Xiaoyan Peng

Hunan University

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i-10 index

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Chenyu Zhu

University of Electronic Science and Technology of China

Xinyue Hou

University of Electronic Science and Technology of China

Yue Li

McGill University

Zhiyong Li

Inner Mongolia University

Yifan Li

Zhejiang University of Technology

Wenquan Xu

Anqing Normal University

Xiaoying Tian

Tsinghua University

Hui Peng

University of Nottingham Ningbo China

Peng Wang

Hunan First Normal University

Xuan He

Kunming University of Science and Technology

Cooperative Institutions

Hunan University

Central South University

University of Electronic Science and Technology of China

Tsinghua University

Chinese Academy of Sciences

Chongqing University

Wuxi Institute of Technology

Guizhou University

Wuhan University

Fuzhou University

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Knowledge graph for different platforms with radar: construction and application

Yue Li Xinyue Hou Chenyu Zhu Yong Tian Xiaoyan Peng

At present, the analysis and identification of unknown targets mostly rely on manual labor, which can not make fast and accurate decisions for the current situation. As a knowledge representation method in the era of big data, knowledge graph has the capabilities of semantic processing, open interconnection, and logical combing. In this paper, we construct a knowledge graph of different platforms with radar through three main steps: data acquisition, knowledge extraction, and knowledge storage, and then propose a knowledge reasoning framework. Based on it, when faced with massive data, combined with the obtained related intelligence information, we can effectively analyze and identify the type of target platform, and then estimate the opponent's actions and intentions, and assist us to make command decisions.

Knowledge graph

10.1117/12.2634555

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Citations (0)

Driver mental load identification model Adapting to Urban Road Traffic Scenarios

Transportation Safety and Environment (2023)

Jing Huang Wei Wei Xiaoyan Peng Lin Hu Huiqin Chen

Abstract Objective At present, most research on driver mental load identification is based on a single driving scene. However, the driver mental load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual driving process. We proposed a driver mental load identification model which adapts to urban road traffic scenarios. Methods The model includes a driving scene discrimination sub-model and driver load identification sub-model, in which the driving scene discrimination sub-model can quickly and accurately determine the road traffic scene. The driver load identification sub-model selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification sub-model. Results The results show that the driving scene discrimination sub-model using five vehicle features as feature subsets has the best performance. The driver load identification sub-model based on the best feature subset reduces the feature noise, and the recognition effect is better than the feature set using a single source signal and all data. The best recognition algorithm in different scenarios tends to be consistent, and the support vector machine (SVM) algorithm is better than the K-nearest neighbors (KNN) algorithm. Conclusion The proposed driver mental load identification model can discriminate the driving scene quickly and accurately, and then identify the driver mental load. In this way, our model can be more suitable for actual driving and improve the effect of driver mental load identification.

Identification

Feature (linguistics)

10.1093/tse/tdac076

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Data-Driven Tire-Road Friction Estimation for Electric-Wheel Vehicle With Data Category Selection and Uncertainty Evaluation

IEEE Transactions on Industrial Electronics (2024)

Liang Chen Zhaobo Qin Yougang Bian Manjiang Hu Xiaoyan Peng

Tire balance

10.1109/tie.2024.3440510

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Citations (1)

Knowledge Graph Based Adversarial Radar Threat Assessment

Chenyu Zhu Yue Li Xinyue Hou Peng Wang Xiaoyan Peng

In the field of military equipment knowledge, there are a large number of equipment models, weapon types, working parameters, and other time-frequency-space data information, among which there is a lot of valuable information. At present, when combat-related personnel face this massive knowledge, they cannot efficiently obtain the key knowledge, which means that they cannot provide effective guidance based on the potential key knowledge. To solve this problem, based on the investigation and analysis of the existing knowledge graph construction method, we excavate and extract military equipment knowledge, instantiate and correlate different weapon equipment, and construct the knowledge graph of military equipment. Its construction can not only deeply study the key technical difficulties of the graph in this field, but also has strong strategic support for the future development of this field. In the end, we propose a threat assessment for target radar with a TransE inference model based on the knowledge graph.

