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第十四届中国卫星导航年会

作者： Dengao Li Danyang Shi Jumin Zhao Fanming Wu Liangquan Yan Ran Feng Xinfang Zhang Jinhua Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

The zenith tropospheric delay（ZTD） obtained by global navigation satellite system（GNSS） atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3（GPT3） model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3TD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network（TCN） for improving the hourly global pressure and temperature 2（HGPT2） model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2TD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCNTD obtained from the proposed model is higher than GPT3TD and HGPT2TD when using the GNSSTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious.

关键词： GNSS ZTD GPT3 HGPT2 TCN

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid model

Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid m...

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第十四届中国卫星导航年会

作者： Dengao Li Xinfang Zhang Jumin Zhao Fanming Wu Ran Feng Jinhua Zhao Danyang Shi College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content(TEC)in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network(LSTM)and the fast training speed of gated recurrent unit network(GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour(HD)are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error(RMSE)of the prediction results of this BLSLSTM-GRU model is decreased by 23.07%,17.48% and 9.46%compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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第十四届中国卫星导航年会

作者： Fanming Wu Dengao Li Jinhua Zhao Ran Feng Danyang Shi Xinfang Zhang Jumin Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

There is a growing interest to use Global Navigation Satellite System(GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is *** sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile,combining GNSS-PWV time domain features(PWV value, PWV increment,PWV decrement, rate of PWV increment, rate of PWV decrement) and colocated meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong’s HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models.

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Distributed Broad Learning System With Quantized and Censored Communications

Distributed Broad Learning System With Quantized and Censore...

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Chinese Automation Congress (CAC)

作者： Jinyan Liang Wu Ai College of Science Guilin University of Technology Guilin China Center for Data Analysis and Algorithm Technology Guilin University of Technology Guilin China

ISBN: (纸本)9781665426480

As a novel neural network model, the broad learning system (BLS) has been used extensively due to its simple structure and few model hyperparameters. This paper presents a distributed BLS algorithm with quantized and censored communications (DQC-BLS). The DQC-BLS algorithm is based on the alternating direction method of multipliers (ADMM). In order to reduce the high communication cost, the DQC-BLS algorithm improves the communication mechanism between the in-networks agents by introducing quantization and communication-censoring strategies. Quantization reduces the number of bits for each transmission whereas communication censoring reduces the number of communications. The experimental results verify that the DQC-BLS algorithm can reduce communication costs while maintaining similar performance on the test set.

关键词： Learning systems Costs Quantization (signal) Automation Neural networks Convex functions

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Distributed Reduced Kernel Extreme Learning Machine

Distributed Reduced Kernel Extreme Learning Machine

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Chinese Automation Congress (CAC)

作者： Manrui Zhou Wu Ai College of Science Guilin University of Technology Guilin China Center for Data Analysis and Algorithm Technology Guilin University of Technology Guilin China

ISBN: (纸本)9781665426480

Extreme learning machines (ELM) are popular in the field of pattern recognition and machine learning. The kernel extension of ELM (KELM) presents a better performance than traditional ELM. Although the KELM is able to solve complex nonlinear problems, it is time-consuming and memorydemanding when dealing with a large-size kernel matrix. Introducing reduced kernel technique can dramatically cut down the computational load and memory usage. However, as the amount of training data grows exponentially, it is not effective for a single worker to store the kernel matrix, making it infeasible for data mining in a centralized manner. In this paper, a distributed reduced kernel method for training ELM over decentralized data (DRKELM) is proposed. In the DRKELM, we randomly assign data to different nodes. The communication between nodes is fixed and does not depend on the size of the training data on each node, but on the network topology. Different from the existing reduced kernel ELM, the DRKELM is a fully distributed training algorithm based on the method of alternating direction method of multiplier (ADMM). Experiment with the large scale data set finds that the distributed method can achieve almost the same results as the centralized algorithm and even takes less time to a large extent. It greatly reduces the computation time consumption.

关键词： Training Machine learning algorithms Extreme learning machines Network topology Distributed databases Training data Machine learning

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A Decentralized algorithm for Deep Echo State Networks

A Decentralized Algorithm for Deep Echo State Networks

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Chinese Automation Congress (CAC)

作者： Wenqi Qiu Wu Ai College of Science Guilin University of Technology Guilin China Center for Data Analysis and Algorithm Technology Guilin University of Technology Guilin China

ISBN: (纸本)9781665426480

As a class of recurrent neural networks (RNNs), echo state networks (ESNs) have been studied extensively in recent years, particularly in time-series prediction and non-linear system identification. It is well-known that deep learning (DL) has been applied to ESNs and shows more outstanding performance than conventional ESNs. However, due to the large-scale dataset and complexity required to train deep learning models, the time required to train deep ESNs based on traditional centralized algorithms is highly extended. Hence, a decentralized training algorithm combining DL and ESNs is presented in this paper to aim at multiple time scales processing of temporal data. We propose this algorithm based on an optimization program called the alternating direction method of multipliers (ADMM) and decentralized average consensus (DAC) procedure. The results based on a large-scale artificial dataset prove that it has more advantages in generalization accuracy and training time than the centralized experimental scheme.

关键词： Training Deep learning Recurrent neural networks Distance learning Time series analysis Predictive models Prediction algorithms

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance 17

Self-supervised fast reconstruction method for diffusion MRI...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wu, Yuxin Zhang, Jian Wang, Lihui Cao, Ying Wu, Linhai Wu, Yunjia Ye, Chen Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 France

ISBN: (纸本)9798350387384

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor. © 2024 IEEE.

关键词： Self-supervised learning

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition

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Frontiers of Information technology & Electronic Engineering 2023年第2期24卷 299-313页

作者： Chunxi LI Yingying FU Xiangke CUI Quanbo GE Logistics Engineering College Shanghai Maritime UniversityShanghai200135China School of Economics and Management Beijing Jiaotong UniversityBeijing102603China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT) Nanjing University of Information Science and TechnologyNanjing210044China

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety *** is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from *** to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low *** response to the above problems,data drivers and machine learning theories are *** the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is ***,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging ***,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking ***,the data run by the actual engineering system are verified for the proposed charging time prediction *** results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.

关键词： Charging mode Charging time Random forest Long short-term memory(LSTM) Simplified particle swarm optimization(SPSO)

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