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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 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.)

In view of the single-link manipulator system with quantized input, a predefined performance adaptive neural network control method based on Funnel control is proposed in this study. Different from the traditional Funnel control scheme, this method can ensure that the system reaches the predefined performance index within the predetermined time by constructing a new performance function, and the neural network and dynamic surface control technology are used to solve the unknown nonlinear items in the system and the differential explosion problem in the traditional backstepping control method. Theoretical analysis shows that the proposed control scheme not only eliminates the negative effects caused by quantized inputs, but also ensures the stability of the system. By adjusting the design parameters in the MATLAB simulation experiment, the predetermined time convergence of the tracking error is realized, which verifies the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

Agricultural sensors can greatly contribute to future technologies and systemic innovation in smart agriculture. However, the types and precision of sensors are limited to monitoring the agricultural environment with complex and diverse objects. The large and redundant monitoring data has also resulted in the low reliability of information perception. In this study, an improved radial basis function neural network (RBFNN) and Chernobyl disaster optimizer (ICDO) multi-sensor data fusion was proposed to improve the accuracy and reliability of single-sensor measurement. Firstly, an improved Chernobyl catastrophe optimization was performed on the neural network model. The good-point set theory was introduced to improve the initial population quality of the CDO, particularly for accuracy and speed. The adaptive Laplacian crossover operator was added to enhance the search performance. The better adaptive behavior was achieved in the high convergence speed. And then, the individual learning and differential evolution strategy were used to redefine the location update equation, in order to balance the local and global exploration. Secondly, the RBF neural network model was optimized by ICDO, in order to improve the stability of the model. Finally, the nonlinear mapping of the RBF neural network model was used to realize the multi-sensor data fusion with high accuracy. Three experiments were conducted to verify the improved model. The first one was to verify the ICDO. A large improvement was obtained in the solution accuracy and optimization stability, compared with particle swarm optimization (PSO), gray wolf optimization (GWO), firefly algorithm (FA), dung beetle optimizer (DBO), and subtraction average-based optimizer (SABO). The second one was to evaluate the quality of the atmospheric environment. Specifically, the atmospheric data was collected outside the South Subtropical Botanical Garden in Mazhang District, Zhanjiang City, Guangdong Province, China, from September 1, 2022, to September 30, 2023. The goodness of fit reached 0.999 for the prediction of atmospheric environmental quality, the mean square error was as low as 0.348, and the mean absolute percentage error was reduced to 0.729%. The third one was to classify the greenhouse environment. The data was collected in the greenhouses of the South Asian Tropical Botanical Garden. The accuracy rate of greenhouse environment classification was 99.21% with a precision rate of 99.91%. The data fusion was suitable for both indoor and outdoor environments, indicating better adaptability and high accuracy. This finding can also provide solid technical support to agricultural sensor data fusion in the field of precision agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong 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.)

Copyright of Journal of East China Jiaotong University is the property of Journal of East China Jiaotong 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.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong 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.)

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.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry 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.)

Flexible structures cause the dynamic parameters of flexible space manipulators to change with time, which reduces the accuracy of tracking control. The lighter mass and the larger ratio of length to radius may result in the vibration of flexible space manipulators during their movement. To solve the above problems, a dynamic model of a flexible space manipulator considering two‑dimensional deformation and disturbance torque is established, and a simplified non‑linear dynamic formula is derived. On this basis, a control law is designed to identify and compensate for the time‑varying term and disturbance torque in the flexible space manipulator using the radial basis function (RBF) neural network. Then, using the hyperbolic tangent function as the approximation rate, a sliding mode control strategy is proposed. Finally, through simulation and ground physical prototype experiment, it can be concluded that in the design of control laws for flexible space manipulators, the control strategy with neural network compensation effectively reduces the impact of disturbance torque on the flexible space manipulator. By using the tanh function instead of the sgn function, the fluctuation of input torque can be reduced, and the effectiveness of the RBF neural network compensation sliding mode control strategy is verified. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong 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.)

