Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
In view of optimizing the configuration of each unitâ??s capacity for energy storage in the\nmicrogrid system, in order to ensure that the planned energy storage capacity can meet the\nreasonable operation of the microgridâ??s control strategy, the power fluctuations during the gridconnected\noperation of the microgrid are considered in the planning and The economic benefit of\nhybrid energy storage is quantified. A multi-objective function aiming at minimizing the power\nfluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage\nsystem in the hybrid energy storage system (HESS) is established. The improved particle swarm\nalgorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid\nexperimental platform. By comparing the power fluctuations of the battery and the supercapacitor\nin the HESS, the power distribution is directly reflected. Comparing with the traditional mixed\nenergy storage control strategy, it shows that the optimized hybrid energy storage control strategy\ncan save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and\nthe performance of the power fluctuation of the renewable energy is also improved. It proves that\nthe proposed capacity configuration of the HESS has certain theoretical significance and practical\napplication value....
Obtaining high convergence and uniform distributions remains a major challenge in most\nmetaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle\nswarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved\nlearning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity\nof external archives and current populations. To improve the global optimal solution, different\nlearning strategies are proposed for non-dominated and dominated solutions. An indicator is\npresented to measure the distribution width of the non-dominated solution set, which is produced by\nvarious algorithms. Experiments were performed using eight benchmark test functions. The results\nillustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence\nand distributions than the other two algorithms, and the distance width indicator is reasonable and\neffective....
Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS).\nIn this paper, we provide a camera-based deep learning method that accurately detects other vehicles\nin the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough\nof deep learning algorithms shows extraordinary performance when applied to many computer\nvision tasks. Many new convolutional neural network (CNN) structures have been proposed and\nmost of the networks are very deep in order to achieve the state-of-art performance when evaluated\nwith benchmarks. However, blind spot detection, as a real-time embedded system application,\nrequires high speed processing and low computational complexity. Hereby, we propose a novel\nmethod that transfers blind spot detection to an image classification task. Subsequently, a series of\nexperiments are conducted to design an efficient neural network by comparing some of the latest\ndeep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using\nthe blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning\nmodel and evaluate its performance on the dataset....
Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively,\nscheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel\nprogram. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to\nsolve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the\nmakespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority\nof the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering\nhow to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to\naddress static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria\nnamed SteepestAscent Learning Criterion andNextAscent Learning Criterionwherewe use the concepts of penalty and reward for\nlearning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling\nimproves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion\nduring the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides\nalways better scheduling against existing well-known scheduling approaches....
Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of\nknown nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization\nscheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is\ncompared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm\n(BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show\nthat the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time,\nand the number of localized nodes....
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