New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

The following is not a conversation with a bank clerk. " Instead, let me introduce you to customized credit loans," "Do you want me to connect you to the screen of using COVID-19 support funds and checking balance?" These are the contents of consultations with AI chatbots at financial institutions. Chatbot, which used to be an additional tool for adding convenience to life, is now at the center of our lives.

We enjoy various forms of leisure every day. Human play culture continues to change according to the development of technology and social environment. In line with the changing society, humans experience socialization, and the market continues to change in response to human new ways of life.

Coffee has become one of the most popular beverages since the 1960s. These days, coffee is more about pleasure, experience, success, lifestyle, high concentration, and social status. Bhumiratana et al. (2014) state that individuals consume coffee with the intention of eliciting positive high-energy emotions, which can help in reducing fatigue, increasing alertness, improving work performance, and promoting a focused mental state, such as feeling motivated, productive, in-control, or clear-minded. Aguirre (2016) and Bhumiratana et al. (2014) also suggest that people drink coffee to stay awake. As coffee has popularized, café has become important as a place for people meetings. However, customers may tend to evaluate coffee as a service product depending on what service encounter they faced. They might perceive the taste and quality of coffee differently by following their mindset and stereotypes about perfect coffee. Customers pay attention to many factors in consuming coffee depending on service encounters. They may pay less for coffee prepared by robots and evaluate coffee prepared by humans as more precious. Therefore, this study proposes a specific factor theoretical framework of customers’ mindset about coffee prepared by human baristas and robot-barista. The study aims to determine how customers evaluate coffee prepared by baristas and robot-barista. Specifically, to which characteristics they pay attention in barista or robot-barista cases. Then, the study examines relationship between expected and perceived coffee characteristics and intentions to revisit café. Finally, the study compares customers' expectations about coffee depending on contact (barista) or untact (robot-barista) service.

With the rise of social media platforms, influencer marketing has become an essential tool for marketers to promote their products and services. Value co-creation behavior of influencers involves collaborating with their followers and brands to create content that provides value to their audience. This approach can help to build stronger relationships with followers and drive engagement and sales for the brands they work with.

Magnetic flux ropes, often observed during intervals of interplanetary coronal mass ejections, have long been recognized to be critical in space weather. In this work, we focus on magnetic flux rope structure but on a much smaller scale, and not necessarily related to interplanetary coronal mass ejections. Using near-Earth solar wind advanced composition explorer (ACE) observations from 1998 to 2016, we identified a total of 309 small-scale magnetic flux ropes (SMFRs). We compared the characteristics of identified SMFR events with those of normal magnetic cloud (MC) events available from the existing literature. First, most of the MCs and SMFRs have similar values of accompanying solar wind speed and proton densities. However, the average magnetic field intensity of SMFRs is weaker (~7.4 nT) than that of MCs (~10.6 nT). Also, the average duration time and expansion speed of SMFRs are ~2.5 hr and 2.6 km/s, respectively, both of which are smaller by a factor of ~10 than those of MCs. In addition, we examined the geoeffectiveness of SMFR events by checking their correlation with magnetic storms and substorms. Based on the criteria Sym-H < -50 nT (for identification of storm occurrence) and AL < -200 nT (for identification of substorm occurrence), we found that for 88 SMFR events (corresponding to 28.5 % of the total SMFR events), substorms occurred after the impact of SMFRs, implying a possible triggering of substorms by SMFRs. In contrast, we found only two SMFRs that triggered storms. We emphasize that, based on a much larger database than used in previous studies, all these previously known features are now firmly confirmed by the current work. Accordingly, the results emphasize the significance of SMFRs from the viewpoint of possible triggering of substorms.

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.