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关键词： Object detection  

摘要： Logging of incoming/outgoing vehicles serves as a piece of critical information for root-cause analysis to combat security breach incidents in various sensitive organizations. RFID tagging hampers the scalability of vehicle tracking solutions on both logistics as well as technical fronts. For instance, requiring each incoming vehicle(departmental or private) to be RFID tagged is a severe constraint and coupling video analytics with RFID to detect abnormal vehicle movement is non-trivial. We leverage publicly available implementations of computer vision algorithms to develop an interpretable vehicle tracking algorithm using finite-state machine formalism. The state-machine consumes input from the cascaded object detection and optical character recognition(OCR) models for state transitions. We evaluated the proposed method on 75 video clips of 285 vehicles from our system deployment site. We observed that the detection rate is most affected by the speed and the type of vehicle. The highest detection rate is achieved when the vehicle movement is restricted to follow a movement restrictions(SOP) at the checkpoint similar to RFID tagging. We further analyzed 700 vehicle tracking predictions on live-data and identified that the majority of vehicle number prediction errors are due to illegible-text, image-blur, text occlusion and out-of-vocab letters in vehicle numbers. Towards system deployment and performance enhancement, we expect our ongoing system monitoring to provide evidences to establish a higher vehicle-throughput SOP at the security checkpoint as well as to drive the fine-tuning of the deployed computer-vision models and the state-machine to establish the proposed approach as a promising alternative to RFID-tagging. © 2022, CC BY.

摘要： In order to solve the problems of highway traffic congestion and frequent traffic accidents, a technology method for highway information collection, processing, and control based on RFID Internet of Things is proposed. The highway information collection and processing control technology designed in this paper is based on radio frequency identification technology to realize the collection of highway vehicle information, analyze, and process the collected information, and finally get the road condition information. Through the extraction and analysis of the traffic flow information in the video surveillance, the background difference method is used to extract the relevant vehicle factors in the video, including the extraction of traffic flow information, vehicle model extraction, and vehicle speed extraction. Traffic detection provides convenience. The experimental data shows that from Monday to Friday on weekdays, the road congestion rates are 2.72%, 1.94%, 2.77%, 2.27%, and 3.47% respectively;the average vehicle speed on weekdays is 89.96 km/h, and the test results are fast and accurate. The proposed method integrates road information collection and road information service, realizes traffic detection when there are vehicles, and realizes detection and perception of large-scale road traffic systems. Travelers provide real-time traffic information and navigation services.

关键词： Wireless sensor networks  

摘要： In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays.

关键词： Robot applications  

摘要： Water utilities across the globe are concerned with the inspection and replacement of buried metallic water pipes due to corrosion-related structural damages. Internal pipe linings are commonly used as a renewal method to improve structural strength as they are regarded to be a less expensive alternative to costly and time-consuming pipe replacements. However, linings are also prone to failure as well. Therefore, water authorities regularly monitor lining performance, where defect evolution over a long period of time is an important parameter to note. It requires an accurate in-pipe robot localization technology. In this article, we propose a novel method for in-pipe robot localization and tag mapping that uses battery-free UHF-RFID sensor wireless signals. It utilizes a signal mapping approach in combination with a tailored pose-graph simultaneous localization and mapping algorithm. Evaluation results of a field-extracted pipe sample from Sydney Water’s distribution network show the proposed approach is capable of localizing the robot within 2.5cm accuracy in a 50m equivalent pipe with an unknown UHF-RFID distribution. The proposed approach outperformed other reported similar work in the literature [Figur presented]. © 2022, CC BY.

关键词： RFID   工业4.0   个性化定制   可视化   智能制造  

摘要： 在工业4.0平台上,可以通过在人机界面HMI产品定制界面输入产品的具体参数,在生产过程中通过RFID读写器将产品信息写入电子标签。每一模块生产时都能读出当前在制品前一道工序的信息,根据定制要求完成本模块的生产。生产完成后通过RFID读写器将生产信息写入定制产品的电子标签中,并在HMI上显示产品信息,可以实现产品的个性化定制生产和可视化管理。文章梳理了RFID系统的内涵和意义、应用研究,对RFID在工业4.0平台实现产品个性化定制及可视化管理的应用进行了详细分析,最后梳理了RFID系统对工业4.0的促进作用。

关键词： 射频识别   机器学习   长短期记忆神经网络   粒子群优化   前馈神经网络   定位识别  

摘要： 针对传统室内固定资产定位方法存在定位时间长、定位误差较大以及定位成本高等问题,提出一种基于射频识别技术和机器学习的室内固定资产定位方法。首先,构建基于长短记忆神经网络的固定资产感知识别模型,完成对固定资产设备的区域级识别粗定位,实现对某个区域内固定资产设备数量的自动清点;其次,面向粗定位结果在区域内的固定资产设备,设计基于粒子群优化反向传播神经网络的定位模型,实现对固定资产设备的精准定位;最后,在高校实验室环境下开展了实测实验。实验结果表明,该方法对室内固定资产的识别F1值可达0.98,平均定位误差约0.5 m,满足建筑智能中对室内固定资产的管理要求,具有资产管理成本低、定位精度高、抗干扰能力强等特点。

关键词： 溯源   物联网   无线传感网   射频识别  

摘要： 文章以茶叶产业为背景,利用物联网技术升级改造茶叶产业链中的溯源环节,讨论在传统茶叶交易基础上设计一套针对消费者、经销商、茶叶生产者等多用户的行之有效的解决方案,达到针对茶叶商品有源可溯,提高茶叶认知度和广泛传播的目的。

关键词： RFID   EPC Gen2   标签数   降维  

摘要： 为了能够在低成本的RFID单片机上运行碰撞标签数的估计算法,需要降低RFID盘存标签数的计算复杂度。本文推导分析了在总时隙长度足够大的前提下,碰撞标签的数量主要取决于碰撞时隙占比,与空闲时隙占比或单标签时隙占比的相关性较小。通过对比分析表明,在对精度要求不高的情况下,计算碰撞标签所依赖的变量维度可以从3维下降到1维,从而简化盘存标签数的计算。

关键词： 无纸化   射频识别技术   空调  

摘要： 以射频识别技术为基础,结合无纸化计技术应用,开发空调产线无纸化系统,与公司MES、ERP等系统进行信息互通互联,在生产线各岗位配置线边终端设备,实现生产信息实时展示,生产工艺操作在线指导,在制品检验信息在线录入等功能,实现生产状况的无纸化管理,提升空调生产线的信息化水平。

关键词： RFID技术   箱式储丝   防差错  

摘要： 制丝车间成品烟丝箱采用进口黄木材质制成,经过多轮老化后,可以用于烟丝的醇化。主要分析RFID技术在烟草箱式储丝过程中的应用,实现在智能化工厂内200 kg的烟丝箱高速有效运转的方案,通过烟丝牌号批次的防差错等功能,实现烟丝高架库的智能运行。