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Copyright of Journal of Frontiers of Computer Science & Technology 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 Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics 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.)

Data asset valuation is of strategic significance to the development of data elementalization, in order to clarify the contribution rate of data asset valuation indicators and balance the accuracy and interpretability of machine learning models, a data preprocessing-feature selection-back propagation neural network ( DP-FS-BP) prediction framework prediction framework was proposed, and the Shapley Additive exPlanations( SHAP) algorithm was used to explain the metric contribution of the prediction model. Taking the transaction block data collected by Youe data network as an example, data preprocessing and feature selection were used to clean the data and select indicators, and then the values of R², root mean squared error( RMSE) and mean absolute error( MAE) were compared with the original data on linear regression, support vector machine( SVM), decision tree, k-nearest neighbors( KNN), random forest, XGBoost and DP-FS-BP models. The results show that the DP-FS-BP model obtains the most ideal prediction results, and has a significant advantage over other models in prediction accuracy. The results of explaining the BP neural network model using the SHAP algorithm show that the average absolute values of SHAP values for scientific research techniques and data sample sizes are 209. 25 and 191. 24, respectively, ranking first and second. By visualizing the contribution rate of features to the output, a decision-making basis is provided for establishing a corresponding data asset value evaluation index system. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication 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.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction 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.)

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data formats, analytical complexities, and high labour costs, hindering the achievement of data standardization. Large language models (LLM), featuring powerful performance in natural language processing comprehension and handling complex tasks, have made great academic and industrial progress in recent years. This study initially investigated the application performance of LLMs in information extraction from locomotive overhaul data, with the following three reveals, as the universal information extraction (UIE) LLM is suitable for information extraction in the field of locomotive overhaul; expanding the size of locomotive data helps improve the UIE performance in information extraction from locomotive overhaul data; balancing the types of fault labels does not notably help improve this performance. Subsequent explorations concentrated on difficulties in data annotation. The script writing method was utilized for automated annotation of data, and ChatGLM was leveraged to standardize locomotive overhaul data, yielding Bleu-4, Rouge-1, Rouge-2, and Rouge-L metrics of 86.87%, 89.60%, 87.54%, and 94.26%, respectively, in alignment with the requirements of engineering applications. Further developments introduced an auxiliary data standardization pre-processing tool to streamline the standardization process by encapsulating the LLM.

In order to ensure the consistency and completeness of monitoring data in the safety monitoring system of coal mines, a design scheme for continuous transmission of disconnected data was proposed in response to faults in the monitoring substation equipment. The methods for fault recognition of data transmission, data preprocessing, data storage strategies, and data transmission protocols were studied. Based on the principle of invariable nonstorage, analog data with invalid values was cleaned up by bit storage compression of discrete transient data. Two-level storage of data was achieved by using FLASH and ferroelectric memory, and the data storage format and strategy were described. Network transmission protocols and bus transmission protocols were formulated separately to implement data continuation functions. The experimental results showed that after the main communication of the substation was interrupted, the monitoring data could be continuously stored for 7 days, and the correct rate of data continuation after the end of the failure reached 100%. This solution effectively solved the problem of missing data caused by equipment failure in the substation, ensured the integrity of monitoring data, and improved the reliability of the monitoring system.

Aiming at the problem that unlabeled samples cannot be utilized by machine learning due to the low detection frequency of sinter chemical indexes, a prediction model for sinter chemical indexes that makes full use of the useful information in the samples is proposed. Firstly, the unlabeled samples are transformed into labeled samples by combining Gaussian mixture model (GMM) and K-nearest neighbor (KNN) algorithm, and then combined with long short-term memory (LSTM) unit for predicting three chemical indexes, namely, total Fe mass fraction, FeO mass fraction, and alkalinity of sinter. By comparing with the three models of back propagation neural network (BPNN), recurrent neural network (RNN), and LSTM, the results show that the proposed model has a low prediction error. The prediction hit rates of total Fe mass fraction and FeO mass fraction reach 98. 73% and 95. 33%, respectively within the allowable error of ±0. 5%, and the prediction hit rate of alkalinity is 98. 13% within the allowable error of ±0. 05, demonstrating high prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Ambient noise seismology uses the ambient noise signals recorded by seismic stations to calculate the cross-correlation between stations, and thereby derive information about geological structures. In recent years, it has been widely used in fields such as Earth structure and oil and gas exploration. Seismic noise data processing often requires preprocessing calculations to reduce interference from instruments and seismic signals, which involves various signal processing calculations. As seismic stations are increasingly deployed in China, the continuous accumulation of seismic waveform files has greatly increased the time required for preprocessing calculations. To address the issue of computational time, a parallel seismic noise preprocessing algorithm has been proposed based on a graphics processing unit (GPU) heterogeneous computing platform. The parallel algorithm designed a parallel computing framework in three dimensions: stations, time, and segments. It implemented computational kernel functions for the calculation process in preprocessing and achieved adaptive processing of large batches of files through batch calculations. Experimental results show that the parallel preprocessing algorithm achieved an acceleration ratio of about 95 times, with good acceleration ratio and parallelism. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of 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 Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

