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Multi-Objective DER Planning Framework as a TEP Alternative: An Augmented Epsilon-Constraint Approach

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IEEE Transactions on Industry Applications 2025年

作者： Saber, Hossein Ranjbar, Hossein Mazaheri, Hesam Sharif University of Technology Center of Excellence in Power System Control and Management Electrical Engineering Department Tehran11365-11155 Iran University of Adelaide School of Electrical and Mechanical Engineering Adelaide Australia Texas A&M University Department of Electrical and Computer Engineering College StationTX United States

Recently, the construction of new transmission lines has faced extreme challenges due to ever-increasing environmental concerns and construction costs. Investing in distributed energy resources (DERs) offers a viable alternative to reduce reliance on new transmission lines. This study introduces a multi-objective bi-level DER planning model as an alternative to transmission network reinforcement. The upper-level problem aims to minimize the total cost of DERs, mitigate the congestion cost of the transmission network, and improve the system reliability while ensuring the profitability of the DER investment. In the lower-level problem, the wholesale electricity market is cleared to determine the optimal locational marginal prices and the operational status of the bulk power system. A scenario-based approach is employed to account for uncertainties in future loads, renewable generation, and generator bid prices. The bi-level problem is reformulated as a single-level optimization using the strong duality condition, with non-linear terms linearized through the big-M method and complementary slackness conditions. The augmented epsilon-constraint method is then employed to solve the multi-objective optimization problem. Finally, the effectiveness and applicability of the proposed DER planning model are evaluated using the IEEE 24-bus test system, offering insights from both system operators and DER investors. © 1972-2012 IEEE.

关键词： Multiobjective optimization

A reachability-decidable petri net modeling method and its application to flexible manufacturing systems

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A reachability-decidable petri net modeling method and its a...

International Conference on Automation in Manufacturing, Transportation and Logistics (iCaMaL 2023)

作者： Yue Su Liang Qi MengChu Zhou Remigiusz Wiśniewski Faculty of Innovation Engineering Macau University of Science and Technology Macau People's Republic of China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao People's Republic of China Institute of Control and Computation Engineering University of Zielona Góra Poland

ISBN: (数字)9781837242054

Petri nets (PNs) are graphical and mathematical tools used to model various discrete event systems and analyze their properties. Reachability is their fundamental property. When we use a state equation to determine a state’s (marking’s) reachability, the existence of legal firing sequences (LFSs) corresponding to nonnegative integer solutions (NISs) of the state equation needs to be determined. Our previous work has proposed an algorithm to determine the LFSs’ existence in polynomial time. However, when a state equation has an infinite number of NISs, we cannot check all NISs to determine a marking’s reachability. Hence, this work studies the relationship between the structure of an ordinary PN and the number of NISs of its state equation. Then, a method is proposed to modify the structure of the PN such that the state equation has no more than one NIS. The modified PN is used as the final model of the system, which maintains the functions of its modeled system. Its reachability can be determined in polynomial time. This work shows the application of the proposed method to a flexible manufacturing system’s modeling and analysis.

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A distributed generalized Nash equilibrium seeking algorithm based on extremum seeking control

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arXiv 2022年

作者： Xiao, Feng Cai, Xin Wei, Bo State Key Laboratory of Alternate Electrical Power System Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical Engineering Xinjiang University Urumqi830047 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

In this paper, a distributed non-model based seeking algorithm which combines the extremum seeking control (ESC) jointly with learning algorithms is proposed to seek a generalized Nash equilibrium (GNE) for a class of noncooperative games with coupled equality constraint. The strategy of each agent is restricted by both the coupled inter-agent constraint and local inequality constraints. Thanks to the ESC, it is unnecessary to know the specific expressions of agents’ cost functions and local constraints and to know the strategies of other agents for the implementation of the proposed GNE seeking algorithm. To deal with the coupled constraints, only the Lagrange multiplier is transmitted among agents with some prior information about the coupled constraints. Moreover, a diminishing dither signal is designed in the seeking algorithm to remove undesirable steady-state oscillations. The non-local convergence of the designed seeking algorithm is analyzed via the singular perturbation theory, averaging analysis and Lyapunov stability theory. Numerical examples are given to verify the effectiveness of our proposed *** Codes 91A99, 93A14, 93A16 Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

DACB-NET: DUAL ATTENTION GUIDED COMPACT BILINEAR CONVOLUTION NEURAL NETWORK FOR SKIN DISEASE CLASSIFICATION

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arXiv 2024年

作者： Ahmad, Belal Usama, Mohd Ahmad, Tanvir Saeed, Adnan Khatoon, Shabnam Chen, Min School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Departments of Diagnostics and Intervention and Biomedical Engineering Umea University Sweden School of Information and Control Engineering Xi'an University of Architecture and Technology Xian China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China School of Management Science and Engineering China University of Geosciences Wuhan China

This paper introduces the three-branch Dual Attention-Guided Compact Bilinear CNN (DACBNet) by focusing on learning from disease-specific regions to enhance accuracy and alignment. A global branch compensates for lost discriminative features, generating Attention Heat Maps (AHM) for relevant cropped regions. Finally, the last pooling layers of global and local branches are concatenated for fine-tuning, which offers a comprehensive solution to the challenges posed by skin disease diagnosis. Although current CNNs employ Stochastic Gradient Descent (SGD) for discriminative feature learning, using distinct pairs of local image patches to compute gradients and incorporating a modulation factor in the loss for focusing on complex data during training. However, this approach can lead to dataset imbalance, weight adjustments, and vulnerability to overfitting. The proposed solution combines two supervision branches and a novel loss function to address these issues, enhancing performance and interpretability. The framework integrates data augmentation, transfer learning, and fine-tuning to tackle data imbalance to improve classification performance, and reduce computational costs. Simulations on the HAM10000 and ISIC2019 datasets demonstrate the effectiveness of this approach, showcasing a 2.59% increase in accuracy compared to the state-of-the-art. © 2024, CC BY.

