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International Conference on Instrumentation Control and Automation (ICA)

作者： Muhammad Farhan Fathurrahman Arne Catteeuw Sidharta Gautama Angel Lopez Aguirre Endra Joelianto Herman Y. Sutarto Department Of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia FlandersMake@UGent–Corelab ISyE Lommel Belgium Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.

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designing business models for augmented reality 28

Designing business models for augmented reality

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28th International Conference for the International Association of Management of Technology: Managing Technology for Inclusive and Sustainable Growth, IAMOT 2019

作者： Röltgen, Daniel Wortmann, Fabio Grote, Eva-Maria Dumitrescu, Roman Fraunhofer Institute for Mechatronic Systems Design Product Engineering Germany

ISBN: (纸本)9789388237543

Almost all sectors of the manufacturing industry are affected by a shift in which the traditional product portfolio is being replaced by highly integrated product service systems. This shift–also referred to as "servitization in manufacturing"–is accelerated by innovation potentials that arise from the progress of digitally enabled technologies. A technology with a considerable potential for the digital refinement of the product service business is Augmented Reality (AR). It enables real-world environments to be enriched with additional virtual content. For example, repair instructions can be visualized as a 3D animation superimposed on a physical machine, enabling service technicians to carry out complex tasks more time and cost efficiently. However, the integration of AR into the product service portfolio requires adjustments to the business model: (1) Companies have to identify promising use cases for AR that create value for the customer, thereby defining the value proposition of the business model. (2) The identified use cases need to be translated into products and services, integrated into the existing product service portfolio. (3) Promising business models have to be designed, taking into account the technology-specific challenges posed by AR (e.g. ensuring user acceptance, finding revenue models to cover the investment costs for AR devices). Despite the large variety of potential AR applications, business models successfully exploiting AR are still the exception, indicating that the development of sustainable business models is a significant challenge. With this in mind, in this paper, we present an approach for designing business models for AR. The approach consists of a procedure model providing step-by-step instructions on how to develop business models for AR and additional tools (e.g. catalogue with AR use cases, business model cards), that help to tackle the technology-specific challenges caused by AR. It combines a methodical procedure with technolo

关键词： Repair

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Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning

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Applied Sciences (Switzerland) 2025年第10期15卷 5373-5373页

作者： Tzotzis, Anastasios Nedelcu, Dumitru Mazurchevici, Simona-Nicoleta Kyratsis, Panagiotis Department of Product and Systems Design Engineering University of Western Macedonia Kila Kozani50100 Greece Department of Manufacturing Engineering "Gheorghe Asachi" Technical University Iasi700050 Romania

This study presents an experimental and computational investigation into the machinability of additively manufactured (AM) fiber-reinforced PETG during external CNC turning. A series of machining trials were conducted under dry conditions, with cutting speed (Vc), feed (f), and depth-of-cut (ap) as the primary input parameters. The corresponding surface roughness (Ra) and tool-tip temperature (T) were recorded as key output responses. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to model the process behavior, utilizing a 3–3–3 architecture with triangular membership functions. The resulting models demonstrated high predictive accuracy across training, testing, and validation datasets. Experimental results revealed that elevated feed rates and depth-of-cut significantly increase surface roughness, while combinations of high cutting speed and feed contribute to elevated tool temperatures. Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both Ra and T simultaneously. The Pareto-optimal front indicated that optimal performance could be achieved within the range of 100–200 m/min for Vc, 0.054–0.059 mm/rev for f, and 0.512–0.516 mm for ap. The outcomes of this research provide valuable insights into the machinability of reinforced polymer-based AM components and establish a robust framework for predictive modeling and process optimization. © 2025 by the authors.

关键词： Genetic algorithms

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Energy efficient motion design and task scheduling for an autonomous vehicle 22

Energy efficient motion design and task scheduling for an au...

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22nd International Conference on engineering design, ICED 2019

作者： Xidias, Elias Azariadis, Philip Department of Product and Systems Design Engineering University of the Aegean Greece

This paper describes an approach for designing an energy efficient motion and task scheduling for an autonomous vehicle which is moving in complicated environments in industrial sector or in large warehouses. The vehicle is requested to serve a number of workstations while moving safely and efficiently in the environment. In the proposed approach, the overall problem is formulated as a constraint optimization problem by using the Bump-Surface concept. Then, a Pareto-based multi-objective optimization strategy is adopted, and a modified genetic algorithm is developed to determine the Pareto optimum solution. The efficiency of the developed method is investigated and discussed through simulated experiments. © 2019 design Society. All rights reserved.

