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1. A Deep Long Short-Term Memory Network for Bearing Fault Diagnosis Under Time-Varying Conditions

   https://doi.org/10.1115/DETC2022-88808  

   Zhou, Kai (August 2022, ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology. 

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2. A Study of Linear Programming and Reinforcement Learning for One-Shot Game in Smart Grid Security

   https://doi.org/10.1109/IJCNN.2018.8489202  

   Paul, Shuva; Ni, Zhen (July 2018, 2018 International Joint Conference on Neural Networks (IJCNN))

   Smart grid attacks can be applied on a single component or multiple components. The corresponding defense strategies are totally different. In this paper, we investigate the solutions (e.g., linear programming and reinforcement learning) for one-shot game between the attacker and defender in smart power systems. We designed one-shot game with multi-line- switching attack and solved it using linear programming. We also designed the game with single-line-switching attack and solved it using reinforcement learning. The pay-off and utility/reward of the game is calculated based on the generation loss due to initiated attack by the attacker. Defender's defense action is considered while evaluating the pay-off from attacker's and defender's action. The linear programming based solution gives the probability of choosing best attack actions against different defense actions. The reinforcement learning based solution gives the optimal action to take under selected defense action. The proposed game is demonstrated on 6 bus system and IEEE 30 bus system and optimal solutions are analyzed. 

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3. Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

   https://doi.org/10.1109/cvprw63382.2024.00007  

   Wei, Chuheng; Wu, Guoyuan; Barth, Matthew J (June 2024, IEEE)

   A significant challenge in the field of object detection lies in the system's performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces "Feature Corrective Transfer Learning", a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model's ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm. 

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4. Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

   https://doi.org/10.1109/ISGTAsia54193.2022.10003627  

   Tan, Bendong; Zhao, Junbo; Liu, Weijia; Duan, Nan (January 2023, 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia))

   The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme. 

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5. An S-band Automatically Tunable Bandpass Filter Based on a Machine Learning Approach

   Adhikari, Pintu and (October 2021, 2021 IEEE 21st Annual Wireless and Microwave Technology Conference (WAMICON))

   This paper presents a novel automatic tuning mechanism that eliminates hand-tuning and is suitable for electronically-tunable microwave filters. The proposed method is based on a deep Q-learning approach using physics-based filter characteristic parameters like resonant frequency, bandwidth, insertion loss, and return loss. The whole tuning process is done automatically and does not require any pre-tuning or human expertise. Furthermore, unlike single-frequency post-production tuning techniques, the presented methodology is applicable to continuously-tunable filters covering a wide frequency range. This method is experimentally demonstrated on a 2-3.5 GHz evanescent-mode electronically-tunable bandpass filter. To the best of our knowledge, this is the first demonstration of such an automatic tuning mechanism where the user can specify any frequency of interest and the filter tunes automatically to that frequency within the entire operating range of the filter. 

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