Yu Si , Zhaofeng He , Fan Zhang , Xiaoyun Sun , Yong Chen , Haiqing Zheng
{"title":"基于超宽带变压器神经网络的经济高效的超宽带滑坡实时监测方法","authors":"Yu Si , Zhaofeng He , Fan Zhang , Xiaoyun Sun , Yong Chen , Haiqing Zheng","doi":"10.1016/j.engappai.2025.111851","DOIUrl":null,"url":null,"abstract":"<div><div>Landslides rank among the most destructive natural phenomena, posing substantial risks to human safety, infrastructure, and ecological systems. Their frequent occurrence in topographically complex regions demands urgent development in real-time monitoring solutions. Current monitoring methodologies, however, are constrained by prohibitive costs, limited temporal resolution, and high-power consumption. These factors create substantial implementation barriers to implementing landslide monitoring systems. To address these limitations, this study proposes an economical real-time monitoring method leveraging ultra-wideband (UWB) technology for landslide detection. The implementation of a dual-Microcontroller Unit (MCU) distributed hardware architecture enables high-accuracy ranging capabilities and high real-time performance. To enhance the spatial resolution of UWB systems in landslide monitoring, we propose an optimized sensor deployment structure and a novel deep learning architecture called Ultra-wideband Transformer (UWBformer). This network utilizes differential UWB-ranging data to predict spatial displacement at monitoring locations, specifically the displacement distance, horizontal angle, and pitch angle. UWBformer incorporates a spatial multi-head attention mechanism and a dual-channel architecture processing both time-domain and frequency-domain features. It is specifically designed to mitigate ranging error propagation and enhance prediction stability by focusing on relative distance changes rather than absolute ranging accuracy. Empirical results demonstrate UWBformer's superior performance in predicting displacement distance, horizontal angle, and pitch angle, outperforming the conventional Caffery-Taylor (C-T) localization approach and established deep learning benchmarks. Field tests incorporated <span><math><mrow><mn>3</mn><mi>σ</mi></mrow></math></span> criterion and Kalman filtering alongside to pre-process raw measurements, thereby enhancing data stability. Comprehensive validation across field tests demonstrates UWBformer's capability to maintain accurate spatial displacement estimation under harsh environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111851"},"PeriodicalIF":7.5000,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cost-effective and real-time landslide monitoring method based on ultra-wideband using ultra-wideband transformer neural network\",\"authors\":\"Yu Si , Zhaofeng He , Fan Zhang , Xiaoyun Sun , Yong Chen , Haiqing Zheng\",\"doi\":\"10.1016/j.engappai.2025.111851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Landslides rank among the most destructive natural phenomena, posing substantial risks to human safety, infrastructure, and ecological systems. Their frequent occurrence in topographically complex regions demands urgent development in real-time monitoring solutions. Current monitoring methodologies, however, are constrained by prohibitive costs, limited temporal resolution, and high-power consumption. These factors create substantial implementation barriers to implementing landslide monitoring systems. To address these limitations, this study proposes an economical real-time monitoring method leveraging ultra-wideband (UWB) technology for landslide detection. The implementation of a dual-Microcontroller Unit (MCU) distributed hardware architecture enables high-accuracy ranging capabilities and high real-time performance. To enhance the spatial resolution of UWB systems in landslide monitoring, we propose an optimized sensor deployment structure and a novel deep learning architecture called Ultra-wideband Transformer (UWBformer). This network utilizes differential UWB-ranging data to predict spatial displacement at monitoring locations, specifically the displacement distance, horizontal angle, and pitch angle. UWBformer incorporates a spatial multi-head attention mechanism and a dual-channel architecture processing both time-domain and frequency-domain features. It is specifically designed to mitigate ranging error propagation and enhance prediction stability by focusing on relative distance changes rather than absolute ranging accuracy. Empirical results demonstrate UWBformer's superior performance in predicting displacement distance, horizontal angle, and pitch angle, outperforming the conventional Caffery-Taylor (C-T) localization approach and established deep learning benchmarks. Field tests incorporated <span><math><mrow><mn>3</mn><mi>σ</mi></mrow></math></span> criterion and Kalman filtering alongside to pre-process raw measurements, thereby enhancing data stability. Comprehensive validation across field tests demonstrates UWBformer's capability to maintain accurate spatial displacement estimation under harsh environments.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"160 \",\"pages\":\"Article 111851\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197625018536\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197625018536","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Cost-effective and real-time landslide monitoring method based on ultra-wideband using ultra-wideband transformer neural network
Landslides rank among the most destructive natural phenomena, posing substantial risks to human safety, infrastructure, and ecological systems. Their frequent occurrence in topographically complex regions demands urgent development in real-time monitoring solutions. Current monitoring methodologies, however, are constrained by prohibitive costs, limited temporal resolution, and high-power consumption. These factors create substantial implementation barriers to implementing landslide monitoring systems. To address these limitations, this study proposes an economical real-time monitoring method leveraging ultra-wideband (UWB) technology for landslide detection. The implementation of a dual-Microcontroller Unit (MCU) distributed hardware architecture enables high-accuracy ranging capabilities and high real-time performance. To enhance the spatial resolution of UWB systems in landslide monitoring, we propose an optimized sensor deployment structure and a novel deep learning architecture called Ultra-wideband Transformer (UWBformer). This network utilizes differential UWB-ranging data to predict spatial displacement at monitoring locations, specifically the displacement distance, horizontal angle, and pitch angle. UWBformer incorporates a spatial multi-head attention mechanism and a dual-channel architecture processing both time-domain and frequency-domain features. It is specifically designed to mitigate ranging error propagation and enhance prediction stability by focusing on relative distance changes rather than absolute ranging accuracy. Empirical results demonstrate UWBformer's superior performance in predicting displacement distance, horizontal angle, and pitch angle, outperforming the conventional Caffery-Taylor (C-T) localization approach and established deep learning benchmarks. Field tests incorporated criterion and Kalman filtering alongside to pre-process raw measurements, thereby enhancing data stability. Comprehensive validation across field tests demonstrates UWBformer's capability to maintain accurate spatial displacement estimation under harsh environments.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.