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共 417 条记录，以下是361-370 订阅

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Evaluation of the Qucs Software for MSR-ERO Electric Propulsion Power processing Assembly Modelling and Design Check

Evaluation of the Qucs Software for MSR-ERO Electric Propuls...

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european space Power conference (ESPC)

作者： Dominique Nicolas Paul Kirch Frédéric Lescoat PPS department Airbus Defence and Space SAS Toulouse France ESA

This paper intends to describe the activity of electrical chain modelling and validation by simulation of the MSR-ERO PPS subsystem. The electrical chain modelling comprises, on one hand, the thruster assembly modelling, the PPU-to-thruster-assembly modelling and the PPU-supplies modelling. On the other hand, the XFCU (Xenon Flow Control Unit) modelling is also comprised. The analyzes also focus on modelling of the thruster start-up waveforms as well as modelling of the beam-out event waveforms. Various simulations are performed to check the modelling with respect to expected electrical behaviour of the thruster. It shall be noticed that the activity is ongoing and will generate further updates as the execution of coupling tests and processing of the associated measurement data will be performed.

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Spectrally Optimized Feature Identification (SOFI): A Novel Band Selection Method for Hyperspectral Image Analysis

Spectrally Optimized Feature Identification (SOFI): A Novel ...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International conference on

作者： Rohit Chugh Rama Rao Nidamanuri Usha Rani Nelakuditi Malavika Dileep Asha Davood Dept. of Earth and Space Sciences Indian Institute of Space Science & Technology Thiruvananthapuram India Dept. of Electronics & Communication Engineering Vignan University Guntur Andhra Pradesh India Marian College of Engineering Thiruvananthapuram India

Hyperspectral data acquired from ground, drone or low-altitude airborne platforms have evolved to be the data of choice for various spectral imaging applications. The enormous interest of the scientific and industrial community in using hyperspectral imaging calls for automated processing and analysis methods. Enabling the use of external reference spectral libraries for hyperspectral image processing, statistical optimal bands selection (OBS) is the approach for handling dimensionality. The existing OBS algorithms are data and application specific. We present an adaptive evolutionary computing-based OBS algorithm, Spectrally Optimized Feature Identification (SOFI), to identify spectral bands optimal for different application domains. We demonstrate the practical applicability of the proposed algorithm by implementing it for identifying appropriate spectral bands for crop discrimination on a diverse landscape using airborne hyperspectral imagery acquired over a part of Anand district, India.

关键词： Image analysis data analysis Imaging Crops Feature extraction Libraries Optimization

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Synergizing airborne Hyperspectral Imaging and ground truth data for environmental monitoring: Insights from the CERAD campaign 10

Synergizing airborne Hyperspectral Imaging and ground truth ...

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10th International conference on Remote Sensing and Geoinformation of the Environment, RSCy 2024

作者： Mettas, C. Themistocleous, K. Prodromou, M. Kountouri, J. Kalogirou, E. Evagorou, E. Tzouvaras, M. Christoforou, M. Loulli, E. Charalampous, G. Eliades, M. Pittaki, Z. Varvaris, G. Fotiou, K. Abate, D. Christofi, D. Theocharidis, C. Neophytides, St. Polydorou, T. Neofytou, E. Koumoulidis, D. Papoutsa, C. Mavrovouniotis, M. Krauß, T. Gstaiger, V. Köhler, C. Hadjimitsis, D. ERATOSTHENES Centre of Excellence Franklin Roosevelt 82 3012 Limassol3036 Cyprus Cyprus University of Technology Archiepiskopou Kyprianou 30 Limassol3036 Cyprus Münchener Str. 20 Weßling82234 Germany

ISBN: (纸本)9781510681491

This work presents the Cyprus Flight Campaign of ERATOSTHENES Centre of Excellence and DLR (CERAD) that took place in October 2023 within the framework of the EXCELSIOR H2020 Widespread Teaming Phase 2 project titled "ERATOSTHENES: EXcellence Research Centre for Earth SurveiLlance and space-Based Monitoring of the EnviRonment". The campaign's main goal was to acquire about 100.000 high-resolution stereo 3K images and hyperspectral HySpex images, complemented by ground truth measurements to perform high-resolution hyperspectral analysis and 3D mapping. The campaign aimed at the capacity development of ERATOSTHENES Centre of Excellence staff on processing these imagery, cross-calibration and validation of sensors, and analysis of land, water, and cultural heritage sites with hyperspectral sensors. This campaign captured high-resolution hyperspectral imagery across a wide spectral range (420-2500nm) in several parts of Cyprus (Paphos and Limassol Districts). Parallel to this airborne campaign, the research team of ERATOSTHENES Centre of Excellence conducted a ground-based measurement campaign, which included the collection of spectroradiometric measurements (HR 1024 and GER 1500), water samples for laboratory analysis of water (e.g., dissolved organic matter) and soil (e.g., texture, pH, organic content) samples, GPS tracking, soil moisture and meteorological sensors and on-board UAV multispectral cameras. The collected data will support various applications, such as calibration and validation of satellite products, environmental monitoring, vegetation analysis, and disaster risk assessment. According to the literature, the use of airborne hyperspectral imaging is essential since the airborne remote sensing data acts as a bridge between large-scale satellite and point-scale field observations. Furthermore, hyperspectral imaging is a simultaneous acquisition of spatial images in several spectrally adjacent bands and a highly multidisciplinary and complex field. The pres

