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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： He, Tao Zhang, Shixiong Li, Hao Yan, Zhijun Liu, Deming Sun, Qizhen School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan430074 China Optics Valley Laboratory Huazhong University of Science and Technology School of Optical and Electronic Information National Laboratory for Optoelectronics National Engineering Laboratory for Next Generation Internet Access System Wuhan430074 China

A multi-source signals separation algorithm is proposed to identify the potential threatening signals submerged in the strong background noises. Finally, the recognition rate of the mixed signals is improved from 62.8... 详细信息

ISBN: (纸本)9781957171180

关键词： Source separation

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Research on Optimization Algorithm for Fire Emergency Evacuation in Experimental Buildings of Universities

Research on Optimization Algorithm for Fire Emergency Evacua...

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Chinese Control and Decision Conference, CCDC

作者： Xincheng Han Hongyan Ma Wei He Shun Wang School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

College laboratory buildings have dense population, numerous electrical devices, and high fire risk. However, the existing evacuation A* algorithm has the disadvantages of long time and poor adaptability. In order to effectively solve the problems of long evacuation simulation time, low efficiency, and inability to change evacuation directions according to fire conditions in college laboratory buildings, this paper proposes an improved A* algorithm. This paper improves the heuristic function in the A* algorithm by introducing a logarithmic decay factor. By changing the weight of the estimated cost, it reduces the number of unnecessary search nodes and steers the search algorithm in the correct direction, thereby improving search efficiency. Secondly, it introduces a bidirectional A* search strategy to reduce search nodes and improve the efficiency of the algorithm. Simulation results show that the improved A* algorithm has fewer traversed nodes, shorter search paths, and higher search efficiency, which verifies the effectiveness and feasibility of the improved A* algorithm in experimental building fire evacuation. This study provides a solution for fire evacuation in college laboratory buildings.

关键词： Heuristic algorithms Simulation Buildings Sociology Laboratories Search problems Path planning

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EdgeNN: Efficient Neural Network Inference for CPU-GPU Integrated Edge Devices

EdgeNN: Efficient Neural Network Inference for CPU-GPU Integ...

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International Conference on data engineering

作者： Chenyang Zhang Feng Zhang Kuangyu Chen Mingjun Chen Bingsheng He Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China School of Computing National University of Singapore

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://***/ChenyangZhang-cs/EdgeNN.

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TransFR: Transferable Federated Recommendation with Pre-trained Language Models

arXiv

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arXiv 2024年

作者： Zhang, Honglei Liu, He Li, Haoxuan Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University China School of Computer and Information Technology Beijing Jiaotong University China Center for Data Science Peking University China

Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent architecture for privacy-preserving recommendations. In conventional FRs, a dominant paradigm is to utilize discrete identities to represent users/clients and items, which are subsequently mapped to domain-specific embeddings to participate in model training. Despite considerable performance, we reveal three inherent limitations that can not be ignored in federated settings, i.e., non-transferability across domains, unavailability in cold-start settings, and potential privacy violations during federated training. To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data. Specifically, it first learns domain-agnostic representations of items by exploiting pre-trained models with public textual corpora. To tailor for federated recommendation, we further introduce an efficient federated fine-tuning and a local training mechanism. This facilitates personalized local heads for each client by utilizing their private behavior data. By incorporating pre-training and fine-tuning within FRs, it greatly improves the adaptation efficiency transferring to a new domain and the generalization capacity to address cold-start issues. Through extensive experiments on several datasets, we demonstrate that our TransFR model surpasses several state-of-the-art FRs in terms of accuracy, transferability, and privacy. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Air quality prediction based on ICEEMDAN-SSA-LSTM

Air quality prediction based on ICEEMDAN-SSA-LSTM

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Chinese Control and Decision Conference, CCDC

作者： Yanling Qin Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the problem that the Air Quality Index (AQI) is difficult to predict, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) is proposed. First, the AQI sequence was decomposed by ICEEMDAN. Second, the initial learning rate, max training epoch and the number of hidden layers of the LSTM model are optimized by the SSA algorithm to establish the SSA-LSTM model. Last, the results decomposed were imported into the SSA-LSTM model to predict AQI. Compared with the prediction results of the LSTM, CEEMD-LSTM, and CEEMDAN-SSA-LSTM model, the accuracy (measured by correlation coefficients) of the CEEMDAN-SSA-LSTM is 98%. The result confirmed that the ICEEMDAN-SSA-LSTM can obtaining an improvement of the correlation coefficients and reduce the mean square error, which is valuable for engineering applications.

关键词： Training Correlation coefficient Adaptation models Accuracy Empirical mode decomposition Atmospheric modeling Predictive models

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Face anti-spoofing based on multi-modal and multi-scale features fusion

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The Journal of China Universities of Posts and Telecommunications 2022年第6期29卷 73-82页

作者： Kong Chao Ou Weihua Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550025China Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guizhou Institute of TechnologyGuiyang 550003China Guizhou Science and Technology Information Center Department of Science and Technology of Guizhou ProvinceGuiyang 550002China

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.

关键词： face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)

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Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing

SSRN

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SSRN 2024年

作者： Li, Qi Guo, Dan Qian, Wei Zhou, Yuanen Zhao, Haifeng Sun, Xiao Wang, Meng School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

The remote photoplethysmography (rPPG) technique enables the estimation of vital signs such as heart rate by analyzing pulse-induced subtle skin color variation from facial videos. Robustly deriving cardiac pulse information with head motion (\eg, rotation and swings) interference, remains a critical challenge, as it inevitably introduces complex noise disturbances such as facial geometric deformation and head motions. Most existing approaches primarily focus on how to extract the weak pulse signals, overlooking the detrimental effects induced by such noises. In response to this, we propose a Temporal Gated Face Alignment Network (TGFAN) to adaptively counteract motion noise in long-term video sequences. As key modules in TGFAN, the bidirectional temporal face alignment block first captures inter-frame motion discrepancies and achieves temporal alignment, and then extracts motion-robust pulse features by spatiotemporal convolution. Furthermore, we also propose a temporal gating mechanism, which can dynamically guide motion-disturbed segments for face alignment to alleviate local temporal interference. Experimental evaluations on four public datasets demonstrate the superior performance of our proposed model under both intra-dataset and cross-dataset tests. Our codes will be available at \url{https://***/Eadith/TGFAN}. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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The State of Charge Estimation of Lithium-ion Battery Pack Based on PSO-KELM

The State of Charge Estimation of Lithium-ion Battery Pack B...

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Chinese Control and Decision Conference, CCDC

作者： Mengyuan Chen Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurately estimating the State of Charge (SOC) is of great significance for ensuring the safe operation of lithium-ion batteries and preventing overcharging or discharging. However, as SOC is the internal state of the battery unit and cannot be directly measured, it is difficult to obtain accurate SOC values. To improve the estimation accuracy of SOC, this paper establishes a prediction model using Kernel Extreme Learning Machine (KELM), and uses Particle Swarm Optimization (PSO) to optimize the kernel function parameter S and regularization coefficient C of KELM and determine the optimal values, a SOC prediction method for lithium-ion batteries based on PSO-KELM is proposed. The input of the PSO-KELM model is battery voltage, current, and temperature, the output is the actual SOC value. The results show that the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE) of SOC prediction are reduced to 2.76%, 2.18%, and 6.89%, increase coefficient of determination (R 2 ) to 0.9919. The PSO-KELM model improves prediction accuracy compared to ELM and KELM models, and has good convergence and generalization properties.

关键词： Lithium-ion batteries Accuracy Extreme learning machines Estimation Predictive models Battery charge measurement data models

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