building energy management building information modelling change management data mining energy efficiency Energy efficient building energy management energy performance indicator flexibility measurement Genetic algorithm industry 4.0 industry 4.0 maturity assessment Internet of Things knowledge based energy management knowledge management linked data machine learning manufacturing Ontology ontology engineering ontology population product configuration product lifecycle management production planning and control requirement engineering resource efficiency resource efficient manufacturing semantic data integration smart cities virtual engineering
2021 |
Wicaksono Hendro; Boroukhian, Tina; Bashyal Atit A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning Book Chapter Forthcoming Springer, Forthcoming. BibTeX | Tags: energy transition, machine learning, Ontology @inbook{Wicaksono2021, title = {A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning}, author = {Wicaksono, Hendro; Boroukhian, Tina; Bashyal, Atit }, year = {2021}, date = {2021-12-31}, publisher = {Springer}, keywords = {energy transition, machine learning, Ontology}, pubstate = {forthcoming}, tppubtype = {inbook} } |
Wicaksono Hendro; Yuce, Baris; McGlinn Kris Smart Cities and Buildings Book Chapter Forthcoming Taylor and Francis, Forthcoming. BibTeX | Tags: energy efficiency, machine learning, Ontology @inbook{Wicaksono2021b, title = {Smart Cities and Buildings}, author = {Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris}, year = {2021}, date = {2021-12-31}, publisher = {Taylor and Francis}, keywords = {energy efficiency, machine learning, Ontology}, pubstate = {forthcoming}, tppubtype = {inbook} } |
2020 |
Wicaksono, Simon Fritz; Matthias Jaenicke; Jivka Ovtcharova; Hendro Context-sensitive Assistance in Requirements-based Knowledge Management Conference NLPIR 2020: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, ACM, 2020. Abstract | Links | BibTeX | Tags: knowledge management, machine learning, requirement engineering @conference{Wicaksono2020, title = {Context-sensitive Assistance in Requirements-based Knowledge Management}, author = {Simon Fritz; Matthias Jaenicke; Jivka Ovtcharova; Hendro Wicaksono }, url = {https://doi.org/10.1145/3443279.3443306}, doi = {10.1145/3443279.3443306}, year = {2020}, date = {2020-12-17}, booktitle = {NLPIR 2020: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval}, pages = {47–54}, publisher = {ACM}, abstract = {In this paper, a concept of a digital assistance system is presented which, based on computer linguistic methods, supports the user in the tasks of requirement-based knowledge management. The concept is divided into six modules that offer context-sensitive support in the identification, documentation, linking, modification and reuse of requirements and the associated knowledge. Since this concept was developed as part of the BMBF-funded SME Innovative Project DAM4KMU, which is primarily aimed at German SMEs, the concept developed was specially designed for processing German-language texts. The digital assistance system pursues the goal, on the one hand, of increasing the quality of the documentation by supporting the user in the creation of complete formulations. On the other hand, with the help of the most modern language models, possible relationships between the information should be identified and linked to each other in a partially automated manner. In addition, the integration of web crawling technologies should make the knowledge available on the Internet available in a context-sensitive manner, in order to lift possible innovations on the one hand and not to forget possible non-considered boundary conditions on the other. The automatic linking of all information is intended to ensure a continuous exchange of knowledge, which should reduce misunderstandings and non-communicated changes to requirements or goals to a minimum. }, keywords = {knowledge management, machine learning, requirement engineering}, pubstate = {published}, tppubtype = {conference} } In this paper, a concept of a digital assistance system is presented which, based on computer linguistic methods, supports the user in the tasks of requirement-based knowledge management. The concept is divided into six modules that offer context-sensitive support in the identification, documentation, linking, modification and reuse of requirements and the associated knowledge. Since this concept was developed as part of the BMBF-funded SME Innovative Project DAM4KMU, which is primarily aimed at German SMEs, the concept developed was specially designed for processing German-language texts. The digital assistance system pursues the goal, on the one hand, of increasing the quality of the documentation by supporting the user in the creation of complete formulations. On the other hand, with the help of the most modern language models, possible relationships between the information should be identified and linked to each other in a partially automated manner. In addition, the integration of web crawling technologies should make the knowledge available on the Internet available in a context-sensitive manner, in order to lift possible innovations on the one hand and not to forget possible non-considered boundary conditions on the other. The automatic linking of all information is intended to ensure a continuous exchange of knowledge, which should reduce misunderstandings and non-communicated changes to requirements or goals to a minimum. |
