This page has only limited features, please log in for full access.

Unclaimed
Tingli Su
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 26 June 2021 in Sensors
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (13):4391.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 12 June 2021 in Computational Intelligence and Neuroscience
Reads 0
Downloads 0

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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Xue-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.

Review
Published: 16 March 2021 in Sensors
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (6):2085.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 13 March 2021 in Energies
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (6):1596.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 11 February 2021 in Entropy
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (2):219.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 29 February 2020 in Sensors
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (5):1334.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 14 February 2020 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (4):1433.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 07 February 2020 in Mathematics
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (2):214.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 06 February 2020 in IET Intelligent Transport Systems
Reads 0
Downloads 0

Pedestrian motion recognition is one of the important components of an intelligent transportation system. Since commonly used spatial-temporal features are still not sufficient for mining deep information in frames, this study proposes a three-stream neural network called a spatial-temporal-relational network (STRN), where the static spatial information, dynamic motion and differences between adjunct keyframes are comprehensively considered as features of the video records. In addition, an optimised pooling layer called convolutional vector of locally aggregated descriptors layer (Conv-VLAD) is employed before the final classification step in each stream to better aggregate the extracted features and reduce the inter-class differences. To accomplish this, the original video records are required to be processed into RGB images, optical flow images and RGB difference images to deliver the respective information for each stream. After the classification result is obtained from each stream, a decision-level fusion mechanism is introduced to improve the network's overall accuracy via combining the partial understandings together. Experimental results on two public data sets UCF101 (94.7%) and HMDB51 (69.0%), show that the proposed method achieves significantly improved performance. The results of STRN have far-reaching significance for the application of deep learning in intelligent transportation systems to ensure pedestrian safety.

ACS Style

Shiyu Peng; Tingli Su; Xuebo Jin; Jianlei Kong; Yuting Bai. Pedestrian motion recognition via Conv‐VLAD integrated spatial‐temporal‐relational network. IET Intelligent Transport Systems 2020, 14, 392 -400.

AMA Style

Shiyu Peng, Tingli Su, Xuebo Jin, Jianlei Kong, Yuting Bai. Pedestrian motion recognition via Conv‐VLAD integrated spatial‐temporal‐relational network. IET Intelligent Transport Systems. 2020; 14 (5):392-400.

Chicago/Turabian Style

Shiyu Peng; Tingli Su; Xuebo Jin; Jianlei Kong; Yuting Bai. 2020. "Pedestrian motion recognition via Conv‐VLAD integrated spatial‐temporal‐relational network." IET Intelligent Transport Systems 14, no. 5: 392-400.

Journal article
Published: 01 February 2020 in ISA Transactions
Reads 0
Downloads 0

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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Yuting 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.

Conference paper
Published: 04 December 2019 in International Conference on Communication, Computing and Electronics Systems
Reads 0
Downloads 0

The state estimation method is one of the important techniques for target tracking and those techniques based on Kalman filtering have attracted the attention of many researchers. Among these existing methods, the very basic and necessary premise is that the model of the target is with high precision and good prior knowledge of process noise as well as measurement noise are also available. However, it is quite difficult to achieve in the practical applications. Therefore, in this paper, the Gated Recurrent Unit (GRU)-based estimation method is proposed to estimate the actual moving trajectory via the simulated GPS data with noise. GRU has a good advantage in the processing of time series data, and it has a memory function for data information. In the experiment part, by comparing with the traditional Kalman method, it is found that the GRU can obtain better estimation results.

ACS Style

Xuebo Jin; Aiqiang Yang; Tingli Su; Jianlei Kong. GRU-Based Estimation Method Without the Prior Knowledge of the Noise. International Conference on Communication, Computing and Electronics Systems 2019, 927 -935.

AMA Style

Xuebo Jin, Aiqiang Yang, Tingli Su, Jianlei Kong. GRU-Based Estimation Method Without the Prior Knowledge of the Noise. International Conference on Communication, Computing and Electronics Systems. 2019; ():927-935.

