با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
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
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020
tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario.
Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems
Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty
برنامه ریزی بهینه از یک میکروگرید مبتنی بر قابل تجدید با توجه به سیستم فتوولتائیک و ذخیره انرژی باتری در عدم قطعیت-2020
This paper suggests a new energy management system for a grid-connected microgrid with various renewable energy resources including a photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT) and battery energy storage system (BESS). For the PV system operating in the microgrid, an innovative mathematical modelling is presented. In this model, the effect of various irradiances in different days and seasons on day-ahead scheduling of the microgrid is evaluated. Moreover, the uncertainties in the output power of the PV system and WT, load demand forecasting error and grid bid changes for the optimal energy management of microgrid are modelled via a scenario-based technique. To cope with the optimal energy management of the grid-connected microgrid with a high degree of uncertainties, a modified bat algorithm (MBA) is employed. The proposed algorithm leads to a faster computation of the best location and more accurate result in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The simulation results demonstrate that the use of practical PV model in a real environment improve the accuracy of the energy management system and decreases the total operational cost of the grid-connected microgrid.
Keywords: Photovoltaic | Energy management | Battery energy storage system | Uncertainty | Optimization | Microgrid
An echo state network architecture based on quantum logic gate and its optimization
معماری شبکه ای حالت اکو مبتنی بر دروازه منطق کوانتومی و بهینه سازی آن-2020
Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor’s 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance.
Keywords: Quantum computation | Echo state network | Particle swarm optimization | Time series | Financial applications
Bi-level optimal sizing and energy management of hybrid electric propulsion systems
اندازه گیری بهینه دو سطح و مدیریت انرژی سیستم های پیشران برقی هیبریدی-2020
Hybrid electric propulsion systems attract considerable research interest because of their potential to reduce fuel consumption, greenhouse gas emission, and net present cost. However, independent optimization for component sizing or energy management may lead to performance degradation. The present study proposes a multiobjective bi-level optimization that performs component sizing at the upper level and energy management at the lower level simultaneously. Multiobjective particle swarm optimization is developed for the upper level because of its merits in computational time and generational distance. An adaptive equivalent consumption minimization strategy, which has a light computational load, has been modified for the lower level by updating the equivalence factor based on the battery stage of charge and engine efficiency. Real-time hardware-in-the-loop experiments are carried out to validate the effectiveness of the optimization. The results of the proposed bi-level optimization are compared with two independent single-level optimizations. The optimal solution of the proposed method is significantly superior to the single-level optimizations. Furthermore, the result of the singlelower- level optimization is closer to that of the bi-level optimization than that of the single-upper-level optimization.
Keywords: Multiobjective bi-level optimization | Hybrid electric propulsion system | Modified adaptive equivalent consumption | minimization strategy | Fuel consumption | Greenhouse gas emission | Net present cost
Intelligent energy management system for conventional autonomous vehicles
سیستم هوشمند مدیریت انرژی برای وسایل نقلیه معمولی خود مختار -2020
Autonomous vehicles have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Although battery-powered electric and hybrid-electric autonomous vehicles assume more widespread use than conventional autonomous vehicles, energy management is harder and more significant for conventional autonomous vehicles. As such, it is necessary to investigate how to manage energy consumption in conventional autonomous vehicles. In this paper, an energy management system is constructed and analyzed by using a road-power-demand model and an intelligent system to reduce fuel consumption for a conventional autonomous vehicle. The road-power-demand model utilizes three impact factors (i) environment-conditions (ii) driver-behavior, and (iii) vehicle-specifications. The proposed intelligent energy management system includes a fuzzy-logic-system with the aim of generating the desired engine torque, based on the vehicle road power demand and a PID controller to control the air/fuel ratio, by changing the throttle angle. Results show that the intelligent energy management system reduces the vehicle energy consumption from 7.2 to 6.71 L/100 km. Next, the parameters of the fuzzy-logic-system are intelligently optimized by the particle-swarm-optimization method and new results indicate that the vehicle energy consumption is reduced by around 9.58%.
Keywords: Autonomous vehicle | Intelligent energy management | Control strategies | Conventional autonomous vehicle | Fuzzy logic system | Particle swarm optimization | Artificial Intelligence
A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization
یک الگوریتم ژنتیکی زنبورعسل مصنوعی برای بهینه سازی داده های بزرگ مبتنی بر بازسازی سیگنال-2020
In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.
