Extension of the gap statistics index to fuzzy clustering

The well-known gap statistic index proposed by Tibshirani et al. has successfully applied in many clustering evaluations. However, the gap statistic index cannot evaluate the clustering partitions from any fuzzy clustering algorithm. This is because fuzzy clustering cannot provide the within-cluster similarity measure that is used in the gas statistic index. Thus, the applicable range of the gap statistic index is very limited. In this paper, we present a new method that extends the gap statistic index to fuzzy clustering by using fuzzy membership notations. Our proposed method can extend the applicability of the gap statistic index, and outperform other existing fuzzy indices in several aspects. Experiments on eight sets of synthetic and real datasets are used to verify the applicability and efficiency of the proposed method.     

A novel artificial bee colony algorithm with Powell's method

Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

一种实时语声控制器的设计与实现

针对ANALOG DEVICES公司的数字信号处理芯片ADSP2181,给出一种实现实时语声控制器的方法.详细描述ITU-T G.728语声信号的能量的计算和软件设计方法.     

Modeling sediment resuspension and transport induced by storm wind in Apalachicola Bay, USA

Sediment is an important environmental factor for aquatic ecosystem and oyster productivities of Apalachicola Bay located in Florida, USA. Based on the data analysis in this study, surface wind speed is highly correlated to the turbidity of water column, which results from sediment resuspension and transport in the Apalachicola Bay. In this paper, an application of a 3D sediment transport model to predict the wind-induced sediment transport in Apalachicola Bay is described. The sediment model is coupled with a 3D hydrodynamic module in the Environmental Fluid Dynamics Code (EFDC) model that provides information on estuarine circulation and salinity transport under normal temperature conditions. The hydrodynamic model was calibrated with field observations of water levels and salinity. The sediment transport model solves the transport equation with sources and sinks terms to describe sediment deposition and resuspension. The coupled hydrodynamic and sediment transport models were used to investigate wind-induced total suspended sediments (TSS) resuspension and transport in the bay. For the period June 1–July 30, 2005 two storm events with strong winds gave model results of TSS concentrations that compared well with the field observations. Model simulations reasonably reproduce the sudden increase of sediment concentrations during the storm events. Maximum sediment concentrations in the bay during the two storm events were 10 times or more than those in the pre-storm conditions. Spatial sediment transport from model simulations indicate active sediment resuspension and transport near areas of highly productive oyster beds. The model predictions of TSS and salinity can be used as inputs to an oyster dynamic model (Wang, H., Huang, W., Harwell, M., Edmiston, L., Johnson, E., Hsieh, P., Milla, K., Christensen, J., Stewart, J., Liu, X., 2008. Modeling eastern oyster population dynamics in response to changing environment in Apalachicola Bay, Florida. Journal of Ecological Modeling 211, 77–89) to support the ecological study of oyster growth and mortality in the aquatic ecosystem of Apalachicola Bay.