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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3284-3296页

作者： Chen, Dingyuan Yang, Cuili Qiao, Junfei Beijing University of Technology Faculty of Information Technology Beijing Laboratory for Intelligent Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Institute of Artificial Intelligence Beijing100124 China

Wastewater treatment process (WWTP) is an important mean to prevent water pollution and improve ecological environment. Dissolved oxygen (DO) and nitrate nitrogen (NO3-N) concentrations are the main indicators to affect the effluent quality (EQ). In order to achieve high accuracy control, the artificial intelligence based asymmetric constraint control method with actuator saturation processing technology is proposed in WWTP. First, the fuzzy neural network (FNN) model is used to estimate the unknown situations, in which the maximum correlation entropy criterion is introduced into the adjustment of model structure to deal with the dynamic changes of WWTP. Second, the unified actuator saturation processing model is established to achieve the stable tracking performance. Third, the asymmetric barrier Lyapunov function (BLF) is introduced into the controller design. Not only the DO and NO3-N concentrations, but also the tracking error are kept within the asymmetric constraint range to guarantee the control performance. Finally, the effectiveness of the proposed method is verified via the dynamical and constant set-points simulation experiments of benchmark simulation model 1 (BSM1). © 2020 IEEE.

关键词： Wastewater treatment

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SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1717-1719页

作者： Jiufang Chen Ye Yuan Xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

关键词： representation preserving undirected

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Research on Regional Collaborative Prediction of Air Quality Based on LSTM-FC 7

Research on Regional Collaborative Prediction of Air Quality...

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7th International Conference on Computer information Science and Application technology, CISAT 2024

作者： Xu, Guoqing Zhang, Heng Yu, Lu Zhao, Meng Lei, Chaowen Huang, Yuxuan Shanghai Aerospace Electronic Technology Institute Key Laboratory of Intelligent Computing Technology Shanghai China

ISBN: (纸本)9798350375107

At present, the air quality problem has attracted the attention of all walks of life. Exploring the changing trends of air pollution and building a reasonable prediction model for predicting changes in air pollutants are of great significance for atmospheric environment governance. On the basis of Long Short-Term Memory Neural Network (LSTM) and Full Connected Neural Network (FC), this paper uses the method of multi-information fusion prediction to explore the data correlation law between multi-regional data sources. By introducing the distance weight matrix, the prediction accuracy of our model is improved by an average of 7.7% compared with LSTM-FC. Our model can predict a variety of air pollutants in multiple regions with high precision, realize the effective prediction of air quality, and effectively meet the needs of meteorological observation and atmospheric governance. © 2024 IEEE.

关键词： Prediction models

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A hybrid attention model based on first-order statistical features for smoke recognition

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Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： GUO Nan LIU JiaHui DI KeXin GU Ke QIAO JunFei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to *** tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the propose

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

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Positioning Error Compensation via Channel Knowledge Map for UAV Communication

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IEEE Internet of Things Journal 2025年第11期12卷 16977-16989页

作者： Zhang, Chiya Wang, Ting He, Chunlong Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Metro Flow Prediction with Hierarchical Hypergraph Attention Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3012-3021页

作者： Wang, Jingcheng Zhang, Yong Hu, Yongli Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing100124 China

Graph convolutional neural networks, which aim to learn the spatial correlation within multivariate time-series data, have achieved significant advancements in traffic prediction. Despite the proliferation of various spatiotemporal graph neural networks, a fundamental question of the predefined graphs still remain to be explored: how can the graph be dynamically optimized as time shifts? Especially for tasks such as rail transit where travel patterns of passengers change periodically. In other words, the spatial correlation of multivariate traffic time-series data is closely intertwined with temporal features, which are tricky to reflect through static graphs with handcrafted association. To adaptively learn the spatial correlation in feature domain, we propose hierarchical hypergraph attention networks (HHGATs). The hierarchical nature of the framework is reflected in both temporal and spatial dimensions. At different time spans, the model explores the hidden hyperedges with a local hypergraph attention mechanism and optimizes the hypergraph with a global attention mechanism. The nodelevel prediction results are then fused after the downstream tasks of spatiotemporal hypergraph convolutional networks. The effectiveness and accuracy of the proposed model are evaluated on historical datasets of Beijing and Hangzhou. Compared to the baselines, the proposed approach consistently demonstrates superior performance in various evaluation scenarios. © 2020 IEEE.

