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Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

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arXiv 2023年

作者： Mao, Qianren Zhao, Shaobo Li, Jiarui Gu, Xiaolei He, Shizhu Li, Bo Li, Jianxin Zhongguancun Laboratory Beijing China School of Software Beihang University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training autoencoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

Fluid Antenna Relay Assisted Communication Systems Through Antenna Location Optimization

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arXiv 2024年

作者： Xu, Ruopeng Chen, Yixuan Kang, Jiawen Xu, Minrui Yang, Zhaohui Huang, Chongwen Niyato, Dusit College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. [IPCAN Hangzhou China School of Automation Guangdong University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

In this paper, we investigate the problem of resource allocation for fluid antenna relay (FAR) system with antenna location optimization. In the considered model, each user transmits information to a base station (BS) with help of FAR. The antenna location of the FAR is flexible and can be adapted to dynamic location distribution of the users. We formulate a sum rate maximization problem through jointly optimizing the antenna location and bandwidth allocation with meeting the minimum rate requirements, total bandwidth budget, and feasible antenna region constraints. To solve this problem, we obtain the optimal bandwidth in closed form. Based on the optimal bandwidth, the original problem is reduced to the antenna location optimization problem and an alternating algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm and the sum rate can be increased by up to 125% compared to the conventional schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation

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IEEE Transactions on Medical Imaging 2025年

作者： Ming, Yu Wu, Zihao Yang, Jie Li, Danyi Gao, Yuan Gao, Changxin Xia, Gui-Song Li, Yuanqing Liang, Li Yu, Jin-Gang South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Southern Medical University Department of Pathology Zhujiang Hospital Guangzhou510280 China Southern Medical University Department of Pathology Nanfang Hospital Guangzhou510515 China Wuhan University School of Electronic Information Wuhan430072 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430072 China Pazhou Laboratory Guangzhou510335 China

Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible, and we hope to leverage these external datasets to assist nucleus instance segmentation on the target dataset which only has very limited annotation. To achieve this goal, we adopt the meta-learning based FSL paradigm, which however has to be tailored in two substantial aspects before adapting to our task. First, since the novel classes may be inconsistent with those of the external dataset, we extend the basic definition of few-shot instance segmentation (FSIS) to generalized few-shot instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of nucleus segmentation, including touching between adjacent cells, cellular heterogeneity, etc., we further introduce a structural guidance mechanism into the GFSIS network, finally leading to a unified Structurally-Guided Generalized Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a couple of publicly accessible datasets demonstrate that, SGFSIS can outperform other annotation-efficient learning baselines, including semi-supervised learning, simple transfer learning, etc., with comparable performance to fully supervised learning with around 10% annotations. © 1982-2012 IEEE.

关键词： Semi-supervised learning

Event-based Video Reconstruction via Spatial-Temporal Heterogeneous Spiking Neural Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yu, Jiajie Lu, Xing Guo, Lijun Wang, Chong Li, Guoqi Qian, Jiangbo Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Nottingham Ningbo China Ningbo315100 China Ningbo University Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo315211 China Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Beijing100045 China

Event cameras detect per-pixel brightness changes and output asynchronous event streams with high temporal resolution, high dynamic range, and low latency. However, the unstructured nature of event streams means that humans cannot analyze and interpret them in the same way as natural images. Event-based video reconstruction is a widely used method aimed at reconstructing intuitive videos from event streams. Most reconstruction methods based on traditional artificial neural networks (ANNs) have high energy consumption, which counteracts the low-power advantage of event cameras. Spiking neural networks (SNNs) are a new generation of event-driven neural networks that encode information via discrete spikes, which leads to greater computational efficiency. Previous methods based on SNNs overlooked the asynchronous nature of event streams, leading to reconstructions that suffer from artifacts, flickering, low contrast, etc. In this work, we analyze event streams and spiking neurons and explain poor reconstruction quality. We specifically propose a novel spatial-temporal heterogeneous (STH) spiking neuron suitable for reconstructing asynchronous event streams. The STH neuron adjusts the membrane decay coefficient adaptively and has better spatiotemporal perception. In addition, we propose a temporal-frequency calibration module (TFCM) based on the Fourier transform to improve the contrast of the reconstructions. On the basis of the above proposed neuron and module, we construct two SNN-based models, referred to as the STHSNN and TFCSNN. The goal of the former is to reduce the artifacts and flickering in reconstructions, whereas the latter focuses on enhancing the contrast. The experimental results demonstrate that our models can yield reconstructions in various scenarios, achieving better quality and lower energy consumption than previous SNNs. Specifically, the TFCSNN and STHSNN achieve top-2 performance among the SNN-based models, with energy consumption reductions of 3.

关键词： Image reconstruction

DGLNet: A Cross-Modal Global-Local Collaborative Network for Chest Radiology Report Generation

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SSRN

SSRN 2022年

作者： Liu, Lijun Wang, Lizhen Liu, Li Peng, Wei Fu, Xiaodong Huang, Qingsong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science and Engineering School of Information Science and Engineering Yunnan University Kunming650228 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

Automatic chest radiology report generation is crucial for improving diagnostic efficiency, enhancing diagnostic quality, and reducing misdiagnoses or missed diagnoses. Existing approaches either resize the high-resolution radiology image into smaller sizes or segment it into multiple patches for separate processing, which may lose fine local details or global contextual information, and the accuracy of report generation will be limited. In addition, the direct fusion of visual features with sentence features reduces the accuracy of aligning fine-grained visual features with text features. In this paper, we focus on designing a cross-modal global-local collaborative network to capture more local fine details and global contextual information, which comprises a global-to-local network and a local-to-global network that takes the resized entire image and segmented image patches as respective inputs. The former handles the extraction of global visual features, the fusion of global and local visual features, and the fusion of visual features into report sentences. The latter implements the extraction of local visual features, the fusion of local to global visual features, and the fusion of visual features to report words. Experimental results on two publicly available datasets, IU X-Ray and MIMIC-CXR, show that our proposed network outperforms state-of-the-art image captioning-based methods in most evaluation metrics. © 2022, The Authors. All rights reserved.

关键词： Extraction

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

A Brain Tumor Segmentation Approach with Adaptive Threshold Optimization Numerical Spiking Neural P Systems

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A Brain Tumor Segmentation Approach with Adaptive Threshold ...

IEEE International Conference on Systems, Man and Cybernetics

作者： Jianping Dong Gexiang Zhang Haina Rong Giancarlo Fortin Min Chen School of Automation Chengdu University of Information Technology Chengdu China School of Electrical Engineering Southwest Jiaotong University Chengdu China University of Calabria Rende CS Italy School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Magnetic resonance imaging (MRI) with the high-resolution in computer-aided diagnostic technology is widely used to provide doctors with diagnostic advice, especially in brain tumor segmentation. In addition, MRI multi-sequence images of brain tumors also provide better image data support for studying brain tumor segmentation. In this paper, an adaptive threshold segmentation numerical optimization spiking neural P system (ATONSNPS or ATONSN P system) is designed to dynamically adjust the threshold quantity. In addition, the ATONSN P system and connectivity algorithm are combined to finish multi-sequence brain tumor segmentation. Experimental results on BraTS2019 show that the multi-sequence brain tumor segmentation approach can achieve more effective segmentation of brain tumor images comparing with several benchmark algorithms.

关键词： Image segmentation Adaptive systems Magnetic resonance imaging Heuristic algorithms Brain tumors Medical services Anatomical structure Benchmark testing Optimization