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نتیجه جستجو - برداشت انرژی

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
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
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