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Xiao-Yi Wang
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

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Journal article
Published: 23 August 2021 in Agriculture
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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.

ACS Style

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 Style

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 (8):802.

Chicago/Turabian Style

Xue-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.

Journal article
Published: 12 June 2021 in Computational Intelligence and Neuroscience
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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.

Journal article
Published: 01 April 2021 in Knowledge-Based Systems
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A deep belief echo state network is an effective deep learning framework for solving time series prediction problems. The suitable structure of the hidden layers determines the prediction performance of the neural network. However, a neural network structure designed using artificial experience has difficulty meeting application requirements. To address this problem, this paper proposes a self-organizing deep belief modular echo state network (SDBMESN) model for time series prediction with high accuracy. The basic framework of this model includes two parts: a deep belief network for deep feature extraction and a modular echo state network with subreservoirs for time series prediction. To find a suitable neural network structure, a neuron significance based on mutual information is designed to measure the degree of information of the neurons, and then a self-organizing mechanism is designed to realize the dynamic adjustment of the hidden layer neurons and subreservoirs. In addition, the robust loss function is used to improve the robustness of the prediction. The simulation results of nonlinear system modeling, sunspot prediction and algal bloom prediction demonstrate that the SDBMESN has good prediction performance and robustness.

ACS Style

Huiyan Zhang; Bo Hu; Xiaoyi Wang; Jiping Xu; Li Wang; Qian Sun; Zhaoyang Wang. Self-organizing deep belief modular echo state network for time series prediction. Knowledge-Based Systems 2021, 222, 107007 .

AMA Style

Huiyan Zhang, Bo Hu, Xiaoyi Wang, Jiping Xu, Li Wang, Qian Sun, Zhaoyang Wang. Self-organizing deep belief modular echo state network for time series prediction. Knowledge-Based Systems. 2021; 222 ():107007.

Chicago/Turabian Style

Huiyan Zhang; Bo Hu; Xiaoyi Wang; Jiping Xu; Li Wang; Qian Sun; Zhaoyang Wang. 2021. "Self-organizing deep belief modular echo state network for time series prediction." Knowledge-Based Systems 222, no. : 107007.

Journal article
Published: 29 March 2021 in Microbial Risk Analysis
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In food processing, it is essential to guarantee the safety of microbial hazards. Microorganisms exist, transit and continuously grow along the food processing with hybrid evolutionary characteristics and uncertainties. Thus, a particular risk assessment is essential to effectively predict and evaluate the risk of microbial hazards in food processing. For such a purpose, we propose a comprehensive dynamic risk assessment approach based on a stochastic hybrid system (SHS). First, we formulate a dynamic evolution of microorganisms in food processing according to the SHS model. Second, we employ a Monte Carlo simulation to obtain the probability density functions of process characteristic information for microorganisms during processing. Additionally, we design a novel risk indicator of “hazard degree” to quantify the potential risks of microorganisms based on this process information. Finally, we present a case study of wheat flour processing to estimate the risk of mixed mildew on the basis of the SHS approach. Experimental results show that the proposed approach is feasible in modeling the hybrid evolution and handling uncertainties in predictions of microbial hazards. This study should prove to be a valuable reference to ensure food safety for risk management and decision-making departments.

ACS Style

Qian Chen; Zhiyao Zhao; Xiaoyi Wang; Ke Xiong; Ce Shi. A dynamic risk assessment approach based on stochastic hybrid system: Application to microbial hazards in food processing. Microbial Risk Analysis 2021, 100163 .

AMA Style

Qian Chen, Zhiyao Zhao, Xiaoyi Wang, Ke Xiong, Ce Shi. A dynamic risk assessment approach based on stochastic hybrid system: Application to microbial hazards in food processing. Microbial Risk Analysis. 2021; ():100163.

Chicago/Turabian Style

Qian Chen; Zhiyao Zhao; Xiaoyi Wang; Ke Xiong; Ce Shi. 2021. "A dynamic risk assessment approach based on stochastic hybrid system: Application to microbial hazards in food processing." Microbial Risk Analysis , no. : 100163.

Journal article
Published: 13 March 2021 in Energies
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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: 15 January 2021 in Sensors
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Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

ACS Style

Xiaomin Zhang; Zhiyao Zhao; Zhaoyang Wang; Xiaoyi Wang. Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals. Sensors 2021, 21, 581 .

