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| 1 | Ore Image Segmentation Method Based on U-Net and Watershed显示文摘Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task. | Hui Li Chengwei Pan Ziyi Chen Aziguli Wulamu Alan Yang | 2020 | Computers, Materials & Continua2020，，10： | 2 |
| 2 | A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation显示文摘The Extreme Learning Machine(ELM) is an effective learning algorithm for a Single-Layer Feedforward Network(SLFN). It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning(NCL) to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs(HE2 LM) for classification has different ELM algorithms including the Regularized ELM(RELM), the Kernel ELM(KELM), and the L2-norm-optimized ELM(ELML2). The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers. | Adnan O.M.Abuassba Yao Zhang Xiong Luo Dezheng Zhang Wulamu Aziguli | 2017 | Tsinghua Science and Technology2017，22，6： | 1 |
| 3 | Efficient Processing of Skyline Group Queries over a Data Stream显示文摘In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications,and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream. | Xi Guo Hailing Li Aziguli Wulamu Yonghong Xie Yajing Fu | 2016 | Tsinghua Science and Technology2016，21，1： | 1 |
| 4 | Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory显示文摘The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation. | Hui Li Cailin Shi Xin Liu Aziguli Wulamu Alan Yang | 2020 | Computers, Materials & Continua2020，，8： | 0 |
| 5 | Robust Cultivated Land Extraction Using Encoder-Decoder显示文摘Cultivated land extraction is essential for sustainable development and agriculture.In this paper,the network we propose is based on the encoder-decoder structure,which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation solutions.The encoder consists of two part:the first is the modified Xception,it can used as the feature extraction network,and the second is the atrous convolution,it can used to expand the receptive field and the context information to extract richer feature information.The decoder part uses the conventional upsampling operation to restore the original resolution.In addition,we use the combination of BCE and Loves-hinge as a loss function to optimize the Intersection over Union(IoU).Experimental results show that the proposed network structure can solve the problem of cultivated land extraction in Yinchuan City. | Aziguli Wulamu Jingyue Sang Dezheng Zhang and Zuxian Shi | 2020 | Journal of New Media2020，2，4： | 0 |
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