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A logic-based flexibility services tool to support a rich renewable distribution network operation

Sustainable Energy, Grids and Networks 2025年 43卷

作者： Bruno Canizes Fábio Castro Vitor Silveira Zita Vale Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto 4200-072 Portugal LASI – Intelligent Systems Associate Laboratory 431 Porto 4200-072 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto 4200-072 Portugal

In recent years, the low-voltage power distribution networks and overall power systems have been undergoing substantial transformations. What were once mere innovation trends are now solidifying into the new norm, driven by advancements in technology and the decreasing costs of manufacturing. Moreover, the role of distribution system operators has been elevated with the widespread deployment of smart meters, coupled with a growing emphasis on engaging citizens as pivotal contributors in shaping the future of energy markets and system operations. This evolving topic underscores the importance of devising innovative approaches to explore potential mechanisms for offering additional services within distribution networks, particularly at low-voltage levels. This paper presents a novel logic-based heuristic approach aimed at tackling voltage and congestion challenges in low-voltage distribution networks. The approach revolves around bolstering the involvement of small-scale users in demand response initiatives and enhancing the flexibility of dispatchable distributed renewable energy sources to serve as flexibility services. To showcase the efficacy of the proposed model, a case study was conducted on a 236-bus low-voltage distribution network, chosen to reflect real-world conditions. The results demonstrate a substantial improvement in voltage profiles and a noteworthy reduction in congestion levels, when comparing the scenarios pre- versus post-optimization, and flexibility versus no flexibility, validating the effectiveness of the approach.

关键词： Congestion management Demand flexibility Dispatchable renewable generation flexibility Low-voltage distribution network Voltage profile

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

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SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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An Auto-Encoding Generative Adversarial Networks for Generating Brain Network 2020

An Auto-Encoding Generative Adversarial Networks for Generat...

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The Fourth International Symposium on Image computing and Digital Medicine

作者： Shan Zhang Peng Cao Lili Dou Jinzhu Yang Dazhe Zhao Computer Science and Engineering Northeastern University Shenyang China and Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China China Artificial Intelligence and Clinical Innovation Research Neusoft of Medical Systems Shenyang China China

ISBN: (纸本)9781450389686

Generative adversarial networks (GANs) are powerful generative models that have led to breakthroughs in image generation. The aim of our work is to adapt the idea of GANs to the graph data based on fMRI, which can solve the issue of limited data and improve the classification performance in the domain of the Autism spectrum disorder (ASD) diagnosis. In this work, we present a data augmentation method that generates synthetic graph data with the proposed graph generation model named α-GCNGAN, which is able to handle the intrinsic challenge of generating brain network with considering flexible context-structure. Extensive experiments on Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate the effectiveness and generalizability of α-GCNGAN.

关键词： Generative adversarial network Autism Spectrum Disorders fMRI Data Augmentation

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A Feedback-based Print Quality Improving Strategy for FDM 3D Printing: An Optimal Design Approach

Research Square

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research Square 2021年

作者： Tami, Tariku Sinshaw Xiong, Gang Fang, Qihang Dong, Xisong Shen, Zhen Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with the layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects is important. This paper presents a feedback-based error compensation strategy, which integrates fuzzy inference system and grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is selected due to its higher convergence speed and best fitness value. The integral absolute error (IAE) is used as an objective function and the global minimum is achieved in the iteration interval [86, 100]. The outputs of this optimization problem is used to achieve the next objective. Second, a closed-loop quality monitoring approach comprising of inner-loops and an outer-loop is utilized. The three inner-loops are used to monitor the errors during pre-printing, printing, and post-printing, respectively. The outer-loop, on the other hand, is responsible for monitoring the aggregated errors in all the three 3D printing phases. The error compensation system simulation in Matlab is run for 10 seconds, and the results show that the "normal" range deformation factors are reached within less than 2 seconds for the inner-loops, whereas the outer-loop deformation factor is achieved within 7 seconds. The responses are within the acceptable time range. © 2021, CC BY.

