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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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E3D: A Scaled-up Framework for Human Action Recognition 7

E3D: A Scaled-up Framework for Human Action Recognition

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Singh, Tushar Anunay Jain, Vayam Singh, Anand Mohan Aggarwal, Nilesh Singh, Sarthak Delhi Technological University Department of Electrical Engineering Delhi India Delhi Technological University Department of Mechanical Engineering Delhi India Delhi Technological University Department of Applied Physics Delhi India Delhi Technological University Department of Applied Mathematics Delhi India

ISBN: (纸本)9798350302882

The research represents an E3D architecture created by upscaling 2D EfficientNet architecture into a three-dimensional one. E3D is a simple yet productive network that utilises a custom and novel Depthwise 3D convolution layer on which the entire algorithm depends. A depthwise convolution layer is devised for simultaneously understanding the spatial and temporal information for a large-scale processed frame sequence dataset by leveraging and scaling EfficientNetB0. This homogeneous architecture summarises the 101 distinct actions from two publicly available action classification datasets, HMDB-51 and UCF-101. The manuscript further explains the functionality of E3D and how its trained features outshine the other state-of-the-art classification benchmark in terms of performance, efficiency and deployment characteristics by achieving 98.74% and 86.49% accuracy on the UCF-101 dataset and HMDB-51 dataset, respectively;with this, the proposed architecture is computationally cheaper than the other action recognition architectures. © 2023 IEEE.

关键词： Convolution

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Combining Deep Networks with Model-Based Scene Segmentation for Reliably Detecting Rocks on Railway Tracks

Combining Deep Networks with Model-Based Scene Segmentation ...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Vincenzo Carletti Pasquale Foggia Speranza Ranieri Alessia Saggese Camilla Spingola Mario Vento Department of Information and Electrical Engineering and applied Mathematics University of Salerno Italy

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

Railway transportation is crucial to global mobility, serving as a sustainable conduit for transporting goods and passengers; unfortunately, it is vulnerable to different threats, like the presence of rocks on railway tracks. Accidents caused by rocks can disrupt transport schedules, damage infrastructure, and even lead to injuries or fatalities. Addressing these safety challenges is very important for ensuring the operation of rail networks, which in turn enhances overall mobility and minimizes delays, but also for protecting lives. In this paper, we propose a novel approach to detect rocks on the railway tracks using a camera mounted on the train's front-end. In particular, we exploit domain-specific knowledge regarding intrinsic characteristics of the camera, its positioning with respect to the rails and rail-track characteristics, together with the output of a deep neural network for semantic segmentation, to identify the railway track in the image. Once the track area is identified, it is partitioned into patches and a convolutional neural network is applied to analyze these patches and to detect the presence of rocks; furthermore, in order to enhance the robustness of the detection process, we employ a majority voting mechanism across a sequence of video frames. The experimentation, conducted over six videos augmented with the presence of rocks, confirm the effectiveness of the proposed approach.

关键词： Rails Technological innovation Schedules Semantic segmentation Transportation Rocks Cameras Safety Convolutional neural networks Security

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Tell what you hear from what you see - video to audio generation through text 24

Tell what you hear from what you see - video to audio genera...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiulong Liu Kun Su Eli Shlizerman Department of Electrical & Computer Engineering University of Washington Seattle Department of Applied Mathematics and Department of Electrical & Computer Engineering University of Washington Seattle

ISBN: (纸本)9798331314385

The content of visual and audio scenes is multi-faceted such that a video stream can be paired with various audio streams and vice-versa. Thereby, in video-to-audio generation task, it is imperative to introduce steering approaches for controlling the generated audio. While Video-to-Audio generation is a well-established generative task, existing methods lack such controllability. In this work, we propose VATT, a multi-modal generative framework that takes a video and an optional text prompt as input, and generates audio and optional textual description (caption) of the audio. Such a framework has two unique advantages: i) Video-to-Audio generation process can be refined and controlled via text which complements the context of the visual information, and ii) The model can suggest what audio to generate for the video by generating audio captions. VATT consists of two key modules: VATT Converter, which is an LLM that has been fine-tuned for instructions and includes a projection layer that maps video features to the LLM vector space, and VATT Audio, a bi-directional transformer that generates audio tokens from visual frames and from optional text prompt using iterative parallel decoding. The audio tokens and the text prompt are used by a pretrained neural codec to convert them into a waveform. Our experiments show that when VATT is compared to existing video-to-audio generation methods in objective metrics, such as VGGSound audiovisual dataset, it achieves competitive performance when the audio caption is not provided. When the audio caption is provided as a prompt, VATT achieves even more refined performance (with lowest KLD score of 1.41). Furthermore, subjective studies asking participants to choose the most compatible generated audio for a given silent video, show that VATT Audio has been chosen on average as a preferred generated audio than the audio generated by existing methods. VATT enables controllable video-to-audio generation through text as well as suggest

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AV-cloud: spatial audio rendering through audio-visual cloud splatting 24

AV-cloud: spatial audio rendering through audio-visual cloud...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Mingfei Chen Eli Shlizerman Department of Electrical & Computer Engineering University of Washington Seattle Department of Electrical & Computer Engineering and Department of Applied Mathematics University of Washington Seattle

