On-line quality control in automated welding operations is an important factor contributing to higher productivity, lower costs and greater reliability of the welded components. The sound signal of plasma arc welding was acquired at high speed and investigated with the aid of computers. It is shown that the amplitude of the sound signal greatly varies with the variation of the status of the weld pool. The analysis of frequency domain indicated that the area below the curve of the low frequency band of the power density spectra, which may reflect the oscillation properties of the weld pool, is largest on the transition phase. The high frequency dominant peaks of the sound signal, which may relate to the plasma jet pulsation, is highest on the keyhole-formed phase. The method for detecting the behaviors of the weld pool keyhole was developed. The experimental results indicate that the acoustic emission is a usable, practical information source in the penetration quality detection of plasma arc welding.KEY WORDS: plasma arc welding; power spectrum; sound signature; process control. Fig. 1. Schematic drawing of the experimental system.

With the development of vehicle network technology, transportation companies have generated GPS-based driver behavior data for operating vehicles, providing potential for research on driver behavior and road safety risk perception. Research on road safety risk perception at the present stage generally involves fuzziness and subjectivity, the degree of risk is difficult to quantify, and big data-based road safety risk perception has not yet been used practically. To address this problem, this paper proposes a method of quantifying the degree of road safety risk based on the hierarchical analysis method and fuzzy comprehensive evaluation method under the drive of abnormal driving behavior data, constructs a scientific and reasonable urban road safety risk identification comprehensive evaluation model, and explores important factors affecting road safety. The research results have a certain reference effect for traffic management departments to accurately locate dangerous sections in the urban road network.

Traffic collisions are one of the leading causes of traffic congestion. In the case of urban intersections, traffic accidents can even result in widespread traffic paralysis. To solve this problem, we developed an autoencoder-based model for identifying intersection entrance accidents by analyzing the characteristics of traffic volume. The model uses the standard deviation of the intersection entrance lanes’ traffic volume as an input parameter and identifies intersection entrance accidents by comparing predicted data to actual measured data. In this paper, the detection rate and average detection time are chosen to evaluate the effectiveness of algorithms. The detection rate of the autoencoder model reaches 94.33%, 95.47%, and 81.64% during the morning peak, evening peak, and daylight off-peak periods, respectively. Compared to the support vector machine and the random forest, autoencoder has better performance. It is evident that the research presented in this paper can effectively enhance the detection effect and has a shorter detection time of intersection entrance accidents.

In view of the spatial and temporal imbalance of residents’ travel demands and challenges of optimal bus capacity allocation, in this paper the grand station express bus scheduling mode is introduced in the direction of heavy passenger flow during peak hours. Coordinated scheduling combining whole-journey and grand station express buses is adopted, and the station correlation calculation model is used to determine the optimal stops of the grand station express bus. Thus, a two-way bus scheduling optimization model for peak passenger flow is established with the goal of minimizing the total cost of passenger travel and enterprise operation. Finally, the nonlinear inertia weight dynamic cuckoo search algorithm is selected for the model’s solution, and the established scheduling optimization model is solved by combining basic data such as the study line’s bus Integrated Circuit (IC) card data. The effectiveness of the model is verified through a comparative study and evaluation of the solution.