Linear manifold clustering for high dimensional data based on line manifold searching and fusing

High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.

Coal mine gas monitoring system based on wireless sensor network

Based on the nowadays＇ condition, it is urgent that the gas detection cable communication system must be replaced by the wireless communication systems. The wireless sensors distributed in the environment can achieve the intelligent gas monitoring system. Apply with multilayer data fuse to design working tactics, and import the artificial neural networks to analyze detecting result. The wireless sensors system communicates with the control center through the optical fiber cable. All the gas sensor nodes distributed in coal mine are combined into an intelligent, flexible structure wireless network system, forming coal mine gas monitoring system based on wireless sensor network.

Data fusing and joint training for learning with noisy labels

It is well known that deep learning depends on a large amount of clean data.Because of high annotation cost,various methods have been devoted to annotating the data automatically.However,a larger number of the noisy labels are generated in the datasets,which is a challenging problem.In this paper,we propose a new method for selecting training data accurately.Specifically,our approach fits a mixture model to the per-sample loss of the raw label and the predicted label,and the mixture model is utilized to dynamically divide the training set into a correctly labeled set,a correctly predicted set,and a wrong set.Then,a network is trained with these sets in the supervised learning manner.Due to the confirmation bias problem,we train the two networks alternately,and each network establishes the data division to teach the other network.When optimizing network parameters,the labels of the samples fuse respectively by the probabilities from the mixture model.Experiments on CIFAR-10,CIFAR-100 and Clothing1M demonstrate that this method is the same or superior to the state-of-the-art methods.

Predicting sandy soil moisture content with hyperspectral imaging

In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 nm)and spatial(1620×841 pixels)information from sandy soil samples with variable SMC levels in the laboratory.Principal component analysis(PCA)transformation,K-means clustering,and several other image processing methods were performed to obtain a region of interest(ROI)of soil sample from the original HSI data.Then,256 optimal spectral wavelengths were selected from the average reflectance of the ROI,and 28 textural features were extracted using a gray-level co-occurrence matrix(GLCM).Data dimensionality reduction was conducted on both the spectral information and textural information by using a partial least square algorithm.Six latent variables(LVs)extracted from the spectral information,four LVs extracted from the textural information and fused data were used to build regression models with a three-layer BPNN,respectively.The results showed that all of the three calibration models achieved high prediction accuracy,particularly when using spectral information with R^(2)_(C)=0.9532 and RMSEC=0.0086.However,validation models demonstrate that predicting SMC using fused data is more effective than using spectral reflectance and textural features separately,with a R^(2)_(P)=0.9350 and RMSEP=0.0141,thus proving that the HSI technique is capable of detecting SMC.