Aiming at the attitude cooperative control problem of multi-spacecraft system, the attitude estimation of the spacecraft is estimated by invariant extended Kalman filter (InEKF), and the attitude estimation of the spacecraft is made independent of the transfer matrix. Then, the multi-spacecraft special orthogonal group (SO(3)) attitude model is constructed by combining the undirected communication topology, and the attitude coordination error on SO (3) is deduced. Among them, an attitude coordination control algorithm based on the consistency theory is proposed based on the special orthogonal group SO(3). The radial basis function neural network is used to tit and compensate virtual disturbances. The proposed algorithm considers spatial disturbances to ensure that the collaborative controller is applicable to the entire attitude space. Multi-satellite attitude collaboration, tracking desired attitude, and collaboration under the constraints of actuator capabilities are realized. The system is analyzed based on LaSalle invariant set theory, proving that the system has global asymptotic stability. Finally, the effectiveness of the proposed attitude collaborative control algorithm is verified by using a six-satellite spacecraft system. [ABSTRACT FROM AUTHOR]

To meet the high-performance trajectory tracking requirements of underactuated autonomous underwater vehicle (AUV) under complex disturbances and parameter uncertainty, a three-dimensional trajectory tracking controller method with predefined dynamic performance and convergence time is proposed. Firstly, by extending the forward position channel of the underactuated AUV, and a multi-input multi-output trajectory tracking control-oriented model is developed. Subsequently, by combining dynamic process functions with predefined-time control theory, a dynamic performance-predefined trajectory tracking control system is established, which allows the transient quality of AUV trajectory tracking to be determined by dynamic process functions, and the actual convergence time of tracking errors to be predefined by a single control parameter. Finally, to avoid control singularities and the " differential explosion" phenomenon, the controller design incorporates the absolute value correction method and radial basis function neural network (RBFNN) fitting method. Numerical simulation results indicate that the proposed controller significantly improves the disturbance rejection and transient quality of underactuated AUV, achieving fact, smooth, and High-performance trajectory tracking. [ABSTRACT FROM AUTHOR]

Focusing on the issue of error detection in terminal guidance radar under sea clutter background, research on sea clutter suppression and discrimination is conducted. The target identification and classification algorithm based on Tri-feature training is adopted, and the target amplitude, peak duration range and fluctuation rate are used as feature quantities. The radial basis function (RBF) is selected as the kernel function, which has strong nonlinear mapping ability and can better represent the relationship between data in high-dimensional space. Then, the support vector machine ( SVM) target identification and classification classifier design and experimental data verification are carried out. Verified by publicly available measured data, the accuracy rate of the proposed algorithm has reached over 97%. Compared with the traditional template matching recognition method, the target identification and classification algorithm based on Tri-feature training has higher identification accuracy, which proves the effectiveness and progressiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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 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.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & 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.)

Owing that the conventional temperature drift error (TDE) estimation method for capacitive micro-electro-mechanical system (MEMS) accelerometers (CMA) has demerits of inaccurate TDE traceability and tedious model establishing process, low accuracy and high complexity are introduced inevitably. A precise TDE estimation method of CMA based on micro-deformation analysis is proposed. Firstly, TDE is traced accurately with micro-deformation analysis, and a modified TDE estimation model is established with radial basis function neural network (RBFNN). Secondly, an accurate model identification method is proposed under Expert-Fuzzy assisted decision-making based on expert experience and fuzzy theory, which offers effective structural configuration guidance to the modified model. Then, temperature experiment is designed to test CMA, and the conventional and modified models are evaluated in performance by comparing their bias stability. Experimental results show that the establishing process for the modified model is greatly simplified, and its bias stability is improved by 35%. It ensures that TDE of CMA can be estimated much more precisely, which decouples temperature dependency of Si-based material and improves the environmental adaptability of CMA. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of 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.)

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.)

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

In order to establish a shelf--life prediction model that can be applied to a variety of fresh aquatic products at the same time. Using back propagation (BP) neural network model, genetic algorithm-back propagation (GA-BP) neural network model, radial basis function(RBF) neural network model, extreme learning machine (ELM) neural network model and support vector machine for regression (SVR) model to predict the remaining shelf life of four aquatic products respectively. First measure 4 indicators of tuna, salmon, turbot and sea bream at 0, 4 °C and 10 °C, in order to construct training samples and test samples. Including sensory score, total number of colonies, TVB-N value, K value, pH value. After correlation analysis, the pH indicator with the lowest correlation was eliminated, and other indicators were selected as input indicators for each model. Then, determine the network topology and parameters of each model, and train each model. Finally, this study used each model trained to predict the remaining shelf life of the test sample. The result showed that the order of model prediction accuracy was: SVR prediction model > RBF neural network model > GA-BP neural network model > ELM neural network model > BP neural network model. The prediction accuracy of the BP neural network model was the worst, mean square error (MSE) was 9.5127X10-4, mean absolute error (MAE) was 0.0197, mean absolute percentage error (MAPE) was 0.0825, R2 was 0.9766. The prediction accuracy of the SVR prediction model was the best, its prediction error was within 12%, MSE=2.2971 x10-4, MAE=0.0128, MAPE =0.0631, R²=0.9944, it could well predict the remaining shelf life of 4 aquatic products. This study provides a theoretical basis for the quality control of aquatic products. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 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 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.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of 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.)

