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Based on the construal level theory, the purpose of this study is to prove the effect of interaction between construal level and visual crowding on consumers' buying intention. The study tries to explain the reasons behind the different buying intention toward visual crowding among consumers with different construal level.

This study was conducted through two situational simulation experiments. The main data analysis methods are ANOVA and bootstrap analysis.

(1) the matching of construal level and visual crowding has a significant effect on consumers’ buying intention. (2) Perceptual fluency mediates the interaction between the construal level and visual crowding on buying intention.

This study measures consumers' buying intention through situational experiments but does not measure consumers' buying behavior through real scenarios.

According to the study conclusions, consumers prefer visually crowded packaging that matches their construal level. Enterprises should consider the impact of the construal level on the effect of packaging stimulation.

This study enriches the theory related to construal level and highlights the mediating role of perceptual fluency. The addition of perceptual fluency explains the mechanism by which visual crowding affects consumers' buying intention. This extends the research on the antecedents and effects of perceptual fluency.

This study innovatively introduces visual crowding into packaging and matches visual crowding to construal level, explaining why different consumers buy different visually crowded packaging.

The purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.

The statistic features and DCT coefficient can be absolutely used in arc sound signal processing; and these features of arc sound signal can accurately react the modification of arc height during the GTAW welding process.

This paper tries to make a foundation work to achieve monitoring arc length through arc sound signal. A new way to remove high frequency noise of arc sound signal is produced. It proposes some effective statistic features and a new way of frequency analysis to build the prediction model.