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publications
Data Augmentation for Industrial Multivariate Time Series via a Spatial and Frequency Domain Knowledge GAN
Published in 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), 2022
The study utilized an AutoEncoder to reduce the correlation between variables, enabling the selection of representative variables for examining mutual information among those with a correlation value exceeding 0.7. Additionally, wavelet decomposition was employed to transform the data from the time domain to the frequency domain, facilitating the extraction of the variance component. For evaluation, the GAN-TEST and maximum mean discrepancy (MMD) metrics were used, demonstrating that the difference between the original and generated data in MMD was below 0.1, with an accuracy difference in GAN-TEST of less than 10%.
Recommended citation: J. C. Lin and F. Yang, "Data Augmentation for Industrial Multivariate Time Series via a Spatial and Frequency Domain Knowledge GAN," 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), Vancouver, BC, Canada, 2022, pp. 244-249, doi: 10.1109/AdCONIP55568.2022.9894177.
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TEST
Published in Robotics and Automation Letters (RA-L), 2022
The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results.
Recommended citation: L. Kastner et. al. (2022) "Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments" Robotics and Automation Letters.
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Generating Election Data Using Deep Generative Models
Published in AI4SG workshop at AAAI-23, 2023
The study involved generating election data using deep generative models by utilizing strict order complete (SOC) election data, which contains comprehensive information about elections. The SOC election data was converted into images to account for the relationships between the rankings of alternatives. A conditional Deep Convolutional GAN (cDCGAN) was employed for learning, and an edge-trimming penalty was proposed to prevent overfitting or underfitting. Additionally, the quality of the generated data was assessed using an Auto-Encoder reconstruction error method, and a memorization metric was applied to evaluate the occurrence of overfitting
Recommended citation: Lin, Jui Chien, et al. "Generating Election Data Using Deep Generative Models."
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LLM-augmented Preference Learning from Natural Language
Published in 2024 Economics and Computation workshop, 2024
The study focused on using large language models (LLMs) to tackle the scarcity of preference data. By generating preference data and testing various LLMs, the research identified optimal prompts to enhance understanding of preferences in texts. LLama2 was used to condense extensive text, emphasizing preference detection, while BERT’s output, guided by instructive sentences, improved classification accuracy. Techniques like masking and segment embedding were also employed to aid in entity comparison.
Recommended citation: Kang, I., Ruan, S., Ho, T., Lin, J. C., Mohsin, F., Seneviratne, O., & Xia, L. (2023). LLM-augmented Preference Learning from Natural Language. arXiv preprint arXiv:2310.08523.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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