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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview 17

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts 5

Building blocks for complex tasks: Robust generative event e...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Zhou, Sitong Yetisgen, Meliha Ostendorf, Mari University of Washington Electrical & Computer Engineering United States University of Washington Biomedical and Health Informatics United States

ISBN: (纸本)9781959429883

This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ BERT-based task-specific classification layers. We then develop methods that reduce the inference cost of the model, making largescale corpus processing more feasible for clinical applications. Specifically, we introduce a generative technique that decomposes complex tasks into smaller subtask blocks, which improves a single-pass model when combined with multitask training. In addition, we leverage target-domain contexts during inference to enhance domain adaptation, enabling use of smaller models. Analyses offer insights into the benefits of different cost reduction strategies. © 2023 Association for Computational Linguistics.

关键词： Cost reduction

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Navigating Complexity: Data Structure Impact on Path Planning Algorithms in Autonomous Systems 6

Navigating Complexity: Data Structure Impact on Path Plannin...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Emad, Hend Mabrouk, Mai S. Elnokrashy, Ahmed F. Nile University Biomedical Informatics Giza Egypt Nile University Computer Science Giza Egypt Benha University Electrical Engineering Benha Egypt

ISBN: (纸本)9798350378511

Navigating a busy cityscape with a fleet of autonomous vehicles requires each to seamlessly maneuver through traffic with split-second decisions. Path planning is the backbone of such advanced machinery applications, from mobile robots to unmanned ground vehicles, where the choice of data structure plays a pivotal role in determining memory usage, planning time, and algorithm reliability. This research rigorously evaluates Grassfire, Dijkstra, A *, and RRT* algorithms based on key metrics using real GPS readings, across diverse environment representations and obstacle conditions. Our findings provide guidance for selecting the optimal algorithms and data structures tailored to specific environmental complexities. By evaluating the performance of these algorithms under various environmental conditions, the study offers insights that can help researchers and practitioners choose the most suitable algorithms and data structures for their autonomous vehicle applications. The ability to match the algorithm and data structure to the specific environmental challenges faced by autonomous vehicles is crucial for ensuring efficient and reliable path planning, which is the backbone of advanced machinery applications. © 2024 IEEE.

关键词： Robot programming

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces 38

Exploring the trade-off between deep-learning and explainabl...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cubillos, Luis H. Revach, Guy Mender, Matthew J. Costello, Joseph T. Temmar, Hisham Hite, Aren Zutshi, Diksha Wallace, Dylan M. Ni, Xiaoyong Kelberman, Madison M. Willsey, Matthew S. van Sloun, Ruud J.G. Shlezinger, Nir Patil, Parag Draelos, Anne Chestek, Cynthia A. Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan United States Biointerfaces Institute Neuroscience Institute University of Michigan United States Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite thi

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Causal Effects of Environmental Exposures and Biological Traits on the Difference between Phenotypic and Chronological Ages

Causal Effects of Environmental Exposures and Biological Tra...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Pala, Daniele Xie, Yuezhi Xu, Jia Zhang, Yuqin Shen, Li University of Pavia Department of Computer Electrical and Biomedical Engineering Pavia Italy University of Pennsylvania Department of Biostatistics Epidemiology and Informatics Philadelphia United States

ISBN: (纸本)9798350337488

Aging is a physiological process associated with numerous cardiovascular, degenerative and neurological conditions. The current demographic changes in the world, characterized by an increasing average age especially in Europe and North America, are causing an increment in the prevalence of such conditions, leading to a public health problem as more resources are required to manage treatments. Recent research has defined a new score named Phenotypic Age, i.e. an adjusted age that takes into account the current health status considering a series of biomarkers, that can be useful to provide a more precise estimation of the probability of developing aging-related conditions. Prevention of such conditions can be performed more efficiently by studying the mechanisms that lead to an increased phenotypic age rather than attempting to treat a patient that has already a high health risk. In this work, we combine pairwise association techniques and mediation analysis to define a strategy to investigate the inner causal mechanisms that lead from specific environmental exposures to an increasing gap between phenotypic and chronological age, considering the influence of biological variables. Four environmental exposures and 11 biological traits have been identified in the NHANES dataset, and each trait has been tested as a mediation variable for each exposure. Almost all mediations reported significant indirect effects with specific results that provide new insights into the causal mechanisms that lead to a deranged aging rate. © 2023 IEEE.

关键词： Aging Exposome Mediation Analysis Phenotypic Age Public Health

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A comprehensive evaluation of large language models in mining gene relations and pathway knowledge

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Quantitative Biology 2024年第4期12卷 360-374页

作者： Muhammad Azam Yibo Chen Micheal Olaolu Arowolo Haowang Liu Mihail Popescu Dong Xu Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouriUSA Bond Life Sciences Center University of MissouriColumbiaMissouriUSA Institute for Data Science and Informatics University of MissouriColumbiaMissouriUSA Department of Biomedical Informatics Biostatistics and Medical EpidemiologyUniversity of MissouriColumbiaMissouriUSA

Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug *** literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the ***-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge *** study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological *** evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway *** indicated a significant disparity in model ***-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,***-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,*** KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for *** study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model *** work also provides a case study and insight into using LLMs das knowledge *** code is publicly available at the website of GitHub(Muh-aza).

关键词： biomedical text mining gene-gene interaction KEGG pathway large language model

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Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kusnik, Damian Smolka, Bogdan Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350322972

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios. © 2023 IEEE.

关键词： Pixels

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Adaptive Technique of Impulsive Noise Removal in Color Digital Images 10

Adaptive Technique of Impulsive Noise Removal in Color Digit...

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10th International Conference on electrical and Electronics engineering, ICEEE 2023

作者： Smolka, Bogdan Mendrela, Monika Kusnik, Damian Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350304299

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios. © 2023 IEEE.

关键词： Image enhancement

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