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arXiv 2025年

作者： Li, Jingwen Chandrasekar, Aravind Rocha, Mariana Li, Chao Chen, Yuqing Department of Computer Science Fudan University China School of AI and Robotics AuroraTech Labs United States Vision Intelligence Group Federal University of Rio de Janeiro Brazil Dept. of Computer Science Eastern Asia Institute of Technology Beijing China

We present a novel approach for controllable, region-specific style editing driven by textual prompts. Building upon the state-space style alignment framework introduced by StyleMamba, our method integrates a semantic segmentation model into the style transfer pipeline. This allows users to selectively apply text-driven style changes to specific segments (e.g., "turn the building into a cyberpunk tower") while leaving other regions (e.g., "people" or "trees") unchanged. By incorporating region-wise condition vectors and a region-specific directional loss, our method achieves high-fidelity transformations that respect both semantic boundaries and user-driven style descriptions. Extensive experiments demonstrate that our approach can flexibly handle complex scene stylizations in real-world scenarios, improving control and quality over purely global style transfer methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces

SSRN

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SSRN 2024年

作者： Chen, Qiuyi Tsilifis, Panagiotis Fuge, Mark Department of Mechanical Engineering University of Maryland College ParkMD20742 United States Robotics AI & Computer Vision Group General Electric Vernova Advanced Research NiskayunaNY12309 United States

Solving inverse problems in scientific and engineering fields has long been intriguing and holds great potential for many applications, yet most techniques still struggle to address issues such as high dimensionality, nonlinearity and model uncertainty inherent in these problems. Recently, generative models such as Generative Adversarial Networks (GANs) have shown great potential in approximating complex high dimensional conditional distributions and have paved the way for characterizing posterior densities in Bayesian inverse problems, yet the problems’ high dimensionality and high nonlinearity often impedes the model’s training. In this paper we show how to tackle these issues with Least Volume—a novel unsupervised nonlinear dimension reduction method—that can learn to represent the given datasets with the minimum number of latent variables while estimating their intrinsic dimensions. Once the low dimensional latent spaces are identified, efficient and accurate training of conditional generative models becomes feasible, resulting in a latent conditional GAN framework for posterior inference. We demonstrate the power of the proposed methodology on a variety of applications including inversion of parameters in systems of ODEs and high dimensional hydraulic conductivities in subsurface flow problems, and reveal the impact of the observables’ and unobservables’ intrinsic dimensions on inverse problems. © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces

arXiv

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arXiv 2024年

关键词： Inverse problems

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Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces

SSRN

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SSRN 2024年

关键词： Inverse problems

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Testing autonomous underwater vehicle compatibility with bidirectional acoustic tags for biotelemetry

Testing autonomous underwater vehicle compatibility with bid...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Gerard Batet David Sarria Marta Real Spartacus Gomariz Joaquin Del Rio Narcis Palomeras Ivan Masmitja SARTI Research Group EEL Universitat Politècnica de Catalunya Computer Vision and Robotics Institute Universitat de Girona Institut de Ciencies del Mar Consejo Superior de Investigaciones Científicas

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Few advancements in underwater species tracking have occurred in recent years. For instance, acoustic biotelemetry systems were initially employed in the late ’70s, and since then, only minimum improvements have been made in terms of range and battery life. The development of a bidirectional acoustic tag, combined with other technologies such as autonomous surface and underwater vehicles, promises a significant leap forward in the field, offering enhanced data precision at a reduced operational cost.

关键词： Biomedical telemetry Working environment noise Noise Receivers Marine ecosystems Acoustics Underwater tracking Batteries Monitoring Testing

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Enhanced Electrical Signature Analysis (e-ESA) for Offshore Wind Permanent Magnet Generators

Enhanced Electrical Signature Analysis (e-ESA) for Offshore ...

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IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Jaime Renedo Anglada Weizhong Yan Bojun Feng Manoj Shah Electrical Systems GE Vernova Advanced Research Niskayuna NY USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Aerospace Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

Offshore wind turbines can generate large amount of renewable energy, but the operation and maintenance (O&M) can be challenging due to the harsh and not easily accessible environment, which can increase operational costs. This work aims to develop methodologies that use the electrical signals of the power converters to detect faults that are not readily detected by existing protection systems of the generator. Currently there are condition monitoring systems based on vibration, thermal and electrical sensors but they can't detect faults like turn shorts and increased crimp resistance in direct-drive permanent magnet generators (PMGs). Detecting these faults early before they evolve into catastrophic failures can help to optimize O&M operations, prevent unplanned downtime and therefore reduce the operational expenditures (OPEX). The detection methodology uses the harmonic content of the electrical signals. The case study of a 6 MW turbine presents simulation results under healthy and unhealthy conditions, and the field data collected from operational wind farms. The results of the features considered show a promising separability between healthy and unhealthy conditions, which indicates that this methodology could be implemented for condition monitoring of PMGs in offshore wind farms.

关键词： Condition monitoring Vibrations Renewable energy sources Permanent magnet generators Thermal resistance Simulation Wind farms Thermal sensors Sensor systems Wind turbines

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the Electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

arXiv

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arXiv 2024年

作者： Lin, Qinliang Luo, Cheng Niu, Zenghao He, Xilin Xie, Weicheng Hou, Yuanbo Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China WAVES Research Group Ghent University Belgium University of Leicester United Kingdom

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://***/LinQinLiang/DeCoWA. © 2024, CC BY-NC-SA.

关键词： Semantics

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Fast-moving object counting with an event camera

arXiv

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arXiv 2022年

作者： Bialik, Kamil Kowalczyk, Marcin Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed. © 2022, CC BY.

关键词： Cameras

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PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

TechRxiv

引用

TechRxiv 2022年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars backbone. © 2022, CC BY.

关键词： Object detection

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