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This paper presents an experimental and finite‐element investigation of ballistic limit of thin single and layered aluminium target plates. Blunt‐, ogive‐ and hemispherical‐nosed steel projectiles of 19 mm diameter were impacted on single and layered aluminium target plates of thicknesses 0.5, 0.71, 1.0, 1.5, 2.0, 2.5 and 3 mm with the help of a pressure gun to obtain the ballistic limit in each case. The ballistic limit of target plate was found to be considerably affected by the projectile nose shape. Thin monolithic target plates as well as layered in‐contact plates offered lowest ballistic resistance against the impact of ogive‐nosed projectiles. Thicker monolithic plates on the other hand, offered lowest resistance against the impact of blunt‐nosed projectiles. The ballistic resistance of the layered targets decreased with increase in the number of layers for constant overall target thickness. Axi‐symmetric numerical simulations were performed with ABAQUS/Explicit to compare the numerical predictions with experiments. 3D numerical simulations were also performed for single plate of 1.0 mm thickness and two layered plate of 0.5 mm thickness impacted by blunt‐, ogive‐ and hemispherical‐nosed projectiles. Good agreement was found between the numerical simulations and experiments. 3D numerical simulations accurately predicted the failure mode of target plates.

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident's habits.