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نتیجه جستجو - بهینه سازی

تعداد مقالات یافته شده: 612
ردیف عنوان نوع
1 بازاریابی جاذبه ای دیجیتال: اندازه گیری عملکرد اقتصادی تجارت الکترونیکی خواروبار در اروپا و آمریکا
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 30
این تحقیق به بررسی رابطه هزینه-نتیجه اقدامات بازاریابی جاذبه ای مورد استفاده تجارت الکترونیکی خواروبار می پردازد. این تحلیل بر اساس به کارگیری مدل درفمن و استینر (1954) برای بودجه تبلیغات بهینه است که مولفین آن را با بازاریابی دیجیتال تطبیق می دهند و با تحلیل آماری تجاری تایید میکنند. با توجه به 29 شرکت عمده در شش کشور در افق زمانی شش سال، تحلیل ترکیبی تکنیک های بهینه سازی موتور جستجو و بازاریابی موتور جستجو هدف جذب کارکنان به صفحات وب شرکت ها را دنبال می کند. نتایج تایید می کند که تجارت الکترونیکی بازاریابی جاذبه ای دیجیتال را بهینه سازی می کند. تفاوت ها بسته به نوع فرمت و سطح کشور فرق دارند.
واژگان کلیدی: بازاریابی جاذبه ای | بازاریابی دیجیتال | تجارت الکترونیک | خرده فروشی | عملکرد اقتصادی | بهینه سازی سرمایه گذاری بازاریابی.
مقاله ترجمه شده
2 بهینه سازی شرایط فرآیند تولید کربن فعال بسیار متخلخل از ضایعات پوست خرما به منظور حذف آلاینده های موجود در آب
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 32
در این مطالعه ، فرآیند تهیه کربن فعال بسیار متخلخل (AC) از پوست خرما از طریق روش سطح پاسخ، بهینه سازی شد. شرایط بهینه آماده سازی AC از طریق روش ترکیبی تجزیه حرارتی با فعال سازی شیمیایی با استفاده از اسید فسفریک در حدود 3 ساعت زمان فعال سازی ، 400 درجه سانتیگراد درجه حرارت فعال سازی و 40وزنی برای مقدار عامل فعال بدست آمد. بالاترین مقادیر سطح خاص و تعداد ید تحت شرایط بهینه عبارتند از902 متر مربع در گرم و 983 میلی گرم در گرم، که تخلخل بسیار بالای ساختار AC را تأیید می کند. همچنین AC آماده به دلیل مساحت زیاد و وجود گروههای عملکردی اسیدی در سطح آن ، توانایی چشمگیری در از بین بردن آلاینده های مختلف از جمله آرسنیک (V) ، متیلن آبی ، متیل نارنجی و کوئرستین داشت. سرانجام ، شاخص تجاری محاسباتی در حدود 451 مترمربع در هر واحد مواد به دست آمد که کاربرد پوست خرما را به عنوان یک پیش درآمد ارزان قیمت و امیدوار کننده برای آماده سازی تجاری AC تأیید می کند.
واژه های کلیدی: پوست خرما | روش سطح پاسخ | سطح خاص | شماره ید | کوئرستین
مقاله ترجمه شده
3 Industrial smart and micro grid systems e A systematic mapping study
سیستم های هوشمند و ریز شبکه صنعتی و یک مطالعه نقشه برداری منظم-2020
Energy efficiency and management is a fundamental aspect of industrial performance. Current research presents smart and micro grid systems as a next step for industrial facilities to operate and control their energy use. To gain a better understanding of these systems, a systematic mapping study was conducted to assess research trends, knowledge gaps and provide a comprehensive evaluation of the topic. Using carefully formulated research questions the primary advantages and barriers to implementation of these systems, where the majority of research is being conducted with analysis as to why and the relative maturity of this topic are all thoroughly evaluated and discussed. The literature shows that this topic is at an early stage but already the benefits are outweighing the barriers. Further incorporation of renewables and storage, securing a reliable energy supply and financial gains are presented as some of the major factors driving the implementation and success of this topic.
