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1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2024年第4期32卷 593-623页

作者： Chen, Jianglong Song, Weiwei Zuo, Xiaoqing Zhao, Kang Jin, Baoxuan Zhu, Daming Dai, Bolan Kunming Univ Sci & Technol Fac Land Resources Engn Kunming 650500 Yunnan Peoples R China Dept Nat Resources Yunnan Prov Kunming 650224 Yunnan Peoples R China China Shipbldg Corp Res Inst 722 Wuhan 430079 Hubei Peoples R China

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation;(2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability.

关键词： Multi-dimensional convolutional autoencoder clustering feature representation linear separability

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Interpretable regional meteorological feature extraction enhances deep learning for extended 120-h PM2.5 forecasting

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JOURNAL OF CLEANER PRODUCTION 2024年 483卷

作者： Liu, Xinyi Pu, Xueting Lu, Chengwei Zhang, Han Li, Tao Grieneisen, Michael L. Li, Jucheng Ma, Ning Yan, Chang Zhan, Yu Yang, Fumo Sichuan Univ Dept Environm Sci & Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Carbon Neutral Future Technol Chengdu 610065 Peoples R China Chengdu Acad Environm Sci Chengdu 610072 Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610041 Peoples R China Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

While deep learning models perform well in short-term PM2.5 forecasting, their performance tends to decay significantly with increasing forecast spans. This study proposes a "Domain-to-Point" approach to enhance deep learning models for extended 120-h PM2.5 forecasting. We used convolutional autoencoders (ConvAEs) to extract regional meteorological features (RMFs) from both surrounding and future meteorological data provided by an advanced weather forecasting system. The RMFs and temporal indicators were used as predictor variables in a time-attention-based bidirectional long short-term memory neural network to forecast the next 24- to 120-h PM2.5. For four megacities in China, the ConvAEs compressed the high-dimensional meteorological data into a lower-dimensional representation, while preserving most of the relevant information (R-2 > 0.75). Using the RMFs as inputs substantially mitigated the performance decay of the PM2.5 forecasting models over extended forecast spans, enabling accurate longer-term forecasting, with R-2 improvements of 14.7-79.2% for the 120-h forecasts. This study further revealed that errors in the meteorological forecasts are the primary cause of performance decay, suggesting that improving the accuracy of meteorological forecasts could be an effective approach for enhancing PM2.5 forecasting performance. With Beijing as a case study, model interpretation using the Shapley-additive-explanations method demonstrated the crucial role played by RMFs in accurately forecasting a typical air pollution episode and identifying the main meteorological factors affecting pollution. The proposed "Domain-to-Point" approach enhances the capability of deep learning models for longer-term PM2.5 forecasting, thus facilitating the development of preventative measures to safeguard public health against air pollution.

关键词： PM2.5 forecasting External feature engineering Meteorological feature extraction Deep learning convolutional autoencoder

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Price prediction in China stock market: an integrated method based on time series clustering and image feature extraction (Aug, 10.1007/s11227-023-05562-z, 2023)

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 10191-10191页

作者： Guan, Bowen Zhao, Chencheng Yuan, Xianghui Long, Jun Li, Xiang Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Econ & Finance Xian Peoples R China Xi An Jiao Tong Univ Jinhe Ctr Econ Res Xian Peoples R China

Stock time-series data has the characteristics of high dimensionality and nonlinearity, which brings great challenges to stock forecasting. Aiming at the impact of stock correlation and the prediction information contained in stock image features, we propose a long short-term memory model based on clustering and image feature extraction, named Kmeans-CAE-LSTM. Firstly, the Kmeans algorithm is used for stock clustering, where the most correlated stocks are found. Secondly, a convolutional autoencoder (CAE) is applied to extract stock price image features. Finally, the stock technical data and image features are respectively input into the double-layer long-term short-term memory network to predict the stock price of the next trading day. The empirical research results on 11 industries in China’s stock market show that the hybrid model has achieved the best prediction effect, which further proves the predictive ability of stock image data and can provide investors with new ideas for stock prediction and asset portfolio.

