Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in machine learning, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analyst to design and develop energy-aware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs

Grey-box identification for photovoltaic power systems via particle-swarm algorithm

Amongst renewable generators, photovoltaics (PV) are becoming more popular as the appropriate low cost solution to meet increasing energy demands. However, the integration of renewable energy sources to the electricity grid possesses many challenges. The intermittency of these non-conventional sources often requires accurate forecast, planning and optimal management. Many attempts have been made to tackle these challenges; nonetheless, existing methods fail to accurately capture the underlying characteristics of the system. There exists scope to improve present PV yield forecasting models and methods. This paper explores the use of apriori knowledge of PV systems to build clear box models and identify uncertain parameters via heuristic algorithms. The model is further enhanced by incorporating black box models to account for unmodeled uncertainties in a novel grey-box forecasting and modeling of PV systems

Modelling powder-binder segregation in powder injection moulding

Powder injection moulding (PIM) is a shape forming technique for advance ceramic or metal that allows low cost and complex shape manufacturing. The segregation between powder and binder is a common occurrence during PIM which leads to the inhomogeneity in the green bodies. A multiphase flow numerical model has been developed based on Eulerian approach to simulate this phenomenon in the injection stage of silicon nitride-based ceramics. A viscosity model based on experimental data of the feedstock is employed in the numerical model. Simulated results from the numerical model have been compared with experimental results. A powder distribution analysis is compared with density distribution test of the green bodies with similar process parameters and flow trends is compared experimental short shots

Modelling powder-binder segregation in powder injection moulding

Powder injection moulding (PIM) is a shape forming technique for advance ceramic or metal that allows low cost and complex shape manufacturing. The segregation between powder and binder is a common occurrence during PIM which leads to the inhomogeneity in the green bodies. A multiphase flow numerical model has been developed based on Eulerian approach to simulate this phenomenon in the injection stage of silicon nitride-based ceramics. A viscosity model based on experimental data of the feedstock is employed in the numerical model. Simulated results from the numerical model have been compared with experimental results. A powder distribution analysis is compared with density distribution test of the green bodies with similar process parameters and flow trends is compared experimental short shots

Evaluation of Student and Staff Perceptions on L&T Models Across Multiple Disciplines

Moving towards Education 4.0, there has been a gradual shift in learning and teaching (L&T) practices worldwide towards active and deep learning (Gardiner, 2015). With technological advancements, different models of learning and teaching utilising digital mediums have evolved, alongside with frameworks to support transitions into enhanced blended learning (Adekola, Dale, & Gardiner, 2017). It was proposed that the students’ learning needs and expectations must be considered in the L&T pedagogy. In Ithaca S+R and the Univer¬sity System of Maryland, parallel comparisons of traditional versus blended courses were conducted (Griffiths, Chingos, Mulhern, & Spies, 2014). In this study, students on the blended courses performed slightly better or as well as those on the traditional courses but enjoyed the course less. At the University of Glasgow Singapore, L&T with different modes of blended instruction was explored. Four courses in Computing Science, Nursing, Mechatronics and Civil Engineering, which were hosted on different learning management systems, FutureLearn, Moodle and xSiTe, were considered. Across these courses, varying lesson plans and proportion of digital versus Face-to-face (F2F) interactions were provided. Lesson plans ranged from supplementary learning with videos to active and blended learning. Two surveys were developed to evaluate the staffs’ and students’ experiences. These included MCQs with a Likert-scale, as well as open ended questions. In this study, quantitative data was imported into Excel for visualisation, while qualitative data was subjected to categorisation and analysis (Braun & Clarke, 2006). Results were collated from at least fifty respondents in each course. The evaluation study for the students was developed on the following areas: (1)Accessibility; (2)Acceptance Levels; (3)Learner’s Gain; (4)Learner’s Experience; (5)Learner’s Perception; (6)Viewing Duration; (7)Repeated Viewing; (8)Useful to Learning; (9)Higher Level Learning; and (10)Acceptance levels on proportion of Videos versus F2F interactions. Similar questions were posed to lecturers. Some of the key findings are as follows: (i) All four lecturers believe that the videos helped to raise the level of classroom discussion and channelled F2F consultation time to enhance the L&T gain for students. (ii) Most learners used a laptop for video viewing. This is closely followed by the smartphone, especially for Nursing. (iii) More than 93% of the learners believe that videos are helpful in their learning. (iv) Concept reinforcement was ranked to be most important approach for successful learning outcomes. Students also appreciate foundational materials and content to evoke active learning and critical thinking. (v) Over 78% of the students felt that they had to repeat the viewing of videos to grasp the concepts. (vi) Across all disciplines, more than 88% of the students felt that videos are useful to learning. Above 79% felt that they are learning at a higher level. (vii) Above 81% of the students are comfortable to engage in blended learning and felt that the optimal proportion of F2F consultation versus video time would be between 40% to 60%. In conclusion, it is evident that students are generally comfortable to engage in blended learning, if a good balance of digital and F2F interaction is provided. Students enjoy learning at their own pace and time. Many of the students felt that the digital content enabled them to review their learning and reinforce their understanding. Improvement in summative assessment scores is also demonstrated, where blended learning is offered to students. This project has provided the necessary guidance needed to develop successful courses for active and blended learning and demonstrates L&T examples with different pedagogical approaches. The results will be studied for future course development and lesson planning across all joint SIT-Glasgow degree programmes

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

Following the first three industrial revolutions, Industry 4.0 (I4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0

Path length optimized search trees with logarithmic performance

SIGLEDEGerman

Performance of Active Learners at the University of Glasgow Singapore: An Empirical Evidence

In this study, we identified active learners in the Mechatronics and Mechanical Design Engineering Programme at the University of Glasgow Singapore and studied their performance from 2012 to 2015 for 3 batches of students. We used a level 3 course, Mechanics of Materials and Structures, that is both at taught at the University of Glasgow, UK and the University of Glasgow Singapore to identify the active learners. In this course, as part of the learning activities, the learners are encouraged to collaborate by posting questions and answering their peers’ questions in the online tool PeerWise (http://peerwise.cs.auckland.ac.nz/). The active learners are then identified through their level of participation in PeerWise and afterwards their performances are studied. The course exam grades, overall level 3 GPAs, and final year project grades of the active learners and their less active peers are compared and evaluated. The results of the study revealed that students with higher levels of activity, as determined from PeerWise, not only scored significantly higher marks on the course exam but also obtained higher overall GPA during their Level 3 of their degree programme. In addition, the active learners performed better for their final year projects as compared with their less active peers

Dynamic performance of IEEE 802.15.4 devices under persistent WiFi traffic

Recent studies have provided coexistence and interaction models between IEEE 802.15.4 and IEEE 802.11 standards. However, the performance of IEEE 802.15.4 devices under WiFi interference are evaluated based on limit parameters i.e. Packet Reception Rate, which does not exhibit the dynamic interactions in the wireless channel. In this paper, we conduct a series of experiments to demonstrate the dynamic interactions between the IEEE 802.15.4 and IEEE 802.11 bgn standards on relevant devices. The performance of four existing Link Quality Estimators (LQEs) of IEEE 802.15.4 nodes under the IEEE 802.11 bgn interference is analyzed. We show that IEEE 802.15.4 transmission failures are largely due to channel access failures rather than corrupted data packets. Based on the analysis, we propose a new LQE - Packet Reception Rate with Clear Channel Assessment - by merging the Clear Channel Assessment count with the Packet Reception Rate. In comparison to existing LQEs, results show that the new estimator distinguishes persistent IEEE 802.11 bgn traffic more robustly