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arXiv 2024年

作者： Nikitina, Viktoriya De Marchi, Alberto Gerdts, Matthias University of the Bundeswehr Munich Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Neubiberg85577 Germany

Models involving hybrid systems are versatile in their application but difficult to optimize efficiently due to their combinatorial nature. This work presents a method to cope with hybrid optimal control problems which, in contrast to decomposition techniques, does not require relaxing the integrality constraints. Based on the discretize-then-optimize approach, our scheme addresses mixed-integer nonlinear problems under mild assumptions. The proposed numerical algorithm builds upon the augmented Lagrangian framework, whose subproblems are handled using successive mixed-integer linearizations with trust regions. We validate the performance of the numerical routine with extensive investigations using hybrid optimal control problems from different fields of application. Promising preliminary results are presented for a motion planning task with hysteresis and a Lotka-Volterra fishing problem with total *** Codes 65K05, 90C06, 90C11, 90C30 © 2024, CC BY.

关键词： Mixed-integer linear programming

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Unsupervised Dual-Layer Aggregation for Feature Fusion on Image Retrieval Tasks

Unsupervised Dual-Layer Aggregation for Feature Fusion on Im...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Ademir Moreno Daniel Carlos Guimarães Pedronette Department of Statistics Applied Mathematics and Computing São Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

The revolutionary advances in image representation have led to impressive progress in many image understanding-related tasks, primarily supported by Convolutional Neural Networks (CNN) and, more recently, by Transformer models. Despite such advances, assessing the similarity among images for retrieval in unsupervised scenarios remains a challenging task, mostly grounded on traditional pairwise measures, such as the Euclidean distance. The scenario is even more challenging when different visual features are available, requiring the selection and fusion of features without any label information. In this paper, we propose an Unsupervised Dual-Layer Aggregation (UDLA) method, based on contextual similarity approaches for selecting and fusing CNN and Transformer-based visual features trained through transfer learning. In the first layer, the selected features are fused in pairs focused on precision. A sub-set of pairs is selected for a second layer aggregation focused on recall. An experimental evaluation conducted in different public datasets showed the effectiveness of the proposed approach, which achieved results significantly superior to the best-isolated feature and also superior to a recent fusion approach considered as baseline.

关键词： Visualization Image retrieval Transfer learning Estimation Euclidean distance Image representation Transformers Convolutional neural networks Optimization

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Affordable mixed-integer Lagrangian methods: optimality conditions and convergence analysis

arXiv

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arXiv 2024年

作者： De Marchi, Alberto University of the Bundeswehr Munich Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Neubiberg85577 Germany

Necessary optimality conditions in Lagrangian form and the augmented Lagrangian framework are extended to mixed-integer nonlinear optimization, without any convexity assumptions. Building upon a recently developed notion of local optimality for problems with polyhedral and integrality constraints, a characterization of local minimizers and critical points is given for problems including also nonlinear constraints. This approach lays the foundations for developing affordable sequential minimization algorithms with convergence guarantees to critical points from arbitrary initializations. A primal-dual perspective, a local saddle point property, and the dual relationships with the proximal point algorithm are also advanced in the presence of integer *** Codes 65K05, 90C06, 90C11, 90C30 © 2024, CC BY.

关键词： Nonlinear programming

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A Discrete Perspective Towards the Construction of Sparse Probabilistic Boolean Networks

arXiv

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arXiv 2024年

作者： Fok, Christopher H. Wong, Chi-Wing Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Pokfulam Road Hong Kong

Boolean Network (BN) and its extension Probabilistic Boolean Network (PBN) are popular mathematical models for studying genetic regulatory networks. BNs and PBNs are also applied to model manufacturing systems, financial risk and healthcare service systems. In this paper, we propose a novel Greedy Entry Removal (GER) algorithm for constructing sparse PBNs. We derive theoretical upper bounds for both existing algorithms and the GER algorithm. Furthermore, we are the first to study the lower bound problem of the construction of sparse PBNs, and to derive a series of related theoretical results. In our numerical experiments based on both synthetic and practical data, GER gives the best performance among state-of-the-art sparse PBN construction algorithms and outputs sparsest possible decompositions on most of the transition probability matrices being tested. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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Opposition flow control for reducing skin-friction drag of a turbulent boundary layer

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Physical Review Fluids 2024年第6期9卷 064602-064602页

作者： Giulio Dacome Robin Mörsch Marios Kotsonis Woutijn J. Baars Faculty of Aerospace Engineering Delft University of Technology 2629 HS Delft The Netherlands Institute of Applied Mathematics and Scientific Computing University of the Bundeswehr Munich 85577 Neubiberg Germany

This work explores the dynamic response of a turbulent boundary layer to large-scale reactive opposition control, at a friction Reynolds number of Reτ≈2240. A surface-mounted hot-film is employed as the input sensor, capturing large-scale fluctuations in the wall-shear stress, and actuation is performed with a single on/off wall-normal blowing jet positioned 2.4δ downstream of the input sensor, operating with an exit velocity of vj=0.4U∞. Our study builds upon the work of Abbassi et al. [Int. J. Heat Fluid Flow 67, 30 (2017)] and includes a control-calibration experiment and a performance assessment using PIV- and PTV-based flow field analyses. With the control-off calibration-experiment conducted a priori, a transfer kernel is identified so that the velocity fluctuations that are to-be-controlled can be estimated. The controller targets large-scale high-speed zones in an “opposing” mode and low-speed zones in a “reinforcing” mode. A desynchronized mode was tested for reference and consisted of a statistically similar control mode, but without synchronization to the incoming velocity fluctuations. An energy-attenuation of about 40 % is observed for the opposing control mode in the frequency band corresponding to the passage of large-scale motions. This proves the effectiveness of the control in targeting large-scale motions: an energy-intensification of roughly 45% occurs for the reinforcing control mode instead, while no change in energy, within the wall-normal range targeted, appears with the desynchronized control mode. Moreover, direct measures of the skin-friction drag are inferred from PTV data. Results indicate that the opposing control logic yields the lowest wall-shear stress (3% lower than the desynchronized control, and 10% lower than the uncontrolled flow). Finally, a FIK-decomposition of the skin-friction coefficient revealed that the off-the-wall turbulence follows a consistent trend with the PTV-based wall-shear stress measurements, although biased by