The strategic rare metal molybdenum ore has the characteristics of low grade, complex fractions and fine embedded grain size, with its valuable metals difficult to separate and recover. The flotation, as one of the main beneficiation methods for separation and recovery of microfine grain molybdenum ore, and its flotation molybdenum concentrate grade has been the key product indicator for the processing plant. The majority of domestic processing plants adopt a shift system sampling, manual assay to get the concentrate grade results, but this way seriously lags behind the flotation process, it is difficult to meet the production of the process for real-time monitoring and operational guidance. LSTM is a special kind of recurrent neural network, and the introduction of the gate mechanism effectively pass or selectively forget information in long time sequences, solving the long-term dependency, gradient disappearance and explosion problems in RNN. This paper analyses and collates datas from various platform sources in the Dongpo processing plant. Combined with the process and mechanism of flotation in the processing plant, screening out a number of variables that affect flotation molybdenum concentrate grade as model inputs are screened out. The input variables are subjected to data pre-processing such as outlier determination, missing value filling and data noise reduction to build a high-quality molybdenum flotation concentrate grade database. The soft measurement model adopts PyCharm software coding, uses BatchNorm batch normalization to process sample data, adds Dropout regularization to prevent overfitting, establishes LSTM-based soft measurement model for flotation molybdenum concentrate grade, updates neural network structure parameters by the forward propagation algorithm, and the predictive performance index results of the Linear model and CNN model are compared. The results show that the LSTM-based soft measurement model for flotation molybdenum concentrate grade has high prediction accuracy, smooth sample data error fluctuation, small floating range, and strong model generalization ability. The average absolute percentage error MAPE is 1. 13%, the root mean square error RMSE is 0. 7049 % and coefficient of determination R² is 0. 876 3, which realizes the online prediction of flotation molybdenum concentrate grade. [ABSTRACT FROM AUTHOR]

The flexibility of the power grid can be significantly promoted by the participation of power customers in dispatch. However, as the uncertainty of customer behavior,the development of demand response services is limited. To solve this problem,the framework of incentive-based demand response is constructed in this paper. The way that load aggregators integrate demand-side resources to participate in the power market is elaborated. And the behavior of power customers responding to incentive policies is transformed into demand elasticity. Then,a data-driven demand elasticity prediction method based on the integrated long short-term memory (LSTM) is proposed. Meanwhile,to improve the performance of the prediction model,the original data is smoothed and scaled,and the weight coefficients of the loss function are added. The simulation results show that,compared with the traditional LSTM algorithm and the k-proximity prediction method,the average forecasting error with the proposed model for the demand elasticity is reduced by 5.33% and 28.8%,and mean absolute percentage error (MAPE) for the total load prediction is reduced by 2.06% and 3.09%. Additionally,based on integrated LSTM,the influence of smoothing and scaling data preprocessing on prediction accuracy is analyzed. The results show that the prediction accuracy can be significantly promoted by data preprocessing.

Metabolomics technique, as an important part of systems biology, aims to identify and quantify all endogenous small molecule metabolites in organisms at certain condition. The continuous iteration of mass spectrometry and nuclear magnetic resonance system facilitate great progress in metabolomics technologies. Among them, mass spectrometry and related metabolomic techniques have been the most widely used due to their ability to detect thousands of metabolites in biological fluids, cells and tissues simultaneously, without complex pre-processing steps for sample preparation. Therefore, the development of tools for mass spectrometry-based metabolomics data analysis has been a hot topic in metabolomics research in the past decade. In this review, we systematically summarized the research progress in four main aspects of gas/liquid chromatography tandem mass spectrometry (GC/LC-MS)-based metabolomics data analysis, including metabolomics data preprocessing, statistical analysis of metabolomics data, metabolic pathway enrichment analysis, and identification of unknown metabolites. We mainly introduced the commonly used analysis strategies and software related with MS-based metabolomic data analysis; and highlighted the cutting-edge innovation about molecular networking-, artificial intelligence-and databases-based metabolite identification. Finally we gave a brief future perspective about MS-based metabolomic data analysis, and believe that new developed strategies, which integrate the known biochemical reactions, molecular networking tools, and genetic loci information regulating the metabolite biosynthesis, will promote the number and accuracy of identified metabolites. This review will provide new ideas for deeper exploration of new methods for metabolomic data analysis and biological significance from metabolomic data.

