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|Title:||STUDY OF HAND WRITING ACTIVITY UNDER LOW CYCLE VIBRATION USING ARTIFICIAL TIVTELLIGENT TECHNIQUE|
|Keywords:||MECHANICAL INDUSTRIAL ENGINEERING;HAND WRITING ACTIVITY;LOW CYCLE VIBRATION;ARTIFICIAL INTELLIGENT TECHNIQUE|
|Abstract:||Trains provide better comfort than any other mode of transport. Businessmen, students and workers effectively utilize their travelling time by performing sedentary activities like reading, writing, working on laptop etc. A number of recent studies have shown that passengers' performances were affected by train vibrations. Vibration measurements and questionnaire surveys have been conducted simultaneously in order to understand the effect of vibration on performing writing activity. The effect of vibration on the ability to write is presented. Recent studies have revealed that both reading and writing activities are affected moderately even at low levels of vibration (<0.40 m/s2 r.m.s.). Passengers used different types of postures to attenuate the vibration and thus performed their activities. Laboratory studies showed that, in general, the maximum difficulty occurred between 2.5 Hz and 5.0 Hz for performing both writing and as well as reading activities. The level of difficulties in performing activities was dependent on the level of vibration amplitudes but however the reading was generally rated as less difficult than writing. The difficulty was found to be higher when the work was performed on the lap than on the table. The main objective of the study is to determine the distortion in writing letters under different vibrating and posture condition in the laboratory developed as a mockup of a railway vehicle. The effect of vibration on writing activity was investigated by asking the subjects to overwrite on five different alphabets of capital and small letters of English language. The distortion in writing was found out by using image processing method. The results showed significant differences between the tasks and postural conditions. Furthermore, the distortions in writing letters were compared with distortion predicted by the use back propagation neural network tool, which is a tool of ANN.|
|Research Supervisor/ Guide:||Saran, V. H.|
Harsha, S. P.
|Appears in Collections:||MASTERS' DISSERTATIONS (MIED)|
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