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42nd Chinese Control Conference, CCC 2023

作者： Li, Zhi Sun, Tao Ma, Song Yang, Bin Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789887581543

Remaining useful life (RUL) prediction can improve the availab.lity and efficiency of equipment or system by providing timely maintenance suggestions. In this paper, an improved hybrid attention learning system for RUL prediction is developed. By introducing adaptive weighting, a hybrid attention mechanism with self attention and external attention is designed. The mechanism enhances the attention to samples with similar performance degradation patterns, and improves the feature extraction ability of the learning system for sensor data with strong temporal correlation. Finally, we conducted a series of simulation experiments on the commercial modular aviation propulsion system simulation (C-MAPSS) dataset to verify the effectiveness and superiority of the proposed method in RUL prediction. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Learning systems

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An Investigation of Zero-Sequence Current Suppression Strategy for AC Power Aging Topology

An Investigation of Zero-Sequence Current Suppression Strate...

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Energy, Electrical and Power engineering (CEEPE), International Conference on

作者： Wenxia Jiang Qingbin Tong Lei Wang Mingchao Zhou School of Electrical Engineering Beijing Jiaotong University Beijing China Key Lab. of Vehicular Multi-Energy Drive Systems Ministry of Education Beijing China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

Due to the existence of zero-sequence current loop in the AC power aging system with a common DC link, the zero-sequence voltage source constituted by the common-mode voltages generated by the modulation of the converters on both sides generates zero-sequence currents in the loop, which affects the operation efficiency and stability of the system. Therefore, a zero-sequence current suppression method based on proportional resonance control is proposed. Based on the establishment of a mathematical model of the AC power aging system and its zero-sequence circuit with a common DC link, the zero-sequence current is suppressed by designing a zero-sequence current closed-loop system based on the proportional resonance control to control the zero-sequence component of the output voltage of the load converter. By constructing a simulation model of the AC power aging system with a common DC link, the zero-sequence current suppression capability of the proposed zero-sequence current suppression strategy is analyzed in depth under different operating conditions, and the effectiveness of the proposed zero-sequence current suppression strategy is verified.

关键词： Power engineering Resonant frequency RLC circuits Aging Resonance Mathematical models Stability analysis

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Distilling Dynamic Spatial Relation Network for Human Pose Estimation 32

Distilling Dynamic Spatial Relation Network for Human Pose E...

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32nd British Machine Vision Conference, BMVC 2021

作者： Wu, Kewei Wang, Tao Xie, Zhao Guo, Dan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation against various unreliable joints is critical. To this end, we propose a Distilling Dynamic Spatial Relation network (DDSR), which builds pose-based graph representation by exploiting the feature of spatial relation from the location distribution of joints. We use a dynamic message propagation mechanism to update the spatial relation on edges. Specifically, to filter out the noisy predictions, we select the joints with high confidence;to enhance the spatial relation in a large receptive field, we propagate multi-stage messages among joints. Besides, to reduce the computation cost of the multi-stage message propagation, we design a cross-resolution distillation framework. We use a new spatial distillation loss to verify the spatial relation between the teacher model and the student model. Experimental results on COCO and MPII datasets show that our method is superior to the state-of-the-art methods. The visualization results further verify the interpretability of our spatial relation. © 2021. The copyright of this document resides with its authors.

关键词： Distillation

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Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System 2021

Electronic Medical Record Sharing System Based on Hyperledge...

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5th International Conference on Compute and data Analysis, ICCDA 2021

作者： Li, Lei Yue, Zhengxiang Wu, Gongqing Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781450389112

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data. © 2021 ACM.

关键词： Blockchain

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IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is availab.e at https://***/Jungle123456/IKGN.

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Puwei Wang Zhouxing Sun Ying Zhan Haoran Li Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China Guizhou University of Finance and Economics Guiyang City China

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.

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Fine-Grained Recognition with Incremental Classes 5

Fine-Grained Recognition with Incremental Classes

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Zhou, Yang-Zhun Pca Lab The Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Info. of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781450396899

This work focuses on dealing with fine-grained recognition problems when incremental classes emerge. The task is desirable to not only distinguish subordinate visual classes based on discriminative but subtle object parts, but also recognize new coming sub-classes without suffering from catastrophic forgetting. In this paper, we first propose to localize both object- and part-level image regions for capturing powerful fine-grained patterns. Then, these fine-grained regions are fed into a bilateral network consisting of a stable branch and a flexible branch for supporting observed and incremental sub-classes recognition respectively. Moreover, a cumulative adaptation strategy is further equipped to adjust the network training during the incremental sessions. Meanwhile, to better retain the modeling capability of observed classes, we also replay samples from previous classes by a hallucination approach. Experiments are conducted on three popular fine-grained recognition datasets and results of the proposed method can reveal its superiority over state-of-the-arts. © 2022 ACM.

关键词： Generative adversarial networks

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665468190

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and lab.led X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods. © 2022 IEEE.

关键词： Forecasting

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Gradient sparsification for efficient wireless federated learning with differential privacy

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Science China(Information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collab.ratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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A Numerical Simulation Study on Bubble Size Distribution in a Variable Diameter Pipeline based on CFD-PBM 43

A Numerical Simulation Study on Bubble Size Distribution in ...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Lei Chen, Xu Shi, Jiamei Zhang, Xuyang Zhang, Shuo Dalian University of Technology The Key Lab. of Intelligent Contr. and Optimization for Indust. Equipment of Ministry of Education Dalian116024 China Electric Power Science Research Institute of State Grid Inner Mongolia East Electric Power CO. Ltd China China Kunlun Contracting & Engineering Corp China

ISBN: (纸本)9789887581581

This study aims to investigate the characteristics of bubble size distribution within a variable diameter pipeline. The pipeline is divided into five main sections: inlet section, contraction section, intermediate section, expansion section, and outlet section. A combination of numerical simulation and computational fluid dynamics (CFD) is employed to understand the impact of different pipe geometries on bubble behavior. This paper uses a set of boundary conditions and methods which are proven correct from an existing study for simulation. In the FLUENT 19.0 software, the Eulerian two-phase flow model combined with the standard k-varepsilon turbulence model is used to simulate the bubble size distribution within a pipe and the results were compared across different numerical methods: CM, QMOM, EQMOM, CQMOM, and ECQMOM. The combination is based on the inner mechanism in CFD solver. The significance of this study lies in its contribution to the common variable diameter pipeline systems in industrial processes. A deeper understanding of bubble dynamics is crucial for optimizing design and operation. This work not only provides new theoretical guidance for gas-liquid two-phase flow but also serves as a practical guide for chemical, petroleum, and other fields involving multiphase flow. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Bubble columns

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