2019 |
Rasyid Alfandino; Ulil Albaab, Mochammad Rifki; Falah Muhammad Fajrul Panduman Yohanes Yohanie Fridelin; Yusuf Alviansyah Arman; Basuki Dwi Kurnia; Tjahjono Anang; Budiarti Rizqi Putri Nourma; Sukaridhoto Sritrusta; Yudianto Firman; Wicaksono Hendro ; Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform Inproceedings 2019 International Electronics Symposium (IES), pp. 672-675, IEEE, 2019. Links | BibTeX | Tags: industry 4.0, Internet of Things, machine learning @inproceedings{Rasyid2019, title = {Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform}, author = {Rasyid, Alfandino; Ulil Albaab, Mochammad Rifki; Falah, Muhammad Fajrul ; Panduman, Yohanes Yohanie Fridelin; Yusuf, Alviansyah Arman; Basuki, Dwi Kurnia; Tjahjono, Anang; Budiarti, Rizqi Putri Nourma; Sukaridhoto, Sritrusta; Yudianto, Firman; Wicaksono, Hendro}, url = {https://ieeexplore.ieee.org/document/8901626}, doi = {10.1109/ELECSYM.2019.8901626}, year = {2019}, date = {2019-09-28}, booktitle = { 2019 International Electronics Symposium (IES)}, pages = {672-675}, publisher = {IEEE}, keywords = {industry 4.0, Internet of Things, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Rasyid Alfandino; Ulil Albaab, Mochammad Rifki; Falah Muhammad Fajrul; Panduman Yohanes Yohanie Fridelin; Yusuf Alviansyah Arman; Basuki Dwi Kurnia; Tjahjono Anang; Budiarti Rizqi Putri Nourma; Sukaridhoto Sritrusta; Yudianto Firman; Wicaksono Hendro Pothole visual detection using machine learning method integrated with internet of thing video streaming platform Conference 2019 International Electronics Symposium (IES) , 2019. BibTeX | Tags: machine learning, virtual engineering @conference{Rasyid2019b, title = {Pothole visual detection using machine learning method integrated with internet of thing video streaming platform}, author = {Rasyid, Alfandino; Ulil Albaab, Mochammad Rifki; Falah, Muhammad Fajrul; Panduman, Yohanes Yohanie Fridelin; Yusuf, Alviansyah Arman; Basuki, Dwi Kurnia; Tjahjono, Anang; Budiarti, Rizqi Putri Nourma; Sukaridhoto, Sritrusta; Yudianto, Firman; Wicaksono, Hendro}, year = {2019}, date = {2019-09-28}, booktitle = {2019 International Electronics Symposium (IES) }, keywords = {machine learning, virtual engineering}, pubstate = {published}, tppubtype = {conference} } |
2012 |
Wicaksono, Hendro; Ovtcharova, Jivka Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining Inproceedings Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM), 2012. Abstract | BibTeX | Tags: data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning @inproceedings{Wicaksono2012b, title = {Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining}, author = {Hendro Wicaksono and Jivka Ovtcharova }, year = {2012}, date = {2012-11-02}, booktitle = {Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM)}, abstract = {The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables.}, keywords = {data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables. |
Wicaksono, Hendro; Rogalski, Sven; Ovtcharova, Jivka Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing Inproceedings Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pp. 108-114, INSTICC SciTePress, 2012, ISBN: 978-989-8565-30-3. Abstract | Links | BibTeX | Tags: energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology @inproceedings{Wicaksono2012c, title = {Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing}, author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova}, url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=VdNAwL50fGw=&t=1}, doi = {10.5220/0004141601080114}, isbn = {978-989-8565-30-3}, year = {2012}, date = {2012-10-07}, booktitle = {Proceedings of the International Conference on Knowledge Engineering and Ontology Development}, volume = {1}, pages = {108-114}, publisher = {SciTePress}, organization = {INSTICC}, abstract = {In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology.}, keywords = {energy efficiency, energy management, knowledge acquisition, knowledge capturing, machine learning, manufacturing, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } In recent years ontologies have been used for knowledge representation in different domains, such as energy management and manufacturing. Researchers have developed approaches in applying ontologies for intelligent energy management in households. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. Energy management in a manufacturing company involves different organizational entities and technical processes. This paper proposes an approach to applying ontology for intelligent energy management in discrete manufacturing companies. The ontology provides a formal knowledge representation that is accessible by different human stakeholders as well as machines in the company. This paper also demonstrates the methods used to construct and to process the ontology. |