关键词： Forecasting

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PFedKD: Personalized Federated Learning via Knowledge Distillation using Unlabeled Pseudo Data for Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 16314-16324页

作者： Li, Hanxi Chen, Guorong Wang, Bin Chen, Zheng Zhu, Yongsheng Hu, Fuqiang Dai, Jiao Wang, Wei Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation 3 Shangyuancun Beijing China China Railway Information Technology Group Corporation Limited Institute of Computing Technologies Beijing10081 China

With the rapid advancement of wearable devices and IoT technologies, sensor data generated by edge devices has surged. This data is crucial for advancing IoT applications, including health status monitoring, abnormal behavior detection, and environmental monitoring. However, traditional centralized learning requires uploading data to a central server, raising security and privacy concerns and hindering data application. Federated learning (FL) offers a solution by enabling collaborative model training on IoT devices without transferring data from the local device. In practice, edge devices generate data that is often highly heterogeneous, making it challenging for the global FL model to capture local data distributions accurately, leading to significant performance degradation. Additionally, imbalanced edge device resources and limited bandwidth can cause data transmission delays or interruptions, impacting application feasibility. To address these issues, we propose PFedKD, a novel personalized FL algorithm based on knowledge distillation, aimed at enhancing the model's generalization ability and reducing communication overhead in heterogeneous IoT data environments. PFedKD constructs a public dataset using unlabeled pseudo data to extract knowledge from each client, training personalized models that fit local data distributions. This method controls dataset size while enhancing performance. During communication, only logits and class prototypes are transmitted, ensuring high communication efficiency. Sharpness Aware Minimization is introduced in local model training to optimize generalization. Additionally, we design a weight distribution mechanism based on client sample quality evaluation that optimizes knowledge aggregation and model personalization. Extensive experiments demonstrate that PFedKD significantly outperforms state-of-the-art baselines in both learning performance and communication efficiency. © 2025 IEEE.

关键词： Wearable technology

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

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IEEE Transactions on Affective computing 2024年第2期16卷 624-638页

作者： Chen, Yin Li, Jia Shan, Shiguang Wang, Meng Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. Recognizing the potential in leveraging SFER knowledge for DFER, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we have achieved a new state of the art. © 2010-2012 IEEE.

关键词： Face recognition

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Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled Mobile Edge computing Networks

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China Communications 2024年第11期21卷 150-162页

作者： Sun Gangcan Sun Jiwei Hao Wanming Zhu Zhengyu Ji Xiang Zhou Yiqing School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Beijing Key Laboratory of Mobile Computing and Pervasive Device and the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

关键词： eavesdropping massive multiple input multiple output mobile edge computing partial offloading secure computation efficiency

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A Hierarchical Control Scheme for Active Power-assist Lower-limb Exoskeletons

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Journal of Bionic Engineering 2024年第5期21卷 2184-2198页

作者： Jing Deng Wenzheng Jiang Haibo Gao Yapeng Shi Mantian Li State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150001HeilongjiangChina Shenzhen Academy of Aerospace Technology Shenzhen518063China Faculty of Computing Harbin Institute of TechnologyHarbin150001HeilongjiangChina Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic UniversityShenzhen518055China

Effectively controlling active power-assist lower-limb exoskeletons in a human-in-the-loop manner poses a substantial chal-lenge,demanding an approach that ensures wearer autonomy while seamlessly adapting to diverse wearer *** paper introduces a novel hierarchical control scheme comprising five integral components:intention recognition layer,dynamics feedforward layer,force distribution layer,feedback compensation layer,as well as sensors and *** intention rec-ognition layer predicts the wearer's movement and enables wearer-dominant movement through integrated force and position *** force distribution layer effectively resolves the statically indeterminate problem in the context of double-foot support,showcasing flexible control *** dynamics feedforward layer mitigates the effect of the exoskeleton itself on ***,the feedback compensation layer provides reliable closed-loop *** approach mitigates abrupt changes in joint torques during frequent transitions between swing and stance phases by decomposed *** this innovative hierarchical control scheme on a hydraulic exoskeleton platform through a series of experiments,the results demonstrate its capability to deliver assistance in various modes such as stepping,squatting,and jumping while adapting seamlessly to different terrains.

关键词： Hierarchical control Active power-assist Exoskeleton robots Dynamics feedforward Force distribution Feedback compensation

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