Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage remaining useful life prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed model is designed to iteratively predict the number of cycles required for the battery to reach the end of its useful life, based on available data. The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Experimental results demonstrate that the proposed ST-MAN model outperforms existing CNN and LSTM-based methods, achieving state-of-the-art performance in predicting the remaining useful life of Li-ion batteries. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries, including automotive and renewable energy

Influencing factors for the human development index in West Java using geographically and temporally weighted regression kernel functions

Human Development Index (HDI) is a competitive index that serves as one of the crucial metrics for evaluating the effectiveness of enhancing the quality of human resources. HDI values from different areas can be compared. This study aims to spatially and temporally explore the HDI data from districts or cities in West Java and examine the factors that influence HDI in each of these districts or cities using the GTWR Great Circle Distance Fixed Kernels model. In this study, we used a combination of cross-sectional data from districts or cities in West Java and time series data with seven annual periods from 2015-2021. The GTWR Great Circle Distance Fixed Kernels model was expected to display coefficient values at each location and time simultaneously, providing more in-depth information and analysis results at each location and time. The analysis results using the GTWR Great Circle Distance Fixed Kernels model show that HDI in West Java carries a positive influence on the location and time. This finding should be of particular concern to the relevant government, particularly the factors presenting a natural effect on HDI based on location and time. The positive influence obtained by an area at a particular time will also have a positive impact on other regions, and if there is a negative influence, it will undoubtedly affect other regions as well. Analysis of the HDI model in West Java using the GTWR Great Circle Distance Fixed Exponential Kernel model also presents better results in comparison to the Global OLS model and the GTWR model without the Great Circle Fixed Exponential Kernel. The final parameter estimator results are displayed in the form of a geographic map to facilitate ease of understanding

Investigating experimental and environmental factors to provide a mechanistic understanding of benthic algal biomass accumulation in freshwater streams

2019 Spring.Includes bibliographical references.To view the abstract, please see the full text of the document

Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

Working group on ecosystem assessment of Western European shelf seas (WGEAWESS)

The ICES Working Group on Ecosystem Assessment of Western European Shelf Seas (WGEA-WESS) aims to provide high quality science in support to holistic, adaptive, evidence-based man-agement in the Celtic seas, Bay of Biscay and Iberian coast regions. The group works towards developing integrated ecosystem assessments for both the (i) Celtic Seas and (ii) Bay of Biscay and Iberian Coast which are summarized in the Ecosystem Overviews (EOs) advice products that were recently updated. Integrated Trend Analysis (ITA) were performed for multiple sub-ecoregions and used to develop an understanding of ecosystem responses to pressures at varying spatial scales. Ecosystem models (primarily Ecopath with Ecosim; EwE) were developed and identified for fisheries and spatial management advice. The updated Celtic Seas EO represents a large step forward for EOs, with the inclusion of novel sections on climate change, foodweb and productivity, the first application of the new guidelines for building the conceptual diagram, inclusion of socio-economic indicators, and progress made toward complying with the Transparent Assessment Framework (TAF). We highlight ongoing issues relevant to the development and communication of EO conceptual diagrams. A common methodology using dynamic factor analysis (DFA) was used to perform ITA in a comparable way for seven subregions. This was supported by the design and compilation of the first standardized cross-regional dataset. A comparison of the main trends evidenced among subregions over the period 1993–2020 was conducted and will be published soon. A list of available and developing EWE models for the region was also generated. Here, we re-port on the advances in temporal and spatial ecosystem modelling, such as their capacity to model the impacts of sector activities (e.g. renewables and fisheries) and quantify foodweb indi-cators. We also reflect on model quality assessment with the key run of the Irish sea EwE model. The group highlighted the hurdles and gaps in current models in support of EBM, such as the choice of a relevant functional, spatial, and temporal scales and the impacts of model structure on our capacity to draw comparisons from models of different regions. The group aims to ad-dress these issues in coming years and identify routes for ecosystem model derived information into ICES advice.info:eu-repo/semantics/publishedVersio