Wicaksono, Hendro; Rogalski, Sven; Ovtcharova, Jivka Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises Journal Article 2012. Abstract | BibTeX | Tags: data mining, energy efficiency, energy management, knowledge management, machine learning, Ontology @article{Wicaksono2012d, title = {Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises}, author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova}, year = {2012}, date = {2012-09-13}, abstract = {Energy efficiency in accordance with the economization of production costs is an important com-petitive factor in the energy-intensive industry. Energy management is a way to achieve this, but most of the manufacturing companies face problems in implementing it due to the lack of standard-izations in their operation portfolio. Energy related information are managed separately and in an unstructured manner. Managements have low visibility to the usage of energy in the operation due to the knowledge gap between managers and operators. Operators are often not aware whether their activities and decisions create excessive energy usage because of the different knowledge among them. This paper introduces a novel method based on knowledge management approach to address the problem using an ontology knowledge base and semi-automatic knowledge acquisition using data mining technique. In this paper the application of the approach in a small medium sized stain-less steel manufacturer will be presented. }, keywords = {data mining, energy efficiency, energy management, knowledge management, machine learning, Ontology}, pubstate = {published}, tppubtype = {article} } Energy efficiency in accordance with the economization of production costs is an important com-petitive factor in the energy-intensive industry. Energy management is a way to achieve this, but most of the manufacturing companies face problems in implementing it due to the lack of standard-izations in their operation portfolio. Energy related information are managed separately and in an unstructured manner. Managements have low visibility to the usage of energy in the operation due to the knowledge gap between managers and operators. Operators are often not aware whether their activities and decisions create excessive energy usage because of the different knowledge among them. This paper introduces a novel method based on knowledge management approach to address the problem using an ontology knowledge base and semi-automatic knowledge acquisition using data mining technique. In this paper the application of the approach in a small medium sized stain-less steel manufacturer will be presented. |
Wicaksono, Hendro; Aleksandrov, Kiril; Rogalski, Sven An Intelligent System for Improving Energy Efficiency in Building Using Ontology and Building Automation Systems Book Chapter Kongoli, Florian (Ed.): IntechOpen, 2012, ISBN: 978-953-51-0685-2. Links | BibTeX | Tags: building energy management, data mining, energy efficiency, machine learning, Ontology @inbook{Wicaksono2012, title = {An Intelligent System for Improving Energy Efficiency in Building Using Ontology and Building Automation Systems}, author = {Hendro Wicaksono and Kiril Aleksandrov and Sven Rogalski }, editor = {Florian Kongoli}, url = {https://www.intechopen.com/books/automation/an-intelligent-system-for-improving-energy-efficiency-in-building-using-ontology-and-building-automa}, doi = {10.5772/48006}, isbn = {978-953-51-0685-2}, year = {2012}, date = {2012-07-25}, publisher = {IntechOpen}, keywords = {building energy management, data mining, energy efficiency, machine learning, Ontology}, pubstate = {published}, tppubtype = {inbook} } |
2010 |
Wicaksono, Hendro; Rogalski, Sven; Kusnady, Enrico Knowledge-based intelligent energy management using building automation system Inproceedings 2010 Conference Proceedings IPEC, IEEE, Singapore, Singapore, 2010, ISSN: 1947-1262. Abstract | Links | BibTeX | Tags: building automation system, data mining, energy management, knowledge-based system, machine learning, Ontology @inproceedings{Wicaksono2010b, title = {Knowledge-based intelligent energy management using building automation system}, author = {Hendro Wicaksono and Sven Rogalski and Enrico Kusnady}, url = {http://ieeexplore.ieee.org/document/5696994/}, doi = {10.1109/IPECON.2010.5696994}, issn = {1947-1262}, year = {2010}, date = {2010-10-29}, booktitle = {2010 Conference Proceedings IPEC}, publisher = {IEEE}, address = {Singapore, Singapore}, abstract = {The rise of energy needs and energy prices due to shortage of energy resources force companies and households to consume energy more efficiently. In recent years building automation systems are commonly used to allow better comfort for the occupants, and also to improve security in the building. This paper describes an approach for improving energy efficiency in private and business buildings by utilizing building automation systems. The goal of the approach is to provide an intelligent system that can help the occupants to monitor and control their energy consumption and also to notice their energy saving potentials. This paper also introduces method of knowledge-driven energy analysis that offers more intelligent mechanism as improvement of classical data-driven analysis, which is commonly used by existing energy analysis solutions.}, keywords = {building automation system, data mining, energy management, knowledge-based system, machine learning, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } The rise of energy needs and energy prices due to shortage of energy resources force companies and households to consume energy more efficiently. In recent years building automation systems are commonly used to allow better comfort for the occupants, and also to improve security in the building. This paper describes an approach for improving energy efficiency in private and business buildings by utilizing building automation systems. The goal of the approach is to provide an intelligent system that can help the occupants to monitor and control their energy consumption and also to notice their energy saving potentials. This paper also introduces method of knowledge-driven energy analysis that offers more intelligent mechanism as improvement of classical data-driven analysis, which is commonly used by existing energy analysis solutions. |