Copyright of Railway Standard Design is the property of Railway Standard Design 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.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

With the growth of the aging population in China, the tracking and detection of the real-time behavior trajectory of the elderly have become a hot spot in the current smart community research. A YOLOv5 roadside pedestrian fall detection method based on optical flow and posture was proposed to solve the interference of electric vehicle flow and pedestrian shadow. Firstly, the original data was input to YOLOv5 network by video frame extraction to preprocess the video data of pedestrian monitoring and realize the reconstruction of pedestrian background. Then the reference frames of optical flow and pedestrian pose were extracted as their motion features. Finally, these features were judged and the fall detection network based on information fusion was used to recognize the fall feature. The comparative experiments were carried out in different frame sequences or backgrounds. The proposed method was conducted on the pedestrian fall public datasets (such as multiple cameras fall and Le2i). The results show that the accuracy and recall are improved by 9% and 10% respectively compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences 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.)

When using near-infrared reflectance spectrum to identify coal gangue, the change of detection distance between spectrum acquisition device and working face and dust interference will affect near-infrared reflectance spectrum. In order to select the best pre-processing method for near infrared reflectance spectrum for coal gangue, samples of anthracite and gangue with similar appearance are collected. A spectrum acquisition device consisting of near infrared spectrometer, collimator and halogen lamp is set up in the laboratory to acquire near infrared reflectance spectrum of coal gangue at different detection distances (1.2,1.5,1.8 m) and dust concentrations (200, 500, 800 mg/m3). Through the analysis of near-infrared reflectance spectrum characteristics of coal gangue, it is found that the detection distance and dust concentration change have no obvious impact on the waveform of near-infrared reflectance spectrum curve and the position of absorption valley of coal gangue. The absorption wavelength point of spectral characteristics will not be changed. However, the reflectance of near-infrared reflectance spectrum of coal gangue will be significantly affected. The spectral reflectance will decrease with the increase of detection distance and dust concentration, which will cause near-infrared reflectance spectrum drift of coal gangue. In order to enhance the absorption characteristics of near-infrared reflectance spectrum of coal gangue, the spectrum data are preprocessed by differential, standard normal variable transformation and polynomial smoothing methods. The preprocessed near-infrared reflectance spectrum data of coal gangue are input to the particle swarm optimization BP neural network model for coal gangue identification. The experimental results show that the differential preprocessing method has the best optimization effect on the near-infrared reflectance spectrum data of coal gangue collected under the change of detection distance and dust concentration, and can eliminate the impact of detection distance and dust concentration on the spectral reflectance effectively.

The imbalance classification is a common problem in the field of data mining.In general,the skewed distribution of data makes the classification effect of the classifier unsatisfactory.As an efficient data mining tool,convolutional neural network is widely used in classification tasks.However,if the training process is adversely affected by data imbalance,it will cause the classification accuracy of minority classes to decrease.Aiming at the classification problem of two-class unbalanced data,this paper proposes a hybrid method for unbalanced classification problems based on cost-sensitive convolutional neural networks.The proposed method first combines the density peak clustering algorithm with SMOTE,and preprocesses the data through oversampling to reduce the imbalance of the original data set.Then the cost sensitive is used to give different weights to different categories in the unbalanced data.Additionally,the Euclidean distance between the predicted value and the label value is considered.The proposed method assigns different cost losses to the majority class and the minority class in the unbalanced data to construct cost sensitivity convolutional neural network model to improve the recognition rate of convolutional neural network for minority classes.Six different datasets are used to verify the effectiveness of the proposed method.The experimental results show that the proposed method is able to improve the classification performance of the convolutional neural network model on unbalanced data.

Copyright of Railway Standard Design is the property of Railway Standard Design 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.)

