با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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1 |
Scaling laws of electromagnetic and piezoelectric seismic vibration energy harvesters built from discrete components
مقررات مقیاس بندی برداشت انرژی لرزه ای الکترومغناطیسی و پیزو الکتریک ساخته شده از اجزای گسسته-2020 This paper presents a theoretical study on the scaling laws of electromagnetic and
piezoelectric seismic vibration energy harvesters, which are assembled from discrete
components. The scaling laws are therefore derived for the so called meso-scale range,
which is typical of devices built from distinct elements. Isotropic scaling is considered for
both harvesters such that the shape of the components and of the whole transducers do
not change with scaling. The scaling analyses are restricted to the case of linearly elastic
seismic transducers subject to tonal ambient vibrations at their fundamental natural frequency,
where the energy harvesting is particularly effective. Both resistive-reactive and
resistive optimal electric harvesting loads are considered. The study is based on equivalent
formulations for the response and power harvesting of the two transducers, which employ
the so called electromagnetic and piezoelectric power transduction factors, P2
cm and P2 pe.
The scaling laws of the transduction coefficients and electrical and mechanical parameters
for the two transducers are first provided. A comprehensive comparative scaling analysis is
then presented for the harvested power, for the power harvesting efficiency and for the
stroke of the two harvesters. Particular attention is dedicated to the scaling laws for the
dissipative effects in the two harvesters, that is the Couette air losses and eddy currents
losses that develop in the electromagnetic harvester and the material, air and dielectric
losses that arise in the piezoelectric harvester. The scaling laws emerged from the study,
are thoroughly examined and interpreted with respect to equivalent mechanical effects
produced by the harvesting loads. Keywords: Seismic vibration energy harvesting | Vibration energy harvesting scaling laws | Electromagnetic seismic vibration harvester | Piezoelectric seismic vibration harvester |
مقاله انگلیسی |
2 |
Adaptive data and verified message disjoint security routing for gathering big data in energy harvesting networks
داده تطبیقی و تایید امنیت پیام متلاشی شدن مسیریابی برای جمع آوری داده های بزرگ در شبکه های برداشت انرژی-2020 To improve the data arrival ratio and the transmission delay and considering that the capacity for
determining malicious nodes and energy are limited, a security disjoint routing-based verified message
(SDRVM) scheme is proposed. The main contributions of SDRVM are as follows: (a) two connected
dominating sets (a data CDS and a v-message CDS) are created for disseminating data and verified
messages (v-messages), respectively, based on the remaining energy of nodes. (b) Nodes record the
ID information in data packets with a specified probability, namely, the marking probability, which is
adjusted according to the remaining energy of the nodes. (c) The duty cycle of the nodes is adjusted,
and the energy of the nodes is divided into three levels. In the data CDS, the duty cycle of the sensor
nodes is the longest and the duty cycle of the nodes that do not belong to either of the CDSs is
the shortest. (d) If the energy of the sensor nodes is sufficient, data packets are transmitted several
times and the v-messages that are stored in the nodes are transmitted to the destination nodes. The
proposed scheme has been evaluated using different parameters where the results obtained prove its
effectiveness in comparison to the existing solutions. Keywords: Energy harvesting networks | Security | Disjoint routing | Marking probability | Network lifetime |
مقاله انگلیسی |
3 |
برداشت انرژی خورشیدی زیرپوستی - روشی جدید برای تامین نیروی ایمپلنتهای الکتریکی مستقل
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 29 برداشت خورشیدی زیرپوستی این پتانسیل را دارد که نیاز به تعویض دورهای باتری را همانطور که در بیماران دارای ضربان ساز قلبی مورد نیاز است، مرتفع سازد. خروجی توان قابل دستیابی ماژول خورشیدی زیرپوستی در عمق کاشت، ویژگیهای پوست نوری و به بخش مهمی در ویژگیهای سلول خورشیدی کاهش مییابد. برای تخمین توان خروجی سلولهای خورشیدی زیر پوستی تحت قرار گرفتن در معرض نور خورشید در اواسط عرض جغرافیایی به عنوان تابعی از عمق کاشت و اندازه صفحه خورشیدی، از شبیه سازی توزیع نور مونت کارلو استفاده شد. برای تاریکترین نوع پوست، تامین انرژی روزانه یک ضربان ساز قلبی مدرن (۰.۸۶۴ ژول در تقاضای توان ۱۰ وات) میتواند توسط یک سلول خورشیدی2 cm2 که به صورت زیر پوستی در عمق ۳ میلی متر وقتی که در معرض تنها ۱۱ دقیقه، تابش روشن آسمان هنگام ظهر قرار میگیرد، تامین شود. مطالعه ما نشان میدهد که برداشت خورشیدی با سلولهای خورشیدی نسبتا کوچک در صورتی که برای تاثیر زیرپوستی طیفی بهینه شده باشد، پتانسیل قدرت ضربان سازها در تمام انواع پوست در زمانهای تابش دهی معقول را دارد. برداشت انرژی خورشیدی برای تامین انرژی ایمپلنت های الکترونیکی بسیار امیدوار کننده است.