关键词： Multiobjective optimization

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Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

arXiv

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arXiv 2021年

作者： Singhal, Trisha Liu, Junhua Blessing, Lucienne T.M. Lim, Kwan Hui SUTD-MIT International Design Centre Singapore University of Technology and Design Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore

The scientific world is changing at a rapid pace, with new technology being developed and new trends being set at an increasing frequency. This paper presents a framework for conducting scientific analyses of academic publications, which is crucial to monitor research trends and identify potential innovations. This framework adopts and combines various techniques of Natural Language Processing, such as word embedding and topic modelling. Word embedding is used to capture semantic meanings of domain-specific words. We propose two novel scientific publication embedding, i.e., PUB-G and PUB-W, which are capable of learning semantic meanings of general as well as domain-specific words in various research fields. Thereafter, topic modelling is used to identify clusters of research topics within these larger research fields. We curated a publication dataset consisting of two conferences and two journals from 1995 to 2020 from two research domains. Experimental results show that our PUB-G and PUB-W embeddings are superior in comparison to other baseline embeddings by a margin of ∼0.18-1.03 based on topic coherence. © 2021, CC BY.

关键词： Natural language processing systems

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Classification of vessel activity in streaming data 20

Classification of vessel activity in streaming data

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14th ACM International Conference on Distributed and Event-Based systems, DEBS 2020

作者： Kontopoulos, Ioannis Chatzikokolakis, Konstantinos Tserpes, Konstantinos Zissis, Dimitris Department of Informatics and Telematics Harokopio University Athens Greece MarineTraffic Athens Greece MarineTraffic Athens Greece Department of Product and Systems Design Engineering University of the Aegean Syros Greece

ISBN: (纸本)9781450380287

In this paper we motivate the need for real-time vessel behaviour classification and describe in detail our event-based classification approach, as implemented in our real-world industry strong maritime event detection service at ***. A novel approach is presented for the classification of vessel activity from real-time data streams. The proposed solution splits vessel trajectories into multiple overlapping segments and distinguishes the ones in which a vessel is engaged in trawling or longlining operation (e.g. fishing activity) from other segments that a vessel is simply underway from its departure towards its destination. We evaluate the effectiveness of our tool on real-world data, demonstrating that it can practically achieve high accuracy results. We present our results and findings intended for both researchers and practitioners in the field of intelligent ship tracking and surveillance. © 2020 ACM.

关键词： Machine learning

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Assessing the weathering-induced degradation on the structural integrity of high-density polyethylene

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Materials Today: Proceedings 2023年 93卷 844-849页

作者： Nikitas Lourmpas Konstantinos Bailas Paraskevas Papanikos Demetris F. Lekkas Emmanuel Ch. Kyriakakis Eleni K. Efthimiadou Nikolaos D. Alexopoulos Research Unit of Advanced Materials Department of Financial Engineering School of Engineering University of the Aegean 41 Kountouriοtou str. 82132 Chios Greece Department of Product and Systems Design Engineering School of Engineering University of the Aegean Konstantinoupoleos 1 Syros 84100 Greece Waste Management Laboratory Department of Environmental Studies University of the Aegean Mytilene 81100 Greece Kyriakakis E. & Co. O.E. “Viosar” Athens Aigaleo 12244 Greece Inorganic Chemistry Laboratory Department of Chemistry National and Kapodistrian University of Athens Panepistimiopolis Zografou 157 71 Greece

Marine plastic pollution is a growing environmental concern, with plastic debris in the ocean having negative impacts on marine life and human health. One of the major challenges in addressing this problem is the slow degradation rate of plastics in the marine environment. In this study, the degradation of high-density polyethylene (HDPE), being extremely common in marine debris, is investigated when exposed to either (a) marine or (b) atmospheric environmental exposure. The weathering effects for this type of plastic were examined through ultraviolet spectrophotometer and tensile investigations. A chain-breaking activity in the microstructure of the weathered HDPE specimens was revealed. During the investigated eight (8) months of atmospheric and sea weathering, an approximate 10 % and 6 % decrease in ultimate tensile strength σ UTS was noticed for the two different environments, respectively. Higher decrease was noticed for the respective elastic modulus E (Young’s modulus) that was approximate 23 % and 21 %, respectively. Based on the available experimental test results, a linear decrease rate equation of all the investigated mechanical properties was proposed, to give valuable information regarding the residual tensile mechanical properties of HDPE after weathering exposure.

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Education innovation through material innovation in primary education: The ‘Grow-it-yourself’ workshop 21

Education innovation through material innovation in primary ...

