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Sliced Latin hypercube designs are useful for computer experiments with qualitative and quantitative factors, model calibration, cross validation, multi-level function estimation, stochastic optimization and data pooling. Orthogonality and second-order orthogonality are crucial in identifying important inputs. Besides orthogonality, good space-filling properties are also necessary for Latin hypercube...
Fault detection in wireless sensor networks is a crucial and challenging task. Many detection approaches relying on specific rules or inference models have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, these approaches may require high communication overhead or computational cost, and many potential faulty sensors that may not...
Fault detection plays a crucial role in wireless sensor networks (WSNs). Many fault detection approaches requiring a priori knowledge of network faults have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, many faulty sensors that may not generate anomalous sensor readings, and potential failures with unknown types and symptoms...
Sensors easily become faulty and unreliable subject to limited battery and insecurity. Data Fault is one of traditional faults in the wireless sensor networks. Data fault mainly uses distributed method through exchanging neighbors' measurements and voting for decision. But the detection accuracy performance is easily influenced by unbalanced fault distribution. Based on this, we propose the k-means...
Sliced Latin hypercube designs are very useful for running a computer model in batches, ensembles of multiple computer models, computer experiments with qualitative and quantitative factors, cross-validation and data pooling. However, the presence of highly correlated columns makes the data analysis intractable. In this paper, a construction method for sliced (nearly) orthogonal Latin hypercube designs...
Monitoring traffics between applictions deployed in a distributed computing system (DCS) can help analyzers perceive the dynamic load of each application, and detect the anomalies in all the running processes. However, due to the factors of high dimension and strong periodicity, the traffic data is difficult to visualize and interpret. In this paper, we propose a traffic monitoring approach based...
In the current broadband multimedia applications, researchers and practitioners have mainly focused their designs on coping with static or slowly evolving traffic demands. Even though many such applications involve fast moving terminals, fast moving traffic conditions have not yet been taken into account which results in unrealistic simulations. It is therefore imperative to design a new channel simulation...
This paper was proposed a modified blind OFDM system parameters estimation method. The method was based on the periodicity of mobile autocorrelation function, and it estimated the OFDM symbol duration by using of the cyclic frequency test. Moreover, it limited the searching range of cyclic frequency which reduced the computational complexity. Simulation results show that the proposed time-parameter...
Survey data are used to examine the influence of distinct categories of internal factors (such as attitudes, personal norms, perceived behavior barriers, knowledge) and external factors (such as socioeconomic characteristics, living conditions and situation factors) on citizen's pro-environmental behavior. Results from the descriptive Analysis indicate that most of respondents consider the environmental...
For the motif significant testing in biological sequences, Bayesian testing based on moment estimate is presented. The motif significant testing is converted into the goodness of fit test of the multinomial distribution. While the prior distribution of the multinomial distribution is known as Dirichlet, the estimates of hyper parameters of prior distribution are given using moment estimate. Based...
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