[Objectives] The mechanical impedance of foundations can directly affect the stealth performance of underwater structures,which is commonly obtained by finite element simulation. However, this method is usually very time-consuming and inefficient when applied directly to optimal design. In order to enhance the efficiency,surrogate model technology is used to take the place of computer simulation.[Methods] The mechanical impedance of a typical underwater foundation is studied. The comparison among support vector regression(SVR)method,back propagation neural network(BPNN) method,radial basis function(RBF)method and Kriging model is conducted for their fitting accuracy. Besides,a data preprocessing method is put forward by combining the practical engineering requirement and characteristics of mechanical impedance. The effect of the data preprocessing method on the fitting accuracy of the surrogate model is studied.[Results] According to the calculated results,different surrogate models have different accuracy in fitting the mechanical impedance of the foundation. And the kriging model achieves the best results among four surrogate models. Compared with the initial model,the data preprocessing method has increased the normalized root mean square error and the normalized maximum absolute error by 9.80% and 15.43% respectively to the modified model.[Conclusions] The kriging model has the best applicability on fitting the parameter input-impedance output mapping. And the proposed data preprocessing method can be used to improve the accuracy of the surrogate model.

Copyright of Bearing is the property of Bearing 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.)

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology 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.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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 Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Because feature selection in an unsupervised environment lacks dependence on category information, this paper proposed a disagreement measure (DAM) with the use of the fuzzy rough set theory. DAM measured the degree of difference in meaning of fuzzy equivalence of any two feature sets or features. On this basis, this paper proposed the DAMUFS unsupervised feature selection algorithm. The DAMUFS algorithm could select feature subsets that contained more information under unsupervised conditions, while also ensuring that the attribute redundancy in the feature subset was as small as possible. The experiment compared the classification performance of DAMUFS algorithm with some unsupervised and supervised feature selection algorithms on multiple data sets. And the results prove the effectiveness of DAMUFS algorithm. [ABSTRACT FROM AUTHOR]

During the construction of the cutter suction dredger, the actual construction output is affected by many factors such as soil, hydrology, meteorology and underwater debris. It is difficult to obtain accurate prediction results, and the output of the cutter suction dredger is directly related to the project benefit. Aiming at the shortcomings of the current industry that the output prediction of cutter suction dredgers is too dependent on construction experience and the real -time performance is poor, a prediction method based on extreme learning machine (ELM) was proposed, and a production prediction model was constructed based on actual construction data. The experimental results show that the method has high prediction accuracy and good stability, can be applied to the dredging process of dredgers, and has certain engineering practical value. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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 Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University 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.)

Due to the complicated dredging operation of the cutter suction dredger, most scholars at home and abroad have used the working characteristics of the key equipment of the cutter suction dredger to carry out related research and establish its mathematical model. However, from the basic principles, it is inaccurate to establish the input-output model of the cutter suction dredger control system, which is a multi-parameter, non-linear, large time-delay system, and can not meet the needs of the actual control system. In this paper, the data-driven method is used to solve the black box problem between the control variables and the instantaneous output in the cutter suction dredger control system. The experimental results show that the predicted results of RBF neural network are reliable under different working conditions, and the model can provide accurate nonlinear mathematical model for the next model predictive control. [ABSTRACT FROM AUTHOR]

In order to further improve the prediction accuracy of polar motion, a wavelet decompositionbased method for polar motion prediction is proposed. In this method, the wavelet decomposition is first used to decompose polar motion time-series into approximation and detailed signals. These approximation and detailed signals are then reconstructed to obtain low- and high-frequency components of polar motion. Furthermore, the least-squares (LS) extrapolation of models for the linear trend, Chandler and annual wobbles are used for predicting low-frequency components, while the autoregressive (AR) model is employed to forecast the high-frequency components together with the residuals derived by subtracting the LS extrapo⁃ lation curve from the low-frequency components. Finally, polar motion predictions are made by the combi⁃ nation of the LS extrapolation and AR model. The results show that the polar motion predictions can be improved using the proposed wavelet decomposition-based method, especially medium- and long-term pre⁃ dictions. [ABSTRACT FROM AUTHOR]

For the problem of a single neural network individual learner prone to over-fitting phenomenon and have poor network generalization, a neural network ensemble method based on genetic algorithm is proposed. The genetic algorithm is used as the combination strategy of ensemble learning, and the diversity of individual learners is fully absorbed while ensuring the accuracy of individual learners. By using genetic manipulation and species invasion, the ensemble learner iterative evolved to obtain a neural network learner with globally optimal neural network. The experimental results show that the integrated learner trained by the neural network individual learner integration method has good overall, can effectively avoid network appears over-fitting, and improve classification accuracy rate of the network, which is a neural network ensemble learner with stable and strong generalization ability. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

The characteristics of power communication network equipment's maintenance,alarm and asset data were analyzed,and then the data preprocessing work was done by adopting attributes reduction and clustering method.The related index and influencing factors required by the analysis of equipment repair work were proposed.Meanwhile,the data mining method which combines the techniques of MapReduce and Apriori algorithms was presented.Finally,the proposed data mining method was applied to mine the actual network data.According to the mined results,the network maintenance work was analyzed from the perspectives of maintenance distribution,maintenance effectiveness on operated service,maintenance time-cost and so forth.