Knowledge graph

10.1109/icicse55337.2022.9828881

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Citations (0)

DBN based SD-ARX model for nonlinear time series prediction and analysis

Applied Intelligence (2020)

Wenquan Xu Hui Peng Xiaoying Tian Xiaoyan Peng

Deep belief network

10.1007/s10489-020-01804-2

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Citations (14)

Two3-AnoECG: ECG anomaly detection with two-stream networks and two-stage training using two double-throw switches

Knowledge-Based Systems (2024)

Yifan Li Xiaoyan Peng Weixun Cai Jiacheng Lin Zhiyong Li

The electrocardiogram (ECG) is a highly cost-effective and convenient diagnostic tool that can aid in the diagnosis of a wide range of cardiovascular conditions, such as arrhythmias, myocardial ischemia, myocardial infarction, and heart failure. Its usefulness lies in its ability to provide information about the electrical activity and rhythm of the heart, making it an essential component of the diagnostic process for many cardiac conditions. However, the interpretation of ECG signals often requires a high level of expertise from medical professionals. When reading complex ECGs, noncardiologists often need to consult with cardiologists. Even cardiologists may make errors in their judgments after prolonged reading of ECGs. Therefore, the accurate and timely diagnosis of cardiovascular diseases using ECG signals is a complex task. In this paper, we categorize this problem as a multilabel, multidimensional time-series data anomaly detection task. We propose a method called Two3-AnoECG for ECG anomaly detection, which improves upon existing ECG feature extraction and data segmentation methods and introduces a two-stream network that is trained in two stages using two double-throw switches to control the input and model structure of each stage. We further propose Two3-EnsECG, which combines the anomaly score generated by Two3-AnoECG and multiple baseline methods, to improve the overall performance of anomaly detection in ECG signals. The experimental results demonstrate the effectiveness of our proposed method.

Anomaly (physics)

Feature (linguistics)

10.1016/j.knosys.2024.111396

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Citations (1)

Specific Emitter Identification Based on Temporal Convolutional Network Sequence Processing

IEEE Communications Letters (2023)

Chenyu Zhu Liang Liu Xiaoyan Peng

Specific emitter identification (SEI) is a method to identify radiation sources using radio frequency fingerprints (RFF) during signal transmission. Since transient features are difficult to capture accurately, we often analyze steady-state fingerprints extracted from signals. In this letter, temporal convolutional networks (TCN) are utilized in the SEI field. It performs as well as recurrent neural network (RNN) on traditional sequential tasks, and it retains the excellent classification capabilities of convolutional neural network (CNN), so it can be used to process time series like signals. We can better extract and analyze fingerprints from the signal using the TCN modules instead of the original convolutional layers. Experimental results show that TCNs provide better recognition performance than methods that exclusively use traditional neural networks to process feature sequences.

Feature (linguistics)

Identification

SIGNAL (programming language)

10.1109/lcomm.2023.3312390

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Citations (2)

A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling

Information Sciences (2010)

Min Gan Hui Peng Xiaoyan Peng Xiaohong Chen Garba Inoussa

10.1016/j.ins.2010.07.012

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Citations (72)

Question answering system based on military knowledge graph

Xinyue Hou Chenyu Zhu Yue Li Peng Wang Xiaoyan Peng

In the field of military equipment knowledge, there are a large number of equipment models, weapon types, operating parameters and other time-frequency domain and airspace data information, which potentially contains a lot of valuable information. At present, in the face of these massive domain knowledge, combat related personnel cannot efficiently acquire the key knowledge, which means that they cannot provide effective guidance according to the potential key knowledge. In order to solve this problem, based on the investigation and analysis of the construction methods of the existing knowledge graph, this paper mined and extracted the knowledge of military equipment, instantiated and correlated different weapons and equipment, and constructed the knowledge graph of military equipment and question answering system. The construction of military equipment knowledge graph and question answering system can not only deeply study the key technical difficulties of domain graph, but also has strong strategic support significance for the development of related fields.

Knowledge graph

10.1117/12.2634559

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Citations (7)

A Hybrid Modeling Method Based on Linear AR and Nonlinear DBN-AR Model for Time Series Forecasting

Neural Processing Letters (2021)

Wenquan Xu Hui Peng Xiaoyong Zeng Feng Zhou Xiaoying Tian

10.1007/s11063-021-10651-2

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Citations (37)