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Attention-Based Regularisation for Improved Generalisability in Medical Multi-Centre data 22

Attention-Based Regularisation for Improved Generalisability...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Silva, Daniel Agrotis, Georgios Beets-Tan, Regina Teixeira, Luis F. Silva, Wilson The Netherlands Cancer Institute Department of Radiology Amsterdam Netherlands Inesc Tec Porto Portugal University of Porto Faculty of Engineering Porto Portugal University Hospital of Larissa Greece Grow School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht Netherlands

ISBN: (纸本)9798350345346

Deep learning models are tremendously valuable in several prediction tasks, and their use in the medical field is spreading abruptly, especially in computer vision tasks, evaluating the content in X-rays, CTs or MRIs. These methods can save a significant amount of time for doctors in patient diagnostics and help in treatment planning. However, these models are significantly sensitive to confounders in the training data and generally suffer a performance hit when dealing with out-of-distribution data, affecting their reliability and scalability in different medical institutions. Deep learning research on Medical datasets may overlook essential details regarding the image acquisition procedure and the preprocessing steps. This work proposes a data-centric approach, exploring the potential of attention maps as a regularisation technique to improve robustness and generalisation. We use image metadata and explore self-attention maps and contrastive learning to promote feature space invariance to image disturbance. Experiments were conducted using Chest X-ray datasets that are publicly available. Some datasets contained information about the windowing settings applied by the radiologist, acting as a source of variability. The proposed model was tested and outperformed the baseline in out-of-distribution data, serving as a proof of concept. © 2023 IEEE.

关键词： attention maps contrastive learning generalisability multi-centre data windowing settings

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CLLA: A Fault Diagnosis Method for Abnormal Fluctuation of Rolling Force in Hot Continuous Rolling System 22

CLLA: A Fault Diagnosis Method for Abnormal Fluctuation of R...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Xu, Xinkai Sun, Chenxi Qi, Zheng Chen, Xuejiao Automation Research and Design Institute of Metallurgical Industry Co. Ltd. Beijing China Central Iron & Steel Research Institute China China Iron and Steel Research Institute Technology Group Co. Ltd. Iron and Steel Green Intelligent Technology Center Beijing China

ISBN: (纸本)9798331527471

In this paper, a CLLA (CNN-LSTM-Linear Attention) based fault diagnosis method for the hot strip mill system is proposed. This method introduces a CNN layer to enhance the LSTM's ability to understand the spatial dimensions of the data. Additionally, it introduces a Linear Attention layer to improve the role of important time steps in the LSTM and to meet the need for rapid response in production. The method aims to diagnose faults related to abnormal fluctuations in rolling force caused by the insufficiency of the automatic control system itself in the automated production of steel hot rolling systems. It also aims to provide preventive maintenance to reduce the significant losses incurred by related enterprises. Through experimental verification, the CLLA method proposed in this paper demonstrates better detection accuracy compared to the CNN-LSTM model and LSTM model, achieving 100% accuracy on the dataset. Furthermore, the diagnosis speed is higher than that of CNN-LSTM-Attention, making it suitable for engineering needs. © 2024 IEEE.

关键词： Preventive maintenance

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Research on Task Scheduling Methods for intelligent Connected Vehicles with End-to-End Delay Constraints 22

Research on Task Scheduling Methods for Intelligent Connecte...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Yang, Shichun Chen, Wenlong Zhang, Boao Duan, Qianlong Hao, Bide Yan, Xiaoyu School of Transportation Science and Engineering Bei Hang University Beijing China Research Institute of Aero-Engine Bei Hang University Beijing China

ISBN: (纸本)9798331527471

With the rapidly growing intelligent Connected Vehicle (ICV) industry, modern Advanced Driver Assistance Systems (ADAS) integrate critical and non-critical software functions on the same hardware platform. These functions have complex timing requirements and associated dependencies. To ensure the real-time safety of critical automotive applications, complex task chains with dependencies generated by automotive applications (e.g. sensor data acquisition, data processing, and control execution) must meet the worst end-to-end latency. The resulting task scheduling problem requires the assignment of tasks to available cores for execution and the generation of static schedules that satisfy the timing constraints of the tasks and the end-to-end latency of the task chain. In this paper, a task scheduling and task mapping method based on an intelligent heuristic algorithm is proposed for the ADAS platform, and simulated scheduling based on the EDF algorithm. The optimal task scheduling scheme is selected by calculating the fitness function by the intelligent heuristic algorithm, and the simulated annealing algorithm (SA) is selected as the benchmark for the experiment. The experimental results show that the grey wolf optimizer converges faster and finds a better scheduling scheme, which reduces the total time of scheduling tasks. © 2024 IEEE.