关键词： Soil moisture

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Bit-Parallel (Compressed) Wavelet Tree Construction

Bit-Parallel (Compressed) Wavelet Tree Construction

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data Compression conference (DCC)

作者： Patrick Dinklage Johannes Fischer Florian Kurpicz Jan-Philipp Tarnowski TU Dortmund University Otto-Hahn-Str. 14 Dortmund Germany Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe Germany

The wavelet tree is a data structure that indexes a text over an integer alphabet for efficient rank and select queries. Using the Huffman encoding, it can be stored in zero-order entropycompressed space. We present a highly engineered open source implementation of an efficient sequential construction algorithm that makes use of bit parallelism via vector instructions. On hardware featuring ultrawide registers of up to 512 bits, it outperforms the currently fastest known practical sequential construction algorithms by a factor of up to 2.5.

关键词： data compression Parallel processing data structures Hardware Encoding Registers Indexes

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Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big data 1

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Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain conference, CD-MAKE 2023

作者： Dave, Ryan Angarita-Zapata, Juan S. Triguero, Isaac School of Computer Science University of Nottingham Nottingham United Kingdom Aimsun SLU Barcelona Spain DeustoTech Faculty of Engineering University of Deusto Bilbao Spain University of Granada Granada Spain

ISBN: (数字)9783031408373

ISBN: (纸本)9783031408366

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models to reach optimal performance. For some, the outcome may be desired;however, the complexity and lack of knowledge in the field of ML become a hindrance. This, in turn, has seen an increasing demand for the automation of the complete ML workflow (from data preprocessing to model selection), known as Automated Machine Learning (AutoML). Although AutoML solutions have been developed, Big data is now seen as an impediment for large organisations with massive data outputs. Current methods cannot extract value from large volumes of data due to tight coupling with centralised ML libraries, leading to limited scaling potential. This paper introduces Hyper-Stacked, a novel AutoML component built natively on Apache Spark. Hyper-Stacked combines multi-fidelity hyperparameter optimisation with the Super Learner stacking technique to produce a strong and diverse ensemble. Integration with Spark allows for a parallelised and distributed approach, capable of handling the volume and complexity associated with Big data. Scalability is demonstrated through an in-depth analysis of speedup, sizeup and scaleup. © 2023, IFIP International Federation for Information processing.

关键词： Big data

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Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts 36

Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Pr...

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36th Annual conference on Neural Information processing Systems - Competition Track, NeurIPS 2022

作者： Gruca, Aleksandra Serva, Federico Lliso, Llorenç Rípodas, Pilar Calbet, Xavier Herruzo, Pedro Pihrt, Jiří Raevskyi, Rudolf Šimánek, Petr Choma, Matej Li, Yang Dong, Haiyu Belousov, Yury Polezhaev, Sergey Pulfer, Brian Seo, Minseok Kim, Doyi Shin, Seungheon Kim, Eunbin Ahn, Sewoong Choi, Yeji Park, Jinyoung Son, Minseok Cho, Seungju Lee, Inyoung Kim, Changick Kim, Taehyeon Kang, Shinhwan Shin, Hyeonjeong Yoon, Deukryeol Eom, Seongha Shin, Kijung Yun, Se-Young Le Saux, Bertrand Kopp, Michael K. Hochreiter, Sepp Kreil, David P. Silesian University of Technology Poland Italian Space Agency Italy AEMET/NWC SAF Spain Polytechnic University of Catalonia Spain Faculty of Information Technology Czech Technical University Prague Czech Republic Meteopress s.r.o Czech Republic Nanjing University of Information Science and Technology China Microsoft United States University of Geneva Switzerland DubFormer Netherlands SI Analytics Korea Republic of Korea Republic of European Space Agency Φ-lab Italy Institute of Advanced Research in Artificial Intelligence Austria Machine Learning Institute Johannes Kepler University Linz Austria

