Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 5 Articles
In order to solve the node localization problem in wireless sensor networks, we propose a novel distributed weighted search\nlocalization algorithm (WSLA) in this paper. The WSLA adopts a modified received signal strength indicator-based range model\nto estimate the distances between nodes, utilizes the results of a centroid localization algorithm as the search initial point, and\nemploys a new weighted search method to compute the positions of nodes in a distributed and recursive manner. The key ideas\nof the WSLA include a node localization precision classification scheme, a processing scheme for special nodes, and weight-based\nsearches. Compared with three state-of-art localization algorithmsââ?¬â?namely, maximum likelihood estimation (MLE), edge-based\nsecond-order cone programming + nonconvex sequential greedy (ESOCP + NCSG), and particle swarm optimization (PSO)ââ?¬â?the\nsimulation results show that localization performance of the WSLA is superior to that of MLE, ESOCP + NCSG, and PSO....
Multi-robot task allocation (MRTA) is an important area of research in\nautonomous multi-robot systems. The main problem in MRTA is to allocate a set of\ntasks to a set of robots so that the tasks can be completed by the robots while ensuring\nthat a certain metric, such as the time required to complete all tasks, or the distance\ntraveled, or the energy expended by the robots is reduced. We consider a scenario where\ntasks can appear dynamically and a task needs to be performed by multiple robots to\nbe completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA)\nthat uses a spatial queue-based model to allocate tasks between robots in a distributed\nmanner. We have implemented the SQ-MRTA algorithm on accurately simulated models\nof Corobot robots within the Webots simulator for different numbers of robots and tasks and\ncompared its performance with other state-of-the-art MRTA algorithms. Our results show\nthat the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in\nthe environment, and it either outperforms or performs comparably with respect to other\ndistributed MRTA algorithms....
An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern\nsynthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and\nphase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical\nexamples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and\nflexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm\noptimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method\n(QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation\nalgorithm (PGSA), tabu search algorithm (TSA),memetic algorithm (MA), non dominated sorting GA-2 (NSGA-2),multiobjective\ndifferential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO),\nharmony search algorithm (HSA), seeker optimization algorithm (SOA), andmean variance mapping optimization (MVMO). The\nsimulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels....
Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical\nalgorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces\nthe efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved\nalgorithms of FP-Growth algorithmââ?¬â?Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting\nsteps, and uses another way to achieve).We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental\nresults show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume;\nthe performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm....
The acceleration performance of EV, which affects a lot of performances of EV such as start-up, overtaking, driving safety, and ride\ncomfort, has become increasingly popular in recent researches. An improved variable gain PID control algorithm to improve the\nacceleration performance is proposed in this paper. The results of simulation with Matlab/Simulink demonstrate the effectiveness\nof the proposed algorithm through the control performance of motor velocity, motor torque, and three-phase current of motor.\nMoreover, it is investigated that the proposed controller is valid by comparison with the other PID controllers. Furthermore, the\nAC induction motor experiment set is constructed to verify the effect of proposed controller....
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