Semantics Interaction Control for Constructing Intelligent Ecology of Internet of Things and Critical Component Research

• 作者: Haijun Zhang Yinghui Chen
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: DEEP Hybrid NEURAL Networks DEEP Bidirectional RECURSIVE NEURAL Network Speech Recognition Semantic Control Embedded Internet of Things INTELLIGENT ECOLOGY Construction
• 年，卷（期）: 2018,（11）
• 页码范围: 23-42
• 页数: 20页
• 分类号: TP39

摘要

Intelligent equipment is a kind of device that is characterized by intelligent sensor interconnections, big data processing, new types of displays, human-machine interaction and so on for the new generation of information technology. For this purpose, in this paper, first, we present a type of novel intelligent deep hybrid neural network algorithm based on a deep bidirectional recurrent neural network integrated with a deep backward propagation neural network. It has realized acoustic analysis, speech recognition and natural language understanding for jointly constituting human-machine voice interactions. Second, we design a voice control motherboard using an embedded chip from the ARM series as the core, and the onboard components include ZigBee, RFID, WIFI, GPRS, a RS232 serial port, USB interfaces and so on. Third, we take advantage of algorithms, software and hardware to make machines “understand” human speech and “think” and “comprehend” human intentions to structure critical components for intelligent vehicles, intelligent offices, intelligent service robots, intelligent industries and so on, which furthers the structure of the intelligent ecology of the Internet of Things. At last, the experimental results denote that the study of the semantics interaction controls based on an embedding has a very good effect, fast speed and high accuracy, consequently realizing the intelligent ecology construction of the Internet of Things.