A New Data Augmentation Method for Time Series Wearable Sensor Data Using a Learning Mode Switching-Based DCGAN

Jeon, Haneul; Lee, Donghun

doi:10.1109/lra.2021.3103648

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Haneul Jeon et al [ 12 ] proposed a novel gesture recognition method based on wearable IMU sensors and experimentally demonstrated that the method is appropriate for gesture recognition under significant changes in the subject’s body alignment during gestures. In addition, to improve the accuracy of wearable sensor-based gesture recognition algorithms, Haneul Jeon et al [ 13 ] proposed a DCGAN structure with a mode switcher for data enhancement of time-series sensor data. Although the sensor-based approach can sense gesture activity with high accuracy, the approach is not convenient because it requires the user to wear the corresponding device while drawing the gesture, and the user inevitably forgets or is not comfortable wearing the device.…”

Section: Related Workmentioning

confidence: 99%

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Zhang

Yang

et al. 2023

Sensors

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Traditional human-computer interaction technology relies heavily on input devices such as mice and keyboards, which limit the speed and naturalness of interaction and can no longer meet the more advanced interaction needs of users. With the development of computer vision (CV) technology, research on contactless gesture recognition has become a new research hotspot. However, current CV-based gesture recognition technology has the limitation of a limited number of gesture recognition and cannot achieve fast and accurate text input operations. To solve this problem, this paper proposes an over-the-air handwritten character recognition system based on the coordinate correction YOLOv5 algorithm and a lightweight convolutional neural network (LGR-CNN), referred to as Air-GR. Unlike the direct recognition of captured gesture pictures, the system uses the trajectory points of gesture actions to generate images for gesture recognition. Firstly, by combining YOLOv5 with the gesture coordinate correction algorithm proposed in this paper, the system can effectively improve gesture detection accuracy. Secondly, considering that the captured gesture coordinates may contain multiple gestures, this paper proposes a time-window-based algorithm for segmenting the gesture coordinates. Finally, the system recognizes user gestures by plotting the segmented gesture coordinates in a two-dimensional coordinate system and feeding them into the constructed lightweight convolutional neural network, LGR-CNN. For the gesture trajectory image classification task, the accuracy of LGR-CNN is 13.2%, 12.2%, and 4.5% higher than that of the mainstream networks VGG16, ResNet, and GoogLeNet, respectively. The experimental results show that Air-GR can quickly and effectively recognize any combination of 26 English letters and numbers, and its recognition accuracy reaches 95.24%.

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Section: Related Workmentioning

confidence: 99%

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Zhang

Yang

et al. 2023

Sensors

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“…The overall self-supervised loss is a combination of two temporal contrastive losses and contextual contrastive losses, as shown in Equation (19).…”

Section: Context Comparative Learning Modulementioning

confidence: 99%

“…The need for generalization is especially important for real-world data, which can help networks overcome small [13] or category-imbalanced datasets [14,15]. Most time series data augmentation techniques are based on random transformations of training data, such as adding random noise [16], slicing or cropping [17], scaling [18], random warping in the time dimension [13,15], frequency warping [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Xie,

Tang,

Liu

et al. 2023

Processes

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Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model.

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“…(Step 1 and 2) Train the GAN and Generate Segments. In TS-LSH we use DCGAN [55], a deep convolutional generative adversarial model frequently used for time series augmentation [30,71]. We train a new GAN model for a given seed dataset by splitting the original time series into short and overlapping segments used by the discriminator.…”

Section: Ts-lsh Generation Techniquementioning

confidence: 99%

TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications

Khelifati,

Khayati,

Dignös

et al. 2023

Proc. VLDB Endow.

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Time series databases are essential for the large-scale deployment of many critical industrial applications. In infrastructure monitoring, for instance, a database system should be able to process large amounts of sensor data in real-time, execute continuous queries, and handle complex analytical queries such as anomaly detection or forecasting. Several benchmarks have been proposed to evaluate and understand how existing systems and design choices handle specific use cases and workloads. Unfortunately, none of them fully covers the peculiar requirements of monitoring applications. Furthermore, they fall short of providing an automated way to generate representative real-world data and workloads for testing and evaluating these systems. We present TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. Our key contributions consist of (1) representative queries that meet the requirements that we collected from a water monitoring use case, and (2) a new scalable data generator method based on Generative Adversarial Networks (GAN) and Locality Sensitive Hashing (LSH). We demonstrate, through an extensive set of experiments, how TSM-Bench provides a comprehensive evaluation of the performance of seven leading time series database systems while offering a detailed characterization of their capabilities and trade-offs.

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A New Data Augmentation Method for Time Series Wearable Sensor Data Using a Learning Mode Switching-Based DCGAN

Cited by 6 publications

References 26 publications

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications

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