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 |
---|---|---|
1 |
Energy-aware resource management for uplink non-orthogonal multiple access: Multi-agent deep reinforcement learning
مدیریت منابع آگاه در زمینه انرژی برای دسترسی چندگانه غیر متعاملی به هم پیوسته: یادگیری تقویت عمیق چند عامل-2020 Non-orthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access
demand and the high data rate requirements of the next generation networks. In this paper, we
investigate the joint subchannel assignment and power allocation problem in an uplink multi-user
NOMA system to maximize the energy efficiency (EE) while ensuring the quality-of-service (QoS) of
all users. Different from conventional model-based resource allocation methods, we propose two deep
reinforcement learning (DRL) based frameworks to solve this non-convex and dynamic optimization
problem, referred to as discrete DRL based resource allocation (DDRA) framework and continuous DRL
based resource allocation (CDRA) framework. Specifically, for the DDRA framework, we use a deep Q
network (DQN) to output the optimum subchannel assignment policy, and design a distributed and
discretized multi-DQN based network to allocate the corresponding transmit power of all users. For
the CDRA framework, we design a joint DQN and deep deterministic policy gradient (DDPG) based
network to generate the optimal subchannel assignment and power allocation policy. The entire
resource allocation policies of these two frameworks are adjusted by updating the weights of their
neural networks according to feedback of the system. Numerical results show that the proposed DRLbased
resource allocation frameworks can significantly improve the EE of the whole NOMA system
compared with other approaches. The proposed DRL based frameworks can provide good performance
in various moving speed scenarios through adjusting learning parameters. Keywords: Non-orthogonal multiple access | Resource allocation | Energy efficiency | Deep reinforcement learning | Deep deterministic policy gradient |
مقاله انگلیسی |
2 |
Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor
مدیریت انرژی چند منظوره برای وسایل نقلیه الکتریکی سلول سوختی با استفاده از یادگیری آنلاین ، پیش بینی کننده سرعت مارکوف را افزایش می دهد-2020 As one of promising solutions towards future cleaner transportation, fuel cell electric vehicles have been widely
regarded as an attractive technology in both academia and industry. To enhance the vehicle’s operation efficiency,
this paper proposes a multi-criteria power allocation strategy for a fuel cell/battery-based plug-in hybrid
electric vehicle. Firstly, an adaptive online-learning enhanced Markov velocity-forecast approach is proposed. Its
predictive behaviors can be adjusted accordingly under various driving scenarios through the real-time-identified
transition probability matrices. Subsequently, based only on the previewed trip duration information and
the speed prediction results, a state-of-charge (SOC) reference planning approach is designed to guide the allocation
of battery energy. Combining with the velocity-forecast results and the reference SoC, model predictive
control derives the optimal power-allocation decision through minimizing the multi-purpose objective function
in a finite time horizon. It has been verified that (1) the presented power allocation strategy can reduce over
12.05% H2 consumption and over 94.40% fuel cell power spikes against the commonly used Charge-Depleting/
Charge-Sustaining strategy; (2) despite the existence of mission time estimation errors, the presented control
strategy could still bring performance enhancement over the benchmark strategy, thus demonstrating its feasibility
for real-world implementations. Keywords: Energy management strategy | Fuel cell | Plug-in hybrid electric vehicles | Speed forecasting technique | State-of-charge reference generation |
مقاله انگلیسی |
3 |
A cascaded energy management optimization method of multimode power-split hybrid electric vehicles
یک روش بهینه سازی مدیریت انرژی آبشار از وسایل نقلیه برقی هیبریدی تقسیم قدرت چند حالته-2020 A novel multimode power-split hybrid electric vehicle demonstrates significant advantages in energy
conservation. The complicated structure and multiple operation modes, however, have brought significant
challenges to energy management. The problems of operation mode selection, power allocation, and
operating point selection need to be solved simultaneously. Traditionally, existing energy management
strategies first have determined operation mode and then have solved power allocation and operating
points. Moreover, traditional dynamic programming usually has not considered electric energy consumption
and has deviated from the optimal solution. To solve these problems, a cascaded energy
management strategy that integrates dynamic programming and equivalent consumption minimization
strategy (DP þ ECMS) is proposed. An iterative method is used to optimize the equivalence factor. A
vehicle model based on actual control strategy is established and validated by test results. Then fuel
economy simulations are conducted. Results showed that DP þ ECMS could achieve 19.9% fuel economy
improvement compared with the rule-based strategy for a new European driving cycle. In addition,
simulation results of a Worldwide Harmonized Light-Duty Test Cycle demonstrated that DP þ ECMS
performed well in real road driving conditions. This study introduces a novel multimode power-split
hybrid system and provides a global energy management optimization method. Keywords: Hybrid electric vehicle | Power-split hybrid system | Energy management | Dynamic programming |
مقاله انگلیسی |
4 |
Adaptive real-time optimal energy management strategy for extender range electric vehicle
مدیریت انرژی بهینه زمان واقعی تطبیقی برای وسایل نقلیه برقی دامنه توسعه دهنده-2020 The extender range electric vehicle (EREV) is an effective way to solve the “mileage anxiety” of pure
electric vehicles, and the fuel economy of EREV is the key point of energy optimization. This paper
designed an adaptive real-time optimal energy management strategy for EREV. Firstly, an improved
shooting algorithm is proposed, which can determine the range of the equivalent factor (EF) according to
the power configuration parameters of the vehicle, and then the secant method is used to quickly
calculate the initial value of the EF. Secondly, from the perspective of energy flow, the intrinsic operation
mechanism of equivalent consumption minimization strategy (ECMS) control strategy is revealed, and
the working relationship between the five working modes of EREV is clarified. Thirdly, based on the car
navigation and geographic location information system, the EF is periodically updated to achieve
effective maintenance of the battery state of charge (SOC), so as to obtain the optimal power allocation.