AMA Style

Xiaomin Zhang, Zhiyao Zhao, Zhaoyang Wang, Xiaoyi Wang. Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals. Sensors. 2021; 21 (2):581.

Chicago/Turabian Style

Xiaomin Zhang; Zhiyao Zhao; Zhaoyang Wang; Xiaoyi Wang. 2021. "Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals." Sensors 21, no. 2: 581.

Journal article
Published: 01 December 2020 in IEEE Access
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In this article, an event-triggered state estimation problem for wireless sensor network systems affected by random packet losses and correlated noises is considered. A set of independent Bernoulli variables are used to describe the random packet losses in the measurement transmission. An event-triggered transmission strategy is introduced to decrease limited network bandwidth consumption, and the measurement noise is correlated with the process noises of the same moment and the previous moment. Event-triggered estimator of process noises under the linear minimum variance criterion is derived. Then, an event-triggered state estimation algorithm related to the packet loss rate, noise correlation coefficient and triggering threshold is designed. Sufficient conditions are provided to guarantee convergence of the estimation error covariances of the proposed estimator. Finally, comparative simulation verifies the effectiveness of our algorithm.

ACS Style

Peng Wu; Lu Jiang; Li Wang; Jiping Xu; Xiaoyi Wang. Event-Triggered State Estimation for Wireless Sensor Network Systems With Packet Losses and Correlated Noises. IEEE Access 2020, 8, 216762 -216771.

AMA Style

Peng Wu, Lu Jiang, Li Wang, Jiping Xu, Xiaoyi Wang. Event-Triggered State Estimation for Wireless Sensor Network Systems With Packet Losses and Correlated Noises. IEEE Access. 2020; 8 (99):216762-216771.

Chicago/Turabian Style

Peng Wu; Lu Jiang; Li Wang; Jiping Xu; Xiaoyi Wang. 2020. "Event-Triggered State Estimation for Wireless Sensor Network Systems With Packet Losses and Correlated Noises." IEEE Access 8, no. 99: 216762-216771.

Original paper
Published: 24 October 2020 in Nonlinear Dynamics
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As an unmanned system, the multirotor has gained widespread attention in practical aviation engineering, because of its ease of use, reliability and maintainability. From the perspective of flight reliability and safety, it is significant and essential for a multirotor to detect anomaly occurrences and evaluate the real-time health performance status in a timely manner. Considering the limitations in the modeling of multirotor dynamics and the state estimation of existing methods, a health performance evaluation method of multirotors under wind turbulence is proposed in this paper. First, a stochastic hybrid system (SHS)-based model is established to describe the dynamic behavior of the multirotor, where the flight dynamics, external disturbances due to wind turbulence, and dynamics of discrete modes are included. The real-time probability distribution of the hybrid state of the SHS-based multirotor model is obtained by a modified interacting multiple model particle filter algorithm. Based on this, the real-time overall health performance status of the multirotor during flight is quantitatively evaluated by the classical health degree and the fuzzy health degree as indicators. Finally, a multirotor suffering from different types of sensor anomalies is simulated to demonstrate the availability and effectiveness of the proposed method.

ACS Style

Zhiyao Zhao; Xiaoyi Wang; Peng Yao; Yuting Bai. A health performance evaluation method of multirotors under wind turbulence. Nonlinear Dynamics 2020, 102, 1701 -1715.

AMA Style

Zhiyao Zhao, Xiaoyi Wang, Peng Yao, Yuting Bai. A health performance evaluation method of multirotors under wind turbulence. Nonlinear Dynamics. 2020; 102 (3):1701-1715.

Chicago/Turabian Style

Zhiyao Zhao; Xiaoyi Wang; Peng Yao; Yuting Bai. 2020. "A health performance evaluation method of multirotors under wind turbulence." Nonlinear Dynamics 102, no. 3: 1701-1715.

