دانلود و نمایش مقالات مرتبط با نظارت بر سلامت::صفحه 1
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نتیجه جستجو - نظارت بر سلامت

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 An efficient Elman neural network classifier with cloud supported internet of things structure for health monitoring system
یک طبقه بندی کارآمد شبکه عصبی Elman با ساختار اینترنت اشیاء با پشتیبانی ابری برای سیستم نظارت بر سلامت-2019
To improve the strain on healthcare systems which happens through an aging population and a rise in chronic illness, Internet of Things (IoT) technology has attracted much attention in recent years. Cloud computing along with the IoT concept is a novel trend, for an efficient management and online process- ing of sensor data, and its major problem is the protection of privacy. Normally, the data is acquired from the users (i.e. patients) by the healthcare service provider and distributes them with authorized clinics or healthcare experts and these details were distributed to both pharmaceutical and life health insur- ance companies. Additionally the hackers have susceptible about the data of the patient, at the time of synchronization or cloud transfer is happening with the devices that are interconnected. An efficient El- man Neural Network (ENN) classifier based data protection is given in this work, which forms the cryp- tography and authentication. This suggested work has two varieties of process namely client side and cloud side. In client side, initially, the EEG signal is obtained from human and is processed with the help of the Hyper analytic Wavelet Transform (HWT) with Adaptive Noise Cancellation (ANC) method. Then, through the Elliptic Curve Cryptography (ECC) scheme, signal is safeguarded from forgery. The features were drawn-out from authenticated information in cloud side, and these details were divided as abnor- mal or not. The appropriateness of this work is validated by executing the technique called One Class Support Vector Machine (OCSVM) with IoT that drivens the ECG-dependent health monitoring system in the cloud on both experimental analysis and simulation.
Keywords: IoT | Cloud computing | Elman neural network | Hyper analytic wavelet transform | Adaptive noise cancellation | Elliptic curve cryptography | SVM
مقاله انگلیسی
2 A deep learning method for online capacity estimation of lithium-ion batteries
یک روش یادگیری عمیق برای برآورد ظرفیت آنلاین باتری های لیتیوم یونی-2019
The past two decades have seen an increasing usage of lithium-ion (Li-ion) rechargeable batteries in diverse applications including consumer electronics, power backup, and grid-scale energy storage. To guarantee safe and reliable operation of a Li-ion battery pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method which utilizes deep convolutional neural network (DCNN) for cell-level capacity estimation based on the voltage, current, and charge capacity measurements during a partial charge cycle. The unique features of DCNN include the local connectivity and shared weights, which enable the model to accurately estimate battery capacity using the measurements during charge. To the best of our knowledge, this is one of the first attempts to apply deep learning to the online capacity estimation of Li-ion batteries. Ten-year daily cycling data from eight implantable Li-ion cells and half-year cycling data from 20 18650 Li-ion cells were utilized to verify the performance of the proposed deep learning method. Compared with traditional machine learning methods such as shallow neural networks and relevance vector machine (RVM), the proposed deep learning method is demonstrated to produce higher accuracy and robustness in the online estimation of Li-ion battery capacity.
Keywords: Capacity estimation | Health monitoring | Deep learning | Lithium-ion batteries
مقاله انگلیسی
3 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities
Keywords: Structural health monitoring | Data mining | Artificial intelligence | Probabilistic approach | Signal time delay
مقاله انگلیسی
4 Deep learning and its applications to machine health monitoring
یادگیری عمیق و کاربردهای آن برای نظارت بر سلامت دستگاه-2019
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed
Keywords: Deep learning | Machine health monitoring | Big data
مقاله انگلیسی
5 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities.
Keywords: Structural health monitoring | Data mining | Artificial intelligence| Probabilistic approach | Signal time delay
مقاله انگلیسی
6 Experimental validation of cost-effective vision-based structural health monitoring
اعتبار تجربی از لحاظ نظارت بهداشتی مبتنی بر دید مبتنی بر مقرون به صرفه-2017
Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conven- tional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing.
