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Honglei Wang
School of Management, Guizhou University, Gui Yang 550225, PR China

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Journal article
Published: 30 July 2021 in Advanced Engineering Informatics
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Customers with different delivery times in online and offline channels often lead to conflicts between two channels, which causes the loss of a large number of customers. Therefore, it is necessary to use the difference between the online and offline delivery to achieve the balance of inventory through order transferring. Aimed at this problem, a dual-channel supply chain system consisting of an online channel supplier and an offline retailer has been developed and sales stage has been expanded to two ones in order to explore the endogenous impact mechanism of different delivery periods on order transferring, and the structural properties of the order transferring threshold. Based on this, the optimal ordering strategy has been discussed at the beginning of the sales season by game theory. Secondly, the optimal threshold for order transferring of offline retailers in each stage by dynamic programming has been explored, and the relationship between the threshold and the sales stage has further been investigated in order to achieve a win-win co-operation by determining the optimal co-operative behavior. Finally, a numerical example has analyzed the influence of co-operation on ordering quantity, inventory quantity, and profit. Furthermore, we have also illustrated the existence of a stable interval when the delivery rate difference rate is lower, which simplifies the implementation of this strategy and improves its operability.

ACS Style

Fei Xu; Honglei Wang. Ordering and transferring model of dual-channel supply chain with delivery time difference. Advanced Engineering Informatics 2021, 49, 101311 .

AMA Style

Fei Xu, Honglei Wang. Ordering and transferring model of dual-channel supply chain with delivery time difference. Advanced Engineering Informatics. 2021; 49 ():101311.

Chicago/Turabian Style

Fei Xu; Honglei Wang. 2021. "Ordering and transferring model of dual-channel supply chain with delivery time difference." Advanced Engineering Informatics 49, no. : 101311.

Journal article
Published: 23 May 2021 in Applied Sciences
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High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

ACS Style

Qiaoping Tian; Honglei Wang. Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression. Applied Sciences 2021, 11, 4773 .

AMA Style

Qiaoping Tian, Honglei Wang. Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression. Applied Sciences. 2021; 11 (11):4773.

Chicago/Turabian Style

Qiaoping Tian; Honglei Wang. 2021. "Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression." Applied Sciences 11, no. 11: 4773.

Journal article
Published: 06 May 2020 in Symmetry
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In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.

ACS Style

Shuai Yuan; Honglei Wang. Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data. Symmetry 2020, 12, 762 .

AMA Style

Shuai Yuan, Honglei Wang. Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data. Symmetry. 2020; 12 (5):762.

Chicago/Turabian Style

Shuai Yuan; Honglei Wang. 2020. "Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data." Symmetry 12, no. 5: 762.

Journal article
Published: 02 January 2020 in Applied Sciences
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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

ACS Style

Qiaoping Tian; Honglei Wang. An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction. Applied Sciences 2020, 10, 346 .

AMA Style

Qiaoping Tian, Honglei Wang. An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction. Applied Sciences. 2020; 10 (1):346.

Chicago/Turabian Style

Qiaoping Tian; Honglei Wang. 2020. "An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction." Applied Sciences 10, no. 1: 346.

Journal article
Published: 28 November 2018 in Applied Sciences
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Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

ACS Style

Ansi Zhang; Honglei Wang; Shaobo Li; Yuxin Cui; Zhonghao Liu; Guanci Yang; Jianjun Hu. Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation. Applied Sciences 2018, 8, 2416 .

AMA Style

Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Zhonghao Liu, Guanci Yang, Jianjun Hu. Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation. Applied Sciences. 2018; 8 (12):2416.

Chicago/Turabian Style

Ansi Zhang; Honglei Wang; Shaobo Li; Yuxin Cui; Zhonghao Liu; Guanci Yang; Jianjun Hu. 2018. "Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation." Applied Sciences 8, no. 12: 2416.

Journal article
Published: 07 September 2018 in Applied Sciences
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Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section.

ACS Style

Yong Yao; Honglei Wang; Shaobo Li; Zhonghao Liu; Gui Gui; Yabo Dan; Jianjun Hu. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Applied Sciences 2018, 8, 1584 .

AMA Style

Yong Yao, Honglei Wang, Shaobo Li, Zhonghao Liu, Gui Gui, Yabo Dan, Jianjun Hu. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Applied Sciences. 2018; 8 (9):1584.

Chicago/Turabian Style

Yong Yao; Honglei Wang; Shaobo Li; Zhonghao Liu; Gui Gui; Yabo Dan; Jianjun Hu. 2018. "End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals." Applied Sciences 8, no. 9: 1584.

Journal article
Published: 07 September 2018 in Journal of Cleaner Production
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With the wide spread use of distributed new energy in power systems, energy management problems in micro-grid have become increasingly significant. Offline or static optimization methods are frequently used to solve the problems which are typically discrete and nonlinear. There are few online optimization methods but they are not only complex but consider normally the distributed generations and loads in micro-grid as a whole. The outcome is that the online methods, fail to reflect the composing characteristic of distributed multi-energy and the contribution made by developing new energy to reduce the consumption of traditional fossil energy. Moreover, results obtained using either the offline or the on-line methods can deviate to certain extent from the true values. Using system control theory, this paper treats the management of distributed energy in micro-grid as an optimal control problem and based on deep learning adaptive dynamic programming, establishes a real-time management strategy for distributed energy in micro-grid. Due to the introduction of the concept of closed-loop feedback, the new management control strategy was real- time, and furthermore the achieved accuracy of managing and controlling the objective function was higher, which suggests that the proposed strategy can improve the management of energy in micro-grid. Finally, the real-time and effectiveness of the proposed control strategy are proved using simulation.

ACS Style

Nan Wu; Honglei Wang. Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid. Journal of Cleaner Production 2018, 204, 1169 -1177.

AMA Style

Nan Wu, Honglei Wang. Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid. Journal of Cleaner Production. 2018; 204 ():1169-1177.

Chicago/Turabian Style

Nan Wu; Honglei Wang. 2018. "Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid." Journal of Cleaner Production 204, no. : 1169-1177.

Journal article
Published: 20 June 2018 in Sustainability
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As market competition becomes increasingly fierce, it becomes more and more important for members of the supply chain to maximize market sales and improve the economic benefits of all parties through altruistic cooperation. Considering the complex relationship between online and offline retail channels, this paper proposes a competitive–cooperative strategy based on altruistic behavior for the dual-channel supply chain, by applying the theory of the co-competition game. First, we introduce the problem with respect to the relationship between online and offline retail channels, and establish the competitive–cooperative strategy model based on altruistic behavior. Then, we prove the equilibrium strategy for existence and stability of the proposed model through mathematical deduction. Next, a multi-object optimal model is excluded by applying the Pareto principle, and the NSGA-II-based algorithm is obtained to acquire the Nash equilibrium point. Finally, we present the case testing results, which indicate that the proposed model is robust and can improve the channel efficiency of the supply chain.

ACS Style

Fei Xu; Honglei Wang. Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains. Sustainability 2018, 10, 2103 .

AMA Style

Fei Xu, Honglei Wang. Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains. Sustainability. 2018; 10 (6):2103.

Chicago/Turabian Style

Fei Xu; Honglei Wang. 2018. "Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains." Sustainability 10, no. 6: 2103.