• Applied Chemistry for Engineering
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• v.22 no.2
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• pp.196-202
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• 2011

In this paper, a series of alkyl succinic acid esters for base oil were synthesized by condensation reaction of succinic anhydride and fatty alcohol. The structures of the synthesized esters were confirmed by $^1H-NMR$, FT-IR spectrum and GC analysis. Basic properties of esters such as kinematic viscosity (KV), refractive index (RI), total acid number (TAN) and pour points were measured and lubricating properties such as SRV wear scar diameter (SRV WSD), fraction coefficient (COF) and 4-ball wear (4-ball WSD) were also evaluated. As the results of basic properties, KV, RI and pour point of synthetic esters increased as the carbon chain of the esters increased. Measurement value of total acid number (TAN) was indicated between 0.2~4 mgKOH/g, and that metal working fluids and pressure working oils are acceptable to use as base oil. Also, lubricating properties of the esters showed as follows: 0.391~0.689 mm of SRV WSD, 0.110~0.138 of SRV COF and 0.49~0.55 mm of 4-ball WSD depended on the structure of the esters. In a comparison on the lubrication capacity of the SRV test based on polyester TMPTO, SRV WSD result showed that a better performance caused by the alkyl group. On the other hand, SRV COF test was not influenced of the alkyl group which the capacity of the lubricant was sightly diminished than the comparison material, regardless of the alkyl group.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.