{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:09:35Z","timestamp":1760234975626,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":["20201510100010"],"award-info":[{"award-number":["20201510100010"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Advancement Research Program","award":["20CTAP-C152286-02"],"award-info":[{"award-number":["20CTAP-C152286-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.<\/jats:p>","DOI":"10.3390\/s21134427","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T13:39:22Z","timestamp":1624887562000},"page":"4427","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4532-6269","authenticated-orcid":false,"given":"Jeong Hoon","family":"Rhee","sequence":"first","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang Il","family":"Kim","sequence":"additional","affiliation":[{"name":"ICE Meca Tech Co., Ltd., Sagimakgol-ro 45 Beon-gil 14, Jungwon-gu, Seongnam-si 13209, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang Min","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea Midland Power Co., Ltd., 89-37, Boryeongbuk-ro 160, Boryeong-si 33439, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moon Kyum","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun Mook","family":"Lim","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MMM.2017.2711978","article-title":"Silo and Tank Vision: Applications, Challenges, and Technical Solutions for Radar Measurement of Liquids and Bulk Solids in Tanks and Silos","volume":"18","author":"Vogt","year":"2017","journal-title":"IEEE Microw. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.compag.2017.12.041","article-title":"A novel compressed sensing based quantity measurement method for grain silos","volume":"145","author":"Yigit","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","unstructured":"Lewis, J.D. (2004). Technology Review Level Measurement of Bulk Solids in Bins, Silos and Hoppers, Monitor Technologies LLC."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.13031\/aea.11870","article-title":"Stored grain volume measurement using a low density point cloud","volume":"33","author":"Turner","year":"2017","journal-title":"Appl. Eng. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lewis, J.D. (2007). Ensuring Successful Use of Guided-Wave Radar Level Measurement Technology, Monitor Technologies LLC. Technical Exclusive.","DOI":"10.1016\/S1350-4789(07)70052-1"},{"key":"ref_6","unstructured":"Alpaydin, E. (2009). Introduction to Machine Learning, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.18201\/ijisae.2018637927","article-title":"Operating frequency estimation of slot antenna by using adapted kNN algorithm","volume":"1","author":"Yigit","year":"2018","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compag.2017.12.032","article-title":"Evaluation of support vector machine and artificial neural networks in weed detection using shape features","volume":"145","author":"Bakhshipour","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.eswa.2014.08.048","article-title":"Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks","volume":"42","author":"Chandwani","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_10","unstructured":"Gao, Z., Chin, C.S., Woo, W.L., Jia, J., and Da Toh, W. (2015, January 15\u201317). Genetic algorithm based back-propagation neural network approach for fault diagnosis in lithium-ion battery system. Proceedings of the 6th International Conference on Power Electronics Systems and Applications, Hong Kong, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"74914","DOI":"10.1109\/ACCESS.2020.2988322","article-title":"Improved Genetic Algorithm to Optimize the Wi-Fi Indoor Positioning Based on Artificial Neural Network","volume":"8","author":"Cui","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1016\/j.renene.2019.12.047","article-title":"Short-term wind speed prediction model based on GA-ANN improved by VMD","volume":"156","author":"Zhang","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"120331","DOI":"10.1016\/j.energy.2021.120331","article-title":"Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic altorithm (GA)","volume":"225","author":"Li","year":"2021","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.engappai.2017.06.007","article-title":"Optimization of modular granular neural networks using a firefly algorithm for human recognition","volume":"64","author":"Melin","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.ins.2017.09.031","article-title":"Multi-objective optimization for modular granular neural networks applied to pattern recognition","volume":"460-461","author":"Melin","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.3233\/JIFS-191198","article-title":"Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition","volume":"38","author":"Melin","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.engappai.2006.06.005","article-title":"Optimizing feedforward artificial neural network architecture","volume":"20","author":"Benardos","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.cherd.2012.08.004","article-title":"Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm","volume":"91","author":"Soleimani","year":"2013","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.powtec.2016.01.028","article-title":"Modeling and optimization of a pharmaceutical crystallization process by using neural networks and genetic algorithms","volume":"292","year":"2016","journal-title":"Powder Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6655","DOI":"10.1021\/ie060562c","article-title":"Hybrid Artificial Neural Network\u2212Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor","volume":"45","author":"Istadi","year":"2006","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1016\/j.jiec.2013.10.028","article-title":"Application of artificial neural network and genetic algorithm to modeling and optimization of removal of methylene blue using activated carbon","volume":"20","author":"Karimi","year":"2014","journal-title":"J. Ind. Eng. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/S1385-8947(03)00150-5","article-title":"Hybrid process modeling and optimization strategies integrating neural networks\/support vector regression and genetic algorithms: Study of benzene isopropylation on Hbeta catalyst","volume":"97","author":"Nandi","year":"2004","journal-title":"Chem. Eng. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jfoodeng.2005.08.044","article-title":"Application of genetic algorithm for optimization of vegetable oil hydrogenation process","volume":"78","author":"Izadifar","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_24","unstructured":"Rajasekaran, S., and Pai, G.A.V. (2003). Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications, Prentice-Hall of India Private Limited."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0260-8774(01)00159-5","article-title":"Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms","volume":"53","author":"Chen","year":"2002","journal-title":"J. Food Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.bej.2017.01.010","article-title":"Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor","volume":"120","author":"Pappu","year":"2017","journal-title":"Biochem. Eng. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1016\/j.biortech.2009.09.093","article-title":"Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant","volume":"101","author":"Sivapathasekaran","year":"2010","journal-title":"Bioresour. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.compchemeng.2006.10.012","article-title":"Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent","volume":"31","author":"Pai","year":"2007","journal-title":"Comput. Chem. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1146\/annurev.pc.45.100194.002255","article-title":"Theory and applications of neural computing in chemical science","volume":"45","author":"Sumpter","year":"1994","journal-title":"Annu. Rev. Phys. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.resconrec.2009.08.012","article-title":"Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm","volume":"54","author":"Shatnawi","year":"2010","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1002\/aic.690450609","article-title":"Global optimization of a dryer by using neural networks and genetic algorithms","volume":"45","author":"Hugget","year":"1999","journal-title":"AIChE J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","article-title":"Forecasting with artificial neural networks: The state of the art","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J. Forecast"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rcim.2007.11.004","article-title":"The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks","volume":"25","author":"Nalbant","year":"2009","journal-title":"Robot. Comput. Manuf."},{"key":"ref_34","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2018). Deep Learning Toolbox. The Math Works, Incorp.. User\u2019 Guide."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","year":"1993","journal-title":"Neural Networks"},{"key":"ref_36","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, The University of Michigan Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4427\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:59Z","timestamp":1760163959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4427"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":36,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134427"],"URL":"https:\/\/doi.org\/10.3390\/s21134427","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,6,28]]}}}