusion in Federated Learning

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IEEE Transactions on Mobile computing 2025年

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Computing Technology Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beihang University School of Computer Science and Engineering China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing China JD Technology JD iCity China JD Intelligent Cities Research China Tsinghua University Department of Computer Science and Technology Beijing China Zhongguancun Laboratory Beijing China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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