浏览全部资源
扫码关注微信
Wi-freshness：基于CSI的猪肉新鲜度检测系统研究
Wi-freshness: research on CSI-based pork freshness detecting system
- 物联网学报 2023年7卷第2期 页码：143-152
- 作者机构：
1. 河南工业大学信息科学与工程学院，河南 郑州 450001
2. 河南省粮食光电探测与控制重点实验室（河南工业大学），河南 郑州 450001
3. 粮食信息处理与控制教育部重点实验室（河南工业大学），河南 郑州 450001
- 作者简介：
[ "牛超（1995- ），男，河南工业大学信息科学与工程学院硕士生，主要研究方向为物联网、无线感知、视频安全等" ]
[ "杨卫东（1977- ），男，博士，河南工业大学教授，主要研究方向为无线网络安全、物联网、隐私保护、车载自组织网络" ]
[ "胡鹏明（1994- ），男，河南省粮食光电探测与控制重点实验室（河南工业大学）博士生，主要研究方向为物联网、无线感知" ]
[ "高向上（1997- ），男，河南工业大学信息科学与工程学院硕士生，主要研究方向无线感知、人工智能" ]
[ "沈二波（1984- ），男，河南省粮食光电探测与控制重点实验室（河南工业大学）博士生，主要研究方向无线感知、人工智能" ]
- 基金信息：国家自然科学基金资助项目;The National Natural Science Foundation of China(62172141);国家自然科学基金资助项目;The National Natural Science Foundation of China(61772173);河南省自然科学基金资助项目;The Natural Science Foundation of Henan Province(222300420004);河南省重大公益专项;The Major Public Welfare Project of Henan Province(201300210100);河南省留学人员科研择优资助项目;The Scientific Research Program for Overseas Students of Henan Province(2124003)
DOI：10.11959/j.issn.2096-3750.2023.00332
中图分类号： TP39纸质出版日期：2023-06-30，
网络出版日期：2023-06，
- 稿件说明：
移动端阅览
牛超, 杨卫东, 胡鹏明, 等. Wi-freshness：基于CSI的猪肉新鲜度检测系统研究[J]. 物联网学报, 2023,7(2):143-152.
CHAO NIU, WEIDONG YANG, PENGMING HU, et al. Wi-freshness: research on CSI-based pork freshness detecting system. [J]. Chinese journal on internet of things, 2023, 7(2): 143-152.
牛超, 杨卫东, 胡鹏明, 等. Wi-freshness：基于CSI的猪肉新鲜度检测系统研究[J]. 物联网学报, 2023,7(2):143-152. DOI： 10.11959/j.issn.2096-3750.2023.00332.
CHAO NIU, WEIDONG YANG, PENGMING HU, et al. Wi-freshness: research on CSI-based pork freshness detecting system. [J]. Chinese journal on internet of things, 2023, 7(2): 143-152. DOI： 10.11959/j.issn.2096-3750.2023.00332.
摘要
有效、快速地评价猪肉新鲜度对猪肉品质监测具有重要意义。然而，传统的感官评价方法过于主观，理化分析又耗时过长且具有破坏性。虽然射频信号被用于定位、材料识别、生命体征监测，但在肉类新鲜度检测方面并未受到重视。提出了一种实时、无损、低成本的基于信道状态信息（CSI
channel state information）的猪肉新鲜度检测系统Wi-freshness。该系统基于泛在网络（商用Wi-Fi）部署和实施，是物联网在生鲜农产品领域一个新的应用。Wi-freshness包括CSI数据感知、数据预处理、检测建模和新鲜度检测4个模块。考虑Wi-freshness需要处理的数据特征值不多，以及对预测的实时性要求较高的特点，提出一种基于宽度学习系统（BLS
broad learning system）的检测模型。实验结果表明，Wi-freshness系统能达到93%以上的检测准确率。
Abstract
Effective and rapid detection of pork freshness is important for pork quality.However
traditional sensory evaluation methods are too subjective
physical and chemical analysis methods are time-consuming and destructive.Recently
radio frequency is widely used in the field of location
material identification and human body monitoring
while
the meat freshness detection is ignored.A real-time
non-destructive and low-cost system for pork freshness detecting based on channel state information (CSI) was proposed.It is a new application of internet of things in the field of fresh agricultural products based on ubiquitous network (commercial Wi-Fi).The proposed Wi-freshness consists of four modules: CSI data sensing
data pre-processing
detection modelling and freshness detection.Considering the need for Wi-freshness data characteristic value processing is not much
and high demand for real-time prediction characteristics
a detection model based broad learning system (BLS) was proposed.Experiment shows that Wi-freshness system can achieve more than 93% detection accuracy.
关键词
信道状态信息Wi-Fi猪肉新鲜度检测宽度学习系统
Keywords
channel state informationWi-Fipork freshness detectionbroad learning system
references
LI S Y, CHEN S J, ZHUO B G ,et al. Flexible ammonia sensor based on PEDOT:PSS/silver nanowire composite film for meat freshness monitoring[J]. IEEE Electron Device Letters, 2017,38(7): 975-978.
LU S C, WANG X F, ROCHA Á, . Modeling the fuzzy cold storage problem and its solution by a discrete firefly algorithm[J]. Journal of Intelligent ＆ Fuzzy Systems:Applications in Engineering and Technology, 2016,31(4): 2431-2440.