Keywords: Industrial smart grid | Industrial micro grid | Systematic mapping study | Strategic energy management | Industrial facility optimization | Renewable energy resources
مقاله انگلیسی
4 Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020
Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer. An unsupervised classification model based on principal components analysis (PCA) was employed first for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a bigger challenge in classification based on PCA. A supervised classification model based on KELM was then developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used as the variable selection method to eliminate useless information and improve classification ability of the KELM model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of 94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method for classification and identification of Salvia miltiorrhiza samples in different regions.
Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification
مقاله انگلیسی
5 A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions
یک استراتژی مدیریت انرژی ترکیبی از زمان واقعی خودروهای برقی پلاگین با توجه به شرایط رانندگی-2020
In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with the help of Global Positioning System and Geographical Information System. A blended energy management strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation of the identified driving conditions and the extracted rules, which includes the engine starting scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed strategy can effectively adapt to different driving conditions with the dramatic improvement of fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time implementation
Keywords: Plug-in hybrid electric vehicles | Energy management strategy | Global optimization | Driving condition | Equivalent driving distance coefficient
مقاله انگلیسی
6 Efficient sizing and optimization of multirotor drones based on scaling laws and similarity models
اندازه گیری موثر و بهینه سازی هواپیماهای بدون سرنشین چند موتور بر اساس قوانین مقیاس بندی و مدل های شباهت-2020
In contrast to the current overall aircraft design techniques, the design of multirotor vehicles generally consists of skill-based selection procedures or is based on pure empirical approaches. The application of a systemic approach provides better design performance and the possibility to rapidly assess the effect of changes in the requirements. This paper proposes a generic and efficient sizing methodology for electric multirotor vehicles which allows to optimize a configuration for different missions and requirements. Starting from a set of algebraic equations based on scaling laws and similarity models, the optimization problem representing the sizing can be formulated in many manners. The proposed methodology shows a significant reduction in the number of function evaluations in the optimization process due to a thorough suppression of inequality constraints when compared to initial problem formulation. The results are validated by comparison to characteristics of existing multirotors. In addition, performance predictions of these configurations are performed for different flight scenarios and payloads.
Keywords: Multirotor drones | UAV | Design methodology | Sizing | Monotonicity analysis | Multidisciplinary Design Optimization
مقاله انگلیسی
7 A preference-based demand response mechanism for energy management in a microgrid
مکانیسم پاسخ تقاضا مبتنی بر اولویت برای مدیریت انرژی در یک ریز شبکه -2020
In this work, a preference-based, demand response (DR) multi-objective optimization model based on real-time electricity price is presented to solve the problem of optimal residential load management. The purpose of such a model is threefold: 1) to minimize the costs associated with consumption; 2) to minimize the inconvenience caused to consumers; and 3) to minimize environmental pollution. Potential solutions to the underlying multi-objective optimization problem are subject to a set of electrical and operational constraints related to home appliances categories and the utilization of distributed energy resources (DER) and energy storage systems (ESS). The use of the proposed model is illustrated in a realistic microgrid scenario where suitable solutions were found by the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). These solutions determine new operational periods for home appliances as well as the utilization of DER and ESS for the planning horizon while considering consumer preferences. Besides helping consumers to take advantage of the benefits offered by DR, the experimental results show that the multi-objective DR model together with the NSGA-III algorithm can effectively minimize energy-consumption costs as well as reduce inconvenience costs and environmental pollution.