关键词： 3D printers

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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A Review of Barren Plateaus in Variational Quantum computing

arXiv

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arXiv 2024年

作者： Larocca, Martín Thanasilp, Supanut Wang, Samson Sharma, Kunal Biamonte, Jacob Coles, Patrick J. Cincio, Lukasz McClean, Jarrod R. Holmes, Zoë Cerezo, M. Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Non-Linear Studies Los Alamos National Laboratory NM87545 United States LausanneCH-1015 Switzerland Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Institute for Quantum Information and Matter Caltech PasadenaCA91125 United States IBM Quantum IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States Quantum Artificial Intelligence Laboratory NASA Ames Research Center Moffett Federal Airfield Mountain ViewCA94035 United States Simons Institute for the Theory of Computing University of California BerkeleyCA94720-2190 United States Normal Computing Corporation New YorkNY United States Quantum Science Center Oak RidgeTN37931 United States Google Quantum AI VeniceCA90291 United States Information Sciences Los Alamos National Laboratory NM87545 United States

Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm—choices of ansatz, initial state, observable, loss function and hardware noise—can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon. Copyright © 2024, The Authors. All rights reserved.

关键词： Heuristic methods

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Two-Stage Safe Reinforcement Learning for High-Speed Autonomous Racing

Two-Stage Safe Reinforcement Learning for High-Speed Autonom...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Jingyu Niu Yu Hu Beibei Jin Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture ICT CAS University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Decision making for autonomous driving is a safety-critical control problem. Prior works of safe reinforcement learning either tackle the problem with reward shaping or with modifying the reinforcement learning exploration process. However, the former cannot guarantee the safety during the learning process, while the latter relies heavily on expertise to design exquisite exploration policy. Currently, only short-term decision makings for low-speed driving were achieved in road scenes with basic geometries. In this paper, we propose a two-stage safe reinforcement learning algorithm to automatically learn a long-term policy for high-speed driving that guarantees safety during the entire training. In the first learning stage, model-free reinforcement learning is followed by a rule-based safeguard module to avoid danger at low speed without expert ne-tuning. In the second learning stage, the rule-based module is replaced with a data-driven counterpart to develop a closed-form analytical safety solution for high-speed driving. Moreover, an adaptive reward function is designed to match the different objectives of the two learning stages for faster convergence to an optimal policy. Experiments are conducted on a racing simulator TORCS which has complex racing tracks (e.g. sharp turns, hills). Compared with the state-of-the-art baselines, the results show that our method achieves zero safety violation and quickly converges to a more efficient and stable policy with an average speed of 127 km/h (3.3% higher than the best result of baselines) and an average swing of 3.96 degrees.

关键词： Training Geometry Roads Decision making Reinforcement learning Safety Cybernetics

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Deeply Supervised U-Net with Feature Fusion: Automatic COVID-19 Lung Infection Segmentation from CT Images 2020

Deeply Supervised U-Net with Feature Fusion: Automatic COVID...

引用

The Fourth International Symposium on Image computing and Digital Medicine

作者： Bo Wu Peng Cao Jinzhu Yang Qi Yang Chengxi Yan Wenxin Wang Minglei Yang Lili Dou Computer Science and Engineering Northeastern University Shenyang and Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Beijing China Artificial Intelligence and Clinical Innovation Research Neusoft of Medical Systems Shenyang China Artificial Intelligence and Clinical Innovation Research Neusoft of Medical Systems Shenyang and Neusoft Research of Intelligent Healthcare Technology Shenyang China Neusoft Corporation Shenyang China

ISBN: (纸本)9781450389686

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. In this work, we present a deep learning based framework for automatic segmentation of pathologic COVID-19 associated tissue areas from clinical CT images available from a dataset with 108 cases in China. More specifically, we present an effective multi-scale feature fusion U-Net equipped with ResNet architecture and a deep supervision mechanism to increase the network's capacity for learning richer representations of infected tissue. Our experiments demonstrate that our model achieves an average dice score (0.674), sensitivity (0.733) and Precision (0.714) on the dataset. The experimental results have indicated the effectiveness of the proposed improvements and the potential of our proposed method for real clinical practice.

关键词： U-Net, Computerized Tomography COVID-19, Pneumonia

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A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 307-315页

作者： Guangyu Xiong Xiuqin Shang Gang Xiong Timo R.Nyberg IEEE the Silk Road Business School Sanya University the Department of Industrial Engineering and Management School of ScienceAalto University the State Key Laboratory for Management and Controlof Complex Systems Institute of AutomationChinese Academy of Sciences(CAS) Qingdao Academy of Intelligent Industries the Cloud Computing Center CAS the Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationCAS the School of Science at Aalto University

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing *** on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

关键词： Lean approach non-manufacturing process optimized order handling process removing wastes value chain win-win scenario

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