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DCC 2005: data Compression Conference

作者： Huang, Bormin Ahuja, Alok Huang, Hung-Lung Schmit, Timothy J. Heymann, Roger W. Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Madison WI 53706 United States NOAA National Environmental Satellite Data and Information Service Office of Research and Applications Madison WI 53706 United States NOAA National Environmental Satellite Data and Information Service Office of Systems Development Silver Spring MD 20910 United States

The compression of three-dimensional ultraspectral sounder data is a challenging task given its unprecedented size. We develop a fast precomputed vector quantization (FPVQ) scheme with optimal bit allocation for lossless compression of ultraspectral sounder data. The scheme consists of linear prediction, bit-depth partitioning, vector quantization, and optimal bit allocation. Linear prediction serves as a whitening tool to make the prediction residuals of each channel close to a Gaussian distribution, and then these residuals are partitioned based on bit depths. Each partition is further divided into several sub-partitions with various 2k channels for vector quantization. Only the codebooks with 2m codewords for 2 k-dimensional normalized Gaussian distributions are precomputed. A new algorithm is developed for optimal bit allocation among subpartitions. Unlike previous algorithms [19, 20] that may yield a sub-optimal solution, the proposed algorithm guarantees to find the minimum of the cost function under the constraint of a given total bit rate. Numerical experiments upon the NASA AIRS data show that the FPVQ scheme gives high compression ratios for lossless compression of ultraspectral sounder data. © 2005 IEEE.

关键词： data compression

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Unobservable Settling Vibration on Head-Positioning Control in Hard Disk Drives

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IFAC Proceedings Volumes 2011年第1期44卷 4052-4060页

作者： Takenori Atsumi William C. Messner Hitachi Research Laboratory Research and Development Group Hitachi Ltd. Fujisawa Kanagawa 252-0888 Japan Data Storage Systems Center Carnegie Mellon University Pittsburgh PA 15213 USA

Abstract In this paper, we study the unobserved vibrations of the head in hard disk drives which may lead to destruction of user data. We show that the acceleration input excites the mechanical resonance near the sampling frequency of the hold in the settling characteristics after the track-seeking control. We also show that the degree to which these oscillations are unobservable depends on the sampling frequency of the sampler. Then we define the index of unobservable position based on its RMS amplitude to estimate effect of the unobservable oscillations of mechanical resonances in the positioning control system. These phenomena are confirmed through the experiments using the actual head-positioning system of the hard disk drive. Consequently the vibrations caused by the mechanical resonances around sampling frequency are not only difficult to observe but readily excited by the control input. Therefore, the head-positioning control system must be designed such that the sampling frequency is away from mechanical resonances.

关键词： Unobservable Transient responses Mechatronic systems Sampled-data systems Vibration and modal analysis

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Distributed software development of a cloud solution for collaborative manufacturing networks 23

Distributed software development of a cloud solution for col...

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23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017

作者： Bendas, Dan Saari, Leila Coutinho, Carlos De Juan Marin, Ruben Gisbert, Josep Bernabe Lopes, Luis Data-Driven Solutions VTT Technical Research Centre of Finland Ltd. Oulu Finland ISTAR-IUL Caixa Mágica Software CMS Lisbon Portugal Distributed Systems Instituto Tecnológico de Informática ITI Valencia Spain Research and Development Caixa Mágica Software CMS Lisbon Portugal

ISBN: (纸本)9781538607749

Distributed software development poses extra difficulties in terms of communication, coordination and awareness. Those difficulties usually imply more time consumption that causes productiveness reduction deriving finally in delays. So, projects where such kind of development is needed have to deal with them in order to ensure the success of the project. The case project has adopted this development approach due to its nature. The main goal of the project is to develop a cloud solution for enhancing collaboration and transparency between manufacturing companies forming a collaborative network. In order to implement it, the overall solution has been divided into several pieces, having many interdependencies among them. Thus, different working teams geographically spread from different organizations have participated in the implementation, integration, verification and validation tasks. This paper explains how the case project has organized the working teams, infrastructure, procedures and practices to guarantee the success of the project. © 2017 IEEE.

关键词： Software design

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Department of Agromechatronics Potsdam Germany University of São Paulo (USP) Center of Nuclear Energy in Agriculture (CENA) Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Federal University of Santa Maria (UFSM) Academic Coordination Santa Maria Brazil Federal University of Santa Maria (UFSM) Soil Department Santa Maria Brazil Norwegian Institute of Bioeconomy Research (NIBIO) Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern Universi

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM. © 2025 The Author(s)

关键词： Benchmarking dataset collection Digital soil mapping Machine learning Open data Pedometrics

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Approximate matching for OCR-processed bibliographic data

Approximate matching for OCR-processed bibliographic data

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International Conference on Pattern Recognition

作者： A. Takasu N. Katayama M. Yamaoka O. Iwaki K. Oyama J. Adachi Research and Development Department National Center for Science and Information Systems Tokyo Japan Laboratory for Information Technology NTT DATA Communications Systems Corporation Kawasaki Kanagawa Japan

This paper presents a method for matching bibliographies in references of academic papers obtained as document images with records of bibliographic databases. The main subject of this paper is to handle the erroneous bibliographic data obtained by a document understanding methodology. The presented method can find a candidate record set from referral databases in spite of the errors of string by means of approximate matching which is performed as an exact matching of k substrings of length m chosen from the strings of bibliographic data in references and in databases. For the accuracy /spl alpha/ of the OCR, theoretical observation shows that the accuracy of the presented method is 1-(1-/spl alpha//sup m/)/sup k/ under the assumption that the OCR error occurs randomly and independently in the string. The method is applied to references of 187 Japanese articles and achieves accuracy of 94.05%.