关键词： Financial time series LSTM neural network Hybrid deep learning model convolutional autoencoder Kmeans

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Improvement of impact acoustic inspection for high-speed railway track slabs using time-frequency analysis and non-defective machine learning

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NDT & E INTERNATIONAL 2024年 145卷

作者： Inaba, Kohko Tanigawa, Hikaru Naito, Hideki Railway Tech Res Inst 2-8-38 Hikari ChoKokubunji Tokyo 1858540 Japan Tohoku Univ 6-6-06Aramaki Aza AobaAoba Ku Sendai Miyagi 9808579 Japan

Slab track is one of the most highly used ballastless track forms in Japan ' s high-speed railway system ( Shin- kansen ) and is composed from track slabs (precast RC or PRC member), a filling layer (mixture of cement, asphalt emulsion and fine aggregates) and concrete bed. As they age, it has been reported that the supporting conditions of track slabs change due to damage in the filling layer that in turn affects running stability. It is necessary to evaluate supporting conditions of track slabs accurately during maintenance of slab tracks prior to signs of cracking. Some previous works show the usefulness of impact acoustics and non -defective machine learning using frequency response spectrums as feature values for evaluating supporting conditions of track slabs. In this study, we examine a method of non -defective learning to improve the evaluation of supporting conditions: timefrequency response characteristics of impact acoustics using time -frequency analysis and resulting spectrograms as feature values. As a result, we have improved accuracy rate (from 81.90 % to 86.72 %) and F -value (from 70.79 % to 78.75 %) more precisely than conventional method based on frequency response functions as feature values.

关键词： Slab track Impact acoustic inspection Continuous wavelet transform Principal component analysis convolutional autoencoder

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Multi-stream pyramid collaborative network for spectral unmixing

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第8期45卷 2674-2701页

作者： Wang, Jie Ni, Mengying Wang, Zhixiang Yan, Yu Cheng, Xiang Xu, Jindong Yantai Univ Sch Comp & Control Engn Yantai Peoples R China Peking Univ Sch Informat Sci & Technol Beijing Peoples R China

convolutional autoencoder, which can well model the spatial correlation of the data, have been widely applied to spectral unmixing task and achieved desirable performance. However, the fixed geometric structure of convolution kernels makes it difficult to capture global context. To address this issue, strategies such as dilated convolution or transformer are often employed, but this may result in minor loss of local details. Therefore, we propose a collaborative unmixing network with a multi-scale pyramid structure to capture both global and local features simultaneously. To integrate features from different scales in the unmixing process, we employ a cross-stream fusion feature strategy, which not only promote collaborative representations but also capture long-range dependencies while preserving local details. Meanwhile, we also design the residual spectral attention mechanism to refine the features from different scales and facilitate their fine-grained fusion. In the proposed network, each convolutional stream undergoes effective collaborative training using a convolutional autoencoder structure. The collaborative strategies include cross-stream feature fusion mechanism and alternating training strategy with weight sharing for endmember information. Experiments over three real hyperspectral datasets indicate the effectiveness of our method compared to other unmixing techniques.

关键词： Spectral unmixing deep learning convolutional autoencoder multi-scale feature fusion collaborative strategies

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Four moisture patterns surrounding Atlantic hurricanes revealed by deep learning: Their characteristics and relationship with hurricane intensity and precipitation

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ATMOSPHERIC RESEARCH 2025年 322卷

作者： Matyas, Corene J. Kim, Dasol Zick, Stephanie E. Wood, Kimberly M. Univ Florida Dept Geog Gainesville FL USA Seoul Natl Univ Sci & Technol Dept Environm Engn Seoul South Korea Virginia Tech Dept Geog Blacksburg VA USA Univ Arizona Dept Hydrol & Atmospher Sci Tucson AZ USA