Weather4cast again advanced modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hi-res rain radar movies from multi-band satellite sensors, requiring data fusion, multi-channel video frame prediction, and super-resolution. Accurate predictions of rain events are becoming ever more critical, with climate change increasing the frequency of unexpected rainfall. The resulting models will have a particular impact where costly weather radar is not available. We here present highlights and insights emerging from the thirty teams participating from over a dozen countries. To extract relevant patterns, models were challenged by spatio-temporal shifts. Geometric data augmentation and test-time ensemble models with a suitable smoother loss helped this transfer learning. Even though, in ablation, static information like geographical location and elevation was not linked to performance, the general success of models incorporating physics in this competition suggests that approaches combining machine learning with application domain knowledge seem a promising avenue for future research. Weather4cast will continue to explore the powerful benchmark reference data set introduced here, advancing competition tasks to quantitative predictions, and exploring the effects of metric choice on model performance and qualitative prediction properties. © 2023 A. Gruca et al.

关键词： Rain

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The first release of the Cherenkov Telescope Array Observatory Array Control and data Acquisition software 8

The first release of the Cherenkov Telescope Array Observato...

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conference on Software and Cyberinfrastructure for Astronomy VIII

作者： Oya, I Aubert, P. Baroncelli, L. Bolle, P. Bruno, P. Bulgarelli, A. Caroff, S. Castaldini, L. Conforti, V Costa, A. David, L. De Cesare, G. de Ona Wilhelmli, E. Di Piano, A. Egberts, K. Fernandez, R. Fioretti, V Fukami, S. Garcia, E. B. Garcia, E. Gasparyan, H. Germani, S. Hoischen, C. Incardona, F. Kostunin, D. Lopez, B. Lyard, E. Maurin, G. Melkumyan, D. Munari, K. Murach, T. Muraczewski, A. Nakhjiri, N. Neise, D. Panebianco, G. Parmiggiani, N. Pietriga, E. Pollet, V Sadeh, I Sah, S. Sarkar, A. Schefer, M. Schmidt, T. Soldevila, D. Steppa, C. Torres, D. Tramacere, A. Valles, R. Vuillaume, T. Werner, F. Cherenkov Telescope Array Observ gGmbH Saupfercheckweg 1 D-69117 Heidelberg Germany Univ Savoie Mt Blanc CNRS Lab Annecy Phys Particules IN2P3 F-74000 Annecy France INAF INAF OAS Bologna Via Gobetti 93-3 I-40129 Bologna Italy Deutsch Elektronen Synchrotron DESY Platanenallee 6 D-15738 Zeuthen Germany INAF Osservatorio Astrofis Catania Via S Sofia 78 I-95123 Catania Italy Univ Paris Saclay INRIA CNRS Gif Sur Yvette France Univ Potsdam Inst Phys & Astron Karl Liebknecht Str 24-25 D-14476 Potsdam Germany COSYLAB JSC Control Syst Lab Gerbiceva Ul 64 Ljubljana 1000 Slovenia Swiss Fed Inst Technol Inst Particle Phys & Astrophys Otto Stern Weg 5 CH-8093 Zurich Switzerland CSIC Inst Space Sci ICE Carrer Can Magrans S-N Cerdanyola Del Valles 08193 Spain Inst Estudis Espacials Catalunya IEEC Carrer Can Magrans S-N Cerdanyola Del Valles 08193 Spain Univ Perugia Dipartimento Fis & Geol Perugia Italy Univ Geneva Dept Astron Chemin Ecogia 16 CH-1290 Versoix Switzerland Polish Acad Sci Nicolaus Copernicus Astron Ctr Ul Bartycka 18 PL-00716 Warsaw Poland Max Planck Inst Kernphys D-69029 Heidelberg Germany

ISBN: (纸本)9781510675261;9781510675254

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation atmospheric Cherenkov gamma-ray project. CTAO will be deployed at two sites, one in the Northern and the other in the Southern Hemisphere, containing telescopes of three different sizes for covering different energy domains. The commissioning of the first CTAO Large-sized Telescope (LST-1) is being finalized at the CTAO Northern site. Additional calibration and environmental monitoring instruments such as laser imaging detection and ranging (LIDAR) instruments and weather stations will support the telescope operations. The Array Control and data Acquisition (ACADA) system is the central element for onsite CTAO operations. ACADA controls, supervises, and handles the data generated by the telescopes and the auxiliary instruments. It will drive the efficient planning and execution of observations while handling the several Gb/s camera data generated by each CTAO telescope. The ACADA system contains the CTAO Science Alert Generation Pipeline - a real-time data processing and analysis pipeline, dedicated to the automatic generation of science alert candidates as data are being acquired. These science alerts, together with external alerts arriving from other scientific instruments, will be managed by the Transients Handler (TH) component. The TH informs the Short-term Scheduler of ACADA about interesting science alerts, enabling the modification of ongoing observations at sub-minute timescales. The capacity for such fast reactions - together with the fast movement of CTAO telescopes - makes CTAO an excellent instrument for studying high-impact astronomical transient phenomena. The ACADA software is based on the Alma Common Software (ACS) framework, and written in C++, Java, Python, and Javascript. The first release of the ACADA software, ACADA REL1, was finalized in July 2023, and integrated after a testing campaign with the LST-1 finalized in October 2023. This contribution describes the design and s

