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نتیجه جستجو - PHM

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 X-PHM: Prognostics and health management knowledge-based framework for SME
X-PHM: پیش آگهی و چارچوب دانش مبتنی بر مدیریت سلامت برای SME-2021
Prognostics and Health Management (PHM) is an emerging concept based on industrial data management. The implementation of PHM in small and medium-sized enterprises (SMEs) is currently limited due to data accessibility difficulties. In order to overcome this pitfall, one could formalize the operators’ knowledge and integrate it in the SME’s information system. Thus, the implementation of the PHM will be based on this information system associating data with knowledge. To this end, we propose a collaborative PHM approach (X-PHM) to ensure the extraction of operators’ knowledge and its integration into the PHM process. The decision resulting from this approach is restituted with a concern of explainability. This paper details the proposed approach while focusing on the data management process and its integration in explainable decisions. This new framework is applied in a French SME to understand its production process and facilitate its digital transformation.
Keywords: PHM | Knowledge formalization and integration | Explainable artificial intelligence | SME | Data analysis.
مقاله انگلیسی
2 Ontology-augmented Prognostics and Health Management for shopfloor-synchronised joint maintenance and production management decisions
پیش آگهی و مدیریت سلامت با هستی شناسی تقویت شده برای تصمیمات مدیریت تولید و نگهداری مشترک هماهنگ شده با کف مغازه-2021
In smart factories, guaranteeing shopfloor-synchronised and real-time decision-making is essential to be responsive to the ever-changing internal environment, namely the shopfloor of the production system and assets. At operational level, decisions should balance counter acting objectives of maintenance and production; there- fore, their decision-making processes should be joint and coordinated, to fulfil production requirements considering the health state of the assets. The knowledge of the current state is promoted by the application of Prognostics and Health Management (PHM) as an aid to support informed decision-making. Nevertheless, PHM- purposed information is usually not complete in terms of production requirements. To support joint maintenance and production management decisions, an ontological approach is proposed. The ontology, called ORMA (Ontology for Reliability-centred MAintenance), has a modular structure, including formalisation of asset, pro- cess, and product knowledge. Via suitable relationships, rules, and axioms, ORMA can infer product feasibility based on the current health state of the assets and their functional units. ORMA is implemented in a Flexible Manufacturing Line at a laboratory scale. Therein, an integrated solution, involving a health state detection algorithm that interacts with the ontology, supports human decision-making via a web-based dashboard; joint maintenance and production management decisions can be then taken, relying on diversified information pro- vided by the PHM algorithm as well as the augmentation via ontology reasoning. The proposed ontology-based solution represents a step towards reconfigurability of smart factories where human and automated decision- making processes work in synergy.
keywords: هستی شناسی | استدلال | پیشگویی و مدیریت بهداشت | phm | نگهداری | تولید | Ontology | Reasoning | Prognostics and health management | PHM | maintenance | production
مقاله انگلیسی
3 Area and Power Efficient Pipelined Hybrid Merged Adders for Customized Deep Learning Framework for FPGA Implementation
افزودنیهای ادغام شده ترکیبی خطی و کارآمد پیوندی ترکیبی برای چارچوب یادگیری عمیق سفارشی برای پیاده سازی FPGA-2019
With the rapid growth of deep learning and neural network algorithms, various fields such as communication, Industrial automation, computer vision system and medical applications have seen the drastic improvements in recent years. However, deep learning and neural network models are increasing day by day, while model parameters are used for representing the models. Although the existing models use efficient GPU for accommodating these models, their implementation in the dedicated embedded devices needs more optimization which remains a real challenge for researchers. Thus paper, carries an investigation of deep learning frameworks, more particularly as review of adders implemented in the deep learning framework. A new pipelined hybrid merged adders (PHMAC) optimized for FPGA architecture which has more efficient in terms of area and power is presented. The proposed adders represent the integration of the principle of carry select and carry look ahead principle of adders in which LUT is re-used for the different inputs which consume less power and provide effective area utilization. The proposed adders were investigated on different FPGA architectures in which the power and area were analyzed. Comparison of the proposed adders with the other adders such as carry select adders (CSA), carry look ahead adder (CLA), Carry skip adders and Koggle Stone adders has been made and results have proved to be highly vital into a 50% reduction in the area, power and 45% when compared with above mentioned traditional adders
Keywords: Deep Learning Framework | PHMAC | GPU | Neural Networks | Optimization
مقاله انگلیسی
4 When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections
هنگامی که بینایی دستگاه با بافت شناسی مقابله می شود: ارزیابی مقایسه ای از معماری مدل برای طبقه بندی بخش های بافت شناسی-2017
Article history:Received 25 February 2016Revised 12 August 2016Accepted 26 August 2016Available online 9 September 2016Keywords:Computational histopathology ClassificationUnsupervised feature learning Sparse feature encoderClassification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical vari- ations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classi- fication remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Fea- ture and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage ex- tension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).© 2016 Elsevier B.V. All rights reserved.
Keywords: Computational histopathology | Classification | Unsupervised feature learning | Sparse feature encoder
مقاله انگلیسی
5 Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System
اینترنت اشیاء فدرال و سیستم های نظارتی بیماران فراگیر محاسبات ابری -2017
The exponentially growing healthcare costs coupled with the increasing interest of patients in receiving care in the comfort of their own homes have prompted a serious need to revolutionize healthcare systems. This has prompted active research in the development of solutions that enable healthcare providers to remotely monitor and evaluate the health of patients in the comfort of their residences. However, existing works lack flexibility, scalability, and energy efficiency. This article presents a pervasive patient health monitor ing (PPHM) system infrastructure. PPHM is based on integrated cloud computing and Internet of Things technologies. In order to demonstrate the suitability of the proposed PPHM infrastructure, a case study for real-time monitoring of a patient suffering from congestive heart failure using ECG is presented. Experimental evaluation of the pro posed PPHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system.
مقاله انگلیسی
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