ISBN: (纸本)9798331314385

We propose a novel approach for rendering high-quality spatial audio for 3D scenes that is in synchrony with the visual stream but does not rely or explicitly conditioned on the visual rendering. We demonstrate that such an approach enables the experience of immersive virtual tourism - performing a real-time dynamic navigation within the scene, experiencing both audio and visual content. Current audio-visual rendering approaches typically rely on visual cues, such as images, and thus visual artifacts could cause inconsistency in the audio quality. Furthermore, when such approaches are incorporated with visual rendering, audio generation at each viewpoint occurs after the rendering of the image of the viewpoint and thus could lead to audio lag that affects the integration of audio and visual streams. Our proposed approach, AV-Cloud, overcomes these challenges by learning the representation of the audio-visual scene based on a set of sparse AV anchor points, that constitute the Audio-Visual Cloud, and are derived from the camera calibration. The Audio-Visual Cloud serves as an audio-visual representation from which the generation of spatial audio for arbitrary listener location can be generated. In particular, we propose a novel module Audio-Visual Cloud Splatting which decodes AV anchor points into a spatial audio transfer function for the arbitrary viewpoint of the target listener. This function, applied through the Spatial Audio Render Head module, transforms monaural input into viewpoint-specific spatial audio. As a result, AV-Cloud efficiently renders the spatial audio aligned with any visual viewpoint and eliminates the need for pre-rendered images. We show that AV-Cloud surpasses current state-of-the-art accuracy on audio reconstruction, perceptive quality, and acoustic effects on two real-world datasets. AV-Cloud also outperforms previous methods when tested on scenes "in the wild".

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Improving Learning from Visual Demonstration Methods by Target Localization

Improving Learning from Visual Demonstration Methods by Targ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Pasquale Foggia Francesco Rosa Mario Vento Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper presents a novel approach to multi-task visual-guided imitation learning. Upon evaluating the current state-of-the-art method, we observed its capability to replicate the intent of the demonstrator, but with the flaw of manipulating incorrect objects. To address this issue, our study introduces a new approach based on the assumption that explicitly addressing task-relevant problems, such as target object localization, can enhance system performance. Our validation shows that the proposal overtakes the leading method thanks to its ability to drive the robot motion towards the target object.

关键词： Location awareness Robot motion Visualization System performance Imitation learning Multitasking Proposals Robots

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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative Clustering Algorithm

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： Clustering algorithm wind speed data management power generation

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Conical Transition Graphs for Analysis of Asymptotic Stability in Hybrid Dynamical Systems ⁎

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IFAC-PapersOnLine 2024年第11期58卷 159-164页

作者： Paul K. Wintz Ricardo G. Sanfelice Department of Applied Mathematics University of California Santa Cruz Department of Electrical and Computer Engineering University of California Santa Cruz

A method is proposed for analyzing asymptotic stability in the conical approximation of a hybrid system. Specifically, this paper introduces the conical transition graph (CTG) to simplify the analysis of asymptotic stability in conical approximations by converting solutions to a hybrid system into walks through a discrete graph. By exploiting the fact that pre-asymptotic stability in a conical approximation implies pre-asymptotic stability in the original system, a CTG-based approach can establish asymptotic stability in hybrid systems that have nonlinear flow maps and jump maps without needing to construct a Lyapunov function.

关键词： Stability stabilization of hybrid systems Stability of nonlinear systems

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The First and Second Translation Theorem for Laplace Transform on Time Scale 30

The First and Second Translation Theorem for Laplace Transfo...

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30th Telecommunications Forum, TELFOR 2022

作者： Mirkovic, Tatjana Cirovic, Natasa University of Belgrade School of Electrical Engineering Department of Mathematics Faculty of Maritime Academic Studies Belgrade Belgrade Serbia University of Belgrade School of Electrical Engineering Department of Applied Mathematics Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

In our paper we formulate and prove the first and second translation theorem for time scale Laplace transform, for several elementary functions. We use the definition of Laplace transform on arbitrary time scales as introduced by M. Bohner and A. Peterson. For time scale taken to be the set of real numbers, the classical definition of Laplace transform is obtained. However, taking the set of integers for time scale, the modification of translation theorems for Z-transform is obtained. This approach applies to all time scales and represents unification and extension of classical Laplace and Z-transform, for specified functions. © 2022 IEEE.

关键词： Z transforms

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Deep Contrastive Learning Network for Small-Sample Hyperspectral Image Classification

Journal of Remote Sensing

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Journal of Remote Sensing 2023年第1期3卷 74-90页

作者： Quanyong Liu Jiangtao Peng Genwei Zhang Weiwei Sun Qian Du Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Gas Sensors and Chemometrics State Key Laboratory of NBC Protection for CivilianBeijingChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateUSA

Recently,deep learning methods have been widely used in hyperspectral image(HSI)classification and achieved good ***,the performance of these methods may be limited because of the scarcity of labeled samples in HSI *** solve the small-sample classification problem,a deep contrastive learning network(DCLN)method is proposed in this *** proposed DCLN method first constructs contrastive groups and trains the network through contrastive ***,it uses the trained network to extract spectral-spatial features of HSI pixels and generates pseudo-label for each unlabeled sample based on the spatial-spectral mixing ***,the pseudo-labeled samples with higher confidence are selected and added to the original training set to retrain the *** gradually increasing pseudo-labeled samples and refining the contrastive learning network,the model shows good feature learning ability and classification performance with the limited labeled *** results on 4 public HSI datasets demonstrate that the proposed DCLN method can achieve better performance than existing state-of-the-art methods.

关键词： DCL Deep network

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