Methodologies and application
Published: 29 September 2020 in Soft Computing
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In recent years, the water quality of lakes has been deteriorating, and the water eutrophication phenomenon is particularly serious. Therefore, it is of great significance to evaluate the water eutrophication of lakes accurately to achieve water protection management. Numerous mechanistic or data-driven methods have been proposed to perform evaluations of the water eutrophication status. However, issues with these methods must still be solved, such as the use of unreasonable thresholds for the water eutrophication levels based on arbitrary numerical values and inaccurate threshold intervals in the water eutrophication evaluation criteria. To solve these problems, an integrated water eutrophication evaluation algorithm is proposed in this study. An evaluation model based on a multidimensional trapezoidal cloud model is first established, where numerical intervals are employed to characterize the thresholds of different water eutrophication levels instead of arbitrary numerical values. The Atanassov’s interval-value intuition language numbers are introduced to identify the model parameters, which are calculated based on the data of a specific lake rather than relying on inaccurate classification threshold intervals. Finally, the water quality data of Qionghai were used to validate the proposed method. Experimental results showed that this method can provide a more accurate and reasonable evaluation of water eutrophication compared with other methods.

ACS Style

Jiabin Yu; Zhe Shen; Zhiyao Zhao; Xiaoyi Wang; Jiping Xu; Qian Sun; Li Wang; Guandong Liu. Water eutrophication evaluation based on multidimensional trapezoidal cloud model. Soft Computing 2020, 25, 2851 -2861.

AMA Style

Jiabin Yu, Zhe Shen, Zhiyao Zhao, Xiaoyi Wang, Jiping Xu, Qian Sun, Li Wang, Guandong Liu. Water eutrophication evaluation based on multidimensional trapezoidal cloud model. Soft Computing. 2020; 25 (4):2851-2861.

Chicago/Turabian Style

Jiabin Yu; Zhe Shen; Zhiyao Zhao; Xiaoyi Wang; Jiping Xu; Qian Sun; Li Wang; Guandong Liu. 2020. "Water eutrophication evaluation based on multidimensional trapezoidal cloud model." Soft Computing 25, no. 4: 2851-2861.

Research article
Published: 14 September 2020 in Complexity
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Xue-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.

Research article
Published: 17 August 2020 in Complexity
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Xiao-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.

Journal article
Published: 29 February 2020 in Sensors
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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: 17 February 2020 in Sustainability
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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.

ACS Style

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 Style

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 (4):1494.

Chicago/Turabian Style

Yi 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.

Journal article
Published: 14 February 2020 in Sustainability
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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
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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: 03 February 2020 in IEEE Access
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ACS Style

Huiyan Zhang; Bo Hu; Xiaoyi Wang; Jiping Xu; Li Wang; Qian Sun; Zhiyao Zhao. An Action Dependent Heuristic Dynamic Programming Approach for Algal Bloom Prediction With Time-Varying Parameters. IEEE Access 2020, 8, 26235 -26246.

AMA Style

Huiyan Zhang, Bo Hu, Xiaoyi Wang, Jiping Xu, Li Wang, Qian Sun, Zhiyao Zhao. An Action Dependent Heuristic Dynamic Programming Approach for Algal Bloom Prediction With Time-Varying Parameters. IEEE Access. 2020; 8 ():26235-26246.

Chicago/Turabian Style

Huiyan Zhang; Bo Hu; Xiaoyi Wang; Jiping Xu; Li Wang; Qian Sun; Zhiyao Zhao. 2020. "An Action Dependent Heuristic Dynamic Programming Approach for Algal Bloom Prediction With Time-Varying Parameters." IEEE Access 8, no. : 26235-26246.

Journal article
Published: 01 February 2020 in ISA Transactions
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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.

Journal article
Published: 05 January 2020 in Sensors
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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.

ACS Style

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 Style

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 (1):299.

Chicago/Turabian Style

Yu-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.

Journal article
Published: 05 January 2020 in International Journal of Environmental Research and Public Health
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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.

ACS Style

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 Style

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 (1):360.

Chicago/Turabian Style

Yu-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.

Journal article
Published: 27 October 2019 in Sensors
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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.

ACS Style

Zhaoyang Wang; Xuebo Jin; Xiaoyi Wang; Jiping Xu; Yuting Bai. Hard Decision-Based Cooperative Localization for Wireless Sensor Networks. Sensors 2019, 19, 4665 .

AMA Style

Zhaoyang 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 Style

Zhaoyang Wang; Xuebo Jin; Xiaoyi Wang; Jiping Xu; Yuting Bai. 2019. "Hard Decision-Based Cooperative Localization for Wireless Sensor Networks." Sensors 19, no. 21: 4665.