A raw milk service platform using BP Neural Network and Fuzzy Inference

Abstract An important index for raw milk storage in a dairy farm is the raw milk storage temperature which directly reflects the raw milk quality. Meanwhile, it’s hard to centrally manage numerous dairy farms which are not distributed in the same place. We aim to build a kind of raw milk monitoring and warning equipment, gateway and cloud service platform to solve these problems. The raw milk monitoring and warning equipment and service platform were designed to monitor the raw milk temperature in the refrigerated storage tank and provide a warning alarm if an exception occurred. Data-driven modeling was used for acquiring, cleaning, and utilizing data to solve the raw milk storage problems. The raw milk monitoring and warning management system provided a way of predicting and warning for raw milk storage using BP Neural Network and Fuzzy Inference. The test showed that the BP Neural Network and Fuzzy Inference model built in this paper had a good performance in predicting the raw milk storage temperature and reflecting the variation of raw milk temperature in raw milk storage process. The platform and models provided a method to manage the raw milk in dairies and prevent the raw milk from deteriorating caused by the rising temperature.

[1]  Hai Jin,et al.  SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep-Web Interfaces , 2016, IEEE Transactions on Services Computing.

[2]  Ian F. Akyildiz,et al.  A cross-layer communication module for the Internet of Things , 2013, Comput. Networks.

[3]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[4]  Haiyan Lu,et al.  A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..

[5]  Fulin Wang,et al.  An Unconstrainted Optimization Method Based on BP Neural Network , 2010, 2010 International Conference on E-Product E-Service and E-Entertainment.

[6]  Frederico Araújo Durão,et al.  A systematic review on cloud computing , 2014, The Journal of Supercomputing.

[7]  Masayuki Ida,et al.  SaaS virtualization method and its application , 2016, 2016 International Conference on Information Networking (ICOIN).

[8]  Rajiv Ranjan,et al.  An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art , 2013, Computing.

[9]  Yue-Shan Chang,et al.  Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments , 2013, The Journal of Supercomputing.

[10]  William M. Pottenger,et al.  A framework for understanding Latent Semantic Indexing (LSI) performance , 2006, Inf. Process. Manag..

[11]  Wolfgang Prinz,et al.  User Involvement in Software Development Processes , 2016, Cloud Forward.

[12]  Yuan Xiao-lin,et al.  Research of improved BP algorithm based on self-adaptive learning rate , 2009 .

[13]  Marco Buongiorno Nardelli,et al.  A RESTful API for exchanging materials data in the AFLOWLIB.org consortium , 2014, 1403.2642.

[14]  Bo Zhong,et al.  BP neural network with rough set for short term load forecasting , 2009, Expert Syst. Appl..

[15]  Duminda Wijesekera,et al.  Performance Analysis of Web Services on Mobile Devices , 2012, ANT/MobiWIS.

[16]  Masoud Rahimi,et al.  PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system , 2012, Korean Journal of Chemical Engineering.

[17]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[18]  B.H.M. Sadeghi,et al.  A BP-neural network predictor model for plastic injection molding process , 2000 .

[19]  Gabriele Bavota,et al.  Code smells for Model-View-Controller architectures , 2017, Empirical Software Engineering.

[20]  Zhao Yan-jun Digital Temperature Measurement System Based on DS18B20 , 2007 .

[21]  James J. Jiang,et al.  Rethinking the role of security in client satisfaction with Software-as-a-Service (SaaS) providers , 2015, Decis. Support Syst..

[22]  Liming Zhu,et al.  Non-Intrusive Anomaly Detection With Streaming Performance Metrics and Logs for DevOps in Public Clouds: A Case Study in AWS , 2016, IEEE Transactions on Emerging Topics in Computing.

[23]  Anthony Sulistio,et al.  Private cloud for collaboration and e-Learning services: from IaaS to SaaS , 2010, Computing.

[24]  Tsai-Chung Li,et al.  Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment , 2011, BMC Medical Informatics Decis. Mak..

[25]  G. Bruce Berriman,et al.  An Evaluation of the Cost and Performance of Scientific Workflows on Amazon EC2 , 2012, Journal of Grid Computing.

[26]  Fatimah Ibrahim,et al.  Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients , 2012, Expert Syst. Appl..

[27]  H. Roginski,et al.  The effects of refrigerated storage of raw milk on the quality of whole milk powder stored for different periods , 1997 .

[28]  Dean Zhao,et al.  An optimized classification algorithm by BP neural network based on PLS and HCA , 2014, Applied Intelligence.

[29]  Zhang Zhisheng SHORT-TERM LOAD FORECASTING BASED ON RECURRENT NEURAL NETWORK USING ANT COLONY OPTIMIZATION ALGORITHM , 2005 .

[30]  Shen Zhang,et al.  Improved BP Neural Network for Transformer Fault Diagnosis , 2007 .

[31]  Guevara Noubir,et al.  Practical Forward-Secure Range and Sort Queries with Update-Oblivious Linked Lists , 2015, Proc. Priv. Enhancing Technol..

[32]  K. Jordan,et al.  The effect of storage temperature and duration on the microbial quality of bulk tank milk. , 2016, Journal of dairy science.

[33]  Alexandru Iosup,et al.  A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing , 2009, CloudComp.

[34]  Nai-Wei Lo,et al.  An Attribute-Role Based Access Control Mechanism for Multi-tenancy Cloud Environment , 2015, Wirel. Pers. Commun..

[35]  I. Ahmad,et al.  Prediction of Raw Milk Microbial Quality Using Data Mining Techniques , 2010 .