The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search f...
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Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the...
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As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, ...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.
Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and...
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Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and scalability when performing complex spatially distributed tasks, a novel distributed framework for construction of backbone-based hierarchical communication networks is proposed. Firstly, the proposed method periodically extracts a subset of agents which can form the communication backbone from the original network using only local information, thus partitions the system into backbone agents and non-backbone agents. Furthermore, the global network connectivity of the system is maintained at two levels: connectivity-preserving potential functions are used to maintain existing links in the backbone; connectivity between backbone and non-backbone agents is achieved via a leader-follower formation control scheme with backbone agents as the leaders. Finally, nontrivial numerical simulations are worked out to verify the theoretical results.
An efficient evaluation index system and evaluation model is crucial for effectiveness evaluation of the air defense networked fire control system. This paper, based on pre-research for functions and characteristics o...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model,...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model, which just take the values of targets, UAVs and weapons into account, the fuel consumption is added into consideration to make the model much more practical. An improved genetic algorithm is proposed for such a multi-UAVs multi-targets task allocation. Simulation results show that the algorithm is significantly effective and the allocation result is reasonable.
Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such ...
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