Finally, The fuel economy and real-time performance of the proposed energy management strategy are
simulated and compared. To verify fuel economy, the rule-based control strategy and the power
following control strategy were used as comparison. The results show that the proposed control strategy
has better fuel economy and adaptability. To verify real-time performance, the proportional integral
derivative ECMS (PID-ECMS) and shooting method ECMS (S-ECMS) were used as comparison. The results
show that the proposed strategy is better in both fuel economy and real-time performance. Keywords: Extended range electric vehicle | Real-time optimization | Adaptive energy management | Improved shooting method | Equivalent consumption minimization | strategy |
مقاله انگلیسی |
5 |
Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery
یادگیری تقویتی مبتنی بر معماری هوشمند مدیریت انرژی برای ماشین آلات ساختمانی ترکیبی-2020 Power allocation is of crucial significance to energy management system in the hybrid construction machinery
(HCM). Most of the existing HCM energy management strategies are only formulated based on the predefined
rules, which causes the system unable to adapt to the changeable and complicated working conditions, thus
seriously limiting the energy saving potential of hybrid technology. In this paper, we build a reinforcement
learning-based intelligent energy management architecture for HCM. Given the working conditions and operating
characteristics of HCM, a Q-function updating method combining direct learning and indirect learning is
proposed to enhance the performance and practicability of reinforcement learning. A virtual world model
(VWM) is introduced to approximate the real-world environment and facilitate the identification of data-driven
environment, so as to enhance the real-time performance and adaptability of the architecture. Based on the
characteristics of HCM working conditions, the load cycle is subdivided, and the stationary Markov chain is
employed to yield real-time transfer probability matrices of required power to accelerate the updating of the
environment model. An HCM experiment platform is built, in which the typical signal of working condition is
sampled for simulation. The results indicate that DYNA-Q based architecture outperforms Q-learning and rulebased
strategy (RBS) in terms of adaptivity, real-time performance and optimality. The results also demonstrate
that with the proposed architecture, the working condition of internal combustion engine (ICE) and the chargedischarge
of ultracapacitor are more rational and efficient. Keywords: Hybrid construction machinery | Energy management | Reinforcement learning | Dyna-Q learning | Virtual world model |
مقاله انگلیسی |
6 |
DC programming and DCA for enhancing physical layer security via cooperative jamming
برنامه ریزی DC و DCA برای افزایش امنیت لایه فیزیکی از طریق پارازیت تعاونی-2017 Article history:Received 30 September 2015Revised 10 August 2016Accepted 7 November 2016Available online 18 November 2016Keywords:Physical layer security Cooperative jamming Resource allocationDC programming and DCAThe explosive development of computational tools these days is threatening security of cryptographic algorithms, which are regarded as primary traditional methods for ensuring information security. The physical layer security approach is introduced as a method for both improving confidentiality of the se- cret key distribution in cryptography and enabling the data transmission without relaying on higher-layer encryption. In this paper, the cooperative jamming paradigm - one of the techniques used in the phys- ical layer is studied and the resulting power allocation problem with the aim of maximizing the sum of secrecy rates subject to power constraints is formulated as a nonconvex optimization problem. The objective function is a so-called DC (Difference of Convex functions) function, and some constraints are coupling. We propose a new DC formulation and develop an efficient DCA (DC Algorithm) to deal with this nonconvex program. The DCA introduces the elegant concept of approximating the original non- convex program by a sequence of convex ones: at each iteration of DCA requires solution of a convex subproblem. The main advantage