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This paper aims to characterize the acoustic field radiated by the piston transducer and measure a few parameters through the data visualization method.

Using the theoretical model of the ultrasonic transducer, the acoustic field data were acquired by scanning the ultrasound field of the piston transducer. And the visualized graphs of the ultrasonic data were displayed through 3D graphs including slice, iso‐surface and volume rendering, respectively. Furthermore, a few parameters of the transducer including beam width and spread angle were measured using the visualized data.

The visualized graphs of the acoustic field radiated by the piston transducer show that the data visualization method can expose obviously the space distribution of the ultrasound field and describe directly the cylindrical shape. And this method provides the basis of reliable measurement and assess for the ultrasonic transducer.

This paper presents a kind of measured method of the acoustic parameters using the visualized data. The measurement range has limitation.

This method is possible used in Medical ultrasonic.

This paper presents the visualized description of the acoustic field of the piston transducer and a measurement of two acoustic parameters using the visualized data.

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.