Wicaksono, Hendro; Rogalski, Sven Ontology Supported Intelligent Energy Management System in Buildings Inproceedings Proceeding IEEE International Conference on Industrial Engineering and Business Management (ICIEBM), IEEE 2010. BibTeX | Tags: building automation system, data mining, machine learning, Ontology @inproceedings{Wicaksono2010c, title = {Ontology Supported Intelligent Energy Management System in Buildings}, author = {Hendro Wicaksono and Sven Rogalski}, year = {2010}, date = {2010-10-13}, booktitle = {Proceeding IEEE International Conference on Industrial Engineering and Business Management (ICIEBM)}, organization = {IEEE}, keywords = {building automation system, data mining, machine learning, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } |
Schubert, Viktor; Wicaksono, Hendro; Rogalski, Sven Knowledge-based Product Configuration through Product Life Cycle Oriented Feedback-Driven Requirements Engineering Inproceedings Proceeding 20th International Conference Flexible Automation and Intelligent Manufacturing (FAIM), San Francisco, USA, 2010. BibTeX | Tags: case-based reasoning, data mining, machine learning, Ontology, product configuration, product lifecycle management @inproceedings{Schubert2010, title = {Knowledge-based Product Configuration through Product Life Cycle Oriented Feedback-Driven Requirements Engineering}, author = {Viktor Schubert and Hendro Wicaksono and Sven Rogalski}, year = {2010}, date = {2010-07-14}, booktitle = {Proceeding 20th International Conference Flexible Automation and Intelligent Manufacturing (FAIM), San Francisco, USA}, keywords = {case-based reasoning, data mining, machine learning, Ontology, product configuration, product lifecycle management}, pubstate = {published}, tppubtype = {inproceedings} } |
Wicaksono, Hendro; Rogalski, Sven Wissensbasierte Energieanalyse – Verbesserte Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs Journal Article ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 105 (6), pp. 551-555, 2010. Abstract | Links | BibTeX | Tags: data mining, Energy efficient building, knowledge management, machine learning @article{Wicaksono2010, title = {Wissensbasierte Energieanalyse – Verbesserte Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs}, author = {Hendro Wicaksono and Sven Rogalski}, url = {http://www.hanser-elibrary.com/doi/10.3139/104.110342}, doi = {10.3139/104.110342}, year = {2010}, date = {2010-06-30}, journal = {ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb}, volume = {105}, number = {6}, pages = {551-555}, abstract = {Vor dem Hintergrund einer wesentlich verbesserten Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs – trotz bestehender Gebäudeautomationssysteme – startete im Mai 2009 das Projekt „KEHL – Kontrollierte-Energie-Haushalts-Lösungen“ im Rahmen des Programms „KMU-innovativ“. Die wesentliche Forschungsherausforderung ist dabei, es dem Nutzer auf Basis einer einzigartigen Raum-, Zeit-, Ereignis-Relation zu ermöglichen, gebäudebezogene Gesamtverbräuche in Gebäuden bis auf die Geräteebene aufzusplitten und energetische Auswertungen in Abhängigkeit zum Nutzungsverhalten durchzuführen. Hierdurch werden nutzungsabhängige Energieverbrauchsanalysen ermöglicht, die eine automatisierte energieeffizientere Ansteuerung der vorhandenen Automationssysteme bewirken. Im nachstehenden Beitrag sollen unter anderem auf bisher erzielte Projektergebnisse eingegangen sowie weiterführende Forschungsansätze herausgestellt werden. Schwerpunkt der Ausführungen bilden insbesondere die KEHL-Wissensbasis und die darauf aufbauenden Energieanalysen.}, keywords = {data mining, Energy efficient building, knowledge management, machine learning}, pubstate = {published}, tppubtype = {article} } Vor dem Hintergrund einer wesentlich verbesserten Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs – trotz bestehender Gebäudeautomationssysteme – startete im Mai 2009 das Projekt „KEHL – Kontrollierte-Energie-Haushalts-Lösungen“ im Rahmen des Programms „KMU-innovativ“. Die wesentliche Forschungsherausforderung ist dabei, es dem Nutzer auf Basis einer einzigartigen Raum-, Zeit-, Ereignis-Relation zu ermöglichen, gebäudebezogene Gesamtverbräuche in Gebäuden bis auf die Geräteebene aufzusplitten und energetische Auswertungen in Abhängigkeit zum Nutzungsverhalten durchzuführen. Hierdurch werden nutzungsabhängige Energieverbrauchsanalysen ermöglicht, die eine automatisierte energieeffizientere Ansteuerung der vorhandenen Automationssysteme bewirken. Im nachstehenden Beitrag sollen unter anderem auf bisher erzielte Projektergebnisse eingegangen sowie weiterführende Forschungsansätze herausgestellt werden. Schwerpunkt der Ausführungen bilden insbesondere die KEHL-Wissensbasis und die darauf aufbauenden Energieanalysen. |