2023 |
Wicaksono Hendro; Trat, Martin.; Bashyal Atit; Boroukhian Tina; Felder Mine; Ahrens Mischa; Bender Janek; Groß Sebastian; Steiner Daniel; July Christoph; Dorus Christoph; Zoerner Thorsten Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing Journal Article Forthcoming Robotics and Computer-Integrated Manufacturing, Forthcoming. Abstract | BibTeX | Tags: artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning @article{Wicaksono2023, title = {Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing}, author = {Wicaksono, Hendro; Trat, Martin.; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; Dorus, Christoph; Zoerner, Thorsten}, year = {2023}, date = {2023-12-30}, journal = {Robotics and Computer-Integrated Manufacturing}, abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.}, keywords = {artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning}, pubstate = {forthcoming}, tppubtype = {article} } The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities. |
Aikenov Temirlan; Hidayat, Rahmat; Wicaksono Hendro Power consumption and process cost prediction of customized products using explainable AI Inproceedings Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems., 2023. Abstract | Links | BibTeX | Tags: energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI @inproceedings{Aikenov2023, title = {Power consumption and process cost prediction of customized products using explainable AI}, author = {Aikenov, Temirlan; Hidayat, Rahmat; Wicaksono, Hendro}, url = {https://link.springer.com/chapter/10.1007/978-3-031-38165-2_135}, doi = {https://doi.org/10.1007/978-3-031-38165-2_135}, year = {2023}, date = {2023-08-25}, booktitle = {Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.}, abstract = {Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.}, keywords = {energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI}, pubstate = {published}, tppubtype = {inproceedings} } Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions. |
Beibit Rauan; Fatahi Valilai, Omid; Wicaksono Hendro Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23), pp. 302–308, Association for Computing Machinery, New York, NY, USA, 2023. Abstract | Links | BibTeX | Tags: COVID-19, machine learning, supply chain management @inproceedings{Beibit2023, title = {Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning}, author = {Beibit, Rauan; Fatahi Valilai, Omid; Wicaksono, Hendro}, url = {https://dl.acm.org/doi/10.1145/3587889.3588215}, doi = {10.1145/3587889.3588215}, year = {2023}, date = {2023-06-09}, booktitle = {Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23)}, pages = {302–308}, publisher = {Association for Computing Machinery, New York, NY, USA}, abstract = {The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors.}, keywords = {COVID-19, machine learning, supply chain management}, pubstate = {published}, tppubtype = {inproceedings} } The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors. |
Agung Teguh Wibowo Almais Adi Susilo, Agus Naba Moechammad Sarosa Cahyo Crysdian Imam Tazi Mokhamad Amin Hariyadi Muhammad Aziz Muslim Puspa Miladin Nuraida Safitri Abdul Basid Yunifa Miftachul Arif Mohammad Singgih Purwanto Diyan Parwatiningtyas Hendro Wicaksono Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters Journal Article IEEE Access, 11 , pp. 74590 - 74601, 2023. Abstract | Links | BibTeX | Tags: artificial intelligence, machine learning, PCA @article{Almais2023, title = {Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters}, author = {Agung Teguh Wibowo Almais, Adi Susilo, Agus Naba, Moechammad Sarosa, Cahyo Crysdian, Imam Tazi, Mokhamad Amin Hariyadi, Muhammad Aziz Muslim, Puspa Miladin Nuraida Safitri Abdul Basid, Yunifa Miftachul Arif, Mohammad Singgih Purwanto, Diyan Parwatiningtyas, Hendro Wicaksono}, url = {https://ieeexplore.ieee.org/abstract/document/10123944}, doi = {10.1109/ACCESS.2023.3275852}, year = {2023}, date = {2023-05-12}, journal = {IEEE Access}, volume = {11}, pages = {74590 - 74601}, abstract = {Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $\text{n} < 0$ ), cluster 2 ( $0\le \text{n} < 2$ ), and cluster 3 ( $\text{n}\ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0\le \text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $\text{n}\ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing.}, keywords = {artificial intelligence, machine learning, PCA}, pubstate = {published}, tppubtype = {article} } Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $text{n} < 0$ ), cluster 2 ( $0le text{n} < 2$ ), and cluster 3 ( $text{n}ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0le text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $text{n}ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing. |
2022 |
