Anirudh Tunga

Anirudh Tunga, Michael J. Mueterthies, Jonathan Nistor

Nuclear power plant operators face significant challenges due to unpredictable deviations between offline and online thermal limits, a phenomenon known as thermal limit bias, which leads to conservative design margins, increased fuel costs, and operational inefficiencies. This work presents a deep learning based methodology to predict and correct this bias for Boiling Water Reactors (BWRs), focusing on the Maximum Fraction of Limiting Power Density (MFLPD) metric used to track the Linear Heat Generation Rate (LHGR) limit. The proposed model employs a fully convolutional encoder decoder architecture, incorporating a feature fusion network to predict corrected MFLPD values closer to online measurements. Evaluated across five independent fuel cycles, the model reduces the mean nodal array error by 74 percent, the mean absolute deviation in limiting values by 72 percent, and the maximum bias by 52 percent compared to offline methods. These results demonstrate the model's potential to meaningfully improve fuel cycle economics and operational planning, and a commercial variant has been deployed at multiple operating BWRs.

Muhammad Rizki Oktavian, Anirudh Tunga, Jonathan Nistor et al.

One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If the reactor goes subcritical before the end of a cycle, it may result in early coastdown as the fuel in the core is already depleted. On contrary, if the reactor still has significant excess reactivity by the end of a cycle, the remaining fuels will remain unused. In both cases, the plant may lose a significant amount of money. This work proposes an innovative method based on a data-driven deep learning model to estimate the excess criticality of a boiling water reactor.

Sai Vidyaranya Nuthalapati, Anirudh Tunga

Automatic classification of pests and plants (both healthy and diseased) is of paramount importance in agriculture to improve yield. Conventional deep learning models based on convolutional neural networks require thousands of labeled examples per category. In this work we propose a method to learn from a few samples to automatically classify different pests, plants, and their diseases, using Few-Shot Learning (FSL). We learn a feature extractor to generate embeddings and then update the embeddings using Transformers. Using Mahalanobis distance, a class-covariance-based metric, we then calculate the similarity of the transformed embeddings with the embedding of the image to be classified. Using our proposed architecture, we conduct extensive experiments on multiple datasets showing the effectiveness of our proposed model. We conduct 42 experiments in total to comprehensively analyze the model and it achieves up to 14% and 24% performance gains on few-shot image classification benchmarks on two datasets. We also compile a new FSL dataset containing images of healthy and diseased plants taken in real-world settings. Using our proposed architecture which has been shown to outperform several existing FSL architectures in agriculture, we provide strong baselines on our newly proposed dataset.

Anirudh Tunga, Sai Vidyaranya Nuthalapati, Juan Wachs

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between the hearing and vocally impaired community and the rest of the society. Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. However, recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations, and other movements. Recent pose-based architectures for WSLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information. We tackle the problem of WSLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion. Our proposed architecture explicitly captures the spatial interactions in the video using a Graph Convolutional Network (GCN). The temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT). Experimental results on WLASL, a standard word-level sign language recognition dataset show that our model significantly outperforms the state-of-the-art on pose-based methods by achieving an improvement in the prediction accuracy by up to 5%.