K. Sahari

... superconducting electric motor Chin Kat Meng, Nasri A. Hamid*, Mohd. Yusri Abd. Rahman ... Thefab-ricated coil was directly mounted to the two-leg of the stator and cooled by liquid nitrogen. In addition, the ro-tor was installed to the stator with the support of the ro-tor housing. ...

ABSTRACT This paper focuses on modelling and simulation of building dynamic thermal comfort control for non-linear HVAC system. Thermal comfort in general refers to temperature and also humidity. However in reality, temperature or humidity is just one of the factors affecting the thermal comfort but not the main measures. Besides, as HVAC control system has the characteristic of time delay, large inertia, and highly nonlinear behaviour, it is difficult to determine the thermal comfort sensation accurately if we use traditional Fanger's PMV index. Hence, Artificial Neural Network (ANN) has been introduced due to its ability to approximate any nonlinear mapping. Using ANN to train, we can get the input-output mapping of HVAC control system or in other word; we can propose a practical approach to identify thermal comfort of a building. Simulations were carried out to validate and verify the proposed method. Results show that the proposed ANN method can track down the desired thermal sensation for a specified condition space.

The primary challenge in SCM pedagogy is the learners' interaction with the dynamic nature of supply chain transactions. Once achieved, it is also required to evaluate learners' learning experience based on their performance. In this paper, a combination of outcome-based education (OBE) and simulation-based education is proposed focusing on beer game theory. The analysis is based on 336 runs of

In this study, a heuristic model for robotics integrated production (RIP) is developed for determining economic production quantity (EPQ) based on triple risk-return characteristics of maintenance, safety, and training. Total risk-return index (TRRI) is then proposed to compare the optimal production quantity of a traditional MCM with robotics integrated counterpart. TRRI is introduced as an index independent of the production parameters that is theoretically based on two sub-functions known as the ideal risk function (IRiF) and ideal return function (IReF). EPQ is then determined by simply calculating the absolute value of the difference between these two sub-functions. The findings of a real case study in plastic injection molding industry reveal that, depending on the mass production volume and product cycle times, production mode should be switched between the traditional MCM (T-MCM) and robotics integrated system to optimize the risk-return characteristics of the ideal function. Robotics integrated production model is also proved to be a more economical and reliable alternative to the T-MCM for all product categories in this study except for those with a weekly production volume of less than 8 K units and a respective cycle time of more than 75 s as compared to the other two product families.