关键词： Advanced driver assistance systems

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A Comparative Study of Hough Transform and PCA for Bolt Orientation Detection 22

A Comparative Study of Hough Transform and PCA for Bolt Orie...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Gambale, Antonio Coleman, Sonya Kerr, Dermot Vance, Philip Kerr, Emmett Fermuller, Cornelia Aloimonos, Yiannis School of Computing Engineering & Intelligent Systems Ulster University Londonderry United Kingdom Dept. of Electronic & Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland United States

ISBN: (纸本)9798331527471

In the fields of manufacturing and robotics, accurately determining the orientation of manufacturing components, such as bolts, is a critical yet challenging problem due to the limitations of existing detection methods. This study introduces a novel methodology for addressing this issue, leveraging traditional computer vision techniques, by proposing a streamlined approach that exploits the inherent geometric properties of bolts for orientation detection. Two methods are presented to ascertain the initial axis angle of the bolt: the Progressive Probabilistic Hough Transform (PPHT) and Principal Component Analysis (PCA). These methods are used in conjunction with a novel tip direction detection approach. The results of the study demonstrate consistent accuracy in angle determination, with PPHT and PCA both achieving angular deviations below ±0.5◦ in the simple dataset, and PCA showing enhanced robustness in the dataset degraded by shadows, with a maximum error under ±1.5◦. This research not only reaffirms the viability of fundamental computer vision techniques in modern robotic applications but also sets a precedent for simple, generalisable, and reliable orientation detection solutions. These methods effectively bridge the gap between highly specialised machine learning systems, which often require tailored, complex models and extensive training data, and more universally applicable, straightforward approaches. © 2024 IEEE.

关键词： Bolts

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Multi-Context Dual Hyper-Prior Neural Image Compression 22

Multi-Context Dual Hyper-Prior Neural Image Compression

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Khoshkhahtinat, Atefeh Zafari, Ali Mehta, Piyush M. Akyash, Mohammad Kashiani, Hossein Nasrabadi, Nasser M. West Virginia University Dept. of Computer Science & Electrical Engineering WV United States West Virginia University Dept. of Mechanical & Aerospace Engineering WV United States

ISBN: (纸本)9798350345346

Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-based transform, which lacks the ability to model long-range dependencies, primarily due to the limited receptive field of the convolution operation. To address this limitation, we propose a Transformer-based nonlinear transform. This transform has the remarkable ability to efficiently capture both local and global information from the input image, leading to a more decorrelated latent representation. In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation. To further improve the entropy model, we add a global context that leverages distant relationships to predict the current latent more accurately. This global context employs a causal attention mechanism to extract long-range information in a content-dependent manner. Our experiments show that our proposed framework performs better than the state-of-the-art methods in terms of rate-distortion performance. © 2023 IEEE.

关键词： Attention Entropy model Global context Hyper-prior Neural image compression Transformer

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Accelerated Real-Time Classification of Evolving data Streams using Adaptive Random Forests 22

Accelerated Real-Time Classification of Evolving Data Stream...

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22nd international conference on Field Programmable Technology (ICFPT)

作者： Ridder, Frank Chen, Kuan-Hsun Alachiotis, Nikolaos Univ Twente Fac EEMCS Enschede Netherlands

ISBN: (纸本)9798350359114

Machine learning is increasingly applied to a wide range of real-time applications, with classification tasks playing a critical role in enabling intelligent decision-making. However, the phenomenon of concept drift, in which the underlying data distribution changes over time, presents a significant challenge for maintaining the accuracy of machine learning models in applications with evolving data streams, such as health monitoring or sensor data analysis. The Adaptive Random Forest (ARF) algorithm addresses this issue by coupling multiple Hoeffding Trees with a drift detector to adapt to concept drift. As training a forest of growing decision trees is a high-latency operation, custom-hardware acceleration is needed to meet the stringent latency requirements for real-time use of ARF. To the best of our knowledge, this work describes the first FPGA implementation of the ARF algorithm, focusing on achieving high hardware efficiency, scalability, and adaptability to different datasets. We present a parameterized design that incorporates various levels of parallelism, resource sharing, and pipelining, and delivers 15x-79x faster execution than a 40-core CPU with a maximum accuracy loss of 13%. Furthermore, our design outperforms a state-of-the-art GPU implementation, achieving 3x-21x faster execution while maintaining accuracy scores in the range of 0.3% to 15% of the GPU ARF implementation.

关键词： Adaptive Random Forest data Streams Online learning Concept Drift FPGA

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Comparative Study on intelligent Modulation Recognition Models Based on Raw data 2

Comparative Study on Intelligent Modulation Recognition Mode...