Wicaksono Hendro; Yuce, Baris; McGlinn Kris; Calli Ozum Smart Cities and Buildings Book Chapter Chapter Smart cities and buildings, pp. 239-263, CRC Press, 1st Edition, 2022, ISBN: 9781003204381. Abstract | Links | BibTeX | Tags: energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability @inbook{Wicaksono2022b, title = {Smart Cities and Buildings}, author = {Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum}, url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781003204381-13/smart-cities-buildings-hendro-wicaksono-baris-yuce-kris-mcglinn-ozum-calli}, isbn = {9781003204381}, year = {2022}, date = {2022-05-01}, pages = {239-263}, publisher = {CRC Press}, edition = {1st Edition}, chapter = {Smart cities and buildings}, abstract = {Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. }, keywords = {energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability}, pubstate = {published}, tppubtype = {inbook} } Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. |
2021 |
Wicaksono Hendro; Boroukhian, Tina; Bashyal Atit A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning Book Chapter Freitag, Michael ; Kotzab, Herbert ; Megow, Nicole (Ed.): pp. 155-181, Springer, 2021, ISBN: 978-3-030-88662-2. Abstract | Links | BibTeX | Tags: artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability @inbook{Wicaksono2021, title = {A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning}, author = {Wicaksono, Hendro; Boroukhian, Tina; Bashyal, Atit }, editor = {Freitag, Michael and Kotzab, Herbert and Megow, Nicole}, doi = {10.1007/978-3-030-88662-2_8}, isbn = {978-3-030-88662-2}, year = {2021}, date = {2021-12-31}, pages = {155-181}, publisher = {Springer}, abstract = {The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.}, keywords = {artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability}, pubstate = {published}, tppubtype = {inbook} } The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets. |
Ahmadi Elham; Fatahi Valilai, Omid; Wicaksono Hendro Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach Inproceedings 2021. BibTeX | Tags: data analytics, machine learning, sentiment analysis, sustainability @inproceedings{Ahmadi2020, title = {Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach}, author = {Ahmadi, Elham; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2021}, date = {2021-12-14}, keywords = {data analytics, machine learning, sentiment analysis, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } |
Wicaksono, Hendro Accelerating Energy Transition to Green Electricity through Artificial Intelligence Presentation 24.08.2021. Abstract | Links | BibTeX | Tags: artificial intelligence, data analytics, energy transition, machine learning @misc{Wicaksono2021c, title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence}, author = {Wicaksono, Hendro }, doi = {10.31219/osf.io/tcrkh}, year = {2021}, date = {2021-08-24}, abstract = {The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform.}, keywords = {artificial intelligence, data analytics, energy transition, machine learning}, pubstate = {published}, tppubtype = {presentation} } The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform. |
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. |
Publications and Talks
2023 |
Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing Journal Article Forthcoming Robotics and Computer-Integrated Manufacturing, Forthcoming. |
Power consumption and process cost prediction of customized products using explainable AI Inproceedings Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems., 2023. |
Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23), pp. 302–308, Association for Computing Machinery, New York, NY, USA, 2023. |
Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters Journal Article IEEE Access, 11 , pp. 74590 - 74601, 2023. |
2022 |
Smart Cities and Buildings Book Chapter Chapter Smart cities and buildings, pp. 239-263, CRC Press, 1st Edition, 2022, ISBN: 9781003204381. |
2021 |
A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning Book Chapter Freitag, Michael ; Kotzab, Herbert ; Megow, Nicole (Ed.): pp. 155-181, Springer, 2021, ISBN: 978-3-030-88662-2. |
Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach Inproceedings 2021. |
Accelerating Energy Transition to Green Electricity through Artificial Intelligence Presentation 24.08.2021. |
2020 |
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. |
2019 |
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. |
Pothole visual detection using machine learning method integrated with internet of thing video streaming platform Conference 2019 International Electronics Symposium (IES) , 2019. |
2012 |
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. |
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. |
Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises Journal Article 2012. |
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. |
2010 |
Knowledge-based intelligent energy management using building automation system Inproceedings 2010 Conference Proceedings IPEC, IEEE, Singapore, Singapore, 2010, ISSN: 1947-1262. |
Ontology Supported Intelligent Energy Management System in Buildings Inproceedings Proceeding IEEE International Conference on Industrial Engineering and Business Management (ICIEBM), IEEE 2010. |
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. |
Wissensbasierte Energieanalyse – Verbesserte Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs Journal Article ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 105 (6), pp. 551-555, 2010. |