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Accurately predicting the concentration trend of dissolved gas in oil has a positive effect on the evaluation of transformer status and life assessment. In order to improve the accuracy of dissolved gas in oil prediction, a dissolved gas in oil prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time convolution network (TCN) is proposed in this paper. Firstly, the CEEMDAN method is used to decompose the original sequence of dissolved gas in oil into multiple intrinsic mode functions(IMFs), separating the stable and unstable IMFs. Secondly, TCN are established for IMFs, then predictions based on these trained TCNs are made. Finally, the prediction results of IMFs are overlaid to reconstruct the prediction results of the original sequence. Analysis in this paper shows that the root mean square error, mean absolute error and maximum error of the prediction method are 1.01 μL/L, 1.53 μL/L, 5.54 μL/L respectively, which are reduced by 53.47%, 41.18%, 13.36% compared to the case without using the CEEMDAN. When using CEEMDAN, the three errors are the smallest compared to commonly used recurrent neural networks. The proposed dissolved gas in oil prediction method has higher prediction accuracy and can provide more effective support for condition based maintenance strategy.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatiotemporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used: statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. [ABSTRACT FROM AUTHOR]

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots. However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, a spatiotemporal convolutional network is proposed. The network combines a graph convolutional neural network(GCN) and temporal convolutional network(TCN), specifically, GCNs are used to learn the spatial dependencies of research topics and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experimental results show that, in research topic prediction tasks, the model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines. [ABSTRACT FROM AUTHOR]

Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Photovoltaic power generation fluctuates, photovoltaic output is easily affected by various meteorological characteristics, and traditional TCN networks tend to over-enhance spatial characteristics and weaken individual characteristics. In view of these problem, a short-term photovoltaic power generation prediction model based on VMD and improved TCN is proposed in this study. The original photovoltaic power generation time series is decomposed into several modal components of different frequencies through VMD, and each modal component and the corresponding meteorological data are input to the improved TCN network for modeling and learning. The center frequency method is used to determine the optimal decomposition modal number of VMD. On the basis of the traditional TCN prediction model, DropBlock regularization is used to replace Dropout regularization to achieve the effect of suppressing information synergy in the convolutional layer, and the attention mechanism is introduced to autonomously mine and highlight the impact of key meteorological input characteristics and quantify the impact of various meteorological factors on photovoltaic power generation to improve forecasting precision. Based on the real data of a photovoltaic power station in Jiangsu, the simulation experiments show that the RMSE of the proposed prediction method is 0.62 MW and the MAPE is 2.03%. [ABSTRACT FROM AUTHOR]

This review outlined recent developments in deep learning time series forecasting technology for the needs of industrial applications. With the advances in industrial automation, the storage and analysis of massive production data have become possible. Traditional mechanism modeling methods based on statistics encounter difficulties in dealing with high-dimensional industrial problems. Thus, time series forecasting for complex processes and products has played an important role in industrial scenarios, such as device modeling, production forecasting, remaining life prediction, and precise control, thereby receiving considerable attention from both academia and industry. To methodically review the time series forecasting method and its industrial applications, this review first introduced the three types of time series forecasting, namely, statistical learning, integrated learning, and deep learning, and compared their ease of use, complexity, and applicability issues. Focusing on industrial data analysis and decision-making, this review analyzed the three types of deep learning models, namely, recurrent neural network, convolutional neural network, and encoder–decoder network. The advantages and disadvantages of these three types of models and the applicable industrial environment were given, and how they can be embedded in industrial application sites to reduce costs and improve production efficiency was described. Afterward, to evaluate the performance of different algorithms clearly and comprehensively, statistical metrics and loss functions for the point prediction sequence and shape (motif) prediction problems were presented. At the same time, this review compiled classic public datasets of the industry for researchers to quickly find authoritative assessment data for an industry sector or issue. Taking mining and metallurgy in the process industry as examples, this review presented some widespread problems in this field, such as nonlinearity, long time delays, and unobservability of variables. This review also showed how deep-learning-based time series forecasting techniques can solve the aforementioned problems, build soft sensors, create digital twins, and achieve the visualization of complex processes. This review revealed that the application of deep learning in the process industry requires highly robust and strongly interpretable or explainable algorithms. For the robustness problem, the use of the ordinary differential equation model and Kalman filter method to solve the modeling of irregularly sampled time series and the use of the deep learning method to detect online sensor abnormalities were proposed. For the interpretability problem, sample-based, structure-based interpretable, and external co-explanation methods were introduced. This review also analyzed how explainable techniques can be applied to industrial deep learning models. Finally, the future directions of time series research were discussed in terms of both deep learning methods and industrial applications.

Objective: The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition., Methods: Using 8 patients' data as samples, the RR intervals of patients were predicted by long short-term memory network (LSTM) and gradient lift tree (XGBoost), and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction., Results: Compared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients., Conclusion: LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is easy for a BP neural network (BP-NN) to be trapped into a local minimum point for the time series forecasting problem. To improve the forecasting accuracy，we design a hybrid algorithm which combines the self-adaptive differential evolution algorithm (SDE) with the BP-NN. We adopt the SDE algorithm to search for global intial weights and thresholds of the BP-NN. These values are then employed to further search for the optimal weights and thresholds. The performance of the proposed SDE algorithm is verified through benchmark functions and a well-known real data set is used to verify the effectiveness of the hybrid algorithm. Compared with general neural network，ARIMA and other hybrid models，experimental results indicate that the proposed algorithm can be an effective way to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

In order to improve the forecasting accuracy and enhance the applicability of the time series forecasting approach, this paper proposed a novel weighted combination method, namely ARIMA-WASDN method. This method simultaneously exploited the ARIMA model and WASDN (short for the power-activation feed-forward neuronet equipped with the WASD algorithm) to model, test and forecast the time series. According to the results of testing, two models could be combined into one model in a weighted manner for time series forecasting. Numerical experiment results indicate that the ARIMA-WASDN method can improve the accuracy achieved via either of the models used separately, and the results further illustrate the effectiveness and superiority of the proposed ARIMA-WASDN method in terms of time series forecasting. [ABSTRACT FROM AUTHOR]