کلمات کلیدی: ضربانساز قلب یا پیس میکر | ایمپلنت یا کاشت زیرپوستی | ویژگیهای سلول خورشیدی | سلولهای خورشیدی نقطه کوانتومی | منبع تغذیه ایمپلنت | جایگزینی باتری |
مقاله ترجمه شده |
4 |
Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks
استراتژی تخصیص منابع مبتنی بر یادگیری تقویتی عمیق برای شبکه های شناختی ماشین به ماشین با قدرت برداشت انرژی-2020 Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of
Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access
requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-
Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating
spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption
to achieve Green Communication (GC) became an important research topic. In this paper, we investigate
the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy
efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available
energy in EH-devices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized
Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and
effectively learns from the environment to make allocation decision without the complete and global network
information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)-based
algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes
in terms of average energy efficiency with an acceptable convergence speed. Keywords: Energy Harvesting | M2M communication | Resource allocation | Deep Reinforcement Learning |
مقاله انگلیسی |
5 |
Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگو برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019 Data mining methods have been widely used for structural health monitoring (SHM) and damage identification
for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot
be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in
time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals,
using a through-substrate self-powered sensing technology, is presented within the context of artificial
intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the
data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike
structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a
dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms,
including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within
the developed learning framework for performance assessment of the monitored structures. Different levels
of harvested energy were considered to evaluate the robustness of the SHM system with respect to such
variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based
data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing
binary signals, overcoming the notable issue of energy availability for smart damage identification platforms
being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance
data mining and interpretation techniques in the SHM domain, promoting the practical application of machine
learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart
infrastructure monitoring. Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting |
مقاله انگلیسی |
6 |
Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگوی برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019 Data mining methods have been widely used for structural health monitoring (SHM) and damage identification
for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot
be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in
time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals,
using a through-substrate self-powered sensing technology, is presented within the context of artificial
intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the
data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike
structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a
dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms,
including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within
the developed learning framework for performance assessment of the monitored structures. Different levels
of harvested energy were considered to evaluate the robustness of the SHM system with respect to such
variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based
data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing
