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Small leakages are severe threats to the long distance pipeline transportation. An online small leakage detection method based on chaos characteristics and Least Squares Support Vector Machines (LS-SVMs) is proposed in this paper. For the first time, the relationship between the chaos characteristics of pipeline inner pressures and the small leakages is investigated and applied in the pipeline detection method. Firstly, chaos in the pipeline inner pressure is found. Relevant chaos characteristics are estimated by the nonlinear time series analysis package (TISEAN). Then LS-SVM with a hybrid kernel is built and named as hybrid kernel LS-SVM (HKLS-SVM). It is applied to analyze the chaos characteristics and distinguish the negative pressure waves (NPWs) caused by small leaks. A new leak location method is also expounded. Finally, data of the chaotic Logistic-Map system is used in the simulation. A comparison between HKLS-SVM and other methods, in terms of the identification accuracy and computing efficiency, is made. The simulation result shows that HKLS-SVM gets the best performance and is effective in error analysis of chaotic systems. When real pipeline data is used in the test, the ultimate identification accuracy of HKLS-SVM reaches 97.38% and the position accuracy is 99.28%, indicating that the method proposed in this paper has good performance in detecting and locating small pipeline leaks. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the inertial Mann forward-backward splitting algorithm for solving variational inclusion problem of the sum of two operators, the one is maximally monotone and the other is monotone and Lipschitz continuous. Under standard assumptions, we prove the weak convergence theorem of the proposed algorithm. We show that the algorithm is flexible to use by choosing the variable stepsizes and two different algorithms are shown by choosing constant stepsize and update stepsize. Moreover, we apply our algorithms to solve data classification using the Wisconsin original breast cancer data set as a training set. We also compare our algorithms with the other two algorithms to show the efficiency of the algorithm and show suitably learns the training dataset and generalizes well to a hold-out dataset of the algorithm by considering overfitting. Finally, we apply our algorithms to solve signal recovery and show the efficiency of the algorithm by compare with the other two algorithms. The results of data classification and signal recovery showed that choosing the right stepsizes of the algorithm would be a good efficient for the different problems. 46T99, 47B02, 47H05, 47J25, 49M37. • The paper proposed an advanced forward-backward splitting algorithm in combination with an inertial technique and Mann algorithm for solving variational inclusion problems. • The weak convergence result has been established in Hilbert spaces subject to certain conditions. • It developed an application to predict breast cancer through the proposed algorithm. • It used breast cancer Wisconsin original data set as a training set to show efficiency of the newly developed algorithm by comparing with two other algorithms in term of three key parameters viz. accuracy, recall and precision. • It developed an application to signal recovery and showed the efficiency of the algorithm by comparing with the other two algorithms. [ABSTRACT FROM AUTHOR]

Crop detection from remote sensed images is of major interest for land use and land cover mapping. Classification techniques often require multi-temporal images. However, most of these techniques assume that the cultural cycle occurs at the same dates across plots or for a given crop and do not take into account the sensitivity to initial conditions of the dynamical behaviors. Such hypotheses are not well adapted when a wide diversity of practices is observed for the same crops from one crop field to another, which is often the case in tropical context. To cope with these difficulties, a new classification technique based on the global modeling technique is introduced in this paper. It is first applied to a case study based on chaotic oscillators. It is then tested on crop classification observed from satellite data. The Berambadi watershed (South India) is taken as a case study to test this new classification approach. Crop classification is a difficult problem in Southern India where optical satellite images are scarce during the monsoon season due to cloud cover, and where crop land is divided in parcels (i.e. crop fields) of very small sizes with diversified crops. The Landsat-8 images were used to monitor an ensemble of 104 parcels of ten different crops (irrigated and non-irrigated). Using global modeling, a bank of crop models was first obtained for the ten crops considered in the study. A metric is introduced to compare the observed signal to the obtained crop-models used as reference for each crop dynamic. Based on this metric, the possibility to use global models as references for distinguishing crops is investigated. The results provide a good proof-of-concept and show promising potential for crop classification. [ABSTRACT FROM AUTHOR]