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An IEEE 802.15.4g sun compliant MR-OFDM RF CMOS transceiver for smart grid and CES
Seungsik Lee,Yongho Seo,Byounghak Kim,Sangsung Choi,Changwan Kim IEEE 2013 IEEE TRANSACTIONS ON CONSUMER ELECTRONICS - Vol.59 No.3
<P>This paper proposes an IEEE 802.15.4g SUN OFDM-based 920 MHz RF CMOS transceiver. It can be adopted in energy saving intelligent green homes, which are related to Smart Grid, as well as universal remote controller, building automation, which is related to CEs (Consumer Electronics), and other areas. With the proposed SUN OFDM-based RF transceiver, wireless connectivity among CE devices or among electric metering systems can considerably save electronic energy and make our lives more comfortable. The proposed RF transceiver consists of a RF front-end with an on-chip RF switch, a Tx BBA (Baseband Analog), a Rx BBA, and a PLL. The proposed RF transceiver is implemented in 0.18-μm CMOS technology and consumes 37 mA in Tx mode and 38 mA in Rx mode from a 1.8 V supply voltage. With the fabricated RF transceiver chip, two successful public demonstrations have been carried out, which show the possibility of its use in commercial products.</P>
Kim Seungsik,Gu Nami,Moon Jeongin,Kim Keunwook,Hwang Yeongeun,Lee Kyeongjun 한국통계학회 2023 Communications for statistical applications and me Vol.30 No.5
This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Re-sults showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.