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关键词： 传感器   自动化   智能化   机电  

摘要： 随着科学技术的迅猛发展,传感器技术在机电自动化中越来越多的被运用和创新,并对相关领域做出巨大贡献。文章阐述了传感器的概念和分类,分析了传感器在机械加工、机器人技术、交通监管、汽车制造和轨道交通领域中的运用,明确其在机电自动化控制领域的智能化、多元化、集成化发展。

关键词： 交通规则   未来汽车   高精度地图   智能计算   高精度定位   传感器技术   匝道   车道级导航  

摘要： 未来汽车是什么样的?未来汽车是什么样的?展开想象,智能、自动、安全、舒适,这些字眼都是我们能够直接想到的。是的,借助日益发展的传感器技术与智能计算技术,汽车正在一条全新的道路上快速发展。在不远的将来,自动驾驶车辆将陆续上路,实现更高效、更智能的新型交通模式。自动驾驶车辆的核心要素之一就是高精度定位。借助于精确的位置结合高精度地图,可实现车道级导航,进而更好地服务于车辆变道、匝道行驶,更便捷地适配多种多样的交通规则。

关键词： 单片机   智能化   分布式多点采集   传感器技术   模糊控制  

摘要： 针对无人值守场所室内智能实时监视机柜内部的烟火、温度等情况,采用多传感器的分布式多点数据采集系统,介绍智能火情报警系统的结构和功能设计。系统利用单片机控制模块通过对烟雾、温度以及红外热像检测,在非正常状态下实现声光报警并显示相应的在线状态上传至数据分析处理中心,通过模糊控制和模糊推理对现场状况进一步做出判断和处理。

关键词： 光纤测温传感器   粮情测温   粮食安全   传感器技术  

摘要： 粮食仓储对于我国粮食安全至关重要,近年来数字信号、光电信号技术、精确传感器技术等现代电子应用技术的飞速发展和广泛应用为粮食仓储智能化、信息化、现代化发展明确了方向。传统的粮情检测系统是检查粮仓储粮温度的重要技术手段,一直以来都是粮食仓储建设的重中之重。本文主要介绍了光纤测温传感器技术在粮食仓储中粮情监测方面的应用,光纤测温传感器将有效地反映粮堆内部不同深度带的温度变化规律,能够节省空间、提升测量精度、加强系统可靠性,为未来粮食仓储安全提供新的技术手段,具有广阔的市场应用前景及推广价值。

关键词： 物联网   新工科   传感器技术   教学实践   专业建设   人才培养  

摘要： 物联网技术的飞速发展推动了全球产业布局和商业模式的变革。传感器是构建物联网的基础,而传感器技术是与物联网技术紧密相关的一门新工科专业课程。面对“物联网+”的时代变革,高校新工课专业课程的教学也要顺应时代潮流,以学生发展为中心,以培养学生创新能力驱动课程改革,以学生实际获得感为目标,探索“物联网+”背景下高校新工科专业课教学方法,特别是实现传感器技术课程教学的内涵式发展。

关键词： Python语言   感知技术   射频识别技术   物联网   传感器技术   GPS定位技术   数字信号   信息转化  

摘要： 物联网技术及其原理物联网技术分为感知、传输和控制三个方面。一个典型的物联网应用是通过感知技术获取数据,再传输、存储到物联网服务器上,最后物联网根据这些外界环境的数据做出相应的控制,从而实现与外界环境的“互动”,如图1所示。1.物联网感知技术物联网的感知技术涉及的领域非常广泛,主要包括传感器技术、射频识别技术、GPS定位技术等,所有能够将外界环境或物品的状态信息转化为计算机能处理的数字信号的技术都属于感知技术。

关键词： 物联网技术   无线通讯技术   传感器技术   电力营销管理   数据管理平台  

摘要： 针对电力营销管理信息系统通讯不及时、反应不快捷的问题,提出了物联网通讯的技术方案,将物联网技术、无线通讯技术、传感器技术、数据管理平台有机地结合起来,构建成智能电力营销管理信息系统,使物联网设备(诸如RFID视频识别装置)能够将检测到的底层数据通过通讯协议的接收、分发传递到电力营销管理信息系统平台。本文采用基于J2EE体系的数据管理平台,并且将SSH框架中的B/S模式作为电力营销管理信息系统的数据运行架构,有效地实现物联网数据的发送和接收。试验表明,本文设计的系统数据发送、接收成功率高,系统稳定性较好,误差率较低。

摘要： As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged. Nonetheless, significant obstacles remain before smart home and equivalent technologies reach a sufficiently high Technology Readiness Level (TRL) and thus become mainstream. The challenges that must be overcome are many; the inherent complexity of the smart home, though a constrained environment in many ways, poses many difficulties. Some difficulties are technical—the lack of robust and scalable technologies, for example. Human factors and even sociotechnical issues may drive other problems that arise. Holistic solutions for the smart home will only emerge from transdisciplinary research and *** smart homes demand the availability of reliable, fault-tolerant sensor networks. Detecting sensor failure as early as possible is essential for ensuring trustworthiness and confidence. In [1], a mechanism for sensor failure detection and subsequent sensor isolation based on association rule mining is proposed for event-driven, ambient sensors. Promising results have emerged in the cases of unlabelled datasets and unknown sensor topologies.

关键词： smart factory   internet-of-things   sensors   manufacturing   Industry 4.0  

摘要： The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate;improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory;and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.

关键词： Healthcare services   Patient monitoring   Sensor   Emergency Department ED   Chronic disease   Disaster  

摘要： Background: Scalability challenge in real time healthcare monitoring system relates to several issues. One of the insistent issues is the increasing in the number of patients. Increasing in the patients' number causes long queue and increase the waiting time for the patients in their seeking for healthcare services. Thus, an ethical issue raises as the healthcare providers should provide fast services for all patients. Recent studies have proposed scalable models that are limited to (1) triaging remote patients for the optimal emergency level and (2) prioritizing remote patients with the highest triage level to receive immediate healthcare services. However, these studies have shown limitations, that is, (1) they have not addressed the waiting time for all patients with different triage levels in the same waiting queue;and (2) they have not considered Emergency Department EDs patients. Therefore, considering the remote patients with the treated patients in EDs in one healthcare system is a demand, to efficiently handle all the patients' requests and productively manage the medical resources. Objective: This study aims to reduce the waiting time for the remote patients in telemedicine with considering treated patients in EDs. The study presents a scalable telemedicine model to improve the ability of real time healthcare monitoring system in accommodating the increasing number of patients with chronic heart disease by reducing their waiting time for healthcare services, prioritizing the patients who have the most emergency cases and provide all the patients by fast healthcare services. The proposed model called Triaging and Prioritizing Model "TPM". Method: The proposed model "TPM" considers triaging and prioritizing all patients (remote and EDs patients) as two sequential processes. The TPM was formulated to triage the patients based on hybrid algorithms which combine Evidence-Theory with Fuzzy Cluster Means (FCM) and then prioritize the patients based on dedicated c