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Book ; Online: Multi-Sensor Information Fusion

Jin, Xue-Bo / Gao, Yuan

2020

Abstract: This book includes papers from the section "Multisensor Information Fusion", from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including ... ...

Abstract	This book includes papers from the section "Multisensor Information Fusion", from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning
Keywords	Technology (General) ; Engineering (General). Civil engineering (General)
Size	1 electronic resource (602 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020480966
ISBN	9783039283026 ; 9783039283033 ; 3039283022 ; 3039283030
DOI	10.3390/books978-3-03928-303-3
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

Jin, Xue-Bo / Sun, Shuli / Wei, Hong / Yang, Feng-Bao

2018

Abstract: The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate and useful representation of that object. The data may be independent or redundant, and can be ... ...

Abstract	The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor.Multi-sensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as geospatial information systems, business intelligence, oceanography, discovery science, intelligent transport systems, wireless sensor networks, etc. Recently, thanks to the vast development in sensor and computer memory technologies, more and more sensors are being used in practical systems and a large amount of measurement data are recorded and restored, which may actually be the time series big data. For example, sensors in machines and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization.The goal of this Special Issue is to report on innovative ideas and solutions for the methods of multi-sensor information fusion in the emerging applications era, focusing on development, adoption and applications
Keywords	Electrical engineering. Electronics. Nuclear engineering
Size	1 electronic resource (VIII, 560 p.)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020098769
ISBN	9783038429333 ; 9783038429340 ; 3038429333 ; 3038429341
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Location estimation based on feature mode matching with deep network models.

Bai, Yu-Ting / Jia, Wei / Jin, Xue-Bo / Su, Ting-Li / Kong, Jian-Lei

Frontiers in neurorobotics

2023 Volume 17, Page(s) 1181864

Abstract: Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) ... ...

Abstract	Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.
Language	English
Publishing date	2023-06-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2453002-5
ISSN	1662-5218
ISSN	1662-5218
DOI	10.3389/fnbot.2023.1181864
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.

Jin, Xue-Bo / Gong, Wen-Tao / Kong, Jian-Lei / Bai, Yu-Ting / Su, Ting-Li

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 3

Abstract: Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model ... ...

Abstract	Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.
Language	English
Publishing date	2022-02-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24030335
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

Jin, Xue-Bo / Chen, Wei / Ma, Hui-Jun / Kong, Jian-Lei / Su, Ting-Li / Bai, Yu-Ting

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 20

Abstract: GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise ... ...

Abstract	GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.
Language	English
Publishing date	2023-10-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23208650
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

Jin, Xue-Bo / Wang, Zhong-Yao / Kong, Jian-Lei / Bai, Yu-Ting / Su, Ting-Li / Ma, Hui-Jun / Chakrabarti, Prasun

Entropy (Basel, Switzerland)

2023 Volume 25, Issue 2

Abstract: The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A ... ...

Abstract	The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.
Language	English
Publishing date	2023-01-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e25020247
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework

Kong, Jianlei / Fan, Xiaomeng / Jin, Xue-Bo / Zuo, Min

2022

Abstract: Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for mastering traffic and making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Furthermore, the ...

Abstract	Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for mastering traffic and making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Furthermore, the sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. This paper proposes a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is constructed by combining variational inference with gated recurrent units (GRU) and used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.
Keywords	Computer Science - Machine Learning ; Computer Science - Information Retrieval
Subject code	380
Publishing date	2022-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture

Kong, Jian-Lei / Fan, Xiao-Meng / Jin, Xue-Bo / Su, Ting-Li / Bai, Yu-Ting / Ma, Hui-Jun / Zuo, Min

Agronomy. 2023 Feb. 22, v. 13, no. 3

2023

Abstract: Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term ... ...

Abstract	Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term predictions of temperature and humidity are vital for guiding agricultural activities and improving crop yield and quality. However, existing intelligent models still have difficulties dealing with big weather data in predicting applications, such as striking a balance between prediction accuracy and learning efficiency. Therefore, a multi-head attention encoder-decoder neural network optimized via Bayesian inference strategy (BMAE-Net) is proposed herein to predict weather time series changes accurately. Firstly, we incorporate Bayesian inference into the gated recurrent unit to construct a Bayesian-gated recurrent units (Bayesian-GRU) module. Then, a multi-head attention mechanism is introduced to design the network structure of each Bayesian layer, improving the prediction applicability to time-length changes. Subsequently, an encoder-decoder framework with Bayesian hyperparameter optimization is designed to infer intrinsic relationships among big time-series data for high prediction accuracy. For example, the R-evaluation metrics for temperature prediction in the three locations are 0.9, 0.804, and 0.892, respectively, while the RMSE is reduced to 2.899, 3.011, and 1.476, as seen in Case 1 of the temperature data. Extensive experiments subsequently demonstrated that the proposed BMAE-Net has overperformed on three location weather datasets, which provides an effective solution for prediction applications in the smart agriculture system.
Keywords	Bayesian theory ; agronomy ; crop quality ; crop yield ; data collection ; humidity ; meteorological data ; prediction ; temperature ; time series analysis ; weather forecasting
Language	English
Dates of publication	2023-0222
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2607043-1
ISSN	2073-4395
ISSN	2073-4395
DOI	10.3390/agronomy13030625
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

Jin, Xue-Bo / Robert Jeremiah, Ruben Jonhson / Su, Ting-Li / Bai, Yu-Ting / Kong, Jian-Lei

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 6

Abstract: State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors ... ...

Abstract	State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.
Language	English
Publishing date	2021-03-16
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21062085
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-Channel Fusion Classification Method Based on Time-Series Data.

Jin, Xue-Bo / Yang, Aiqiang / Su, Tingli / Kong, Jian-Lei / Bai, Yuting

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 13

Abstract: Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, ... ...

Abstract	Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster-Shafer evidence theory (D-S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.
Language	English
Publishing date	2021-06-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21134391
Database	MEDical Literature Analysis and Retrieval System OnLINE

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