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High strength steel has high strength and poor plasticity. When it is used in parts with large material thickness, complex shape and many working procedures, higher requirements are put forward for mold design. This paper introduced the forming characteristics, layout scheme, overall structure design of multi-station progressive die and design key points of important parts of a high-strength steel plate car mounting part. The progressive die including blanking, drawing, local forming and bending and other 13 processes was designed. The preforming and secondary forming were adopted to form the reinforcing ribs. The double-row with center carrier layout pattern was adopted to reduce the bending side force, and made the material utilization higher. The sub-die and insert structure’s using was convenient for die machining and debugging. The practice has proved that the die structure is reasonable and reliable, which can guarantee the quality of the products. It can be used as a reference for the design of similar products.

Emotion prediction has been a core task in affective computing, which aims at finding the thorough human mental states by analyzing people's activities. In this paper, we focus on predicting emotions in the public online blogs from different people, by extracting as many reasonable emotions for each blog sentence as possible. Concretely, we consider three different perspectives for analyzing the multiple emotions in a sentence: (i) predict sentence emotions by examining the emotion related topics in a global sense; (ii) predict the sentence emotions from the context‐sensitive word emotions; and (iii) predict sentence emotions by considering the emotional significance in the local bag of words. We build different probabilistic models from each perspective, to separately generate the sentence emotion probabilities. We then integrate these probabilistic models to jointly predict the emotion probabilities. Because the component models are based on different emotional assumptions with distinct features, the integrated predictions should predict emotions from more general perspectives and therefore yield better results. In the experiment, we employ different evaluation criteria to compare the multi‐emotion predictions from the single and integrated models. Compared to the results in the baseline model, our bi‐integrated models achieve 8.69% higher Micro F1 and 7.78% higher Macro F1 scores, on average. Moreover, our tri‐integrated model acquires 10.00% higher Micro F1 and 9.19% higher Macro F1 scores than the baseline results, which proves our assumption, and suggests interesting features in the different emotion perspectives. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.