Chicago/Turabian Style

Xuebo Jin; Aiqiang Yang; Tingli Su; Jianlei Kong. 2019. "GRU-Based Estimation Method Without the Prior Knowledge of the Noise." International Conference on Communication, Computing and Electronics Systems , no. : 927-935.

Journal article
Published: 25 October 2019 in Applied Sciences
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (21):4533.

Chicago/Turabian Style

Xuebo 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.

Journal article
Published: 09 October 2019 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (20):3788.

Chicago/Turabian Style

Yu-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.

Research article
Published: 22 September 2019 in Complexity
Reads 0
Downloads 0

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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Yuting 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.

Special issue
Published: 09 December 2018 in Asian Journal of Control
Reads 0
Downloads 0

Indoor tracking and positioning technology have received significant attention. There are many techniques to track indoor targets, such as Wi‐Fi technology, radio frequency identification (RFID), and inertial navigation technology. However, these technologies cannot accurately track a target with a single sensor due to the uncertainty of the sensors. This paper presents an indoor tracking method using RFID and inertial measurement units (IMU); the estimated trajectory is mainly determined by RFID, and the information about the trajectory based on IMU is added when the estimated trajectory is missing without RFID data. The results show that the fusion estimation algorithm has excellent performance in indoor tracking and is very effective when the target maneuvers, even where some measurements are lost.

ACS Style

Fafa Wang; Tingli Su; Xuebo Jin; Yangyang Zheng; Jianlei Kong; Yuting Bai. Indoor Tracking by RFID Fusion with IMU Data. Asian Journal of Control 2018, 21, 1768 -1777.

AMA Style

Fafa Wang, Tingli Su, Xuebo Jin, Yangyang Zheng, Jianlei Kong, Yuting Bai. Indoor Tracking by RFID Fusion with IMU Data. Asian Journal of Control. 2018; 21 (4):1768-1777.

Chicago/Turabian Style

Fafa Wang; Tingli Su; Xuebo Jin; Yangyang Zheng; Jianlei Kong; Yuting Bai. 2018. "Indoor Tracking by RFID Fusion with IMU Data." Asian Journal of Control 21, no. 4: 1768-1777.

Journal article
Published: 12 September 2018 in Sensors
Reads 0
Downloads 0

In this paper, a novel semi-supervised segmentation framework based on a spot-divergence supervoxelization of multi-sensor fusion data is proposed for autonomous forest machine (AFMs) applications in complex environments. Given the multi-sensor measuring system, our framework addresses three successive steps: firstly, the relationship of multi-sensor coordinates is jointly calibrated to form higher-dimensional fusion data. Then, spot-divergence supervoxels representing the size-change property are given to produce feature vectors covering comprehensive information of multi-sensors at a time. Finally, the Gaussian density peak clustering is proposed to segment supervoxels into sematic objects in the semi-supervised way, which non-requires parameters preset in manual. It is demonstrated that the proposed framework achieves a balancing act both for supervoxel generation and sematic segmentation. Comparative experiments show that the well performance of segmenting various objects in terms of segmentation accuracy (F-score up to 95.6%) and operation time, which would improve intelligent capability of AFMs.

ACS Style

Jian-Lei Kong; Zhen-Ni Wang; Xue-Bo Jin; Xiao-Yi Wang; Ting-Li Su; Jian-Li Wang. Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications. Sensors 2018, 18, 3061 .

AMA Style

Jian-Lei Kong, Zhen-Ni Wang, Xue-Bo Jin, Xiao-Yi Wang, Ting-Li Su, Jian-Li Wang. Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications. Sensors. 2018; 18 (9):3061.

Chicago/Turabian Style

Jian-Lei Kong; Zhen-Ni Wang; Xue-Bo Jin; Xiao-Yi Wang; Ting-Li Su; Jian-Li Wang. 2018. "Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications." Sensors 18, no. 9: 3061.

Research article
Published: 12 August 2018 in Complexity
Reads 0
Downloads 0

An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.

ACS Style

Binbin Wang; Tingli Su; Xuebo Jin; Jianlei Kong; Yuting Bai. 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition. Complexity 2018, 2018, 1 -10.