of the proposed approach is that it leads to strongly convex quadratic subproblems with separate variables in the objective function, which can be tackled by both distributed and centralized methods. One of the major contributions of the paper is to develop a highly efficient distributed algorithm to solve the convex subproblem. We adopt the dual decomposition method that results in computing iteratively the projection of points onto a very simple structural set which can be determined by an inexpensive procedure. The numerical results show the efficiency and the superiority of the new DCA based algorithm compared with existing approaches.© 2016 Elsevier Ltd. All rights reserved. Keywords: Physical layer security | Cooperative jamming | Resource allocation | DC programming and DCA |
مقاله انگلیسی |
7 |
The role of control power allocation in service supply chains: Model analysis and empirical examination
نقش تخصیص قدرت کنترل در زنجیره تامین خدمات: تجزیه و تحلیل مدل و معاینه تجربی-2017 In complex and competitive business environment, there have been many examples of supply chain members
fighting for power. Therefore, researchers have begun focusing on the impact of control power allocation on the
supply chain. This paper examines the allocation of power in different service supply chain relationships,
analyzing the impact of service level on optimal control power allocation and comparing the differences between
the optimal power distribution in service supply chains and that of manufacturing supply chains. We adopt a
mathematical model building method to discuss this issue, verifying the theoretical perspectives through
empirical studies of Chinas largest state-owned logistics company, the China Railway Company, and the private
ownership enterprise, Tianjin SND Logistics Company. We also develop a conceptual model of the influence of
control power on the performance of service supply chains, based on the modeling and case analysis. The
conceptual model shows several results: the control power allocation determines the dominant structure of the
supply chain; the service providers wholesale pricing strategy and the service integrators sales price strategy
present different outcomes under various dominant structures of the supply chain, which will greatly affect the
performance of the corresponding supply chain; and the relationship between the supply chain dominant
structure and the price can be adjusted by the service level.
Keywords:Control power|Service supply chain|Model|analysis|Case study|Conceptual model|Service level |
مقاله انگلیسی |
8 |
Energy-efficient Distributed Relay Selection in Wireless Sensor Network for Internet of Things
انتخاب رله توزیع شده با صرفه جویی در انرژی به شبکه حسگر بی سیم برای اینترنت اشیاء-2017 The growing Internet of Things (IoT) is increasing
wireless sensor networks (WSNs) for different applications.
Energy efficiency and reliability are key factors for multi-hop
path source to sink. A relay node forwards other node’s data to
sink, therefore, it consumes more energy. When relay runs out of
battery it may cause network failure in WSNs of IoT. A relay
selection in multi-hop transmission can play an important role to
increase the network lifetime. A stable, WSN is the fundamental
requirement for IoT data gathering in multi-hop networking
from source to sink. In this paper, an energy efficient distributed
relay selection (EDRS) technique is proposed for multi-hop
WSNs. The energy consumption is reduced by selecting a stable
relay and power allocation for collaboration. In addition, the
proposed EDRS selects a relay node with optimal power levels to
prolong the network lifetime. The simulation results demonstrate,
the EDRS reduces energy consumption and increases network
lifetime.
Keywords: IoT | Energy strategy | Relay node | Network lifetime | Power allocation | Stability | Reliability |
مقاله انگلیسی |
9 |
تخصیص قدرت انرژی -کارآمد در شبکه رادیوشناختی با استفاده از بهینه سازی ازدحام ذرات آشفته همفرگشتی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 28 - تعداد صفحات فایل doc فارسی: 35 در این مقاله، یک سبک و سنگین کردن (ارزیابی) بین منفعت(سودمندی، کاربردپذیری) و مصرف انرژی در شبکه رادیوشناختی(CR) مبتنی بر مدولاسیون تقسیم فرکانس عمود برهم(OFDM) مورد بررسی قرار گرفته است.
