{"title":"基于学习标准原型的记忆增强自编码器验证地铁室内空气质量数据","authors":"Vahid Ghorbani, Shahzeb Tariq, ChangKyoo Yoo","doi":"10.1007/s11814-025-00451-y","DOIUrl":null,"url":null,"abstract":"<div><p>Indoor air quality (IAQ) monitoring in subway stations depends on sensors prone to failures due to confined spaces, cyberattacks, and prolonged use. Soft sensor validation frameworks using statistical or machine learning models can detect, diagnose, and reconstruct faulty data but struggle with complex fault patterns. This study introduces a memory-augmented autoencoder-based framework for reliable IAQ sensor validation, leveraging memorized normal prototypes. To the best of our knowledge, this is the first validation method that utilizes normal prototypes for reconciling corrupted measurements. Tested on real IAQ data from Seoul Metro's C-station, the method achieved a 97.03% detection rate, a 4.33% false alarm rate, and demonstrated potential for 10.25% energy savings while maintaining healthy IAQ.</p></div>","PeriodicalId":684,"journal":{"name":"Korean Journal of Chemical Engineering","volume":"42 10","pages":"2231 - 2252"},"PeriodicalIF":3.2000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validation of Subway Indoor Air Quality (IAQ) Data Using Memory-Augmented Autoencoders with Learned Normal Prototypes\",\"authors\":\"Vahid Ghorbani, Shahzeb Tariq, ChangKyoo Yoo\",\"doi\":\"10.1007/s11814-025-00451-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Indoor air quality (IAQ) monitoring in subway stations depends on sensors prone to failures due to confined spaces, cyberattacks, and prolonged use. Soft sensor validation frameworks using statistical or machine learning models can detect, diagnose, and reconstruct faulty data but struggle with complex fault patterns. This study introduces a memory-augmented autoencoder-based framework for reliable IAQ sensor validation, leveraging memorized normal prototypes. To the best of our knowledge, this is the first validation method that utilizes normal prototypes for reconciling corrupted measurements. Tested on real IAQ data from Seoul Metro's C-station, the method achieved a 97.03% detection rate, a 4.33% false alarm rate, and demonstrated potential for 10.25% energy savings while maintaining healthy IAQ.</p></div>\",\"PeriodicalId\":684,\"journal\":{\"name\":\"Korean Journal of Chemical Engineering\",\"volume\":\"42 10\",\"pages\":\"2231 - 2252\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Korean Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11814-025-00451-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Korean Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11814-025-00451-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Validation of Subway Indoor Air Quality (IAQ) Data Using Memory-Augmented Autoencoders with Learned Normal Prototypes
Indoor air quality (IAQ) monitoring in subway stations depends on sensors prone to failures due to confined spaces, cyberattacks, and prolonged use. Soft sensor validation frameworks using statistical or machine learning models can detect, diagnose, and reconstruct faulty data but struggle with complex fault patterns. This study introduces a memory-augmented autoencoder-based framework for reliable IAQ sensor validation, leveraging memorized normal prototypes. To the best of our knowledge, this is the first validation method that utilizes normal prototypes for reconciling corrupted measurements. Tested on real IAQ data from Seoul Metro's C-station, the method achieved a 97.03% detection rate, a 4.33% false alarm rate, and demonstrated potential for 10.25% energy savings while maintaining healthy IAQ.
期刊介绍:
The Korean Journal of Chemical Engineering provides a global forum for the dissemination of research in chemical engineering. The Journal publishes significant research results obtained in the Asia-Pacific region, and simultaneously introduces recent technical progress made in other areas of the world to this region. Submitted research papers must be of potential industrial significance and specifically concerned with chemical engineering. The editors will give preference to papers having a clearly stated practical scope and applicability in the areas of chemical engineering, and to those where new theoretical concepts are supported by new experimental details. The Journal also regularly publishes featured reviews on emerging and industrially important subjects of chemical engineering as well as selected papers presented at international conferences on the subjects.