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food 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.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION 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 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 Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian 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 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

Objectives: In the process of digital elevation model (DEM) modeling, the existing interpolation methods do not take into account the local topographic characteristics near the breaklines, which makes the elevation of the local area of the breakline smoothed, thus leading to topographic distortion. A weight function with respect to considering the characteristics of breaklines is constructed, and a weight radial basis function (RBF) method is proposed. The proposed method makes full use of the gradient and direction information of the sampling points near the fracture line. Methods: First, the distance between the sampling point and the point to be sought is calculated adaptively by capturing the structure tensor of each sampling point, and then the distance is used to assign a suitable weight to each sampling point, finally the DEM modeling is realized by using weighted interpolation. Results: Examples on 10 public data and 1 private dataset of DEM construction with airborne light detection and ranging (LiDAR) point clouds indicates that the calculation results of each interpolation method gradually decrease with the decrease of sampling points. Compared to RBF and the classical interpolation methods including inverse distance weighting, ordinary Kriging and constrained triangulated irregular network, our proposed method has a better ability to maintain terriain features in the breakline area. Regardless of sample density, our proposed method is always more accurate than the other methods. Conclusions: Overall, the proposed method with the merit of terrain feature preservation is helpful for the construction of high-accurate DEMs, which play an important role in some geoscience applications with data quality as the most important factor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute 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 Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing 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.)

Many kinds of hydrocarbon reservoir damages with complex mechanisms have been encountered in every phase of oil and gas field exploration and development. Conventional core flow test used in evaluating the sensibility damage of a reservoir can give reliable test results, however, this test is both expensive (coring, for instance) and time consuming. Researches have shown that a model established with neural network and random forest algorithm on small-scale samples can be used to save time and money in predicting the properties of samples. In this study, the data of a set of small-scale samples tested in laboratory is obtained from the block X. The training-sets and testing-sets are then selected on the samples. By extensively comparing the results of three algorithms, which are the BP neural network algorithm, the radial basis function neural network algorithm and the random forest algorithm, the random forest algorithm is finally selected as the main method of quantitatively diagnosing the sensitivity damage of hydrocarbon reservoirs. To improve the prediction accuracy, algorithms such as grid search are used in hyperparameter optimization, and data dimensionality reduction is performed based on factor weight. A complete model is finally established based on the studies conducted. The average R² value of the four kinds of reservoir damage model is 0.852, with a prediction accuracy between 90.00% and 95.68%. . [ABSTRACT FROM AUTHOR]

Soil organic carbon(SOC) reservoir reserves of farmland serve as an important indicator for the assessment of the potential of carbon sequestration and emission reduction and turnover of SOC was affected by environmental variables like climate (temperature and precipitation) and terrain(slope and elevation). In order to clarify the impact of environmental variable factors on SOC and achieve spatial interpolation prediction of SOC, provide some theoretical and practical references for understanding the spatial heterogeneity and precision mapping of SOC at small scale, in this study, 7 different approaches including the Inverse Distance Weighting method(IDW), Radial Basis Function method(RBF), Ordinary Kriging(OK), Multiple Linear Regression (MLR), Regression Kriging(RK), regression inverse distance weighting method(MIDW), regression radial basis function method (MRBF) were used to explore the relationship between terrain factors and climate factors and SOC, and thenthe optimal spatial interpolation model to predict the spatial distribution of SOC was further obtained. The results showed that there was a significant negative correlation between SOC content and elevation(-0.255**), temperature(-0.246**), slope(-0.214**), and precipitation (-0.085* ). The relationship between elevation and SOC was the closest. Comparing the predictive performance of the different interpolation models, the root mean square error(RMSE) of MLR was smaller than that of RMSE of OK, RBF, IDW, RK, MRBF, MIDW, and its value was 0.083. The average absolute error(MAE) of MRBF was less than MAE of OK, RBF, IDW, MLR, RK and MIDW, and its value was 2.506. The Pearson correlation coefficient of MRBF was greater than that of OK, RBF, IDW, MLR, RK, and MIDW, and its value was 0.674. Therefore, the prediction effect of SOC based on MRBF method was the best. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical 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 High Pressure Physics is the property of Chinese Journal of High Pressure Physics 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.)