ALI I, NAGALINGAM S, GURD B . A resilience model for cold chain logistics of perishable products[J]. The International Journal of Logistics Management, 2018,29(3): 922-941.
LIU R, XING L J, ZHOU G H ,et al. What is meat in China?[J]. Animal Frontiers, 2017,7(4): 53-56.
XIONG L, HU Y, LIU C ,et al. Detection of total volatile basic nitrogen (TVB-N) in pork using Fourier transform near-infrared (FT-NIR) spectroscopy and cluster analysis for quality assurance[J]. Transactions of the ASABE, 2012,55(6): 2245-2250.
WENG X H, LUAN X Y, KONG C ,et al. A comprehensive method for assessing meat freshness using fusing electronic nose,computer vision,and artificial tactile technologies[J]. Journal of Sensors, 2020: 1-14.
CHANG H Y, LI H, HU Y J . An intelligent method of detecting pork freshness based on digital image processing[C]// Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. Piscataway:IEEE Press, 2015: 107-110.
GRASSI S, BENEDETTI S, OPIZZIO M ,et al. Meat and fish freshness assessment by a portable and simplified electronic nose system (mastersense)[J]. Sensors (Basel,Switzerland), 2019,19(14): 3225.
HAN F K, HUANG X Y, TEYE E ,et al. Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis[J]. Analytical Methods, 2014,6(2): 529-536.
TAHERI-GARAVAND A, FATAHI S, SHAHBAZI F ,et al. A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique[J]. Journal of Food Process Engineering, 2019,42(4): e13039.
SUN X, YOUNG J, LIU J H ,et al. Prediction of pork loin quality using online computer vision system and artificial intelligence model[EB]. 2018.
ZOU L, LIU W N, LEI M ,et al. An improved residual network for pork freshness detection using near-infrared spectroscopy[J]. Entropy (Basel,Switzerland), 2021,23(10): 1293.
CRICHTON S O J, KIRCHNER S M, PORLEY V ,et al. Classification of organic beef freshness using VNIR hyperspectral imaging[J]. Meat Science, 2017(129): 20-27.
KAUSHIK S . An overview of technical aspect for Wi-Fi networks technology[J]. International Journal of Electronics and Computer Science Engineering, 2012,1(1): 28-34.
LASORTE N, BARNES W J, REFAI H H . The history of orthogonal frequency division multiplexing[C]// Proceedings of IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. Piscataway:IEEE Press, 2008: 1-5.
WANG H, ZHANG D Q, WANG Y S ,et al. RT-fall:a real-time and contactless fall detection system with commodity Wi-Fi devices[J]. IEEE Transactions on Mobile Computing, 2017,16(2): 511-526.
WANG Y, LIU J, CHEN Y Y ,et al. E-eyes:device-free location-oriented activity identification using fine-grained Wi-Fi signatures[C]// Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. New York:ACM Press, 2014: 617-628.
WANG X Y, GAO L J, MAO S W ,et al. CSI-based fingerprinting for indoor localization:a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017,66(1): 763-776.
HU P, YANG W, WANG X ,et al. Contact-free wheat mildew detection with commodity Wi-Fi[J]. International Journal of Cognitive Computing in Engineering, 2022,3: 9-23.
MUTWAKI L . Meat spoilage mechanisms and preservation techniques:a critical review[J]. American Journal of Agricultural and Biological Sciences, 2011,6(4): 486-510.
MRLEIN D . Sensory evaluation of meat and meat products:fundamentals and applications[J]. IOP Conference Series:Earth and Environmental Science, 2019,333(1): 012007.
DAS A K, NANDA P K, DAS A ,et al. Hazards and safety issues of meat and meat products[M]// Food Safety and Human Health. Amsterdam: Elsevier, 2019: 145-168.
KOMAROV V, WANG S, TANG J . Permittivity and measurements[M]// Encyclopedia of RF and Microwave Engineering. Hoboken: John Wiley ＆ Sons,Inc., 2005: 36-46.
BEKHIT E, HOLMAN B, GITERU S G ,et al. Total volatile basic nitrogen (TVB-N) and its role in meat spoilage:a review[J]. Trends in Food Science ＆ Technology, 2021(109): 280-302.
WU C S, YANG Z, ZHOU Z M ,et al. PhaseU:real-time LOS identification with Wi-Fi[C]// Proceedings of 2015 IEEE Conference on Computer Communications (INFOCOM). Piscataway:IEEE Press, 2015: 2038-2046.
NEE R V, PRASAD R . OFDM for wireless multimedia communications[EB]. 1999.
HALPERIN D, HU W J, SHETH A ,et al. 802.11 with multiple antennas for dummies[J]. ACM SIGCOMM Computer Communication Review, 2010,40(1): 19-25.
SEN S, RADUNOVIC B, CHOUDHURY R R ,et al. You are facing the Mona Lisa:spot localization using PHY layer information[C]// Proceedings of the 10th International Conference on Mobile Systems,Applications,and Services. New York:ACM Press, 2012: 183-196.
FENG C, XIONG J, CHANG L Q ,et al. WiMi:target material identification with commodity Wi-Fi devices[C]// Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2019: 700-710.
CHEN C L P, LIU Z L . Broad learning system:an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018,29(1): 10-24.
0
浏览量
435
下载量
0
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
- 总访问量：42531 今日访问量：326