A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
House price prediction is of utmost importance in forecasting residential property prices,particularly as the demand for high-quality housing continues to rise.Accurate predictions have implications for real estate in...House price prediction is of utmost importance in forecasting residential property prices,particularly as the demand for high-quality housing continues to rise.Accurate predictions have implications for real estate investors,financial institutions,urban planners,and policymakers.However,accurately predicting house prices is challenging due to the complex interplay of various influencing factors.Previous studies have primarily focused on basic property information,leaving room for further exploration of more intricate features,such as amenities,traffic,and social sentiments in the surrounding environment.In this paper,we propose a novel approach to house price prediction from a multi-source data fusion perspective.Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects,including amenities,traffic,and emotions.We validate our approach using a dataset of 28550 real-world transactions in Beijing,China,providing a comprehensive analysis of the drivers influencing house prices.By adopting a multi-source data fusion perspective and considering a wide range of influential factors,our approach offers valuable insights into house price prediction.The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models,benefiting stakeholders in the real estate market.展开更多
煤矿安全风险辨识文本包含丰富的风险特征描述与专家经验知识,深入挖掘这些文本对实现风险等级预测具有重要价值。针对风险辨识文本存在小样本、短文本及语义复杂问题,提出了一种融合类别描述与增强嵌入的煤矿安全风险预测模型。该方法...煤矿安全风险辨识文本包含丰富的风险特征描述与专家经验知识,深入挖掘这些文本对实现风险等级预测具有重要价值。针对风险辨识文本存在小样本、短文本及语义复杂问题,提出了一种融合类别描述与增强嵌入的煤矿安全风险预测模型。该方法在句子级嵌入维度对文本进行数据增强,有效扩充训练样本;通过构建风险类别描述引入煤矿领域知识,并利用注意力机制对风险类别描述进行动态融合,为煤矿安全风险样本补充专业知识;使用双向长短期记忆(Bidirectional Long Short-Term Memory,Bi-LSTM)网络与Mamba算法对原始文本特征进行深度提取,获取煤矿文本复杂情境下的核心特征;最后使用动态门控机制融合各模块特征,输出预测结果。研究表明,该模型在小规模煤矿风险辨识数据集上准确率和F1均有不错的表现,可基于煤矿安全风险辨识文本为煤矿安全风险等级预测提供支持。展开更多
By comprehensively analyzing the data of geology and mining,Kriging algorithm was introduced to analyze the thematic information of geological data,to rapidly extract mining parameters for predicting mining subsidence...By comprehensively analyzing the data of geology and mining,Kriging algorithm was introduced to analyze the thematic information of geological data,to rapidly extract mining parameters for predicting mining subsidence,and to effectively integrate geomorphology and predict information.As a result,the change information of water body is successfully detected from the prediction of surface subsidence due to mining activity.Analysis shows that the elevation of farmland in the west side of water body will be lower than ever,and the west part farmland will be submerged.However,there is no evidence for impacting the villages.All the information provides a reference for efficiently assessing environmental impact due to mining activity,which can help to govern the subsidence of the area reasonably.展开更多
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
文摘House price prediction is of utmost importance in forecasting residential property prices,particularly as the demand for high-quality housing continues to rise.Accurate predictions have implications for real estate investors,financial institutions,urban planners,and policymakers.However,accurately predicting house prices is challenging due to the complex interplay of various influencing factors.Previous studies have primarily focused on basic property information,leaving room for further exploration of more intricate features,such as amenities,traffic,and social sentiments in the surrounding environment.In this paper,we propose a novel approach to house price prediction from a multi-source data fusion perspective.Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects,including amenities,traffic,and emotions.We validate our approach using a dataset of 28550 real-world transactions in Beijing,China,providing a comprehensive analysis of the drivers influencing house prices.By adopting a multi-source data fusion perspective and considering a wide range of influential factors,our approach offers valuable insights into house price prediction.The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models,benefiting stakeholders in the real estate market.
文摘煤矿安全风险辨识文本包含丰富的风险特征描述与专家经验知识,深入挖掘这些文本对实现风险等级预测具有重要价值。针对风险辨识文本存在小样本、短文本及语义复杂问题,提出了一种融合类别描述与增强嵌入的煤矿安全风险预测模型。该方法在句子级嵌入维度对文本进行数据增强,有效扩充训练样本;通过构建风险类别描述引入煤矿领域知识,并利用注意力机制对风险类别描述进行动态融合,为煤矿安全风险样本补充专业知识;使用双向长短期记忆(Bidirectional Long Short-Term Memory,Bi-LSTM)网络与Mamba算法对原始文本特征进行深度提取,获取煤矿文本复杂情境下的核心特征;最后使用动态门控机制融合各模块特征,输出预测结果。研究表明,该模型在小规模煤矿风险辨识数据集上准确率和F1均有不错的表现,可基于煤矿安全风险辨识文本为煤矿安全风险等级预测提供支持。
基金Project(200911036)supported by the Ministry of Land and Resources research special,ChinaProject(2010YD05)supported by the Fundamental Research Funds for the Central Universities,China
文摘By comprehensively analyzing the data of geology and mining,Kriging algorithm was introduced to analyze the thematic information of geological data,to rapidly extract mining parameters for predicting mining subsidence,and to effectively integrate geomorphology and predict information.As a result,the change information of water body is successfully detected from the prediction of surface subsidence due to mining activity.Analysis shows that the elevation of farmland in the west side of water body will be lower than ever,and the west part farmland will be submerged.However,there is no evidence for impacting the villages.All the information provides a reference for efficiently assessing environmental impact due to mining activity,which can help to govern the subsidence of the area reasonably.
