This page has only limited features, please log in for full access.
Dr. Jin Xue-bo was born in Liaoning in 1972, China. She received the B.E. degree in industrial electrical and automation and the Master degree in industrial automation from Jilin University, Jilin, China, in 1994 and 1997, and the Ph.D. degree in control theory and control engineering from Zhejiang University, Zhejiang, China, in 2004. From 1997 to 2012, she was with College of Informatics and Electronics, Zhejiang Sci-Tech University. Since 2012 she has been with College of Computer and Information Engineering, Beijing Technology and Business University. Her research interests include multisensor fusion, statistical signal processing, video/image processing, robust filtering, Bayesian theory, graph theory and Time series analysis, artificial intelligence, financial automation, target tracking and dynamic analysis. In particular, her present major interest is multisensor fusion, Bayesian estimation and big data tendency analysis. JIN is the Fellow of information fusion branch in China Society of Aeronautics and Astronautics, the China Computer Society and Chinese Society of Artificial Intelligence. She is now the leader of the intelligent cognitive and data analysis research center of Beijing Technology and Business University. JIN has published more than 100 papers in the statistical, mathematics, information fusion and image processing journals.
Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.
Xue-Bo Jin; Wei-Zhen Zheng; Jian-Lei Kong; Xiao-Yi Wang; Min Zuo; Qing-Chuan Zhang; Seng Lin. Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse. Agriculture 2021, 11, 802 .
AMA StyleXue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Min Zuo, Qing-Chuan Zhang, Seng Lin. Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse. Agriculture. 2021; 11 (8):802.
Chicago/Turabian StyleXue-Bo Jin; Wei-Zhen Zheng; Jian-Lei Kong; Xiao-Yi Wang; Min Zuo; Qing-Chuan Zhang; Seng Lin. 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse." Agriculture 11, no. 8: 802.
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.
Xue-Bo Jin; Aiqiang Yang; Tingli Su; Jian-Lei Kong; Yuting Bai. Multi-Channel Fusion Classification Method Based on Time-Series Data. Sensors 2021, 21, 4391 .
AMA StyleXue-Bo Jin, Aiqiang Yang, Tingli Su, Jian-Lei Kong, Yuting Bai. Multi-Channel Fusion Classification Method Based on Time-Series Data. Sensors. 2021; 21 (13):4391.
Chicago/Turabian StyleXue-Bo Jin; Aiqiang Yang; Tingli Su; Jian-Lei Kong; Yuting Bai. 2021. "Multi-Channel Fusion Classification Method Based on Time-Series Data." Sensors 21, no. 13: 4391.
Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.
Xue-Bo Jin; Jia-Hui Zhang; Ting-Li Su; Yu-Ting Bai; Jian-Lei Kong; Xiao-Yi Wang. Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting. Computational Intelligence and Neuroscience 2021, 2021, 1 -13.
AMA StyleXue-Bo Jin, Jia-Hui Zhang, Ting-Li Su, Yu-Ting Bai, Jian-Lei Kong, Xiao-Yi Wang. Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting. Computational Intelligence and Neuroscience. 2021; 2021 ():1-13.
Chicago/Turabian StyleXue-Bo Jin; Jia-Hui Zhang; Ting-Li Su; Yu-Ting Bai; Jian-Lei Kong; Xiao-Yi Wang. 2021. "Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting." Computational Intelligence and Neuroscience 2021, no. : 1-13.
Precision farming aims to optimizing the crop production process and managing sustainable supply chain practices as more efficient and reasonable as possible. Recently, various advanced technologies, such as deep-learning and internet of things (IoT), have achieved remarkable intelligence progress in realistic agricultural conditions. However, crops species recognition can be considered as fine-grained visual classification (FGVC) problem, suffering the low inter-class discrepancy and high intra-class variances from the subordinate categories, which is more challenging than common basic-level category classification depended on traditional deep neural networks (DNNs). This paper presents a fine-grained visual recognition model named as MCF-Net to classifying different crop species in practical farmland scenes. Proposed MCF-Net is consisted of cross stage partial network (CSPNet) as backbone module, three parallel sub-networks, and cross-level fusion module. With multi-stream hybrid architecture utilizing massive fine-granulometric information, MCF-Net obtains preferable representation ability for distinguishing interclass discrepancy and tolerating intra-class variances. In addition, the end-to-end implementation of MCF-Net is optimized by cross-level fusion strategy to accurately identify different crop categories. Several experiments on in-field CropDeepv2 datasets demonstrate that our method favorably against the state-of-the-art methods. The recognition accuracy and F1-score of MCF-Net achieves very competitive results up to 90.6% and 0.962 separately, both of which outperforming contrasted models indicate better recognition accuracy and model stability of our method. Moreover, the overall parameters of MCF-Net are only 807 MByte with achieving a good balance between model’s performance and complexity. It is acceptable and suitable to the implementation of IoT platforms in precision agricultural practices.