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2nd international conference on Image Processing, Computer Vision and Machine learning, ICICML 2023

作者： Xie, Didi Zhao, Yurui Wang, Haoyuan Huang, Zhitao Wang, Xiang National University of Defense Technology College of Electronic Science and Engineering Changsha China People's Liberation 61905 Army of China Guangzhou China

ISBN: (纸本)9798350331417

In recent years, intelligent modulation recognition methods for communication signals based on deep learning have attracted attention due to their advantages in feature extraction and recognition performance. However, there is limited research on the performance boundaries and optimization criteria of neural network models in existing domestic and international studies, and the datasets used in the research are relatively simple. This paper first proposes an intelligent modulation recognition model based on deep learning;Then a super dataset containing 22 traditional and emerging complex modulation modes is generated by simulation;Finally, based on this dataset, a comprehensive comparative analysis is conducted from multiple perspectives to investigate the effects of data slicing, selection and design of network models and parameters on the modulation recognition performance of the model. General patterns were obtained, providing valuable references for subsequent research and model application. © 2023 IEEE.

关键词： data Preprocessing Deep learning intelligent Modulation Recognition Network Model Parameter Optimization

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intelligent Pressure Reconstruction of Centrifugal Compressor Based on Gappy POD and Sparse Measurement 22

Intelligent Pressure Reconstruction of Centrifugal Compresso...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Li, Xuhui Hu, Chenxing Peng, Jingyu School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331527471

The capacity to reconstruct the coherent characteristics of fluid flows from limited observational data holds paramount significance for applications across the physical and engineering sciences. The present study conducted steady-state experiments on a centrifugal compressor, utilizing a densely distributed array of static pressure probes to measure the actual static pressure distribution within the flow field, thereby obtaining the aerodynamic performance characteristics of the centrifugal compressor. Furthermore, the study employed randomly distributed sparse pressure sensors and numerical model data of the centrifugal compressor, applying the Gappy POD method to achieve high-fidelity reconstruction of the static pressure distribution on the impeller shroud wall. By comparing the reconstructed results with the experimental measurements, the overall reliability of the data-driven algorithm was validated, and the differences in the subtle flow field characteristics were identified. This not only deepened the understanding of the complex flow dynamics and their underlying patterns within turbomachinery, but provided important theoretical foundations and technical support for the optimization design, performance prediction, and flow control of turbomachinery. © 2024 IEEE.

关键词： Centrifugal compressors

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A Bayesian Hierarchical Analysis on the Disparity of Emergency Department Visits for COVID-19: A Cohort Study Using National COVID Cohort Collaborative (N3C) data 22

A Bayesian Hierarchical Analysis on the Disparity of Emergen...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Jiang, Sihang Loomba, Johanna Zhou, Andrea Sharma, Suchetha Sengupta, Saurav Brown, Donald E. School of Engineering and Applied Science University of Virginia United States University of Virginia United States School of Data Science University of Virginia United States

ISBN: (纸本)9798350345346

Since the COVID-19 pandemic in 2020, there are numerous studies and researches on the long term effect of COVID-19 on both patient level and social level with disparities noted in infection rates and outcomes. However, differences in the COVID related healthcare decisions after patients present for care (e.g. hospitalization after emergency department visit) has not been studied much at a national level. The National COVID Cohort Collaborative (N3C) provides researchers with abundant data collected from different clinical sites, making it suitable for Bayesian hierarchical modeling while analyzing the disparity in hospitalization after emergency department visit, where prior information or belief could be easily included in the modeling process by adjusting the prior distribution of parameters. In this analysis, we select demographic information (age, sex, race and ethnicity) and the Charlson Comorbidity Index (CCI) as features and study the relationship between these features and whether a patient would be hospitalized after having a COVID related visit to an emergency department (ED). © 2023 IEEE.

关键词： Bayesian analysis COVID-19 related healthcare disparity generalized linear model hierarchical modeling

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Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media data 22

Conceptualizing Suicidal Behavior: Utilizing Explanations of...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Nguyen, Van Minh Nur, Nasheen Stern, William Mercer, Thomas Sen, Chiradeep Bhattacharyya, Siddhartha Tumbiolo, Victor Goh, Seng Jhing Florida Institute of Technology Mathematical Sciences MelbourneFL32901 United States Florida Institute of Technology Electrical Engineering and Computer Science MelbourneFL32901 United States Florida Institute of Technology Mechanical and Civil Engineering MelbourneFL32901 United States

ISBN: (纸本)9798350345346

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using artificial intelligence (AI) techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art language model, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using large language models during inference. © 2023 IEEE.

关键词： Large Language Model Layer Integrated Gradients Natural Language Processing Suicidal Ideation TF-IDF Token Attribution

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