2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)最新文献

{"title":"“Smarterized” urban project process with living lab approach: Exploration through a case study","authors":"Jonathan Lacroix, L. Dupont, C. Guidat","doi":"10.1109/ICE.2017.8279939","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279939","url":null,"abstract":"This paper explore the relation between urban project practices in peri-urban context and innovative ways to develop solutions. Urban project process imposes linear and sequenced engineering framework and regulates stakeholder's links by a regulatory actor's polarization. However, it does not generate innovative solutions linking local and global stakes. Here, starting by a theorical field of urban project and innovation studying, we improve first observations from a qualitative research approach with 51 various regional urban project stakeholders within 4 Focus Groups. This exploratory study provides basis about urban project process and Living Lab interactions and gives a better understanding of the professional context. Based on these findings, we summarize that specific innovation management gives means to allow renew practices in urban project process. Living Lab implementation can generate an open-innovated and collaborative environment centred on “Use”. It gives the design sense to improve solution appropriation by finals users. Also, it allows large stakeholders mobilization to produce, capitalize and distribute “Uses”, as language and knowledge.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning in professional firms a multiple case study from South Africa","authors":"Ludwig Martin","doi":"10.1109/ICE.2017.8279868","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279868","url":null,"abstract":"Developing knowledge and skills among employees is a key factor in developing and sustaining businesses – this is particularly true for professional services firms. This paper reports upon some results of a study conducted among Quantity Surveying firms in South Africa. The aim of the study was to link strategic management to mechanisms for learning and the development of knowledge within such firms. Due to the wide scope of the study, this paper reports upon on part only. The objective of the research was to identify, understand, and describe key components of Management's strategic influence on organization's culture and the development of knowledge within professional quantity surveying firms. The focus was on learning environments rather than a precise measurement of organizational cultures. A multiple case study approach was used. A mix of methods was deployed aiming to gain understanding of the subject under investigation. Evidence for positive top-down support given to employees to develop their knowledge and skills exists, this support is generally congruent with apparent organizational cultures. The mechanism for such support given to employees differs. No clear pattern could be established. Enhancements of learning contexts through Management is possible. This might lead to improved routines to foster learning.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123110358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SmartLife smart clothing gamification to promote energy-related behaviours among adolescents","authors":"Ruben Costa, Pedro Oliveira, António Grilo, Ayla Schwarz, G. Cardon, A. DeSmet, Josué Ferri, Jorge Doménech, Andrew Pomazanskyi","doi":"10.1109/ICE.2017.8280058","DOIUrl":"https://doi.org/10.1109/ICE.2017.8280058","url":null,"abstract":"Inactivity and high sedentary behavior among adolescents are main societal problems. Unhealthy lifestyles place a large burden on society and promoting healthy lifestyles is thus key for health, wellness and economic prosperity. These non-communicable diseases and unhealthy lifestyles furthermore occur more often among lower socio-economic groups, which indicates a need for healthy lifestyle promotion programs to help reduce health inequalities and improve social inclusion. The SmartLife project aims to create a mobile game that requires lower body movement, and is personalized by physiological feedback measured by smart textiles. Personalization via smart textiles can present a game challenge achievable for the current fitness level of the player and can adjust this based on activity levels during game play. This approach can improve current exergames to achieve a higher level of intensity in physical activity, needed to create a health impact, and can do so considering what is achievable for the person and hence reduce drop-out and injury risks.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training in concurrent design the interplay of theory, practice, reflection and infrastructure","authors":"K. A. Strand, T. Hjeltnes","doi":"10.1109/ICE.2017.8279880","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279880","url":null,"abstract":"Concurrent Design is a rather complicated method, which on the one side require theoretical knowledge of how projects best should be carried out, while it on the other side requires practical training and experience in order to utilize the method in a good way. In this paper we present the main findings from a study where we followed two implementations of a Master's degree course of 15 ECTS within Concurrent Design, that includes both theoretical and practical training. The overall objective for the study was to understand how training within Concurrent Design should take place in the best possible way. Our findings suggest there should be an interplay between theoretical training, practical training, collective reflection among those involved, and utilization of an infrastructure which is also adapted to training purposes. The main contribution presented in this paper is the TPRI-Model for improved training in Concurrent Design where the interplay of Theory, Practice, Reflection and Infrastructure are discussed.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124479289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative product's supply chain: How to model it","authors":"Brunelle Marche, Vincent Boly, L. Morel-Guimaraes, J. Ortt","doi":"10.1109/ICE.2017.8279887","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279887","url":null,"abstract":"The emergence of innovative products impacts the ecosystem of the company and more precisely its supply chain. Therefore, anticipating the reorganization of the latter is an important challenge for many innovative companies to ensure the success of their product. From a systematic review of the literature and case studies, this article highlights the descriptive elements of a supply chain. A methodology is developed leading to a representation model for any supply chain. This model is confronted with several case studies to verify its pertinence before being confronted in real time with a case study. The model will be implemented and validated according to the results of this study. Subsequently, a methodology will be developed to help innovative companies anticipate the reorganization of the supply chain supporting their innovation.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115974331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sustainability based optimization model for starting solutions in toolkits for mass customization","authors":"Stephan Hankammer, A. Weber, L. Canetta, Sultan Kaygin Sel, M. Hora","doi":"10.1109/ICE.2017.8279914","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279914","url":null,"abstract":"This article proposes a sustainability based optimization model for starting solutions in toolkits for mass customization. It reviews existing optimization models to analyze and display sustainability in a configurator context and develops an updated and more sophisticated model. The proposed linear programming algorithm describes the customizable product and calculates the sustainability-maximizing configuration depending on the customer's triple bottom line preferences (financial, environmental and social). The proposed model is useful for companies aiming to implement sustainable mass customization to provide customers with a recommendation that corresponds to their specific demands. Moreover, this article helps mass customization firms to accomplish parts of their corporate social responsibility goals by means of a configurator. Our optimization model considers all three sustainability dimensions and gives each a specific “dimensional weight” that corresponds to the preferences of the customer. For academia, this article serves as a basic framework that analyzes general conditions and advantages for applying sustainable mass customization. A step-by-step application explains how to guide the customer through the configuration process by analyzing how the sustainability based optimization model for starting solutions in toolkits for mass customization can be applied within the television manufacturing industry.