Jianlei Kong; Hongxing Wang; Xiaoyi Wang; Xuebo Jin; Xing Fang; Seng Lin. Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Computers and Electronics in Agriculture 2021, 185, 106134 .
AMA StyleJianlei Kong, Hongxing Wang, Xiaoyi Wang, Xuebo Jin, Xing Fang, Seng Lin. Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Computers and Electronics in Agriculture. 2021; 185 ():106134.
Chicago/Turabian StyleJianlei Kong; Hongxing Wang; Xiaoyi Wang; Xuebo Jin; Xing Fang; Seng Lin. 2021. "Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture." Computers and Electronics in Agriculture 185, no. : 106134.
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.
Xue-Bo Jin; Ruben Robert Jeremiah; Ting-Li Su; Yu-Ting Bai; Jian-Lei Kong. The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. Sensors 2021, 21, 2085 .
AMA StyleXue-Bo Jin, Ruben Robert Jeremiah, Ting-Li Su, Yu-Ting Bai, Jian-Lei Kong. The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. Sensors. 2021; 21 (6):2085.
Chicago/Turabian StyleXue-Bo Jin; Ruben Robert Jeremiah; Ting-Li Su; Yu-Ting Bai; Jian-Lei Kong. 2021. "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods." Sensors 21, no. 6: 2085.
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
Xue-Bo Jin; Wei-Zhen Zheng; Jian-Lei Kong; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Seng Lin. Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. Energies 2021, 14, 1596 .
AMA StyleXue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Seng Lin. Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. Energies. 2021; 14 (6):1596.
Chicago/Turabian StyleXue-Bo Jin; Wei-Zhen Zheng; Jian-Lei Kong; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Seng Lin. 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization." Energies 14, no. 6: 1596.
Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.
Xue-Bo Jin; Xing-Hong Yu; Ting-Li Su; Dan-Ni Yang; Yu-Ting Bai; Jian-Lei Kong; Li Wang. Distributed Deep Fusion Predictor for aMulti-Sensor System Based on Causality Entropy. Entropy 2021, 23, 219 .
AMA StyleXue-Bo Jin, Xing-Hong Yu, Ting-Li Su, Dan-Ni Yang, Yu-Ting Bai, Jian-Lei Kong, Li Wang. Distributed Deep Fusion Predictor for aMulti-Sensor System Based on Causality Entropy. Entropy. 2021; 23 (2):219.
Chicago/Turabian StyleXue-Bo Jin; Xing-Hong Yu; Ting-Li Su; Dan-Ni Yang; Yu-Ting Bai; Jian-Lei Kong; Li Wang. 2021. "Distributed Deep Fusion Predictor for aMulti-Sensor System Based on Causality Entropy." Entropy 23, no. 2: 219.
The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.
Xue-Bo Jin; Hong-Xing Wang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization. Complexity 2020, 2020, 1 -14.
AMA StyleXue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong. Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization. Complexity. 2020; 2020 ():1-14.
Chicago/Turabian StyleXue-Bo Jin; Hong-Xing Wang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. 2020. "Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization." Complexity 2020, no. : 1-14.
The expert is a vital role in multicriteria decision-making, which provides source decision opinions. In the existing group decision-making activities, the selection of experts is usually conducted artificially, which relies on personal subjective experience. It has been the urgent demand for an automatic selection of experts, which can help to determine their weights for the follow-up decision calculation. In this paper, an expert classification method is proposed to solve the problem. First, the CatBoost classification algorithm is improved by integrating the 2-tuple linguistic, which can effectively extract the features of samples. Second, the framework of the expert classification is designed. The flow combines the expert resume collection, expert classification, and database update. Third, a decision-making case is analyzed for the expert selection issue. The experiment and result indicate that the proposed classifier performs better than the classic methods. The proposed classification method of the decision experts can support the automatic and intelligent operation of the decision-making activities.