Keywords:Vision-based displacement sensor | Displacement measurement | Structural health monitoring | Damage detection | Modal analysis
مقاله انگلیسی
7 ارزیابی عیب برای بررسی سلامت ساختاری، یافتن عیب، جلوگیری از شکست و پیش بینی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 11
در این مقاله، دو موضوع نگه داری بر حسب شرایط و بررسی سلامت ساختار و پیش بینی ها توضیح داده شده است. شناسایی و تخمین عیوب قدمی بسیار مهم و لازم در زمینه نگه داری بر حسب شرایط است. در این مقاله، آزمایشی به وسیله تجهیزات سفارشی یک تست با ایجاد نواقصی بر روی دوایر بیرونی و درونی یاتاقان غلتکی انجام شده است.در این مقاله همچنین، ارتباط بین اطلاعات بدست آمده از آزمایش ارتعاشات و رابطه بین نواقص ایجاد شده، بدست آمده است. هنگامی که آزمایش بر روی تجهیزات تست طراحی شده برای پیش بینی عیوب انجام شد، روش تحلیلی انتقال موجی نشان داد که ابزار موثری برای تحلیل سیگنال ارتعاشات است. در این مقاله، که توسط روش چگالی طیف قدرت تبعیت می شود، بر روی سیگنال های ارتعاشات ناشی از یک یاتاقان غلتکی معیوب اعمال شده است. پس از یافتن عیب موجود در یاتاقان، مکان و شدت آن، عمر مفید باقی مانده یاتاقان تخمین زده می شود.
کلمات کلیدی: نظارت بر سلامت سازه | تجزیه و تحلیل داده های ارتعاش | تحول موج تحلیلی | باقی مانده عمر مفید
مقاله ترجمه شده
8 Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data
Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data-2015
Numerical algorithms that can assess Engine Health Monitoring (EHM) data in aeroengines are influenced by the high level of uncertainty inherent to gas path measurements and engine-to-engine variability. Among them, fuzzy rule-based techniques have been successfully used due to their robustness towards noisy signals and their capability to learn human-readable rules from data. These techniques are useful in detecting the presence of certain types of abnormal events or general engine deterioration, through the identification of specific combinations of EHM signals associated with these specific cases. However, there are also other types of engine events that manifest themselves as an ordered sequence of otherwise normal combinations of the EHM signals. These combinations are dismissed when considered in isolation as the current existing techniques cannot assess them. In this paper it is proposed to use sequence mining techniques in order to obtain fuzzy rules from uncertain EHM data which can in turn be used to identify the cases where an engine event is determined as a sequence of otherwise normal combinations of EHM signals. The results are subsequently tested on a representative sample of aeroengine data.& 2015 Elsevier Ltd. All rights reserved.
Keywords: Engine health monitoring | Fuzzy rule-based systems | Fuzzy sequence mining | Uncertain data
مقاله انگلیسی
9 تکنیک های اندازه گیری غیر تماسی برای نظارت بر ساختار سلامتی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 14
استفاده از نوسان سنجی لیزر اسکن کننده سه بعدی برای تصویرسازی انتشار امواج تنشی در نمونه هایی که نفوذ غیر سطحی در انها با عیوبی همراه است در این مطالعه ارایه شده است . نتایج ،نیازمندی به درک بهترِ معیارهای سنجش سرعت نوسانات سه بعدی را بمنور دستیابی به بینش بهتر در مورد انتشار موج تنشی در نواحیی تحت نظر تایید میکنند . در ادامه نشان داده خواهد شد که دانش کامل در مورد سرعت ،درکِ متقابل از انواع حالتهای مختلف موج که در ساختار خود با عیوبی روبرو هستند را تسهیل خواهد بخشید . بمنظور تحقق بخشیدن به اهداف این مطالعه ، از یک صفحه الومینیمی با داشتن ناپیوستگی در نفوذ غیر سطحی برای نشان دادن کاربرد این شکل از تکنیک های سنجشِ غیر تماسی برای انتشار امواج تنشی استفاده نمودیم . نشان داده خواهد شد که محتوای چند کیفیتی و زمان پیشرفت برخورد و حالتهای پراکندگی موج را میتوان با این نوع تکنیکِ سنجش مورد ارزیابی قرار داد .
واژگان کلیدی : موج لمب یا امواج سطحی فراصوت | PZT | نوسان سنجی لیزر سه بعدی | آسیب | حفره عمقی جزیی | تجسم | نظارت بر سلامت ساختاری .
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