In our everyday life, we come across various media that facilitate communication. Photography is one such medium used for visual communication. Although it is easy for human beings to look at a picture and describe it, it is often a hard task for a computer to generate a caption automatically if a photograph is fed to it. The recent development in deep learning and neural network has made this problem easier to work on especially if the relevant datasets are provided. This paper attempts to comprehensively summarize and present a unique perspective of the prevalent systems developed to address this problem of image captioning.
Abstract. The current scenario of the industries is that the major losses in efficiency of a machine are due to vibration and friction. To reduce the detrimental effects of vibration we need to decrease the frequency and amplitude of vibration or completely eliminate vibration. To do that one must quantify vibration that already occurs in machinery and structural components. Which is the aim of this paper. The intention of the paper is to obtain and characterize the vibration signature of equipment used in a company and composite material. We have designed a setup to vibrational properties composites, vibrational signature of industrial equipment .To study vibration properties, micro-electrical mechanical systems (MEMS) based accelerometers are used to measure acceleration of the material about the datum when displaced. The data obtained is processed in MATLAB using ARDUINO relayed to computer to convert the data to frequency spectra using Fast-Fourier transforms (FFT). We ultimately compared the vibrational properties of two lathes used at a metal fabrication plant operating at different Conditions and quantified the vibration results using Fast Fourier Transforms (FFT) algorithm. The vibration signatures of a composite is studied along with which various properties like Damping Coefficient, Free Vibration, GFRP, Natural Frequency applications are studied.
Abstract. The current scenario of the industries is that the major losses in efficiency of a machine are due to vibration and friction. To reduce the detrimental effects of vibration we need to decrease the frequency and amplitude of vibration or completely eliminate vibration. To do that one must quantify vibration that already occurs in machinery and structural components. Which is the aim of this paper. The intention of the paper is to obtain and characterize the vibration signature of equipment used in a company and composite material. We have designed a setup to vibrational properties composites, vibrational signature of industrial equipment .To study vibration properties, micro-electrical mechanical systems (MEMS) based accelerometers are used to measure acceleration of the material about the datum when displaced. The data obtained is processed in MATLAB using ARDUINO relayed to computer to convert the data to frequency spectra using Fast-Fourier transforms (FFT). We ultimately compared the vibrational properties of two lathes used at a metal fabrication plant operating at different Conditions and quantified the vibration results using Fast Fourier Transforms (FFT) algorithm. The vibration signatures of a composite is studied along with which various properties like Damping Coefficient, Free Vibration, GFRP, Natural Frequency applications are studied.
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