In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environme...
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ISBN:
(纸本)9783037855447
In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particleswarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.
Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as pro...
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ISBN:
(纸本)9781467375412
Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.
We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a p...
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We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a Differential Evolution algorithm (DEA) and particleswarmoptimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.
An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where...
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ISBN:
(纸本)9781479979929
An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the development of degradation model is difficult, we must be addressed to the identification of new features having an obvious trending quality. in this context, This paper present a new selection method based upon a particle swarm optimization algorithm to identify the advanced prognosis feature and a particle filtering for the prediction of the remaining useful life. The fault growth model is integrated to the particle filter using a Neuro-Fuzzy System with its process noise. This method was validated on a set of experimental data collected from bearings run-to-failure tests.
When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is ...
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ISBN:
(纸本)9781479974559
When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimizationalgorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.
This work investigates the calibration of a stereo vision system based on two PTZ (Pan-Tilt-Zoom) cameras. As the accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationship...
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ISBN:
(纸本)9780819499363
This work investigates the calibration of a stereo vision system based on two PTZ (Pan-Tilt-Zoom) cameras. As the accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationships between rotation axes of the cameras, the major concern is the development of an effective and systematic way to obtain these relationships. We derived a complete geometric model of the dual-PTZ-camera system and proposed a calibration procedure for the intrinsic and external parameters of the model. The calibration method is based on Zhang's approach using an augmented checkerboard composed of eight small checkerboards, and is formulated as an optimization problem to be solved by an improved particleswarmoptimization (PSO) method. Two Sony EVI-D70 PTZ cameras were used for the experiments. The root-mean-square errors (RMSE) of corner distances in the horizontal and vertical direction are 0.192 mm and 0.115 mm, respectively. The RMSE of overlapped points between the small checkerboards is 1.3958 mm.
swarm intelligence based technique has been used in this work for the estimation of parameters of photovoltaic cells using the two-diode model of the photovoltaic cell. particle swarm optimization algorithm was used t...
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ISBN:
(纸本)9783319030029;9783319030012
swarm intelligence based technique has been used in this work for the estimation of parameters of photovoltaic cells using the two-diode model of the photovoltaic cell. particle swarm optimization algorithm was used to fit the calculated current-voltage characteristics of the photovoltaic cells to the experimental one. The estimated parameters were the generated photocurrent, saturation currents, series resistance, shunt resistance and ideality factors. The proposed approach was validated using industrial photovoltaic cells.
In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle ...
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ISBN:
(纸本)9781479951512
In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle swarm optimization algorithm (MGPSO) is proposed to avoid the shortcomings of PSO including premature convergence, poor search accuracy and easily falling into local optimal solution. The gradient searching strategy is introduced to improve the search accuracy of local optima. Grouping and randomly updating strategy are used to improve the searching ability of global optima. Simulation experiments and the example of reservoir optimal operation show that the new algorithm MGPSO obviously outperforms the standard PSO and shuffled frog leaping particleswarmoptimization (SFLPSO), and is effective in solving the optimal operation of hydropower station reservoir.
Polyphase coded radar signals is one of the most important and usually used in MIMO Radar systems. Polyphase coded radar signals with good orthogonal properties having low autocorrelation and a low crosscorrelation pr...
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ISBN:
(纸本)9781479940400
Polyphase coded radar signals is one of the most important and usually used in MIMO Radar systems. Polyphase coded radar signals with good orthogonal properties having low autocorrelation and a low crosscorrelation property is a nonlinear multivariable optimization problem. The proposed technique yield polyphase waveforms with orthogonal properties which effectively removes the interference issues faced by radars and also help in attaining high radar resolution. The oppositional concept is included in the initial population selection in the PSO algorithm and fractional calculus is used for modifying the velocity updation equation in PSO. Peak Sidelobe Ratio (PSLR) and Integrated Energy Sidelobe ratio (ISLR) of the polyphase waveforms are incorporated in the fitness function defined for the PSO. The experimental analysis based on different phases, different sequence lengths, different code set and different cost function are carried out. In all cases, the proposed technique has achieved good results.
A particle swarm optimization algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. At the same time, the proposed ...
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ISBN:
(纸本)9783662450482
A particle swarm optimization algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. At the same time, the proposed algorithm is investigated in experiments which are based on the function optimization of micro-grid's economic operation. Furthermore, the feasibility and effectiveness of the proposed algorithm are showed in the experimental results.
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