Preventive maintenance (PM) is a predetermined task that constitutes any maintenance actions performed before the quantity or quality of product equipment deteriorates. PM is primarily aimed at protecting assets, improving system reliability, and decreasing system downtime. Recently, the implementation of PM is becoming more challenging due to the increase in complexity of manufacturing systems. A case study on the effect of different parameters from a maintenance perspective on industrial shop floor performance is presented. The parameters include number of technicians, number of operations in each machine before maintenance actions, and volume of parts ordered by customers. This present research demonstrates the application of WITNESS simulation software to develop shop floor simulation model and the use of analysis of variance (ANOVA) to measure the significance of such parameters on performance.

Shop floor performance management is a method to ensure the effective utilization of people, processes, and equipment. Changes in the shop floor might have a positive or negative effect on production performance. Therefore, optimal shop floor operation is required to enhance shop floor performance and to ensure the long-term efficiency of the production process. This work presents a case study of a semiconductor industry. The punching department is modeled to investigate the effect of changes in the shop floor on production performance through discrete event simulation. The effects on the throughput rate, machine utilization, and labor utilization are studied by adjusting the volume of parts, number of operators, and flow pattern of parts in a series of models. Simulation results are tested and analyzed by using analysis of variance (ANOVA). The best model under changes in the shop floor is identified during the exploration of alternative scenarios.

The viability of energy storage systems is evaluated from technical, cost and emission reduction perspective in this paper. With the rising penetration rate of renewable energy sources, ESS (energy storage systems) are widely researched on the ability to overcome the mismatch of generation and load profiles.

Under the existing commercial framework of electricity in Malaysia, commercial and industrial cus-tomers are required to pay for the peak power demand charge every month. Usually, the peak demand charge can contribute up to 30% to their electricity bills due to the use of open-cycle gas power plants that deliver expensive electricity to the customers.

In recent high power LED applications, metal core PCB (MCPCB) has replaced FR4 PCB in order to achieve greater heat dissipation ability. However, the low thermal conductivity of dielectric and other complicated structures in MCPCB has not supported to maintain junction temperature in safe level when LED is operated at higher power.

A large amount of heat trapped inside Light Emitting Diode (LED) is the consequence of large thermal resistance between the heat source and the heat sink.

Emotional factor plays an important role in communication. In the field of psychology, it is known that memory and emotions are closely related to each other. In this paper, we present the significance of emotional factors to associative memory in communication and apply it on human–robot interaction problems.

A hybrid intelligent model comprising a modified fuzzy min–max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is presented. The hybrid FMM-CT model is explained.