Moisture plays a key role in the energetics of hurricanes. Using a convolutional autoencoder, a state-of-the-art deep learning approach to spatial pattern classification, with k-means we identified four representative clusters of total column water vapor (TCWV) patterns around North Atlantic hurricanes. These four clusters exhibit distinct spatial distributions of TCWV in terms of amount, symmetry, and areal extent. Cluster 1 has a compact, symmetric, and moderate moisture pattern which we refer to as medium moisture symmetrical. Cluster 2 is high moisture symmetrical as these hurricanes have an abundance of moisture with a widespread and symmetric pattern. Cluster 3 is low moisture asymmetrical as it represents the driest conditions especially in the northwest. Cluster 4 has high moisture near the center but exhibits a pattern with the strongest contrast between dryness in the northwest and wetness in the southeast, thus we label it high moisture asymmetrical. Each cluster has distinct geographical and temporal distributions, indicating differences in dynamic and thermodynamic environmental conditions associated with each cluster's moisture pattern. Additionally, hurricane intensity, size, and precipitation features vary among the four clusters, characteristics which are closely associated with the moisture and environmental conditions of each cluster. Our study's application of a deep learning method in classifying spatial patterns of moisture around hurricanes highlights the importance of moisture conditions in a hurricane's evolution.

关键词： convolutional autoencoder Atlantic hurricane Moisture Pattern clustering Intensity Precipitation

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Quality grading method for Pleurotus eryngii during postharvest storage based on hyperspectral imaging and multiple quality indicators

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FOOD CONTROL 2024年 166卷

作者： Wei, Ziyuan Liu, Haoling Xu, Jinghua Li, Yihang Hu, Jin Tian, Shijie Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China

During storage, Pleurotus eryngii is prone to spoilage, significantly affecting their commercial value. This study addresses the issue of inconsistent quality grading standards and low detection accuracy for Pleurotus eryngii. . It proposes a method for grading the quality of Pleurotus eryngii during post-harvest low-temperature (4 degrees C) storage, achieving rapid quality grading based on hyperspectral imaging technology combined with clustering algorithms and comprehensive evaluation methods. According to 11 physical and chemical indexes analyzed by K-means algorithm and principal component analysis, the samples were divided into three categories: high, medium and inferior. These clustering results were then used as labels to build classification models. We compared the feature extraction capabilities of convolutional autoencoder and competitive adaptive reweighted sampling, as well as the modeling effectiveness of support vector machine, linear discriminant analysis, and partial least squares discriminant analysis. The models' practicality was validated using external data. The results showed that the convolutional autoencoder-support vector machine model performed the best in quality classification, with an accuracy of 91.58%, an F1 score of 91.36%, a precision of 89.65%, and a recall of 90.60%. In summary, this study demonstrates the rationality of using clustering algorithms for quality grading of Pleurotus eryngii and highlights the significant improvement in prediction accuracy and generalization ability achieved by convolutional autoencoder.

关键词： Pleurotus eryngii K -means Principal component analysis Hyperspectral imaging convolutional autoencoder

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A Deep autoencoder-Based Approach for Suspicious Action Recognition in Surveillance Videos

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第3期49卷 3517-3532页

作者： Ahmed, Waqas Yousaf, Muhammad Haroon Univ Engn & Technol Dept Telecommun Engn Taxila 47050 Pakistan Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan Natl Ctr Robot & Automat NCRA Swarm Robot Lab Taxila Pakistan

In the recent era of technological advancements, surveillance cameras are installed in crowded areas to ensure public protection. In the video surveillance context, contents belonging to suspicious actions are very less in course of the surveillance stream. Therefore, manual monitoring of suspicious actions may become very exhaustive, which effects reliability and speed during emergencies due to monitoring tiredness, so the importance of suspicious action detection is very clear. We first address the issue of detecting suspicious activities from the surveillance videos with our proposed CNN-based autoencoder. The features are extracted using a three-dimensional convolutional neural network (C3D) and fed to our proposed autoencoder framework, which detects the localization of activity based on high reconstruction loss. For normal video clips, we have seen low reconstruction loss and the converse is seen for video clips containing suspicious actions. Secondly, we extract these suspicious clips from the long surveillance videos and use them to classify various suspicious actions with the help of our proposed generative adversarial network (GAN). We evaluate the performance of our work with benchmark datasets, namely UT interaction, hybrid crime action (HCA), and UCF crime. The results show the effectiveness of our work and as achieved accuracies are 97.5%, 89.6%, and 47.34% on UT interaction, HCA and UCF crime dataset, respectively.

