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The study aims to improve the stability and accuracy of fire doors by identifying whether they require maintenance or repair. The fire doors can be classified into three types: the vehicle fire opening type, sliding type and evacuation opening type. The inspection of a fire door is normally carried out by visual inspection. Inspecting a fire door from the outside is inconvenient, costly, time consuming and it is not easy to distinguish between a problematic and an acceptable fire door. An automatic fire door inspection system is proposed to aid this inspection procedure by using an image processing technique. It can also provide important data for generating a maintenance plan. This study proposes an approach that combines a visual retrieval method and a deep learning technique. Visual features for fire door inspection are captured using a digital camera and they are then used as input to a CNN. CNN is a kind of artificial neuron that is used to process data such as images, sounds, etc. The final output from the CNN is passed to a Support Vector Machine (SVM). A dataset for evaluation of the fire door images was created and the performance of the system was analyzed using the dataset. The results show that the proposed approach can detect fire doors automatically with an average precision of 92.6% compared to the manual inspection performed by human inspectors. This means that the fire door detection system can be used to overcome the drawbacks of manual inspection.

This paper presents the use of an improved inner product vector (IPV) method for structural damage detection. Various recent vibration-based damage detection methods employ inner product vector (IPV) based on the similarity of a test-structure’s frequency responses, the damage index and the frequencies affected by the damage. The conventional IPV method is vulnerable to the presence of possible modes that share the same frequency response. The new improved IPV method circumvents the above problems by using the inner product function of each frequency response, provided that the elements of the IPV are normalised to one.

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