In the context of DOORS, a medium-scale distributed system, running on tens of 'normal PCs and/or embedded devices, we propose a solution for the problem of efficient allocation of execution and storage resources....
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This paper presents a Hardware-in-the-loop (HIL) simulation methodology for teleoperating the cable-driven manipulator system. This system includes a joystick device, designated as a delta robot, used for controlling ...
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In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based ...
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ISBN:
(数字)9798350395440
ISBN:
(纸本)9798350395457
In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.
MATLAB® releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the system Identification Toolbox™. The emphasis has been on integrating deep learning arc...
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MATLAB® releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the system Identification Toolbox™. The emphasis has been on integrating deep learning architectures and training techniques that facilitate the use of deep neural networks as building blocks of nonlinear models. The toolbox offers neural state-space models which can be extended with auto-encoding features that are particularly suited for reduced-order modeling of large systems. The toolbox contains several other enhancements that deepen its integration with the state-of-art machine learning techniques, leverage auto-differentiation features for state estimation, and enable a direct use of raw numeric matrices and timetables for training models.
Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on th...
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This article presents the problem of designing a nonlinear observer for an active magnetic suspension system. The design process of the nonlinear Luenberger observer (also known as the Kazantzis-Kravaris-Luenberger ob...
This article presents the problem of designing a nonlinear observer for an active magnetic suspension system. The design process of the nonlinear Luenberger observer (also known as the Kazantzis-Kravaris-Luenberger observer) is discussed. Particular attention was paid to the main nonlinearity of the system - the electromagnetic force, which was modeled applying the function describing the change in inductance as a function of the distance of the levitating object from the electromagnet surface. Theoretical analyses were confirmed by the results of experimental studies in which the task of moving the sphere between the given positions using current control was carried out. control tasks were conducted in the real-time regime on an embedded platform. The measured signals and estimated velocity were analyzed in the context of future implementations in control applications.
Blockchain is a booming technology. More and more applications are being developed in the field of banking, security, document storage, smart contracts, etc. This article proposes an exploratory research of blockchain...
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In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing inf...
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Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the res...
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ISBN:
(纸本)9781665480468
Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.
In this paper, the problems related to cooperative control for the multiple mobile robot system (MMRS) is presented. The LIDAR sensor is employed to obtain the 2D map of the indoor space. The formation control and the...
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