关键词： Cherenkov Telescope Array Observatory Software Alma Common Software Supervisory Software data Acquisition

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Latent CLAP Loss for Better Foley Sound Synthesis

Latent CLAP Loss for Better Foley Sound Synthesis

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european Signal processing conference (EUSIPCO)

作者： Tornike Karchkhadze Hassan Salami Kavaki Mohammad Rasool Izadi Bryce Irvin Mikolaj Kegler Ari Hertz Shuo Zhang Marko Stamenovic University of California San Diego San Diego USA The Graduate Center CUNY New York USA Bose Corp. Framingham USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Foley sound generation, the art of creating audio for multimedia, has recently seen notable advancements through text-conditioned latent diffusion models. These systems use multimodal text-audio representation models, such as Contrastive Language-Audio Pretraining (CLAP), whose objective is to map corresponding audio and text prompts into a joint embedding space. AudioLDM, a text-to-audio model, was the winner of the DCASE2023 task 7 Foley sound synthesis challenge. The winning system fine-tuned the model for specific audio classes and applied a post-filtering method using CLAP similarity scores between output audio and input text at inference time, requiring the generation of extra samples, thus reducing data generation efficiency. We introduce a new loss term to enhance Foley sound generation in AudioLDM without post-filtering. This loss term uses a new module based on the CLAP model-Latent CLAP encoder-to align the latent diffusion output with real audio in a shared CLAP embedding space. Our experiments demonstrate that our method effectively reduces the Frechet Audio Distance (FAD) score of the generated audio and eliminates the need for post-filtering, thus enhancing generation efficiency.

关键词： Art Europe Signal processing data collection Diffusion models data models

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Topical Grouping of Thousands of Biomimetics Articles According to Their Goals, Results and Methods 12th

Topical Grouping of Thousands of Biomimetics Articles Acco...

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Proceedings of the 12th International conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Carniel, Théophile Cazenille, Leo Dalle, Jean-Michel Halloy, José Université Paris Cité CNRS LIED UMR 8236 Paris75006 France Agoranov Paris France Sorbonne Université Paris France

ISBN: (纸本)9783031395031

Here we discuss the limitations of current topic modeling methods for scientific documents, which are often too coarse-grained to identify the specific themes of an article accurately. This is particularly challenging in interdisciplinary research, such as in the field of Living Machines, where scientific methods from different fields may be used to achieve a scientific goal. To address this problem, we extend previous methods and propose a new targeted topic modeling method that goes beyond a simple thematic classification. Our data processing relies on ChatGPT to extract information from abstracts of the corpus, which are then classified into thematic clusters using topic modeling algorithms. This approach adds clustering of the articles according to the scientific goal, the type of method used, and the type of results obtained. By using this technique, each document is associated with three targeted themes and one general theme, allowing for more fine-grained clustering. We demonstrate the effectiveness of this method in biomimetics with a representative corpus of 2099 documents. Our framework provides a detailed scientometric analysis of different topics in the field. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data handling

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General Algorithm for Learning from Grouped Uncoupled data and Pairwise Comparison data 29th

General Algorithm for Learning from Grouped Uncoupled Data a...

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29th International conference on Neural Information processing

作者： Kohjima, Masahiro Nambu, Yuta Kurauchi, Yuki Yamamoto, Ryuji NTT Corp NTT Human Informat Labs Yokosuka Kanagawa Japan

ISBN: (纸本)9783031301049;9783031301056

Uncoupled regression is the problem of learning a regression model from uncoupled data that consists of a set of input values (unlabeled data) and a set of output values where the correspondence between the input and output is unknown. A recent study showed that a method using both uncoupled data and pairwise comparison data can learn the optimal model under some assumptions. However, this method cannot use grouped information and may require the implementation (almost) from scratch for some models. In this study, we extend the above existing method for handling group information and derive a general algorithm that can learn a model using the standard regression method by approximating the loss function. The effectiveness of the proposed method is confirmed by synthetic and benchmark data experiments.

关键词： Uncoupled data Regression Bregman divergence

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