Keywords: Big data optimization | Signal decomposition | Artificial Bee Colony
Vanadium redox flow battery parameters optimization in a transportation microgrid: A case study
بهینه سازی پارامترهای باتری بادی جریان وانادیوم در یک شبکه انتقال : یک مطالعه موردی-2020
This paper addresses the concept of vanadium redox flow batteries as stationary energy storage for achieving optimum parameters of energy and cost-effectiveness in transportation microgrids. Such energy storage has two main purposes: to utilize the energy recovered from braking trains, and shave power peaks. With abovementioned purposes, economic feasibility is the main driver of measures to optimize the battery parameters, including joint energy and power capacity, as well as and energy management strategy parameters. The optimization results obtained from the genetic algorithm and particle swarm optimization algorithm were compared, and the comparison demonstrates that the second method operates more sufficiently. The case study shows that the implementation of the proposed battery system in a traction substation allows one to achieve approximately 7 year payback period and decrease peak power and daily consumption by 581 kW and 1.77 MWh, respectively. In addition, sensitivity analysis was conducted to determine the impact of certain factors and battery parameters on the resulting payback period. The results show that the effect of deviation of energy management strategy parameters from optimum values on payback period is four times more profound than deviation of battery parameters, which demonstrates how important energy management strategy is.
Keywords: Vanadium redox flow battery | Transportation system microgrid | Energy management strategy | Traction substation | Regenerative braking | Peak power sheaving
A closed-loop brain–machine interface framework design for motorrehabilitation
طراحی چارچوب رابط مغز و ماشین با حلقه بسته برای توان بخشی در موتور-2020
Brain–machine interfaces (BMIs) can be adopted to rehabilitate motor systems for disabled subjectsby sensing cortical neuronal activities and creating new method. In this paper, to achieve the functionof motor rehabilitation, two generalized BMI frameworks, including decoders, encoders and auxiliarycontrollers, are proposed and compared based on a classical single-joint information transmission model.Firstly, a decoder based on the Wiener filter and an encoder based on a network of spiking neuronsare designed to compensate for the absent information pathway, and a charge-balanced intra-corticalmicrostimulation current is chosen as the input of the spiking neuron network; Secondly, to formulateclosed-loop BMI frameworks, two auxiliary controllers are designed according to the strategy of modelpredictive control, where the controller inputs are the position of joint muscle trajectories and the averagefiring activity trajectories of perceived position vector neurons. Thirdly, considering that several integerparameters are included in the charge-balanced intra-cortical microstimulation current and that theoptimization problem for solving the control inputs also includes these decision variables, a particleswarm optimization algorithm is adopted to solve the hard optimization problem. We compare the motorrecovery effectiveness of the two presented frameworks through these simulations and choose the betterframework for future BMI system design. The proposed frameworks provide a important theoreticalguidance for designing BMI system applied in future life
Keywords:Brain–machine interface | Framework design | Auxiliary controller | Network of spiking neurons | Particle swarm optimization
Analysis of earnings forecast of blockchain financial products based on particle swarm optimization
تحلیل پیش بینی درآمد محصولات مالی بلاکچین بر اساس بهینه سازی ازدحام ذرات-2019
The purpose of this study is to solve the problems of large number of iterations, limitations and poor fitting effect of traditional algorithms in predicting the yield rate of blockchain financial products. In this study, bitcoin yield rate is taken as the research object, and data from June 2, 2016 to December 30, 2018 are collected, totaling 943 pieces. The BP neural network, support vector regression machine algorithm and particle swarm optimization least square vector algorithm are respectively adopted to carry out model simulation and empirical analysis on the collected data, and it is concluded that particle swarm optimization least square vector algorithm has the best fitting effect. Subsequently, the Ethereum (ETH) yield rate is selected as the research object, and the model simulation and empirical analysis are carried out on it, which verifies that the optimized algorithm has better prediction and fitting on the time series. The results show that the particle swarm optimization algorithm among the three algorithms mentioned in this research has the best prediction effect. Therefore, the results of this study have a good fitting effect on the prediction of the yield rate of blockchain financial products, have a good guiding effect on the investors of blockchain financial products, and have a good guiding significance for the study of the yield rate of China’s blockchain financial products.
Keywords: Particle swarm optimization | Blockchain | Financial product | Earnings