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 China Mechanical Engineering is the property of Editorial Board of China Mechanical 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.)

Space-borne search Coil Magnetometer(SCM)is able to detect space low frequency alternating magnetic field. Ground testing system and data preprocessing are important tools for design and development. Firstly, the working principle, data product and research significance of SCM are introduced. Secondly, aground testing system is designed based on theexternal interface of the payload, which implements the communication function between the payload and the computer. By using Visual C++ to call Matlab, a data preprocessing software for SCM is designed, with real-time scientific display function. The system can not only completely receive scientific data but also display the waveforms of different directions and frequency bands in dynamic refresh mode. The method is simple in programming and has comprehensive integrated functions. It has undergone multi-stage satellite testing and experiments. The software running results indicate that the system can monitor and control the working status of the payload in real-time. It has the characteristics of easy maintenance and expandability, and provides test conditions for the development and application of the onboard instrument in the follow-up stage. [ABSTRACT FROM AUTHOR]

The problem on the classification algorithm of imbalanced datasets was analyzed. Common methods of balancing data， including improvement of datasets and the improved algorithm， were summarized. Then a novel algorithm called adaptive random sampling algorithm was put forward based on balance maximization. The classification effect of random forest algorithm was further optimized. Experiments show that the proposed algorithm performs well with the imbalanced data， the new data are fitted with the original data， and it could improve the ability of classifier to deal with the imbalanced data. [ABSTRACT FROM AUTHOR]

The data preprocessing method of the cam profile and data error compensation theory of the cam profile in the reverse design cam mechanism are introduced.According to the introduction of cam,the data of discrete points of the actual cam profile line is mapping,the possible’noise point’is removed,and the cam theoretical profile equation is deduced.Then according to the cam design theory,the angular displacement and angular quasi-acceleration of the cam follower is solved by using numerical computation method.Through comparing the value between angular quasi-acceleration and expected angular quasi-acceleration,the error of the angular quasi-acceleration is obtained,then the angular displacement error is solved.Through removing the angular displacement error,the cam theoretical profile compensation is carried out,and the data of discrete points more closer to the original design of cam profile line is acquired.Thus the scientific method for the reverse design of this kind of cam mechanism is provided.

Aiming at simulation performance verification problem of the flight simulator, the previously collected test flight data and simulation data was studied, the method of simulation model validation data preprocessing was carried on the induction and limitation was pointed out, A set of processing methods to dynamic data and static data of simulator was presented to passage data, remove outliers, sampling points matching, stationarity test and smooth processing, zero mean processing, ergodic property inspection and normality, and these methods were used to the flight test data and simulation data obtained for processing, the data after preprocessing was used for the verification of specific flight performance. The results indicate that the pretreatment method can be gained by the data processing to meet the specific data validation, and the foundation of validation work is laid for the next step. [ABSTRACT FROM AUTHOR]

The original physical examination data has many problems, including ambiguity, noise, incomplete and redundancy information，so it cannot be used for disease risk assessment and prediction directly. Traditional processing methods are not suitable for physical examination data because of its special structure and format. In order to solve these problems and make full use of the valuable information in the data, this paper proposed several methods. It used a compression-based data reduction method to reduce the time and space complexity of the data, and used a field matching algorithm based on segmentation and weights to complete the data cleaning and solved the problem of inconsistency. It used a data transformation method based on linear function to get the consistency and continuity of the history data. It also proves that the proposed filed matching algorithm is more accurate than the traditional method. [ABSTRACT FROM AUTHOR]

In neural network modeling， it may build a wrong model using original data without any treatment or only eliminating the abnormal value， for it could contain the law not to follow the physical metallurgy principle. To make the regularity significant， the original data need to be processed before modeling. In this work， based on the data of the C-Mn steel derived from a large number of data collected from different steel grades， a set of method for data preprocessing was proposed and a model for predicting yield strength of the C-Mn steel was established using LM-BP neural network， which could make the prediction accuracy meet the requirement (94.21%). The effects of the elements content and processing parameters on the yield strength were analyzed by the mean impact value (MIV). The results showed that the yield strength increased with the increase of carbon content and increased with the decrease of final rolling thickness and coiling temperature. [ABSTRACT FROM AUTHOR]

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