Research on data augmentation for vital data using conditional GAN

Shiotani, Maho; Iguchi, Shino; Yamaguchi, Kazuo

doi:10.1109/gcce56475.2022.10014132

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…The calculation steps of population entropy are as follows. The similarity between an individual P i and every other individual P j in the population is calculated by adding up their Euclidean distances, as shown in Equation (21).…”

Section: Adaptive Entropy Control Strategymentioning

confidence: 99%

“…During training, GANs leverage an adversarial loss function that simultaneously minimizes the generator's loss and maximizes the discriminator's loss, leading to the generation of high-quality synthetic data. The effectiveness of GANs has been demonstrated in various applications, such as image and text generation [20], data augmentation [21], and style transfer [22].…”

Section: Introductionmentioning

confidence: 99%

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A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Qian,

Xu,

Chen

et al. 2024

Mathematics

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The multi-objective evolutionary algorithm optimization (MOEA) is a challenging but critical approach for tackling complex data analysis problems. However, prevailing MOEAs often rely on single strategies to obtain optimal solutions, leading to concerns such as premature convergence and insufficient population diversity, particularly in high-dimensional data scenarios. In this paper, we propose a novel adversarial population generation algorithm, APG-SMOEA, which synergistically combines the benefits of MOEAs and Generative Adversarial Networks (GANs) to address these limitations. In order to balance the efficiency and quality of offspring selection, we introduce an adaptive population entropy strategy, which includes control parameters based on population entropy and a learning pool for storing and retrieving optimal solutions. Additionally, we attempt to alleviate the training complexity and model collapse problems common in GANs with APG-SMOEA. Experimental results on benchmarks demonstrate that the proposed algorithm is superior to the existing algorithms in terms of solution quality and diversity of low-dimensional or high-dimensional complex data.

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Section: Adaptive Entropy Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Qian,

Xu,

Chen

et al. 2024

Mathematics

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“…In healthcare, finding high-volume lifelogging data is challenging, and due to privacy and ethical issues, most datasets are private. Synthetic data generation techniques, such as GC [12], CTGAN [13], and TABGAN [14][15][16], have been used for synthetic data generation with a focus on large-scale data sharing, experimentation, and analysis without revealing sensitive information. We have performed a comparative study with statistical metrics to find the best synthetic data generation method from our real MOX2-5 dataset.…”

Section: Aim Of the Studymentioning

confidence: 99%

Semantic Representation of Physical Activity Sensor Observations and Comparative Analysis of Real and Synthetic Datasets: A Proof-of-Concept-Study with MOX2-5 Sensor

Chatterjee,

Gerdes,

Prinz

et al. 2023

Preprint

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Background Daily activity of humans is monitored at a large scale automatically by devices such as mobile phones and wearables. This produces immense amounts of data that can be used to get a better understanding of human behavior over time. To understand this data and its possibilities, a structured and controlled collection process is required. Physical activity monitoring using wearable sensors has attracted prevalent attention in healthcare, sports science, and fitness applications. However, ensuring the availability of diverse and comprehensive datasets for research and algorithm development can be challenging. Objective We emphasize the importance of semantic representation for physical activity sensor observations to enable data interoperability and advanced analytics. In this proof-of-concept study, we propose an approach to improve the usability of physical activity datasets and highlight ethical considerations by generating synthetic datasets using medical-grade (CE certified) sensor. Moreover, our study presents a comparative analysis between real and synthetic activity datasets, evaluating their utilities to address model bias and fairness in predictive analysis. Methods We design and develop an ontology for semantic representation of physical activity sensor observations and predictive analysis on collected data with MOX2-5 activity sensors. The MOX2-5 activity monitoring device can collect and transmit high-resolution activity data such as activity intensity, weight-bearing, sedentary, standing, low physical activity, moderate physical activity, vigorous physical activity, and steps per minute. We collected physical activity data from 16 adults (Male: 12; Female: 4) for 30–45 days (about 1 and a half months). It produced a volume of 539 records which is small. Thus, we utilize different synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN) to enhance the dataset with synthetic data. For both the real and synthetic datasets, we developed a Multilayer Perceptron (MLP) classification model to classify daily physical activity levels. Results The results highlight that semantic ontology is suitable for semantic search, knowledge representation, data integration, reasoning, and capturing the meaning and relationships between data. The analysis proves the hypothesis that the efficiency of predictive models grows with the increasing volume of additional synthetic training data. Conclusions The potential of ontology and Generative AI may accelerate research and innovation in the field of behavioral monitoring. Moreover, the presented data (both real MOX2-5 and its synthetic version) will be helpful in the creation of robust methods for the classification of activity types and different research directions in connection to synthetic data such as model efficiency, detection of generated data and data privacy.

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“…At present, the method based on deep learning has made remarkable achievements in the field of fault diagnosis. The main methods include Convolutional neural network [3][4] generative adversarial network [5][6] , Skowron [7] uses FFT combined with CNN neural network to achieve fault diagnosis under 100 groups of samples. Although the accuracy reaches 95%, the network generalization ability of this method is poor due to the scarcity of data.…”

Section: Introductionmentioning

confidence: 99%

Research on fault diagnosis of motor interturn short circuit based on deep learning

ding,

Chang,

huang

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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Fault diagnosis is an effective means to improve the reliability of the electric drive system of new energy vehicles. At present, inter turn short circuit faults in new energy vehicle motors are mainly detected using single fault features and small sample fault datasets. This method has low fault diagnosis accuracy and poor robustness. In this paper, a combined sample expansion strategy of conditional generative adversarial networks for attention mechanism optimization is proposed, and a fault diagnosis method combining improved Convolutional neural network is proposed. First, the combined characteristic data set of third harmonic and negative sequence current is made, and input into Attention CGAN to realize the expansion of original data samples. Then the expanded data and the original data are combined into a new data set, which is input into the improved CNN for training, and finally the fault diagnosis results are obtained. The experimental results show that compared to traditional fault diagnosis methods, the proposed fault diagnosis method based on combined features and data expansion improves the fault diagnosis accuracy by about 6.92%.

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Research on data augmentation for vital data using conditional GAN

Cited by 5 publications

References 2 publications

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Semantic Representation of Physical Activity Sensor Observations and Comparative Analysis of Real and Synthetic Datasets: A Proof-of-Concept-Study with MOX2-5 Sensor

Research on fault diagnosis of motor interturn short circuit based on deep learning

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