Xiao-Yi Wang; Yi Yang; Yu-Ting Bai; Jia-Bin Yu; Zhi-Yao Zhao; Xue-Bo Jin. Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making. Complexity 2020, 2020, 1 -10.
AMA StyleXiao-Yi Wang, Yi Yang, Yu-Ting Bai, Jia-Bin Yu, Zhi-Yao Zhao, Xue-Bo Jin. Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making. Complexity. 2020; 2020 ():1-10.
Chicago/Turabian StyleXiao-Yi Wang; Yi Yang; Yu-Ting Bai; Jia-Bin Yu; Zhi-Yao Zhao; Xue-Bo Jin. 2020. "Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making." Complexity 2020, no. : 1-10.
Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.
Xue-Bo Jin; Nian-Xiang Yang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model. Sensors 2020, 20, 1334 .
AMA StyleXue-Bo Jin, Nian-Xiang Yang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong. Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model. Sensors. 2020; 20 (5):1334.
Chicago/Turabian StyleXue-Bo Jin; Nian-Xiang Yang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. 2020. "Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model." Sensors 20, no. 5: 1334.
Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.
Yi Yang; Yuting Bai; Xiaoyi Wang; Li Wang; Xuebo Jin; Qian Sun. Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes. Sustainability 2020, 12, 1494 .
AMA StyleYi Yang, Yuting Bai, Xiaoyi Wang, Li Wang, Xuebo Jin, Qian Sun. Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes. Sustainability. 2020; 12 (4):1494.
Chicago/Turabian StyleYi Yang; Yuting Bai; Xiaoyi Wang; Li Wang; Xuebo Jin; Qian Sun. 2020. "Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes." Sustainability 12, no. 4: 1494.
Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.
Xue-Bo Jin; Xing-Hong Yu; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability 2020, 12, 1433 .
AMA StyleXue-Bo Jin, Xing-Hong Yu, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong. Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability. 2020; 12 (4):1433.
Chicago/Turabian StyleXue-Bo Jin; Xing-Hong Yu; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System." Sustainability 12, no. 4: 1433.
Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.
Xue-Bo Jin; Nian-Xiang Yang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction. Mathematics 2020, 8, 214 .
AMA StyleXue-Bo Jin, Nian-Xiang Yang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong. Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction. Mathematics. 2020; 8 (2):214.
Chicago/Turabian StyleXue-Bo Jin; Nian-Xiang Yang; Xiao-Yi Wang; Yu-Ting Bai; Ting-Li Su; Jian-Lei Kong. 2020. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction." Mathematics 8, no. 2: 214.
MEMS (Micro-Electro-Mechanical Systems) gyroscope is the core component in the posture recognition and assistant positioning, of which the complex noise limits its performance. It is essential to filter the noise and obtain the true value of the measurements. Then an adaptive filtering method was proposed. Firstly, noises of MEMS gyroscope were analyzed to build the basic framework of the dynamic noise model. Secondly, the dynamic Allan variance was improved with a novel truncation window based on the entropy features, which referred to the parameters in the noise model. Thirdly, the adaptive Kalman filter was derived from the dynamic noise model. Finally, the simulation and experiment were carried out to verify the method. The results prove that the improved dynamic Allan variance can extract noise feature distinctly, and the filtering precision in the new method is relatively high.
Yuting Bai; Xiaoyi Wang; Xuebo Jin; Tingli Su; Jianlei Kong; Baihai Zhang. Adaptive filtering for MEMS gyroscope with dynamic noise model. ISA Transactions 2020, 101, 430 -441.
AMA StyleYuting Bai, Xiaoyi Wang, Xuebo Jin, Tingli Su, Jianlei Kong, Baihai Zhang. Adaptive filtering for MEMS gyroscope with dynamic noise model. ISA Transactions. 2020; 101 ():430-441.
Chicago/Turabian StyleYuting Bai; Xiaoyi Wang; Xuebo Jin; Tingli Su; Jianlei Kong; Baihai Zhang. 2020. "Adaptive filtering for MEMS gyroscope with dynamic noise model." ISA Transactions 101, no. : 430-441.
Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.
