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1.
Object detection and graspability analysis for robotic bin-picking application in intralogistics
Primož Bencak, Darko Hercog, Tone Lerher, 2023, published scientific conference contribution

Abstract: Robotics has been gaining attention in intralogistics applications in recent years. Automation of intralogistics processes aims to cope with the rising trends of workforce deficiency, aging, and increasing demands that came with the rise of E-commerce. Many improvements aim at bin-picking applications since order-picking requires most contributions while adding little to the products' value. Robotic bin-pickers are showing promising results; however, they are still subject to many limitations. First, the vision system must correctly determine the object's location and orientation. Second, a correct robotic gripper must be chosen. Lastly, appropriate grasping points that lead to successful picking must be selected. In this paper, we explore the influencing parameters of object detection using a 3D vision system. Second, we analyze an actual bin-picking application to determine the most appropriate selection of the robotic gripper. Based on the experiments, we provide the guidelines for selecting the most appropriate robotic bin-picking configuration.
Keywords: intralogistics, robotic bin-picking, detection analysis, graspability analysis
Published in DKUM: 25.07.2023; Views: 330; Downloads: 20
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2.
K-vertex: a novel model for the cardinality constraints enforcement in graph databases : doctoral dissertation
Martina Šestak, 2022, doctoral dissertation

Abstract: The increasing number of network-shaped domains calls for the use of graph database technology, where there are continuous efforts to develop mechanisms to address domain challenges. Relationships as 'first-class citizens' in graph databases can play an important role in studying the structural and behavioural characteristics of the domain. In this dissertation, we focus on studying the cardinality constraints mechanism, which also exploits the edges of the underlying property graph. The results of our literature review indicate an obvious research gap when it comes to concepts and approaches for specifying and representing complex cardinality constraints for graph databases validated in practice. To address this gap, we present a novel and comprehensive approach called the k-vertex cardinality constraints model for enforcing higher-order cardinality constraints rules on edges, which capture domain-related business rules of varying complexity. In our formal k-vertex cardinality constraint concept definition, we go beyond simple patterns formed between two nodes and employ more complex structures such as hypernodes, which consist of nodes connected by edges. We formally introduce the concept of k-vertex cardinality constraints and their properties as well as the property graph-based model used for their representation. Our k-vertex model includes the k-vertex cardinality constraint specification by following a pre-defined syntax followed by a visual representation through a property graph-based data model and a set of algorithms for the implementation of basic operations relevant for working with k-vertex cardinality constraints. In the practical part of the dissertation, we evaluate the applicability of the k-vertex model on use cases by carrying two separate case studies where we present how the model can be implemented on fraud detection and data classification use cases. We build a set of relevant k-vertex cardinality constraints based on real data and explain how each step of our approach is to be done. The results obtained from the case studies prove that the k-vertex model is entirely suitable to represent complex business rules as cardinality constraints and can be used to enforce these cardinality constraints in real-world business scenarios. Next, we analyze the performance efficiency of our model on inserting new edges into graph databases with varying number of edges and outgoing node degree and compare it against the case when there is no cardinality constraints checking. The results of the statistical analysis confirm a stable performance of the k-vertex model on varying datasets when compared against a case with no cardinality constraints checking. The k-vertex model shows no significant performance effect on property graphs with varying complexity and it is able to serve as a cardinality constraints enforcement mechanism without large effects on the database performance.
Keywords: Graph database, K-vertex cardinality constraint, Cardinality, Business rule, Property graph data model, Property graph schema, Hypernode, Performance analysis, Fraud detection, Data classification
Published in DKUM: 10.08.2022; Views: 690; Downloads: 77
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3.
Text analysis with sequence matching
Marko Ferme, Milan Ojsteršek, 2011, original scientific article

Abstract: This article describes some common problems faced in natural language processing. The main problem consist of a user given sentence, which has to be matched against an existing knowledge base, consisting of semantically described words or phrases. Some main problems in this process are outlined and the most common solutions used in natural language processing are overviewed. A sequence matching algorithm is introduced as an alternative solution and its advantages over the existing approaches are explained. The algorithm is explained in detail where the longest subsequences discovery algorithm is explained first. Then the major components of the similarity measure are defined and the computation of concurrence and dispersion measure is presented. Results of the algorithms performance on a test set are then shown and different implementations of algorithm usage are discussed. The work is concluded with some ideas for the future and some examples where our approach can be practically used.
Keywords: sequence matching, subsequence analysis, similarity measure, fuzzy string search, phrase detection
Published in DKUM: 01.06.2012; Views: 2131; Downloads: 59
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4.
Progressive method for color selective edge detection
Peter Rulić, Iztok Kramberger, Zdravko Kačič, 2007, original scientific article

Abstract: Edge detection plays an important role in image analysis systems. We present acolor selective edge detection technique, which consists of two image processing steps. The first step represents pixel-based color detection and the second progressive block-oriented edge detection. The combination of these two steps defines a selective edge detection technique, which enables fast and simple processing of those images captured using arbitrary cameras incomplex scenes with nonstandard illumination. The proposed method was implemented for the detecting of skin color objects and tested on real scene images.
Keywords: image analysis system, image processing, color detection, skin color
Published in DKUM: 31.05.2012; Views: 2054; Downloads: 135
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5.
Crack identification in gear tooth root using adaptive analysis
Aleš Belšak, Jože Flašker, 2007, original scientific article

Abstract: Problems concerning gear unit operation can result from various typical damages and faults. A crack in the tooth root, which often leads to failure in gear unit operation, is the most undesirable damage caused to gear units. This article deals with fault analyses of gear units with real damages. A laboratory test plant has been prepared. It has been possible to identify certain damages by monitoring vibrations. In concern to a fatigue crack in the tooth root significant changes in tooth stiffness are more expressed. When other faults are present, other dynamic parameters prevail. Signal analysis has been performed also in concern to a non-stationary signal, using the adaptive transformation to signal analysis.
Keywords: machine elements, gears, fatigue crack, fault detection, vibrations, adaptive signal analysis, engineering diagnostics
Published in DKUM: 31.05.2012; Views: 2147; Downloads: 78
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