A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

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A Data-Driven Approach for Executing the CG Method on Reconfigurable High-Performance Systems

Architecture of Computing Systems – ARCS 2013 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-36424-2_15 ◽

2013 ◽

pp. 171-182 ◽

Cited By ~ 1

Author(s):

Fabian Nowak ◽

Ingo Besenfelder ◽

Wolfgang Karl ◽

Mareike Schmidtobreick ◽

Vincent Heuveline

Keyword(s):

High Performance ◽

Data Driven ◽

Data Driven Approach ◽

High Performance Systems

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Data-Driven Model-Free Control of Torque-Applying System for a Mechanically Closed-Loop Test Rig Using Neural Networks

Strojniški vestnik – Journal of Mechanical Engineering ◽

10.5545/sv-jme.2019.6499 ◽

2020 ◽

Vol 66 (5) ◽

pp. 337-347

Author(s):

Aida Parvaresh ◽

Mohsen Mardani

Keyword(s):

Experimental Data ◽

Neural Networks ◽

High Performance ◽

Data Driven ◽

Test Rig ◽

Operational Conditions ◽

Model Free ◽

Actuation System ◽

Data Driven Approach ◽

Learning Factors

This paper presents a data-driven approach that utilizes the gathered experimental data to model and control a test rig constructed for the high-powered gearboxes. For simulating a wide variety of operational conditions, the test rig should be capable of providing different speeds and torques; this is possible using a torque-applying system. For this purpose, Electro-Hydraulic Actuators (EHAs) are used. Since applying accurate torque is a crucial demand as it affects the performance evaluation of the gearboxes, precise modelling of the actuation system along with a high-performance controller are required. In order to eliminate the need to solve complex nonlinear equations of EHA that originate from friction, varying properties of flow and similar, a data-driven system based on neural networks is used for modelling. In this manner, the model of the system, which captures the whole dynamic of the system, can be obtained without any simplifying assumptions. The model is validated with experimental data, and the learning factors are set to zero to reduce the high computational costs. After that, another network of neurons is used as a controller. The performance of the proposed controller under normal conditions and in the presence of disturbances are investigated. The results show a good tracking of this controller for various reference inputs in different conditions with acceptable characteristics. Additionally, the obtained results are compared with a conventional proportional-integral-derivative (PID) controller results, and the superior features of the proposed scheme is concluded.

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Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2019.0841 ◽

2020 ◽

Vol 476 (2243) ◽

pp. 20190841

Author(s):

Héctor Andrade-Loarca ◽

Gitta Kutyniok ◽

Ozan Öktem

Keyword(s):

Edge Detection ◽

High Performance ◽

Hybrid Methods ◽

Tomographic Reconstruction ◽

Data Driven ◽

Model Based ◽

Edge Classification ◽

Wide Range ◽

Data Driven Approach ◽

Sparse Approximations

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

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cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large, bacterial whole genome sequencing datasets

BMC Bioinformatics ◽

10.1186/s12859-021-03981-4 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 1

Author(s):

Ryan D. Crawford ◽

Evan S. Snitkin

Keyword(s):

Phylogenetic Analysis ◽

Software Package ◽

Phylogenetic Inference ◽

R Package ◽

Core Gene ◽

Data Driven ◽

Marker Genes ◽

Data Driven Approach ◽

Rapid Generation ◽

User Friendly

Abstract Background The quantity of genomic data is expanding at an increasing rate. Tools for phylogenetic analysis which scale to the quantity of available data are required. To address this need, we present cognac, a user-friendly software package to rapidly generate concatenated gene alignments for phylogenetic analysis. Results We illustrate that cognac is able to rapidly identify phylogenetic marker genes using a data driven approach and efficiently generate concatenated gene alignments for very large genomic datasets. To benchmark our tool, we generated core gene alignments for eight unique genera of bacteria, including a dataset of over 11,000 genomes from the genus Escherichia producing an alignment with 1353 genes, which was constructed in less than 17 h. Conclusions We demonstrate that cognac presents an efficient method for generating concatenated gene alignments for phylogenetic analysis. We have released cognac as an R package (https://github.com/rdcrawford/cognac) with customizable parameters for adaptation to diverse applications.

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