关键词： Gradient methods

A single-cell transcriptomic analysis of Neuroblastomas revealed a selective cGAS-STING pathway suppression in malignant cells

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A single-cell transcriptomic analysis of Neuroblastomas reve...

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sibilio, Pasquale Di Giulio, Stefano Funari, Alessio Paci, Paola Veschi, Veronica Giannini, Giuseppe Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Molecular Medicine Rome Italy Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Karolinska Institutet Stockholm17177 Sweden San Gallicano Dermatological Institute Irccs Microbiology and Virology Rome Italy Istituto Pasteur-Fondazione Cenci Bolognetti Rome Italy

ISBN: (纸本)9798350337488

Neuroblastoma is a disease of disordered development accounting for 15% of childhood cancer deaths. The "cold"immunophenotype frequently occurring of these tumors is likely to contribute to its aggressiveness and refractoriness to treatments, including immune checkpoint blockade, observed in high risk neuroblastoma. The mechanisms that contribute to the cold immunostate have not been elucidated, yet. However, recent studies have reported the involvement of MYCN amplification in reducing the immune infiltrate in high-risk Neuroblastoma tumors. Its action mainly concerns suppressed interferon responses and pro-inflammatory pathways. An important pathway deputed to the activation of type I interferon responses and pro-inflammatory cytokines release is the cyclic GMP-AMP synthase (cGAS) - stimulator of interferon genes (STING) cytosolic DNA sensing pathway. Recent preclinical studies suggest a promising therapeutic effect of cGAS-STING pathway reactivation in Neuroblastomas. However, very little is known about the regulation of the cGAS-STING pathway in Neuroblastoma malignant cells, and its relationship with the MYCN amplification state. To this end, we performed a single-cell transcriptomic analysis of primary human Neuroblastoma cells compared with their normal fetal and embryonic progenitors. The results of the high-resolution analysis of cGAS-STING pathway showed that Neuroblastomas malignant cells have a much lower expression of this pathway compared to normal progenitors and other cell phenotypes populating the tumor microenvironment, possibly acquiring an evolutionary advantage. Moreover, the cGAS-STING pathway anticorrelated with MYCN expression suggesting a putative regulatory mechanism of the STING pathway. © 2023 IEEE.

关键词： immunotherapy Neuroblastoma single-cell transcriptomic

Visualizing the Local Atomic Environment Features of Machine Learning Interatomic Potential

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arXiv 2025年

作者： Shao, Xuqiang Zhang, Yuqi Zhang, Di Gao, Tianxiang Liu, Xinyuan Gan, Zhiran Meng, Fanshun Li, Hao Yang, Weijie School of Control and Computer Engineering North China Electric Power University Baoding071003 China Tohoku University Sendai980-8577 Japan Department of Power Engineering School of Energy and Power Engineering North China Electric Power University Hebei Baoding071003 China School of Science Liaoning University of Technology Jinzhou12100l China

This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions. We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which utilizes the properties of atomic local environments and employs histogram statistics to generate difference vectors. This technique facilitates dataset screening and optimization, effectively minimizing redundancy while maintaining data diversity. We have validated the optimized datasets in high-temperature and high-pressure hydrogen systems as well as the α-Fe/H binary system, demonstrating a significant reduction in computational resource usage without compromising prediction accuracy. Additionally, our method has revealed new structures that emerge during simulations but were underrepresented in the initial training datasets. The redundancy in the datasets and the distribution of these new structures can be visually analyzed through the visualization of difference vectors. This approach enhances our understanding of the characteristics of these newly formed structures and their impact on physical processes. © 2025, CC BY.

关键词： Adversarial machine learning

UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Optimization of Sliding Mode control based on BAT-Algorithm for the DFIG-WT

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Optimization of Sliding Mode Control based on BAT-Algorithm ...

2020 International Conference on computer, control, Electrical, and Electronics engineering, ICCCEEE 2020

作者： Alzain, Omar Busati Liu, Xiangjie Ali, Ayman North China Electric Power Univ. School of Control and Computer Eng. Beijing China International Univ. of Africa Department of Electrical Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

This topic presents a rotor-branch current control (RBC) regarding stator-branch power conduct based on an optimal sliding surface of Sliding Model (SMC) using BAT-Algorithm (BS-SMC) for the grid-Tied Doubly-Fed Induction Generator type that connected with wind turbine (DFIG-WT). The BS-SMC is built to regulate the DFIG system in a dq-reference stage rotor-branch current during many operation scenarios to meet the dynamic factors. The simulated BS-SMC test results for a 1. 5MW DFIG-WT are robust to gave a superior dynamic action i.e lowest than 10% overshoot of rotor-branch current and stator-branch power and other dynamic characteristics than common controllers. Furthermore, BAT algorithm enhanced the global searching and the convergence less 10e-4 in tolerance region during iterations. © 2021 IEEE.

关键词： Electric current control

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

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Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting