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

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 by applying four RNN techniques—long… 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 the calculated frame size is… More >