关键词： Flow control Turbulence control Turbulent boundary layers

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Feature Fusion for Graph Convolutional Networks in Semi-Supervised Image Classification

Feature Fusion for Graph Convolutional Networks in Semi-Supe...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Marina Chagas Bulach Gapski Lucas Pascotti Valem Daniel Carlos Guimarães Pedronette Department of Statistics Applied Mathematics and Computing São Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

In recent years, the volume of multimedia data has been rapidly increasing across various applications. Consequently, classification methods capable of handling scenarios with limited labeled data (e.g., semi-supervised, weakly supervised) have become critically important, particularly because acquiring labeled data is often expensive and time-consuming. Regarding image data, feature extraction approaches are commonly employed in many tasks. Feature extraction involves identifying and extracting key characteristics or patterns, such as edges, textures, shapes, and colors. Nowadays, most extractors consider deep learning strategies, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViT). With various feature extractors available in the literature, there is a wide diversity of features that can be considered. The features extracted from an image depend on the application, the extractor used, and its configuration. Therefore, combining different extractors can be a promising strategy to exploit complementary information. Graph Convolutional Networks (GCNs) are fundamental and promising strategies in the scenario of semi-supervised image classification, being able to leverage labeled and unlabeled data, and exploiting the graph structures that offer valuable information. This work proposes an approach for GCNs in scenarios where labeled data is scarce, combining sets of features and graphs considering different extraction approaches. Among the main contributions, the experimental results reveal that these combinations and the use of manifold learning to process these graphs improve the classification results in most cases.

关键词： Graph convolutional networks Shape Image color analysis Image edge detection Semisupervised learning Feature extraction Transformers Manifold learning Data mining Image classification

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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A SUB-GRID ACTIVITY SENSOR applied TO MIXED MODELING IN LARGE EDDY SIMULATION 12

A SUB-GRID ACTIVITY SENSOR APPLIED TO MIXED MODELING IN LARG...

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12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022

作者： Hasslberger, Josef Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 Neubiberg85577 Germany

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The Mean Time to Absorption on Horizontal Partitioned Sierpinski Gasket Networks

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Analysis in Theory and Applications 2024年第1期40卷 1-21页

作者： Zhizhuo Zhang Bo Wu Zuguo Yu School of Mathematics Southeast UniversityNanjingJianagsu 211189China School of Applied Mathematics Nanjing University of Finance and EconomicsNanjingJiangsu 210023China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan UniversityXiangtanHunan 411105China

The random walk is one of the most basic dynamic properties of complex networks,which has gradually become a research hotspot in recent years due to its many applications in actual *** important characteristic of the random walk is the mean time to absorption,which plays an extremely important role in the study of topology,dynamics and practical application of complex *** the mean time to absorption on the regular iterative self-similar network models is an important way to explore the inﬂuence of self-similarity on the properties of random walks on the *** existing literatures have proved that even local self-similar structures can greatly affect the properties of random walks on the global network,but they have failed to prove whether these effects are related to the scale of these self-similar *** this article,we construct and study a class of Horizontal Par-titioned Sierpinski Gasket network model based on the classic Sierpinski gasket net-work,which is composed of local self-similar structures,and the scale of these structures will be controlled by the partition coefﬁcient ***,the analytical expressions and approximate expressions of the mean time to absorption on the network model are obtained,which prove that the size of the self-similar structure in the network will directly restrict the inﬂuence of the self-similar structure on the properties of random walks on the ***,we also analyzed the mean time to absorption of different absorption nodes on the network toﬁnd the location of the node with the highest absorption efﬁciency.

关键词： Mean time to absorption self-similar network Sierpinski Gasket

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A Dynamic Pyramidal Relaxation Method of AC OPF 9

A Dynamic Pyramidal Relaxation Method of AC OPF

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9th Asia Conference on Power and Electrical Engineering, ACPEE 2024

作者： Tang, Yu-Yang Chen, Liang Du, Hongbo Chen, Sheng-Jie Kou, Caixia Ai, Xiaomeng Zhou, Bo Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications College of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China University of Michigan Department of Industrial and Operations Engineering Ann Arbor United States

ISBN: (纸本)9798350309638

The Alternating Current Optimal Power Flow (AC OPF) is crucial for power system analysis, yet existing algorithms face challenges in meeting the diverse requirements of practical applications. This paper presents a Pyramidal Relaxation (PR) formulation of the Branch Flow Model (BFM) for AC OPF, where surface conical constraints are approximated through a union of tetrahedrons in a Mixed Integer Linear Programming (MILP) relaxation. To reduce problem size and to enhance computational efficiency, we proposed a Dynamic Pyramidal Relaxation (DPR) method, where the cuts (inner approximation and outer approximation) are adaptively added. In practice, the DPR method is embedded into a MILP commercial solver based on the branch-and-cut framework. Case studies in different test systems validate the effectiveness in terms of feasibility, accuracy, and efficiency of our proposed method. © 2024 IEEE.

关键词： Computational efficiency

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