关键词： convolutional autoencoder convolutional neural network Deep learning GAN UCF-crime

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Unsupervised Learning Approach for Risky Driving Behavior Identification on Expressways in C-ITS Environments

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 5146-5155页

作者： Kim, Dongmin Lee, Hwanpil Lee, Jooyoung Korea Adv Inst Sci & Technol Cho Chun Shik Grad Sch Mobil Daejeon 34051 South Korea Korea Expressway Corp Res Inst ICT Convergence Res Div Hwaseong 18489 Gyeonggi Do South Korea Hannam Univ Dept Ind & Management Engn Daejeon 34430 South Korea

Understanding the intricate relationship between driving behaviors, traffic crashes, and human factors is paramount in enhancing road safety. Human error, often stemming from risky driving behaviors, contributes significantly to traffic crashes. Identifying and mitigating these behaviors through advanced technologies and data analysis has become an important concern in the field of traffic safety management. This study introduces an unsupervised learning algorithm for detecting risky driving behaviors on expressways within Cooperative Intelligent Transport Systems (C-ITS) environments, employing deep clustering techniques to analyze individual driving patterns from Probe Vehicle Data (PVD). Utilizing data from 116 vehicles, including buses and heavy trucks, a convolutional Neural Network (CNN)-based autoencoder was employed to extract latent hierarchical features, facilitating the clustering of similar driving patterns. Elementary Driving Behaviors (EDBs) were identified for different vehicle types and driving statuses, serving as a foundation for detecting risky driving behaviors against the proposed criteria. The research revealed a clear positive correlation between detected risky driving behaviors and traffic crashes across the vehicle types. Furthermore, when comparing our model's criteria with traditional safety indexes, our proposed model demonstrated stronger correlations with traffic crashes, indicating its effectiveness in expressway driving environments. This research not only introduces a novel method for identifying risky driving behaviors but also underscores the importance of tailored traffic safety interventions in enhancing C-ITS environments.

关键词： Driver behavior Accidents Data mining Safety management Monitoring Feature extraction Unsupervised learning Probes Microscopy Intelligent transportation systems convolutional autoencoder cooperative intelligent transport systems elementary driving behavior expressways probe vehicle data risky driving behavior

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Health index estimation through integration of general knowledge with unsupervised learning

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 251卷

作者： Bajarunas, Kristupas Baptista, Marcia L. Goebel, Kai Chao, Manuel Arias Delft Univ Technol Fac Aerosp Engn NL-2926 HS Delft Netherlands Zurich Univ Appl Sci Inst Data Anal & Proc Design CH-8401 Winterthur Switzerland SRI Int 3333 Coyote Hill Rd Palo Alto CA 94304 USA Lulea Univ Technol S-97187 Lulea Sweden

Accurately estimating a Health Index (HI) from condition monitoring data (CM) is essential for reliable and interpretable prognostics and health management (PHM) in complex systems. In most scenarios, complex systems operate under varying operating conditions and can exhibit different fault modes, making unsupervised inference of an HI from CM data a significant challenge. Hybrid models combining prior knowledge about degradation with deep learning models have been proposed to overcome this challenge. However, previously suggested hybrid models for HI estimation usually rely heavily on system-specific information, limiting their transferability to other systems. In this work, we propose an unsupervised hybrid method for HI estimation that integrates general knowledge about degradation into the convolutional autoencoder's model architecture and learning algorithm, enhancing its applicability across various systems. The effectiveness of the proposed method is demonstrated in two case studies from different domains: turbofan engines and lithium batteries. The results show that the proposed method outperforms other competitive alternatives, including residual-based methods, in terms of HI quality and their utility for Remaining Useful Life (RUL) predictions. The case studies also highlight the comparable performance of our proposed method with a supervised model trained with HI labels.

关键词： Prognostics Health index Hybrid model Unsupervised learning convolutional autoencoder

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