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21st International Conference on engineering and product design Education, E and PDE 2019

作者： Brosens, Lore Emmanouil, Marina Ghent University Department of Industrial Systems Engineering and Product Design Belgium

ISBN: (纸本)9781912254057

In recent years more STEM (Science, Technology, engineering and Mathematics) topics have been incorporated in mainstream public education. Although the benefits of STEM instruction are broadly recognised in secondary school curricula, STEM topics in primary education are rather limited, leaving a gap in manipulative skills building and in preparation processes for the next school level. This paper reflects on the outcomes of a design workshop attended by 12 primary school students (9 to 12 years old) in Belgium. Mycelium, a fungi-based natural material now used in innovative sustainable applications, served as a means to introduce early learners engineering basics through self-made learning tools. Students grew their own 3-D structures to build a ‘Grow-It-Yourself’ biodegradable playground using mycelium as a primary source. The paper stems from an in-progress research that investigates the opportunities of how mycelium as a material innovation can be used as a medium to create innovation in primary education through a learning-by-design approach. Reflections on the workshop’s instructional guidelines are included along with an extension of the call for support for primary school teachers delivering STEM topics in their classes. © 2019 Institution of engineering designers, The design Society. All rights reserved.

关键词： Mycelium

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Pure Data-Driven Machine Learning Challenges for p FMEA: A Case Study

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IFAC-PapersOnLine 2024年第19期58卷 658-663页

作者： Mahdi Mokhtarzadeh Jorge Rodríguez-Echeverría Zafer Zeren Johan Van Noten Sidharta Gautama Department of Industrial Systems Engineering and Product Design Ghent University 9052 Ghent Belgium FlandersMake@UGent - corelab ISyE Lommel Belgium CodesignS Flanders Make Oude Diestersebaan 133 Lommel 3920 Belgium ESPOL Polytechnic University Escuela Superior Politécnica del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación Campus Gustavo Galindo Km 30.5 Vía Perimetral P.O. Box 09-01-5863 EC090112 Guayaquil Ecuador

Manufacturing processes are susceptible to quality defects, resulting in overall equipment effectiveness reduction. Proactive and reactive methods, such as process failure mode and effects analysis, and root cause analysis, have been developed to eliminate potential causes of failure modes. In this study, data from an assembly case is evaluated using supervised machine learning methods to analyze the challenges of purely data-driven failure mode detection. Assembly step execution times, as indicators, and end-of-the-line quality checklists, as the failure modes, are used to gain insights into failure mode detection. Challenges for data-driven methods are discussed and possible future research streams are proposed.

关键词： FMEA RCA Failure analysis Explainable AI Hybrid intelligence systems

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Optimization of the carbon nanotubes reinforcement in cement-based materials

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Procedia Structural Integrity 2022年 37卷 485-491页

作者： Anastopoulos G. Stylianos Givannaki Th. Faidra Metaxa S. Zoi Papanikos Paraskevas Alexopoulos D. Nikolaos Department of Financial and Management Engineering University of the Aegean 41 Kountouriotou Str. 82100 Chios Greece BETA CAE Systems SA Kato Scholari Thessaloniki GR-57500 Epanomi Greece Department of Chemistry International Hellenic University St. Luke 65404 Kavala Greece Department of Product and Systems Design Engineering University of the Aegean Konstantinoupoleos 1 Ermoupoli 84100 Syros Greece Department of Financial and Management Engineering University of the Aegean Kountouriotou Str. 82100 Chios Greece

During the last decades, composites had been successfully strengthened at the micrometer scale using different types of reinforcements, e.g., carbon or even glass fibers. Over the past years, the development of carbon nanotubes and graphene enabled the reinforcement of cement-based materials at the nanoscale. The produced nanocomposites have increased the mechanical properties as compared to non-reinforced materials and especially on the “effective” properties such as modulus of elasticity and Poisson’s ratio. In several cases and for compatibility issues, it is of imperative importance to “tailor” a nano-reinforced composite with specific material properties, such as the modulus of elasticity and the Poisson’s ratio. To this end, a methodology to identify the optimal material synthesis needs to be effectively addressed. The present article presents an optimization method to quantify the appropriate volume fraction of graphene nanostructures to design a cement-based nanocomposite with a target value of the effective modulus. The Interior Point Optimization (IPOpt) algorithm is used for the optimization iterations applied to the homogenization properties calculated by ANSA® pre-processor. A comparison was made between three different optimization cases, having different maximum number of iterations and a different objective value. The calculated effective modulus and volume fraction were correlated with the target experimental values.

关键词： optimization nanocomposites multi-step method homogenization

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