Yu-Ting Bai; Xue-Bo Jin; Xiao-Yi Wang; Ji-Ping Xu. Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis. International Journal of Environmental Research and Public Health 2020, 17, 360 .
AMA StyleYu-Ting Bai, Xue-Bo Jin, Xiao-Yi Wang, Ji-Ping Xu. Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis. International Journal of Environmental Research and Public Health. 2020; 17 (1):360.
Chicago/Turabian StyleYu-Ting Bai; Xue-Bo Jin; Xiao-Yi Wang; Ji-Ping Xu. 2020. "Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis." International Journal of Environmental Research and Public Health 17, no. 1: 360.
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.
Yu-Ting Bai; Xiao-Yi Wang; Xue-Bo Jin; Zhi-Yao Zhao; Bai-Hai Zhang. A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model. Sensors 2020, 20, 299 .
AMA StyleYu-Ting Bai, Xiao-Yi Wang, Xue-Bo Jin, Zhi-Yao Zhao, Bai-Hai Zhang. A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model. Sensors. 2020; 20 (1):299.
Chicago/Turabian StyleYu-Ting Bai; Xiao-Yi Wang; Xue-Bo Jin; Zhi-Yao Zhao; Bai-Hai Zhang. 2020. "A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model." Sensors 20, no. 1: 299.
Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.
Zhaoyang Wang; Xuebo Jin; Xiaoyi Wang; Jiping Xu; Yuting Bai. Hard Decision-Based Cooperative Localization for Wireless Sensor Networks. Sensors 2019, 19, 4665 .
AMA StyleZhaoyang Wang, Xuebo Jin, Xiaoyi Wang, Jiping Xu, Yuting Bai. Hard Decision-Based Cooperative Localization for Wireless Sensor Networks. Sensors. 2019; 19 (21):4665.
Chicago/Turabian StyleZhaoyang Wang; Xuebo Jin; Xiaoyi Wang; Jiping Xu; Yuting Bai. 2019. "Hard Decision-Based Cooperative Localization for Wireless Sensor Networks." Sensors 19, no. 21: 4665.
It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction.
Xuebo Jin; Nianxiang Yang; Xiaoyi Wang; Yuting Bai; Tingli Su; Jianlei Kong. Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction. Applied Sciences 2019, 9, 4533 .
AMA StyleXuebo Jin, Nianxiang Yang, Xiaoyi Wang, Yuting Bai, Tingli Su, Jianlei Kong. Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction. Applied Sciences. 2019; 9 (21):4533.
Chicago/Turabian StyleXuebo Jin; Nianxiang Yang; Xiaoyi Wang; Yuting Bai; Tingli Su; Jianlei Kong. 2019. "Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction." Applied Sciences 9, no. 21: 4533.
The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of "Circumjacent Monitoring-Blind Area Inference". In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.
Yu-Ting Bai; Xiao-Yi Wang; Qian Sun; Xue-Bo Jin; Ting-Li Su; Jian-Lei Kong. Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network. International Journal of Environmental Research and Public Health 2019, 16, 3788 .
AMA StyleYu-Ting Bai, Xiao-Yi Wang, Qian Sun, Xue-Bo Jin, Ting-Li Su, Jian-Lei Kong. Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network. International Journal of Environmental Research and Public Health. 2019; 16 (20):3788.
Chicago/Turabian StyleYu-Ting Bai; Xiao-Yi Wang; Qian Sun; Xue-Bo Jin; Ting-Li Su; Jian-Lei Kong. 2019. "Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network." International Journal of Environmental Research and Public Health 16, no. 20: 3788.
The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.
Yuting Bai; Xuebo Jin; Xiaoyi Wang; Tingli Su; Jianlei Kong; Yutian Lu. Compound Autoregressive Network for Prediction of Multivariate Time Series. Complexity 2019, 2019, 1 -11.
AMA StyleYuting Bai, Xuebo Jin, Xiaoyi Wang, Tingli Su, Jianlei Kong, Yutian Lu. Compound Autoregressive Network for Prediction of Multivariate Time Series. Complexity. 2019; 2019 ():1-11.
Chicago/Turabian StyleYuting Bai; Xuebo Jin; Xiaoyi Wang; Tingli Su; Jianlei Kong; Yutian Lu. 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series." Complexity 2019, no. : 1-11.