{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/25b5598491fd5e9c77a4cd7ec154f8d53/isw-bibliothek", "tags" : [ "MachineLearning","isw" ], "intraHash" : "5b5598491fd5e9c77a4cd7ec154f8d53", "interHash" : "2865168d49b2d3379d2ed380378521d5", "label" : "A methodology for evaluating feature selection and clustering methods with project-specific requirements", "user" : "isw-bibliothek", "description" : "", "date" : "2025-05-07 16:32:35", "changeDate" : "2025-05-07 16:32:35", "count" : 2, "pub-type": "article", "journal": "International Journal of Production Research","publisher":"Taylor & Francis", "year": "2024", "url": "http://dx.doi.org/10.1080/00207543.2024.2384597", "author": [ "H. von Linde","O. Riedel" ], "authors": [ {"first" : "H.", "last" : "von Linde"}, {"first" : "O.", "last" : "Riedel"} ], "volume": "63","number": "5","pages": "1692\u20131706","abstract": "This paper describes amethodology for ranking feature selection and clusteringmethods with userspecific preferences and taking data properties into account. For a better understanding of this paper, the developed methodology is referred to as the Two Machine Learning Procedures, Preferences and Properties (2ML3P) methodology. The 2ML3P methodology aims to support users from multiple domains, such as engineers, who have little expertise in machine learning (ML). It is also independent from the disciplinary core competencies of the manufacturer, with a strong focus on employability in small andmid-sized enterprises (SME). The foundationof themethodology toevaluate the combination of the twomachine learning classes is described. It focuses on a range of feature selection and clustering methods, their limitations, and their challenges. The paper covers the concept phase by defining the inputs, such as the specific characteristics ofmachine learning classes or the properties of the production data and the user preferences. With applied methodologies such as the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), the preferences of the user as valid input are integrated. The scientific contribution of this methodology is the approach to include user preferences and specific data properties in the selection process of twoMLmethods. As digitalisation progresses, making data-driven decisions in the domains of production and logistics is a goal for many SMEs. This methodology can support a data-driven decision-aid model by providing a guided method, which requires relatively little ML knowledge on the part of the engineer. It allows the user(s) to select the best suited combination of ML methods for a clustering use case.", "issn" : "1366-588X", "doi" : "10.1080/00207543.2024.2384597", "bibtexKey": "von_Linde_2024" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22a6688b02d28ad57f32e658489309379/isw-bibliothek", "tags" : [ "MachineLearning","grk2198","isw","myown" ], "intraHash" : "2a6688b02d28ad57f32e658489309379", "interHash" : "4fdefd5d5c45d2646ee9e64564f524b9", "label" : "Reinforcement Learning of a Robot Cell Control Logic using a Software-in-the-Loop Simulation as Environment", "user" : "isw-bibliothek", "description" : "", "date" : "2020-06-09 11:14:14", "changeDate" : "2020-06-18 12:23:01", "count" : 2, "pub-type": "inproceedings", "booktitle": "2019 Second International Conference on Artificial Intelligence for Industries (AI4I)", "year": "2019", "url": "https://ieeexplore.ieee.org/document/9027783/", "author": [ "Florian Jaensch","Akos Csiszar","Janik Sarbandi","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Janik", "last" : "Sarbandi"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "79-84","abstract": "This paper introduces a method for automatic robot programming of industrial robots using reinforcement learning on a Software-in-the-loop simulation. The focus of the the paper is on the higher levels of a hierarchical robot programming problem. While the lower levels the skills are stored as domain specific program code, the combination of the skills into a robot control program to solve a specific task is automated. The reinforcement learning learning approach allows the shopfloor workers and technicians just to define the end result of the manufacturing process through a reward function. The programming and process optimization is done within the learning procedure. The Software-in-the-loop simulation with the robot control software makes it possible to to interpret the real program code and generate the exact motion. The exact motion of the robot is needed in order to find not just an optimal but also a collision-free policy.", "doi" : "10.1109/AI4I46381.2019.00027", "bibtexKey": "9027783" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c9324e84efe8aab59d8f55f1b95a8d99/isw-bibliothek", "tags" : [ "DigitalTwin","MachineLearning","grk2198","isw","myown" ], "intraHash" : "c9324e84efe8aab59d8f55f1b95a8d99", "interHash" : "359b8b3bc1b319e37551a5aff6196ff4", "label" : "Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation", "user" : "isw-bibliothek", "description" : "", "date" : "2019-05-15 01:21:27", "changeDate" : "2020-01-20 14:24:16", "count" : 4, "pub-type": "inproceedings", "booktitle": "2018 First International Conference on Artificial Intelligence for Industries (AI4I)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8665712/", "author": [ "Florian Jaensch","Akos Csiszar","Annika Kienzlen","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Annika", "last" : "Kienzlen"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "77-80","abstract": "In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.", "doi" : "10.1109/AI4I.2018.8665712", "bibtexKey": "8665712" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/208e7136aaad4731582ec69d690483ae6/isw-bibliothek", "tags" : [ "DigitalTwin","DigitaleFabrik","MachineLearning","grk2198","isw","myown" ], "intraHash" : "08e7136aaad4731582ec69d690483ae6", "interHash" : "5b083907fa1783d06c168d70a4adf590", "label" : "Digital Twins of Manufacturing Systems as a Base for Machine Learning", "user" : "isw-bibliothek", "description" : "", "date" : "2019-01-17 22:17:56", "changeDate" : "2020-01-20 14:27:38", "count" : 4, "pub-type": "inproceedings", "booktitle": "2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8600844/", "author": [ "Florian Jaensch","Akos Csiszar","Christian Scheifele","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Christian", "last" : "Scheifele"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "1-6","abstract": "In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.", "doi" : "10.1109/M2VIP.2018.8600844", "bibtexKey": "8600844" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287ee03a81e0a792cd35cf42f4dd47359/isw-bibliothek", "tags" : [ "CADCAMCNCChain","ISW","MachineLearning","ReinforcementLearning","ToolPath","xcr","xde","xhr" ], "intraHash" : "87ee03a81e0a792cd35cf42f4dd47359", "interHash" : "c73c7ba4b206589bfe506d040073c60d", "label" : "A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths", "user" : "isw-bibliothek", "description" : "", "date" : "2017-01-19 08:35:34", "changeDate" : "2017-01-19 07:35:34", "count" : 2, "pub-type": "inbook", "booktitle": "Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2016","publisher":"Springer Berlin Heidelberg","address":"Berlin, Heidelberg", "year": "2017", "url": "http://dx.doi.org/10.1007/978-3-662-53806-7_1", "author": [ "Caren Dripke","Sara Höhr","Akos Csiszar","Alexander Verl" ], "authors": [ {"first" : "Caren", "last" : "Dripke"}, {"first" : "Sara", "last" : "Höhr"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Alexander", "last" : "Verl"} ], "editor": [ "Jürgen Beyerer","Oliver Niggemann","Christian Kühnert" ], "editors": [ {"first" : "Jürgen", "last" : "Beyerer"}, {"first" : "Oliver", "last" : "Niggemann"}, {"first" : "Christian", "last" : "Kühnert"} ], "pages": "1--8", "isbn" : "978-3-662-53806-7", "doi" : "10.1007/978-3-662-53806-7_1", "bibtexKey": "Dripke2017" } ] }