关键词： Optical character recognition software data mining Image databases Image analysis Text analysis Character recognition Information systems Laboratories Information technology data communication

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Epilepsy Detection Grand Challenge 48

Epilepsy Detection Grand Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chatzichristos, Christos Bhagubai, Miguel Van Paesschen, Wim De Vos, Maarten Un. Hospital Leuven Lab. for Epilepsy Research KU Leuven & Department of Neurology Belgium Signal Processing and Data Analytics KU Leuven STADIUS Center for Dynamical Systems Department of Electrical Engineering Leuven Belgium KU Leuven Department Development and Regeneration Leuven Belgium

ISBN: (纸本)9781728163277

In this paper, we describe the epilepsy detection grand challenge, in association with ICASSP 2023. The challenge was centered on seizure detection using wearable behind-the-ear EEG. Two separate tasks were set for the participants: 1) obtain the best overall performance in seizure detection;2) systematically engineer the data in a data-centric task to optimize a given AI model for seizure detection. © 2023 IEEE.

关键词： Neurology

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GE-CMU: description of the SHOGUN system used for MUC-5 93

GE-CMU: description of the SHOGUN system used for MUC-5

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Proceedings of the 5th conference on Message understanding

作者： Paul S. Jacobs George Krupka Lisa Rau Michael L. Mauldin Teruko Mitamura Tsuyoshi Kitani Ira Sider Lois Childs GE Research and Development Schenectady NY Carnegie Mellon University Pittsburgh PA Management Data Systems Martin Marietta Philadelphia PA

ISBN: (纸本)9781558603363

This paper describes the GE-CMU TIPSTER/SHOGUN system as configured for the TIP-STER 24-month (MUC-5) benchmark, and gives details of the system's performance on the selected Japanese and English texts. The SHOGUN system is a distillation of some of the key ideas that emerged from previous benchmarks and experiments, emphasizing a simple architecture in which the focus is on detailed corpus-based knowledge. This design allowed the project to meet its goal of achieving advances in coverage and accuracy while showing consistently good performance across languages and domains.

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Expert System for Coal Blending

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IFAC Proceedings Volumes 1992年第17期25卷 109-114页

作者： E. Nakata H. Fujimoto K. Terazono Systems Engineering Division Kawasaki Steel Systems Research and Development Corporation Kurashiki 712 Japan Systems Laboratory Systems Planning and Data Processing Department Kawasaki Steel Corporation Tokyo 100 Japan Coke Technology Section Ironmaking Department Kawasaki Steel Corporation Kurashiki 712 Japan

The application of an expert system to plan coal blending for coke production is described. The object of this coal blending plan is to produce coke of stable quality at low cost while maintaining the optimum use of the coal yard. The monthly coal blending planning work previously depended on an experienced operator's expertise, and the expert system approach has helped to reduce planning time and has standardized the planning method. The effectiveness of the expert system in comparison with linear programming was confirmed by the following advantages: 1. Every fact that was difficult to express by a fixed formula could be expressed as a rule. 2. Calculation time was shortened. 3. Every solution could be derived by limiting its searching scope.

关键词： artificial intelligence expert systems mathematical programming production control blending cokemaking

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Analysis of Barkhausen noise failure caused by ESD in a GMR head

Analysis of Barkhausen noise failure caused by ESD in a GMR ...

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Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)

作者： Takehiko Hamaguchi Takayuki Ichihara Takayoshi Ohtsu Information Storage Research Department Central Research Laboratory Hitachi and Limited Japan Disk Storage Development Department Data Storage Systems Division Hitachi and Limited Japan

We used a spin-stand with a pre-amplifier with a built-in ESD simulator circuit to investigate a new magnetic failure mode caused by ESD current. This failure mode originates in Barkhausen noise from unexpected domain walls in the free layer of a giant magnetoresistive (GMR) head. According to a modified machine model (0 Ω, 50 pF), this failure occurs at around 0.5 V, i.e., a quarter of that at which a conventional pinned layer reversal failure occurs.

关键词： Failure analysis Electrostatic discharge Giant magnetoresistance Circuit noise Magnetic noise Magnetic heads Magnetic domain walls Magnetic analysis Circuit simulation Magnetic circuits

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BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation

BoostMotion: Boosting a Discriminative Similarity Function f...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： S.K. Zhou B. Georgescu D. Comaniciu Jie Shao Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA Center for Automation Research University of Maryland College Park MD USA

Motion estimation for applications where appearance undergoes complex changes is challenging due to lack of an appropriate similarity function. In this paper, we propose to learn a discriminative similarity function based on an annotated database that exemplifies the appearance variations. We invoke the LogitBoost algorithm to selectively combine weak learners into one strong similarity function. The weak learners based on local rectangle features are constructed as nonparametric 2D piecewise constant functions, using the feature responses from both images, to strengthen the modeling power and accommodate fast evaluation. Because the negatives possess a location parameter measuring their closeness to the positives, we present a locationsensitive cascade training procedure, which bootstraps negatives for later stages of the cascade from the regions closer to the positives. This allows viewing a large number of negatives and steering the training process to yield lower training and test errors. In experiments of estimating the motion for the endocardial wall of the left ventricle in echocardiography, we compare the learned similarity function with conventional ones and obtain improved performances. We also contrast the proposed method with a learning-based detection algorithm to demonstrate the importance of temporal information in motion estimation. Finally, we insert the learned similarity function into a simple contour tracking algorithm and find that it reduces drifting.

关键词： Boosting Motion estimation Stress Motion measurement Gaussian noise Testing Heart Speckle data systems Automation

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