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116456808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MOOCs as facilitator: Online learning and women in STEM","authors":"Minji Lee, Kathaleen Starr-Mitchell, Lara Nunes, M. Black, Thomas Schmidt","doi":"10.1109/ICE.2017.8279924","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279924","url":null,"abstract":"The scarcity of female students in computer science programs is one of the most troubling challenges facing the discipline today. Enrollment of women in computer science undergraduate courses has fallen by over 50% from its high point in the 1980s, and is believed to be due, in part, to unwelcoming classroom environments. We postulate that the virtual classroom offered by Massively Open Online Courses (“MOOC”s) may provide a more comfortable learning space for many female students. This paper relates the experience of three female upper-level computer science majors when encountering a MOOC classroom for the first time.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131959291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic enrichment of product data supported by machine learning techniques","authors":"R. Costa, Paulo Figueiras, R. Jardim-Gonçalves, Jose Ramos-Filho, C. Lima","doi":"10.1109/ICE.2017.8280056","DOIUrl":"https://doi.org/10.1109/ICE.2017.8280056","url":null,"abstract":"The process of transforming big data into understandable information is the key of sustainable innovation within an Industry 4.0 factory. Machine learning techniques and cyber-physical systems are closely related to realize a new thinking of production management and factory transformation. Textual data collected in machinery logs or product documentation, does not exhibit a rich structure which can be easily understandable by both humans and machines. Therefore, data in an unstructured format needs to be enriched and transformed into a representation schema that exhibits a higher degree of structure, before it can be used and shared. The paper, introduces a novel conceptual framework to create knowledge representations from unstructured data sources, based on enriched Semantic Vectors, using a classical vector space model extended with ontological support. Hence, this research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (i.e., semantic associations) modelled by domain ontologies with the addition of information presented in documents, addresses the challenges concerning data exchange and its understanding within Industry 4.0 scenarios, when supported by semantic technologies. The proposed approach is validated with industrial examples of product data used in the building and construction domain (e.g., technical specifications concerning climate control, electric power and lighting products) showing its benefits in a real-world use case.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guiding manufacturing companies towards digitalization a methodology for supporting manufacturing companies in defining their digitalization roadmap","authors":"Anna De Carolis, M. Macchi, Elisa Negri, S. Terzi","doi":"10.1109/ICE.2017.8279925","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279925","url":null,"abstract":"Within the era of Industry 4.0, digital technologies are seen as the main drivers for manufacturing industry transformation. If on one hand manufacturing companies have to be able to “ride” this wave of transformation in order to remain competitive, on the other hand, before investing in digital technologies, they have to understand what their current situation is and what their needs are with respect to both digital technologies and organizational processes in different functions. Indeed, the success of the transformation process mainly depends on the company ability to be ready to apply the technological change that some of these digital technologies envision. From these considerations, after having figured out their current readiness level for starting the digital transformation fostered by the Industry 4.0, it is possible to state that the next step manufacturing companies have to undertake is to define their transformation roadmap. With the aim to guide them towards this transformation process, a maturity model, called DREAMY (Digital REadiness Assessment MaturitY model) and based on the inspiring principles of the CMMI (Capability Maturity Model Integration) framework, has been developed and utilized. The objectives of this model are twofold. Firstly, it allows the assessment of the current digital readiness of manufacturing companies and the identification of their strengths and weaknesses with respect to implemented technologies and organizational processes. Secondly, it enables the identification of a set of opportunities offered to companies by the digital transformation, considering their strengths and aiming to overcome their weaknesses. Through the application of this methodology into case studies, it has been possible to reach two main results. On one hand, the analyzed manufacturing companies have been aware of their digital readiness level, of their strengths and weaknesses and of the main opportunities they can exploit from the digitalization process starting from their current situation. On the other hand, empirical evidences were gathered on the current level of manufacturing companies' digital readiness and on the possible common traits among the identified opportunities.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Educational data mining perspectives within university big data environment","authors":"Kamelia Stefanova, D. Kabakchieva","doi":"10.1109/ICE.2017.8279898","DOIUrl":"https://doi.org/10.1109/ICE.2017.8279898","url":null,"abstract":"All organizations are working nowadays in a very dynamic and strongly competitive environment. In order to survive and remain competitive, they need to take timely, adequate and informed decisions that are based not only on intuition and past experience. The main challenges for data analysis are related with the specific characteristics of “big data” and the availability of suitable analytical tools for knowledge extraction that would support the processes of taking strategic management decisions. While “big data” are already widely available and used in business, there are only rare cases of utilizing “big data” in the educational sector. The main purpose of this paper is to focus on the challenges related to the analytical processing of “big data” generated and stored at higher education institutions. The paper discusses the unique opportunities that Big Data analysis could give for the educational sector development and the improvements that could scale from a single school, to governmental directions and satisfaction of the labor market. However, big data analytics confronts universities with great challenges as well, related to finding appropriate methods and tools for extracting knowledge and patterns from extremely rich and complex data sets, and integrating the insights into a coherent vision for strategic management decisions.","PeriodicalId":421648,"journal":{"name":"2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129483986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}