From the article: Abstract: An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.
Channel State Information (CSI) analysis for Predictive Maintenance using Convolutiona Neural Network (CNN).
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry.Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce Xyper) aims at developing Explainable Predictive Maintenance algorithms that do not only provide the engineers with a prediction but in addition, with a risk analysis on the components when delaying the maintenance, and what the primary indicators are that the algorithms use to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and also the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane but also the vessel is performing. Thus the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze University of Applied Sciences in Groningen (Hanze UAS), context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that are already developed and available from the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The resulting XAIPre prototype offers significant competitive advantages for maritime companies such as Heerema, by increasing the longevity of machine components, increasing worker safety and decreasing maintenance costs.
