71 research outputs found

    Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

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    An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

    A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

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    Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review

    Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding

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    Elektrisches Widerstandsschweißen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch WĂ€rme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue QualitĂ€tsüberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgeführt werden. Es besteht ein großes industrielles und wirtschaftliches Potenzial, datengetriebene AnsĂ€tze für die QualitĂ€tsüberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die QualitĂ€tskontrolle zu verbessern. Datengetriebene AnsĂ€tze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfügbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene AnsĂ€tze ermöglichen eine zerstörungsfreie, umfassende und prĂ€zise QualitĂ€tsüberwachung, wenn eine bestimmte Menge prĂ€ziser Daten verfügbar ist. Dies kann eine umfassende Online-QualitĂ€tsüberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden Ă€ußerst schwierig ist. Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher AnsĂ€tze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren für erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von DomĂ€nenwissen für die prĂ€diktive QualitĂ€tsüberwachung mit inhomogenen Datenstrukturen, usw. Bestehende ML-Lösungen für ERW liefern keine systematische Vorgehensweise für die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und DatenverstĂ€ndnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen für das Prozess- und DatenverstĂ€ndnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen für das Prozess- und DatenverstĂ€ndnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen für maschinelles Lernen ermöglichen. Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework fĂŒr maschinelles Lernen für ERW vorgeschlagen wird, und demonstriert fünf industrielle AnwendungsfĂ€lle, die das Framework anwenden und validieren. Das Framework ĂŒberprĂŒft die Fragen und DatenspezifitĂ€ten, schlĂ€gt eine simulationsunterstützte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert außerdem die Übertragbarkeit des Frameworks auf einen hochprĂ€zisen Laserprozess. Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert

    #OceanOptimism: Balancing the Narrative About the Future of the Ocean

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    The ocean is facing multiple pressures from human activities, including the effects of climate change. Science has a prominent role in identifying problems and communicating these to society. However, scientists are also increasingly taking an active role in developing solutions, including strategies for adapting to and mitigating climate change, increasing food security, and reducing pollution. Transmitting these solutions to society changes our narrative about the ocean and motivates actions. The United Nations triple initiatives for this decade—the Sustainable Development Goals, the Decade on Ocean Science for Sustainable Development, and the Decade of Ecosystem Restoration—provide the momentum for this change in narrative and focus. Here, we reflect on the search for solutions and the need for better ways of communicating science in a positive way. We synthesize insights from a summer school held during the COVID-19 pandemic and present some examples of successes and failures and the lessons learned from these.#OceanOptimism: Balancing the Narrative About the Future of the OceanpublishedVersio

    A culturicon design model for communication across culture

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    Emoticons are important in Computer-Mediated Communication due to its capability to express emotions/actions without face-to-face meeting. However, existing emoticons are still incompatible and lack some human expressions that limit user’s selection, particularly in terms of culture. Based on the comprehensive literature review conducted, the study regarding emoticons in cultural perspective is limited and there are demand for more cultural-based emoticons to be developed. To solve the issue, this study developed a model named Culturicon Design Model (CDM) by incorporating appropriate cultural dimensions and icon design principles, where Culturicon is the combination of ‘culture’ and ‘icon’. The components of CDM were determined based on previous study’s findings. CDM was then verified through expert review by applying a convergent parallel mixed method that measured the model’s components, flow, and readability, involving 11 experts. Then, CDM was validated by applying an explanatory sequential mixed method involving two phases – validation by designers and validation by end users. Validation by designers measured the components of the model in terms of gain satisfaction, interface satisfaction, task support satisfaction, and emoticon samples’ development, involving five designers. The validation by the end user was performed through focus group discussions, involving eight participants. Thematic analysis was used to analyse focus group’s results. The final version of CDM comprises five cultural dimensions (high power distance, high collectivism, low uncertainty avoidance, moderate masculinity/femininity, and long-term relationships), and eight Human Computer Interaction (HCI) icon design principles (familiar, understandable, attractive, coherent, informative, distinct, memorable, and legible). Focus group’s result showed that the emoticon’s samples represent the cultural elements, fulfilled the HCI icon design principles, and useful in their communication across culture. CDM contributed to the body of knowledge in HCI. It can be a guideline for designers to develop Culturicon in the future, hence providing more emoticon selections from local culture to satisfy end user’s needs

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Annals of Scientific Society for Assembly, Handling and Industrial Robotics

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    This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)
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