مساله کار آیی انرژی در زمینه شبکه CR خیلی مهم میباشد، یعنی در شبکه CR، باید سودمندی حداکثر شده و مصرف انرژی حداقل شود. از آنجا که سبک و سنگین کردن بین این موارد، در این مقاله مد نظر میباشد، بنابراین در این جا تخصیص انرژی؛ بعنوان یک مساله بهینه سازی آورده شده است ، که در آن کار ایی انرژی را توسط یک معیار کارایی انرژیِ جدید که در این مقاله تعریف شده ؛ حداکثر میکند . مساله فرموله شده ، یک مساله نامحدب در مقیاس بزرگ میباشد، که حل آن خیلی مشکل میباشد. در این مقاله، ما یک الگوریتم بهینه سازی ازدحام ذرات(PSO) بهبود داده شده را برای حل مستقیم مساله بهینه سازی مشکلِ در مقیاس بزرگِ ؛ ارائه نمودیم. بدلیل وجود همگرایی ضعیف در PSO اصلی در اطراف نقطه بهینه موضعی، یک نسخه بهبود داده شده که به صورت ترکیبی از تئوری اشفتگی( آشوب) است، در این مقاله ارائه داده شده است، که در آن، تئوری اشفتگی (آشوب) میتواند به جستجوی PSO برای یافتن راه حلها، در اطراف بهترینهای(بهینه ترین) عمومی و تکی(خصوصی، منحصر بفرد) کمک کند. علاوه براین، برای تسریع ِ فرآیند همگرایی، وقتی با یک چنین بهینهسازیِ دارای مقیاس بزرگی مواجه میشویم، مساله اولیه، با استفاده از روش هم فرگشت ، به تعدادی از مسایل کوچکتر شکسته میشود ، و سپس برای اجتناب از تولید راهحلهای امکان ناپذیر، استراتژی تقسیم کردن و سپس حل کردن بکار گرفته میشود. شبیه سازیها نشان دادند که PSO آشوبی هم فرگشتیِ پیشنهادی ، نیاز به تعداد کمتری حدس و خطا دارد و میتواند نسبت به سایر الگوریتمها، به کار ایی انرژی بیشتری دست آید.
کلید واژه ها: شبکه رادیو شناختی | تخصیص توان | بهینه سازی کلی در مقیاس بزرگ | بهینه سازی ازدحام ذرات اشفته هم فرگشتی (CCPSO) |
مقاله ترجمه شده |
10 |
تخصیص کاربر و تخصیص منابع مشترک در شبکه های بی سیم مجازی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 46 در این مقاله تخصیص منابع پویا اتصال رو به پایین در شبکه های بی سیم مجازی چند سلولی را در نظر می گیریم تا کاربران تامین کنندگان خدمات مختلف (بخش ها) درون ناحیه خاص را با یک سری ایستگاه های پایه ای از طریق تقسیم فرکانسی عمودی با دسترسی چندگانه پشتیبانی کنیم. به طور ویژه تخصیص ایستگاه پایه ای مشترک، حمل کننده فرعی و الگوریتم تخصیص نیرو را مطرح می کنیم، تا مجموع مقدار شبکه را بیشینه سازی کنیم، در حالی که حداقل میزان مورد نیاز هر قسمت فراهم می گردد. طبق این فرض که هر کاربر در هر نمونه انتقال نمی تواند به بیش از یک ایستگاه پایه ای متصل گردد، ضریب تخصیص کاربر را معرفی می کنیم تا بیانگر حمل کننده فرعی و تخصیص ایستگاه پایه مشترک به عنوان بردار متغییر بهینه سازی در طرح مسئله باشد. استفاده مجدد از حمل کننده فرعی در سلول های مختلف مجاز است، اما درون یک سلول مجاز نیست. همانطور که مسئله بهینه سازی پیشنهادی به طور ذاتی غیر محدب است، با به کارگیری برآورد محدب متوالی و برنامه نویسی هندسی مکمل، رویکرد تکرار شونده دو مرحله ای کارآمد را با پیچیدگی محاسباتی کم مطرح می کنیم تا مسئله پیشنهادی را حل کنیم. به ازای مسئله داده شده، مرحله 1 بر مبنای تخصیص کاربر بهینه بوده و در نتیجه به ازای تخصیص کاربر به دست آمده، مرحله 2 تخصیص نیرو بهینه را می یابد. نتایج شبیه سازی نشان می دهد که الگوریتم تکرار شونده پیشنهادی عملکرد بهتری نسبت به رویکرد سنتی دارد که در آن هر کاربر به ایستگاه پایه ای اختصاص می یابد که دارای بزرگترین مقدار متوسط قدرت سیگنال است و سپس حمل کننده فرعی مشترک و تخصیص نیرو به ازای کاربران اختصاص یافته هر سلول حاصل می گردد. نتایج شبیه سازی نشان دهنده بهبود در پوشش است که با رویکرد پیشنهادی ارائه می گردد و به ترتیب 57% و 71% به ازای کاربران همسان و ناهمسان است، در نتیجه منجر به طیف کارایی بالاتر در شبکه های بی سیم مجازی می گردد.
واژگان کلیدی: برنامه نویسی هندسی مکمل | برآورد محدب متوالی | تخصیص کاربر و تخصیص منابع مشترک | شبکه های بی سیم مجازی |
مقاله ترجمه شده |