<span style="font-size: 12pt; font-family: "Times New Roman"; mso-bidi-font-size: 9.0pt; mso-fareast-font-family: 宋体; mso-fareast-language: ZH-CN; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This paper proposes a new clustering algorithm based on ant colony to solve the unsupervised clustering problem. </span><span style="font-size: 12pt; font-family: "Times New Roman"; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 宋体; mso-fareast-language: ZH-CN; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to find approximate solutions to difficult combinatorial <a title="Optimization (election of authors)" href="http://www.scholarpedia.org/article/Optimization"><span style="color: windowtext; text-decoration: none; text-underline: none;">optimization</span></a> problems. Clustering Analysis, which is an important method in data mining, classifies a set of observations into two or more mutually exclusive unknown groups. This paper presents an effective clustering algorithm with ant colony which is based on stochastic best solution kept--ESacc. The algorithm is based on Sacc algorithm that was proposed by P.S.Shelokar. It’s mainly virtue that best values iteratively are kept stochastically. Moreover, the new algorithm using Jaccard index to identify the optimal cluster number. The results of several times experiments in three datasets show that the new algorithm-ESacc is less in running time, is better in clustering effect and more stable than Sacc. Experimental results validate the novel algorithm’s efficiency. In addition, Three indices of clustering validity analysis are selected and used to evaluate the clustering solutions of ESacc and Sacc.</span>
License plate location (LPL) plays an important role in the license plate recognition (LPR). The license plate characters contain abundant corners which distribute intensively and regularly. Accord to the feature, a novel method to locate the license plate accurately and fast is proposed in this paper. The method is dived into three steps which are corners detection, finding out candidate license plates and locating the license plate accurately. In the course of locating license plate, three thresholds are used. First, the reasonable threshold is used to eliminate a great number of non-corner pixels to speedup corner-detections. Second, a big threshold is selected with a slid window to find out candidates. Finally, the region of candidate license plates are expanded, all the corners in it were stand out with a smaller threshold, and the license plate is located accurately using clustering analyses. Experiments have been conducted, 1200 license plate images are tested, the accuracy is 98.42% and the average consuming time is 20ms.
This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car' s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been apphed to identify and predict its chaotic movement. A simtflated traffic flow is generated using a car-following model(GM model), and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN ( radial basis function neural network) is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time waffic flow series.
Global land cover data are fundamental for applications, especially ecological environmental assessment and climate change research. Currently available global land cover data products show some deficiencies in data accuracy and spatial and temporal resolution. So we discuss fast automatic classification methods for the study area in Antarctica. A classification method based on a Support vector machine (SVM) and a decision tree (DT) model is proposed. We compare the land cover classification using four common kernel functions for a SVM. The experiment indicates that the SVM classification method using a radial basis function (RBF) leads to the optimal accuracy and running time. In view of existing phenomenon that surface features in shadow areas are easily confused, classification is further improved by using a DT model, at last a majority analysis of the above classification result removes small polygon artifacts to form the final land cover data product. The overall accuracy is 95.82%, higher than the SVM alone and the maximum likelihood method. Land cover classification in Antarctica can be conducted more reliably through our proposed classification method.
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