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IEEE Instrumentation and Measurement Technology Conference

作者： Ivan Kraljevski Frank Duckhorn Yong Chul Ju Constanze Tschoepe Christian Richter Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard *** used dataset is a representative example of a small data which was collected in carefully prepared *** to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional *** results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.

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YOLO-ICP: Deep learning Integrated Pose Estimation for Bin-Picking of Multiple Objects

YOLO-ICP: Deep Learning Integrated Pose Estimation for Bin-P...

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IEEE SENSORS

作者： Huajian Li Ivan Kraljevski Paul Meyer Constanze Tschöpe Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a novel deep learning-integrated pipeline called YOLO-ICP that aims to estimate the six degree of freedom (6-DoF) pose of objects using RGB-D sensors and does not require pose labels to train deep learning networks. YOLO-ICP integrates a real-time object detection algorithm with a point cloud registration method to estimate the pose of multiple objects. We evaluated our approach by quantitatively comparing it with baseline models on the OccludedLINEMOD dataset. Experimental results illustrate that our approach outperforms baseline models in challenging scenarios with textureless and occluded objects. In particular, our pipeline shows superior performance when dealing with small and symmetric objects in terms of the ADD(-S) metric.

关键词： Point cloud compression Deep learning YOLO Solid modeling Pipelines Pose estimation Refining Real-time systems 6-DOF Sensors

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Forecasting Time Series of Financial Indicators Using Artificial Neural Networks

Forecasting Time Series of Financial Indicators Using Artifi...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Savostyanov Aleksandr Grineva Natalia Stroeva Ekaterina Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia

This paper explores the possibility of using several different architectures of artificial neural networks to construct forecasts for financial indicators of a group of countries. The analysis is based on data collect...

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Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification

arXiv

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arXiv 2019年

作者： Mortier, Thomas Wydmuch, Marek Dembczyński, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznań University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility scores. Two of these algorithms make use of structural information in the form of a class hierarchy, which is often available in prediction problems with many classes. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Econometric Modeling of the Company's Intellectual Capital in the Context of Digitalization

Econometric Modeling of the Company's Intellectual Capital i...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Natalia Grineva Svetlana Mikhailova Natalia Kontsevaya Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

An approach to assessing the value of intellectual capital has been developed. Models demonstrating the impact of intellectual capital and its components on the financial performance of companies implementing a digital transformation strategy have been constructed. The influence of R&D investments on company performance has been evaluated, and strategic modeling of enterprise revenue growth rates has been conducted.

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Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors

Convolutional Autoencoders for Health Indicators Extraction ...

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IEEE Sensors

作者： Ivan Kraljevski Frank Duckhorn Constanze Tschoepe Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9781728168012

ISBN: (纸本)9781728168029

We present a method for extracting health indicators from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful life prediction.

关键词： Valves Sensors Hidden Markov models Training Feature extraction Degradation Convolution

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Combining Computer Vision and Word Processing to Classify Film Genres

Combining Computer Vision and Word Processing to Classify Fi...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Nikita Andriyanov Vitaly Dementev Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Security Department Ulyanovsk State Technical University Ulyanovsk Russia

The paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie database, IMDB (https://***/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. The paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric.

关键词： Measurement Deep learning Computer vision Computational modeling Computer architecture Motion pictures Transformers

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Comparison of MS Custom Vision Auto machine learning with Algorithms Implementation Methods

Comparison of MS Custom Vision Auto Machine Learning with Al...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Nikita Andriyanov Yulia Kamalova Vitaly Dementiev Data Analysis and Machine Learning Department Financial University Under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Protection Department Ulyanovsk State Technical University Ulyanovsk Russia

This study is devoted to the acquaintance, application, and implementation of computer vision models in the automated Microsoft Custom Vision service, as well as comparing the results of such models with the results of work personally assembled using the PyTorch/Tensorflow frameworks. The base models were represented by convolutional neural networks, which are applied to the image classification task. The study showed that the functionality of the Custom Vision service, although not great, but they represent really strong models, because without searching for the architecture, tuning the hyperparameters of the convolutional neural network, it is not possible to achieve results comparable to automatic machine learning solutions. However, the disadvantages of such models are the complexity of their deployment. As data for the study, we used databases of images of pollen grains of various plants, as well as aviation security images recorded by introscopes at airports. The experiment showed that in terms of precision and recall metrics, the Custom Vision solution provides an advantage of about 2-3% on balanced data and up to 15% on unbalanced ones. Thus, it is shown that using machine learning cloud services, you can get the primary baseline model, which programmers can further develop and improve, and other users can try to launch in production without knowledge of machine and deep learning technologies if the model quality estimation is quite high.

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Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

Object Detection in 3D Point Clouds via Local Correlation-Aw...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Chengzhi Wu Julius Pfrommer Jürgen Beyerer Kangning Li Boris Neubert Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Center for Machine Learning Fraunhofer IOSB Karlsruhe Germany Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

We present an improved approach for 3D object detection in point clouds data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud, but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks. Contribution-This research improves the 3D object detection performance for point clouds data with local correlation-aware embedding strategies.

关键词： Three-dimensional displays Object detection Feature extraction Task analysis Proposals Correlation Convolution

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Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

arXiv

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arXiv 2023年

作者： Wu, Chengzhi Pfrommer, Julius Beyerer, Jürgen Li, Kangning Neubert, Boris Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Center for Machine Learning Fraunhofer IOSB Karlsruhe Germany Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany

关键词： Embeddings

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