Article
Engineering, Multidisciplinary
Zhiqi Xu, Wei Jiang, Junjun Xu, Dong Wang, Yifei Wang, Zhujian Ou
Summary: This paper proposes an automatic topology identification method based on measurements from widely distributed transformer terminal units (TTUs) to match the topology model in the management system with the actual topology of the distribution network. The method formulates the topology identification problem as an L2-norm minimization problem and addresses non-convexity and nonlinearity by introducing variables and proposing a linearization and convex relaxation approach.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Energy & Fuels
Liwen Qin, Xiaoyong Yu, Haitao Gui, Lifang Wu, Shifeng Ou
Summary: This paper proposes a distribution network measurement super-resolution model based on GCN, which can achieve distribution network situation awareness with limited measurements and perform well when the distribution network topology changes.
Article
Computer Science, Interdisciplinary Applications
Jun Yan, Dongling Geng, Qi Xu, Haijiang Li
Summary: This paper proposes a step-to-step training method to improve the prediction accuracy of a deep learning model for real-time structural topology optimization. By increasing the utilization of optimization history information, the method improves the efficiency of model utilization without increasing the sample set size.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Software Engineering
Aaditya Chandrasekhar, Krishnan Suresh
Summary: In this paper, an approximate length scale filter strategy for topology optimization (TO) is proposed by extending a density-based TO formulation using neural networks (TOuNN). The proposed method enhances TOuNN with a Fourier space projection to approximately control the minimum and/or maximum length scales. The method does not involve additional constraints and automates sensitivity computations using the neural net's library.
COMPUTER-AIDED DESIGN
(2022)
Article
Engineering, Multidisciplinary
Zeyu Zhang, Yu Li, Weien Zhou, Xiaoqian Chen, Wen Yao, Yong Zhao
Summary: The paper introduces a method for topology optimization using neural networks, the TONR framework, which allows flexible design variable updates and sensitivity analysis. With this approach, optimized structures for different optimization problems can be obtained stably without the need to construct a dataset beforehand.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Zeyu Zhang, Wen Yao, Yu Li, Weien Zhou, Xiaoqian Chen
Summary: With the rapid development of artificial intelligence (AI) technology, scientific research has entered a new era of AI. The cross development between topology optimization (TO) and AI technology has been receiving continuous attention. This paper introduces the concept of Implicit Neural Representations from AI into the TO field and establishes a novel TO framework called TOINR.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Mechanics
Thanh N. Huynh, Jaehong Lee
Summary: This article presents a two-stage optimization approach for finding optimal blended composite laminate designs by predicting the optimal thickness distribution. By predicting the optimal thicknesses, the method simplifies the blending optimization problem and improves the combinatorial optimization efficiency.
COMPOSITE STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Gorkem Can Ates, Recep M. Gorguluarslan
Summary: This study proposes a two-stage network model for topology optimization, which effectively reduces structural disconnections and pixel-wise errors, enhancing the predictive performance of DNNs. The optimized framework improves network prediction ability while significantly reducing compliance and volume fraction errors.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Chemistry, Physical
Praveen S. Vulimiri, Hao Deng, Florian Dugast, Xiaoli Zhang, Albert C. To
Summary: This research introduces a novel topology optimization method that uses neural style transfer to optimize both structural performance and geometric similarity. The user can control the influence of neural style transfer on structural performance, allowing for an ideal compromise in design.
Article
Computer Science, Software Engineering
Aaditya Chandrasekhar, Amir Mirzendehdel, Morad Behandish, Krishnan Suresh
Summary: In this paper, a topology optimization framework is proposed to simultaneously optimize the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced composites. By using a mesh-independent representation based on a neural network, the accurate extraction of functionally graded continuous fibers can be achieved, and automatic differentiation can be employed for sensitivity analysis to improve computational efficiency. The effectiveness and computational efficiency of the proposed method are demonstrated through various numerical examples, and it is shown that the optimized continuous fiber-reinforced composites can be directly fabricated at high resolution using additive manufacturing.
COMPUTER-AIDED DESIGN
(2023)
Article
Engineering, Electrical & Electronic
Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang
Summary: This paper proposes a novel deep reinforcement learning algorithm, DRL-GS, for network topology optimization. DRL-GS consists of a verifier, a graph neural network, and a DRL agent to efficiently search and generate topology with satisfactory performance.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ankur Srivastava, Saikat Chakrabarti, Joao Soares, Sri Niwas Singh
Summary: The paper presents an optimization-based method for detecting topology errors in power systems. The method utilizes residual analysis and minimization of normalized measurement residual in state estimation, with the application of matrix inverse lemma. The proposed method is computationally efficient and produces accurate results, with robust performance in the presence of measurement uncertainties and bad data.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Civil
Dongling Geng, Jun Yan, Qi Xu, Qi Zhang, Mengfang Zhou, Zhirui Fan, Haijiang Li
Summary: This paper presents a real-time topology optimization algorithm based on the Moving Morphable Component (MMC) method using a Convolutional Neural Network (CNN). The algorithm uses a new data pre-processing method to preserve the numerical characteristics and smoothness of the structure boundary, facilitating the CNN to capture data features with a limited sample set. The effectiveness of the algorithm has been verified with several examples.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Electrical & Electronic
Siyuan Sun, Gengfeng Li, Chen Chen, Yiheng Bian, Zhaohong Bie
Summary: A novel mathematical formulation of radiality constraints based on maximum density is proposed and proved to be the necessary and sufficient condition for radial topology. The proposed constraints can reduce the number of binary variables and constraints, improving the computational performance of related optimization problems. The effectiveness of these constraints is verified through numerical results on test systems with different node sizes.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Materials Science, Multidisciplinary
Yiping Sun, Zhaoyu Li, Jiadui Chen, Xuefeng Zhao, Meng Tao
Summary: This paper investigates a VAE model-based topology optimization method for optimizing the cavity structure of anechoic coatings. The finite-element method is used to calculate the sound absorption coefficient, and the VAE model is trained to learn the key features of an anechoic coating. The method efficiently generates new anechoic coatings with specific sound absorption properties.
MATERIALS TODAY COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zhi Zeng, Jianyuan Jia, Zhaofei Zhu, Dalin Yu
COMPUTER VISION AND IMAGE UNDERSTANDING
(2016)
Article
Automation & Control Systems
Zhi Zeng, Jianyuan Jia, Wei Chai, Yilong Chen, Zhaofei Zhu, Dalin Yu
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2017)
Article
Computer Science, Artificial Intelligence
Zhi Zeng, Jianyuan Jia, Dalin Yu, Yilong Chen, Zhaofei Zhu
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2017)
Article
Computer Science, Information Systems
Zhi Zeng, Ting Wang, Fulei Ma, Liang Zhang, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun
Summary: This paper investigates the fusion of limited foreground information, temporal consistency, and semantic information for background segmentation. A concise framework is proposed to effectively fuse these two types of information, and theoretical proof shows that this framework is more accurate than using temporal consistency or semantic information alone.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