Keywords: Demand Response | Microgrid | Optimization | NSGA-III | Smart grid
مقاله انگلیسی
8 Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach
بهینه سازی تعمیر و نگهداری انتخابی پویا برای سیستم های چند حالته در یک افق محدود: یک رویکرد یادگیری تقویتی عمیق-2020
Selective maintenance, which aims to choose a subset of feasible maintenance actions to be performed for a repairable system with limited maintenance resources, has been extensively studied over the past decade. Most of the reported works on selective maintenance have been dedicated to maximizing the success of a single future mission. Cases of multiple consecutive missions, which are oftentimes encoun- tered in engineering practices, have been rarely investigated to date. In this paper, a new selective main- tenance optimization for multi-state systems that can execute multiple consecutive missions over a finite horizon is developed. The selective maintenance strategy can be dynamically optimized to maximize the expected number of future mission successes whenever the states and effective ages of the components become known at the end of the last mission. The dynamic optimization problem, which accounts for imperfect maintenance, is formulated as a discrete-time finite-horizon Markov decision process with a mixed integer-discrete-continuous state space. Based on the framework of actor-critic algorithms, a cus- tomized deep reinforcement learning method is put forth to overcome the “curse of dimensionality”and mitigate the uncountable state space. In our proposed method, a postprocess is developed for the actor to search the optimal maintenance actions in a large-scale discrete action space, whereas the techniques of the experience replay and the target network are utilized to facilitate the agent training. The perfor- mance of the proposed method is examined by an illustrative example and an engineering example of a coal transportation system.
Keywords: Maintenance | Dynamic selective maintenance | Deep reinforcement learning | Imperfect maintenance | Multi-state system
مقاله انگلیسی
9 A bi-objective optimization approach for selection of passive energy alternatives in retrofit projects under cost uncertainty
یک روش بهینه سازی دو هدفه برای انتخاب گزینه های انرژی منفعل در پروژه های مقاوم سازی تحت عدم اطمینان هزینه-2020
Improving energy performance of buildings is of particular importance in new construction and existing buildings. Building refurbishment is considered a practical pathway towards energy efficiency as the replacement of older buildings is at a slow pace. There are various ways of incorporating energy conservation measures in buildings through refurbishment projects. As such, we have to choose among various passive or active measures. In this study, we develop an integrated assessment model to direct energy management decisions in retrofit projects. Our focus will be on alternative passive measures that can be included in refurbishment projects to reduce overall energy consumption in buildings. We identify the relative priority of these alternatives with respect to their non- monetary (qualitative) benefits and issues using an analytic network process. Then, the above priorities will form a utility function that will be optimized along with the energy demand and retrofit costs using a multi-objective optimization model. We also explore various approaches to formulate the uncertainties that may arise in cost estimations and incorporate them into the optimization model. The applicability and authenticity of the proposed model is demonstrated through an illustrative case study application. The results reveal that the choice of the optimization approach for a retrofit project shall be done with respect to the extent of variations (uncertainties) in expected utilities (benefits) and costs for the alternative passive technologies.
Keywords: Construction technologies | assive energy measures | Building retrofit | Multi-Objective Optimization | Cost uncertainty | Fuzzy set theory
مقاله انگلیسی
10 Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method
مدیریت بهینه انرژی یک سیستم انرژی تجدیدپذیر ترکیبی مبتنی بر مسکونی با استفاده از کنترل زمان واقعی مبتنی بر قانون و روش بهینه سازی برنامه نویسی پویا 2D-2020
This paper presents a magnetically coupled hybrid renewable energy system (RES) for residential applications. The proposed system integrates the energies of a set of PV panels, a fuel cell stack, and a battery using a multi-winding magnetic link to supply a residential load. It can operate in multiple gridconnected and off-grid operation modes. An energy management unit including an off-line dynamic programming-based optimization stage and a real-time rule-based controller is designed to optimally control the power flow in the system according to the provided energy plan. The system is designed according to the required standards of the grid-connected residential RES. Different sections of the proposed system including steady-state operation, control techniques, energy management method and hardware design are studied in brief. A prototype of the proposed system is developed and experimentally tested for an energy management scenario considering both sunny and cloudy profiles of the PV generation. The energy distribution and cost analysis approved the benefits of the proposed system for residential consumers.
Keywords: Energy management | Real-time | Renewable energy system | Residential
مقاله انگلیسی
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