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This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R2 = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score () (10 ≤ Strong, 5 ≤ Ambiguous < 10, and Weak < 5), according to their influence. As a result, the models considering the input variables of the Strong+Ambiguous group (R2 = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R2 = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p < 0.05 or 0.01). There were no statistically significant results between the Strong group and the Strong + Ambiguous group (R2: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group (: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption.

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Pulse diagnosis, which is one of methods of diagnosis, is an important factor in oriental medicine. However, a problem in diagnosis with the pulse is that there is no objective standard. Therefore, the practitioners pass on the skill and students learn about pulse diagnosis as a method that depends on speech. In this study, the electronic pulse wave reproduction apparatus, which is an objective and accurate means for measuring the pulse, was developed. The previous model reproduced the pulse wave in one part of the point, but it was made by using three pairs of voice coil motors (VCM) in order to similarly express the three parts of the pulse: Cun, Guan and Chi. To evaluate this system, the output of the pulse wave was confirmed in order to reproduce the pulse wave with these settings. Consequently, the targets for slow pulse and rapid pulse have a 7 ms standard deviation, which is within the error tolerance. A voltage value of H(1), utilized to verify vacuous pulse and the replete pulse, has a standard deviation range of 4.7-5.4 mV. This system, which is similar to a person's pulse diagnosis, can be used to educate others in pulse diagnosis both quantitatively and scientifically.

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