binary signals, overcoming the notable issue of energy availability for smart damage identification platforms
being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance
data mining and interpretation techniques in the SHM domain, promoting the practical application of machine
learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart
infrastructure monitoring. Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting |
مقاله انگلیسی |
7 |
شبکهی حسگر نوری-الکترونیکی تغذیهشده از طریق فیبر نوری برای کاربردهای صنعتی خشن
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 27 ادوات الکترونیکی هوشمند که به شیوهی نوری از طریق فیبر نوری تغذیه و به هم متصل میشوند، در مقایسه با سیستمهایی که از اتصالات الکتریکی بهره میگیرند، چندین مزیت برای کاربردهای صنعتی خشن دارند. با وجود مزایایی همچون ایمنی الکترومغناطیسی و سطح بالای ایزوله شدن گالوانیک، فناوریهای انتقال توان بر روی فیبر نوری در حال حاضر صرفاً برای کاربردهای خاص سطح بالا استفاده میشوند، این به خاطر هزینهی بالای آنها در مقایسه با توزیع سنتی توان است. دلیل اصلی برای این امر، استفاده از دیودهای لیزری و سلولهای فتوولتائیک برای اکثر سیستمهای انتقال توان بر روی فیبر است. در نتیجه، یک روش جدید و مقرون به صرفه برای انتقال توان بر روی فیبر با استفاده از تنها LED های استاندارد واحد و دیودهای نوری برای منبع تغذیهی نوری به شیوهای کارآمد، هزینههای سیستم را کاهش داده و آن را به انتخابی مناسب برای کاربردهای بازار کلان همچون اینترنت اشیاء تبدیل خواهد نمود. افزون بر این، چگالی پایین توان نوری درون فیبرها، شبکه را ذاتاً حتی در محیطهای با قابلیت انفجاری شدید، ایمن خواهد ساخت. استفاده از نور LED مرئی و غیرمنسجم، ریسک وارد آمدن آسیب حرارتی به شبکیهی چشم انسان در استانداردهای ایمنی شغلی در صورت شکسته شدن فیبر یا حین Hotplugging را از بین میبرد. یک سیستم نمونه ساخته شد، این شامل یک ایستگاه پایه است که 4 نقطهی حسگر الکترونیکی را به از طریق نوری تغذیه میکند. بین ایستگاه پایه و هر شبکهی حسگر، دادهها را میتوان از طریق یک لینک نوری 2MBaud دوجهته تبادل نمود. این مقاله بطور مختصر تاریخچه و وضعیت کنونی سیستمهای انتقال توان بر روی فیبر را تشریح میکند. چالشهای فنتی توسعه و نیز مراحل ساخت گرههای حسگر الکترونیکی کمتوان و ارزان که به شیوهی نوری تغذیه میشوند و بر محدودیتها و معایب روشهای توان بالای موجود غلبه میکنند نیز شرح داده شده است. جنبههای نوری، الکتریکی و محاسباتی سیستم جدید نیز بیان شده است. افزون بر این، اندازهگیری انرژی و محاسبات بودجهی توان نیز جهت تحلیل و بهینهسازی مصرف انرژی گرههای حسگر جین عملکرد ارائه شده است. نهایتاً مزایای این روش جدید برای کاربردها تولیدی هوشمند در قالب یک مثال تبیین میشود.
کلیدواژه ها: برداشت انرژی | مدیریت انرژی | محیطهای انفجاری | محیطهای صنعتی خشن | ایمنی ذاتی | قطعات الکترونیکی کمهزینه | قطعات الکترونیکی کممصرف | ایمنی شغلی | شبکههای حسگر | انتقال توان بر روی فیبر |
مقاله ترجمه شده |
8 |
Smart health monitoring and management system: Toward autonomous wearable sensing for internet of things using big data analytics
سیستم نظارت و مدیریت هوشمند سلامت: به سوی سنجش پوشیدنی مستقل برای اینترنت اشیا با استفاده از تجزیه و تحلیل داده های بزرگ-2018 The current development and growth in the arena of Internet of Things (IoT) are providing a great
potential in the route of the novel epoch of healthcare. The vision of the healthcare is expansively favored, as it
advances the excellence of life and health of humans, involving several health regulations. The incessant increase of
the multifaceted IoT devices in health is broadly tested by challenges such as powering the IoT terminal nodes used
for health monitoring, real-time data processing and smart decision and event management. In this paper, we
propose a healthcare architecture which is based analysis of energy harvesting for health monitoring sensors and the
realization of Big Data analytics in healthcare. The rationale of proposed architecture is twofold: (1) comprehensive
conceptual framework for energy harvesting for health monitoring sensors, and (2) data processing and decision
management for healthcare. The proposed architecture is three-layered architecture, that comprised (1) energy
harvesting and data generation, data pre-processing, and data processing and application. We also verified the
consistent data sets on Hadoop server to validate the proposed architecture based on threshold limit value (TLV).
