Article
Construction & Building Technology
Wioleta Blaszczak-Bak, Czeslaw Suchocki, Maria Mrowezynska
Summary: Terrestrial Laser Scanning (TLS) is a non-invasive remote sensing technology for examining building wall conditions. This paper presents a TLS point cloud optimization method based on a modified Optimum Dataset method, which reduces the number of observations while maintaining data quality for accurate 3D modeling.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Software Engineering
Zhongping Ji, Xianfang Sun, Yu-Wei Zhang, Weiyin Ma, Mingqiang Wei
Summary: This paper introduces a normal-based modeling framework for bas-relief generation and stylization by processing normal images from a geometric perspective. The method can generate new normal images and build bas-reliefs from a single RGB image and its edge-based sketch lines. Additionally, an auxiliary function is introduced to represent a smooth base surface or generate a layered global shape, expanding the bas-relief shape space.
Article
Computer Science, Software Engineering
Zhenjie Yang, Beijia Chen, Youyi Zheng, Xiang Chen, Kun Zhou
Summary: This method presents a semi-automatic approach to produce human bas-relief from a single photograph. By estimating 3D skeletons and aligning the guide model with image contours, it generates realistic results.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Information Systems
Xiao Liu, Jianhui Nie
Summary: In this paper, an unsupervised bas-relief generation method is proposed, which modifies and reconstructs the normal image of the model using style-transfer neural network and Convolution Neural Network (CNN). The method aims to maintain details and eliminate height jumps. Experimental results show that this method is simple and efficient, and can generate stylized relief models with rich details and good saturation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Zhongping Ji, Wei Feng, Xianfang Sun, Feiwei Qin, Yigang Wang, Yu-Wei Zhang, Weiyin Ma
Summary: This paper introduces a fast bas-relief generation method based on deep learning, which eliminates the need for parameter tuning and maintains rich details. The proposed ReliefNet is designed to solve the essential problem of bas-relief modeling effectively, showcasing its performance through extensive experiments on various high-resolution 3D scenes.
COMPUTER-AIDED DESIGN
(2021)
Article
Automation & Control Systems
Sasa V. Rakovic, Sixing Zhang, Yanye Hao, Li Dai, Yuanqing Xia
Summary: This article presents a model predictive control method for exclusion constraints that ensures strong system theoretic properties and is implemented using computationally highly efficient, strictly convex quadratic programming. The approach utilizes safe tubes to handle intrinsically nonconvex exclusion constraints through closed polyhedral constraints, constructed using the separation theorem for convex sets. The safe tube is obtained practically from the solution of a strictly convex quadratic programming problem and is used to optimize a predicted finite horizon control process efficiently via another strictly convex quadratic programming problem.
Article
Automation & Control Systems
Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Henrik Johansson
Summary: This paper explores distributed bandit online convex optimization with time-varying coupled inequality constraints, focusing on the repeated game between learners and an adversary. By optimizing the global loss functions and coupled constraint functions in multiple iterations, the algorithms are able to achieve sublinear expected regret and constraint violation.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Software Engineering
Yu-Wei Zhang, Ping Luo, Hao Zhou, Zhongping Ji, Hui Liu, Yanzhao Chen, Caiming Zhang
Summary: This paper presents an end-to-end neural solution for modeling portrait bas-relief from a single photograph. The authors address the challenge of lacking bas-relief data by proposing a semi-automatic pipeline to synthesize bas-relief samples. They train different network architectures on synthetic data and select the best-performing one through qualitative and quantitative comparisons. Experiments, comparisons, and evaluations by artists demonstrate the effectiveness and efficiency of the selected network.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Engineering, Electrical & Electronic
Yi-Xuan Zhang, Yong-Chang Jiao, Li Zhang
Summary: New methods are proposed to maximize the directivities of array antennas, including optimizing array pencil beam directivity and addressing nonconvexity in wide beam directivity. A two-stage iterative method is developed to handle sidelobe constraints in wide beam directivity, leading to efficient and excellent performance in solving array directivity maximization problems.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2021)
Article
Automation & Control Systems
Xuyang Wu, He Wang, Jie Lu
Summary: In this article, the authors investigate distributed convex optimization with both inequality and equality constraints. They propose a novel distributed algorithm called IPLUX, which integrates ideas from primal-dual, proximal, and virtual-queue optimization methods. The algorithm achieves an O(1/k) rate of convergence in terms of optimality and feasibility, outperforming alternative methods and eliminating the assumption on the compactness of the feasible region. Simulation results demonstrate that IPLUX exhibits faster convergence and higher efficiency compared to state-of-the-art methods.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Software Engineering
Jiahui Mao, Tingting Li, Feiyu Zhang, Meili Wang, Jian Chang, Xuequan Lu
Summary: This study proposes a novel approach to automatically arrange bas-relief layout, identifying evaluation indicators and applying optimization algorithms to achieve the goal of automated layout arrangement.
COMPUTER ANIMATION AND VIRTUAL WORLDS
(2021)
Article
Multidisciplinary Sciences
Xinli Wu, Yun Zhao, Jiali Luo, Minxiong Zhang, Wenzhen Yang
Summary: This paper presents a novel method for bas-relief modeling from RGB monocular images, which adjusts the concave-convex relationship through regional division and three-dimensional reconstruction, and enables direct printing of 3D bas-relief models.
SCIENTIFIC REPORTS
(2022)
Article
Automation & Control Systems
Antoine Lesage-Landry, Joshua A. Taylor, Duncan S. Callaway
Summary: This study focuses on online optimization with binary decision variables and convex loss functions. A new algorithm, binary online gradient descent (bOGD), is designed and its expected dynamic regret is bounded. The research provides a regret bound applicable for any time horizon and a specialized bound for finite time horizons. The application of bOGD in demand response systems shows significant effectiveness.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Jiawei Zhang, Songyang Ge, Tsung-Hui Chang, Zhi-Quan Luo
Summary: Motivated by the need for decentralized learning, this paper proposes a distributed algorithm for solving non-convex problems with general linear constraints over a multi-agent network. The proposed prox anal dual consensus (PDC) algorithm combines proximal technique and dual consensus method. Numerical results show the good performance of the proposed algorithms for solving two vertical learning problems in machine learning over a multi-agent network.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Michael Muehlebach, Michael I. Jordan
Summary: This class of first-order methods for smooth constrained optimization introduces an analogy to non-smooth dynamical systems. The distinctive features of this approach include avoiding projections or optimizations over the entire feasible set and allowing iterates to become infeasible. By expressing constraints in terms of velocities and optimizing over local convex approximations, this algorithm simplifies the optimization process and expands its applicability to machine learning.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
