Hyun-Sam Shin¹, Jun-Ki Hong^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 235-248, 2023, DOI:10.32604/iasc.2023.033082 - 29 September 2022

Abstract Recently, artificial-intelligence-based automatic customer response system has been widely used instead of customer service representatives. Therefore, it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly. Therefore, we analyzed the performance of the emotion recognition (ER) accuracy as a function of the simulation time using the proposed chunk-based speech ER (CSER) model. The proposed CSER model divides voice signals into 3-s long chunks to efficiently recognize characteristically inherent emotions in the customer’s voice. We evaluated the performance of the ER of voice signal chunks More >

Haipeng Chen¹, Kexiong Liu², Chunyang Ma³, Yu Han⁴, Jian Su^5,*

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 195-204, 2018, DOI:10.32604/cmc.2018.03592

Abstract Recently, object identification with radio frequency identification (RFID) technology is becoming increasingly popular. Identification time is a key performance metric to evaluate the RFID system. The present paper analyzes the deficiencies of the state-of-the-arts algorithms and proposes a novel sub-frame-based algorithm with adaptive frame breaking policy to lower the tag identification time for EPC global C1 Gen2 UHF RFID standard. Through the observation of slot statistics in a sub-frame, the reader estimates the tag quantity and efficiently calculates an optimal frame size to fit the unread tags. Only when the expected average identification time in More >