AMA Style

Binbin Wang, Tingli Su, Xuebo Jin, Jianlei Kong, Yuting Bai. 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition. Complexity. 2018; 2018 ():1-10.

Chicago/Turabian Style

Binbin Wang; Tingli Su; Xuebo Jin; Jianlei Kong; Yuting Bai. 2018. "3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition." Complexity 2018, no. : 1-10.

Conference paper
Published: 01 July 2018 in 2018 37th Chinese Control Conference (CCC)
Reads 0
Downloads 0
ACS Style

Yuting Bai; Baihai Zhang; Senchun Chai; Xuebo Jin; Xiaoyi Wang; Tingli Su. Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network. 2018 37th Chinese Control Conference (CCC) 2018, 1 .

AMA Style

Yuting Bai, Baihai Zhang, Senchun Chai, Xuebo Jin, Xiaoyi Wang, Tingli Su. Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network. 2018 37th Chinese Control Conference (CCC). 2018; ():1.

Chicago/Turabian Style

Yuting Bai; Baihai Zhang; Senchun Chai; Xuebo Jin; Xiaoyi Wang; Tingli Su. 2018. "Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network." 2018 37th Chinese Control Conference (CCC) , no. : 1.

Conference paper
Published: 01 July 2018 in 2018 10th International Conference on Modelling, Identification and Control (ICMIC)
Reads 0
Downloads 0

For high-frequency fluctuations of time-series data, it is necessary to extract and analyze the main trend for many applications. This paper focuses on the main trend analysis by Kalman filter, gives the extraction model and the transform model, and discusses the suitable value for the key parameters to guarantee the system convergence. The high order dimensional dynamics of the main trend are analyzed by the estimate results. The simulations show that the developed method is effective for extracting the main trend of the time-series data and able to explain accurately the characteristics of the main trend.

ACS Style

Xue-Bo Jin; Nian-Xiang Yang; Ting-Li Su; Jian-Lei Kong. Time-Series Main Trend Analysis by Adaptive Dynamics Model. 2018 10th International Conference on Modelling, Identification and Control (ICMIC) 2018, 1 -5.

AMA Style

Xue-Bo Jin, Nian-Xiang Yang, Ting-Li Su, Jian-Lei Kong. Time-Series Main Trend Analysis by Adaptive Dynamics Model. 2018 10th International Conference on Modelling, Identification and Control (ICMIC). 2018; ():1-5.

Chicago/Turabian Style

Xue-Bo Jin; Nian-Xiang Yang; Ting-Li Su; Jian-Lei Kong. 2018. "Time-Series Main Trend Analysis by Adaptive Dynamics Model." 2018 10th International Conference on Modelling, Identification and Control (ICMIC) , no. : 1-5.

Conference paper
Published: 01 July 2018 in 2018 10th International Conference on Modelling, Identification and Control (ICMIC)
Reads 0
Downloads 0

Over the last decade, quantitative investment has been adopted by the international investment community as a new method that combines financial data with mathematics and computer technology. This paper proposes a quantitative investment prediction model that combines multifactor and candlestick models to predict stock prices. Firstly, relevant indicators were identified to construct a stock pool containing stocks with good future trends. Then, the candlestick model was used to decide when to buy or sell stocks in the pool. The method developed here can be used for quantitative investment strategies, and the experimental results show it can have good returns in the investment stock market based on the transaction data.

ACS Style

Nian-Xiang Yang; Xue-Bo Jin; Ting-Li Su; Jian-Lei Kong. Multisource Data Analysis for Stock Prediction. 2018 10th International Conference on Modelling, Identification and Control (ICMIC) 2018, 1 -6.

AMA Style

Nian-Xiang Yang, Xue-Bo Jin, Ting-Li Su, Jian-Lei Kong. Multisource Data Analysis for Stock Prediction. 2018 10th International Conference on Modelling, Identification and Control (ICMIC). 2018; ():1-6.

Chicago/Turabian Style

Nian-Xiang Yang; Xue-Bo Jin; Ting-Li Su; Jian-Lei Kong. 2018. "Multisource Data Analysis for Stock Prediction." 2018 10th International Conference on Modelling, Identification and Control (ICMIC) , no. : 1-6.