Time Series Classification Based on Image Transformation Using Feature Fusion Strategy

Jiang, Wentao; Zhang, Dabin; Ling, Liwen; Lin, Ruibin

doi:10.1007/s11063-022-10783-z

Cited by 17 publications

(3 citation statements)

References 32 publications

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“…The use of data mining [16,17] technology is becoming more widespread. Time series [18,19], which also contains speech and financial data, is one type of typical data. However, time series data frequently increase with time, which reduces the efficiency of the traditional decision tree method to categorize, perform regression analysis on, and forecast this kind of data.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

Ji,

Bao,

et al. 2023

Front. Phys.

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This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

Ji,

Bao,

et al. 2023

Front. Phys.

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show abstract

“…Xu et al [34] proposed an unsupervised anomaly detection model based on NVAE for univariate time series, namely T2IVAE, which transforms the 1D time series into a 2D image as input to capture more information on time-series correlation. Time-series images were generated using RP, GAF, and MTF algorithms for time-series data in some works [35][36][37]. Wen et al [38] developed a timedomain signal-to-image transformation method in which raw signals fulfill the pixels of the image by sequence, known as the gray pixel image.…”

Section: Time Series To Image Transformationmentioning

confidence: 99%

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

Li,

Yong

et al. 2023

Systems

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Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety.

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“…However, traditional methods struggled to handle complex scenes and maintain global consistency, requiring a substantial amount of manual intervention. In recent years, based on better performance and more applicable scenarios, deep learning was widely used in image fusion [4] and other vision field. Li [5] proposed a DenseNet encoder and used CNN for feature fusion and image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Dual-branch visible and infrared image fusion transformer

Shi

Wang

Pan

et al. 2023

Second International Conference on Optoelectronic Information and Computer Engineering (OICE 2023)

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The process of combining features from two images of different sources to generate a new image is called image fusion. In order to adapt to different application scenarios, deep learning was widely used. However, existing fusion networks focued on the extraction of local information, neglected the long-term dependencies. In order to improve the defect, a fusion network based on Transformer was proposed. To accommodate our experimental equipment, we made some modifications to Transformer. A dual-branch autoencoder network was designed with detail and semantic branches, the fusion layer consists of CNN and Transformer, and the decoder reconstructs the features to get the fused image. A new loss function was proposed to train the network. Based on the results, an infrared feature compensation network was designed to enhance the fusion effect. In several metrics that we focus on, we compared with several other algorithms. As the experiments on some datasets, our method had improvement on SCD, SSIM and MS-SSIM metrics, and was basically equal to other algorithms on saliency-based structural similarity, weighted quality assessment, and dge-based structural similarity. From the experimental results, we can see that our method was feasible.

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Time Series Classification Based on Image Transformation Using Feature Fusion Strategy

Cited by 17 publications

References 32 publications

RETRACTED: Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

RETRACTED: Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

Dual-branch visible and infrared image fusion transformer

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