The study reveals that the proposed architecture offer valuable imminent into the field of smart health.
Key Words: IoT, Energy Harvesting, Big Data Analytics |
مقاله انگلیسی |
9 |
A two-queue model for optimising the value of information in energy-harvesting sensor networks
یک مدل دو صفه برای بهینه سازی ارزش اطلاعات در شبکه های حسگر برداشت انرژی-2018 We study the optimal transmission policy of a sensor node in an energy-harvesting wireless sensor network. We consider a hybrid wireless sensor network in which a mobile sink is used to collect data. Taking cues from recent works on the value of information (VoI), we posit that the optimal policy maximises the VoI that can be sent for the node to the sink. We model this system as a discrete-time queueing model with two coupled queues. In particular, the sensor node under study operates energy neutral and harvests energy according to a Bernoulli process. Discretising energy into “energy chunks”, the battery is modelled as a first queue, whereas a second queue is introduced to hold the VoI at the sensor node. From the vantage point of the sensor node, this means that the sensor can only send when the sink is sufficiently close. When this is the case, the sensor decides whether to transmit its data or not depending on the amount of available energy and the value of the information. Focusing on the optimal transmission policy, we formulate the optimal control problem as a Markov Decision Process with a level-dependent block-triangular transition probability matrix. We find the optimal policy which maximises the mean VoI transmitted from the node in the long run and numerically show that it is of threshold type. Further, we assess the value function at optimal policy analytically and provide some properties. Finally, we investigate the structure of the optimal policy and the mean VoI collected from the node for different system parameters by means of some numerical experiments.
keywords: Sensor network |Energy harvesting |Value of information |Markov decision process |
مقاله انگلیسی |
10 |
Microsystem based Energy Harvesting (EH-MEMS): Powering pervasivity of the Internet of Things (IoT) – A review with focus on mechanical vibrations
برداشت انرژی بر پایه میکروسیستم (EH-MEMS): پراکندگی قدرت اینترنت اشیاء (IoT) - بررسی با تمرکز بر ارتعاشات مکانیکی-2017 The paradigm of the Internet of Things (IoT) appears to be the common denominator of all distributed
sensing applications, providing connectivity, interoperability and communication of smart entities (e.g.
environments, objects) within a pervasive network. The IoT demands for smart, integrated, miniaturised
and low-energy wireless nodes, typically powered by non-renewable energy storage units (batteries). The
latter aspect poses constraints as batteries have a limited lifetime and often their replacement is imprac
ticable. Availability of zero-power energy-autonomous technologies, able to harvest (i.e. convert) and
store part of the energy available in the surrounding environment (vibrations, thermal gradients, electro
magnetic waves) into electricity to supply wireless nodes functionality, would fill a significant part of the
technology gap limiting the wide diffusion of efficient and cost effective IoT applications. Given the just
depicted scenario, the realisation of miniaturised Energy Harvesters (EHs) leveraging on MEMS technol
ogy (MicroElectroMechanical-Systems), i.e. EH-MEMS, seems to be a key-enabling solution able to con
jugate both main driving requirements of IoT applications, namely, energy-autonomy and
miniaturisation/integration.
This short review outlines the current state of the art in the field of EH-MEMS, with a specific focus on
vibration EHs, i.e. converters capable to convert the mechanical energy scattered in environmental vibra
tions, into electric power. In particular, the issues in terms of conversion performance arising from EHs
scaling down, along with the challenge to extend their operability on a frequency range of vibrations
as wider as possible, are going to be discussed in the following.
Keywords: Energy Harvesting (EH) | MEMS | Internet of Things (IoE) | Ultra-Low Power (ULP) | Zero-power electronics |
مقاله انگلیسی |