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Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) 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.)

In response to the strong multi-solution problem of conventional seismic exploration methods in geological sweet spot prediction of shale， this paper fully explored one-dimensional whole rock analysis data and well logging data， two-dimensional geological data， three-dimensional post-stack seismic data， and five-dimensional offset vector tile （OVT） orientation information. In addition， the paper researched geological sweet spot identification technology of shale based on fuzzy fusion prediction to improve the accuracy of geological sweet spot prediction of shale. Firstly， the approximate distribution direction of faults in the study area and the distribution area of favorable shale lithofacies were statistically analyzed， and the sensitive azimuthal section was selected to stack the OVT data based on azimuth. Then， the fractures on the plane were analyzed based on pre-stack preferred azimuth， and the two-dimensional prediction of favorable shale lithofacies was carried out using a feedforward neural network. Finally， a fuzzy fusion technology based on an improved Sigmoid and Takagi-Sugeno （TS） function was developed， which could weigh the significance of controlling factors in geological sweet spot identification of shale according to their degree of influence and effectively integrate the predicted results of azimuthal anisotropy fractures on the plane with those of neural network-based shale lithofacies， so as to realize decision-making integration for identifying geological sweet spots of shale. This technology has been applied in the classification and grading evaluation of geological sweet spots of shale in the Lower Submember of the 3rd Member of Shahejie formation （Es3U） in Bonan Depression. Areas with poorly developed fractures and unfavorable lithofacies that could interfere with the analysis of geological sweet spots of shale were filtered out. Based on the predictions of fractures and lithofacies， The geological sweet spots of shale in the study area were categorized into three classes. The areas characterized by the superposition of developed fractures and favorable lithofacies were classified as sweet spots of Class I， which showed a high degree of consistency with actual drilling results and achieved notable application effects. The research results indicate that the classification and grading evaluation can be achieved using the geological sweet spot identification technology of shale based on fuzzy fusion prediction， which improves prediction reliability and provides reliable technical support for shale oil exploration.

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 Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency 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 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.)

To address the problems of uneven transition between foreground and sky regions, inaccurate transmission rate fusion, and unnatural color restoration in image dehazing, an image dehazing algorithm using weighted fusion with the Sigmoid function is proposed. This algorithm uses different attenuation constants in the RGB three channels, and uses Sigmoid as the weight function to fuse the estimated transmission rates of the sky region based on the color attenuation prior and the estimated transmission rates of the foreground region based on the dark channel prior. Finally, the haze-free image is restored through the atmospheric scattering model. Experimental results show that the dehazing effect of this algorithm is better for hazy images containing sky regions, and the restored images are clear and natural in color, improving the subjective visual effect of the images. Objective comparisons also confirm the effectiveness and applicability of this algorithm. [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.)

Small wind power generation systems have the characteristics of nonlinear strong coupling and the application requirements of small weight and low cost. If the traditional blade tip speed ratio method is used for the maximum wind energy capture control of wind turbine, there is a problem of poor dynamic performance when wind speed changes. This paper proposes a maximum wind energy capture method based on an inverse system, which takes the permanent magnet synchronous generator (PMSG) as the object, and introduces a method of inverse system to linearly compensate the original system. This method needs to observe rotor angle, speed and mechanical torque of PMSG. Conventional mechanical sensors will increase the weight, volume and design cost of small wind power generation system, while traditional sensorless technologies such as sliding mode observer have their own problems of large vibration. To solve this problems, an improved sliding mode observer is designed for the proposed method. Simulation results show that the proposed control method has better dynamic decoupling performance than the traditional one, and the improved sliding mode observer can eliminate its own jitter to a great extent, and can achieve accurate observation of PMSG rotor angle, speed and mechanical torque in the full speed range.

In order to improve the global exploration and local development performance of the arithmetic optimization algorithm, an arithmetic optimization algorithm (IX-AOA) based on the non-linear aperture imaging mechanism and the optimal position-guided search strategy with fine-tuning function is proposed. The adaptive adjustment of the arithmetic acceleration optimizer factor is a vertical update strategy based on the exploration and development purpose of the balanced algorithm, adjusts the algorithm exploration and development time allocation through the hyperbolic nature, and then realizes the algorithm's self-update mechanism to achieve the effect of balancing algorithm exploration and development. It uses sigmoid activation in the optimal individual, the weight coefficient of the function adjusts the reasonable control mechanism of the search and development of the offspring position to achieve the goal of enriching high-quality candidate solutions. It introduces the principle of nonlinear aperture imaging to improve the search mechanism to optimize the stagnation of optimization caused by the phenomenon of coverage between candidate solutions. Finally, 8 benchmark functions and some CEC2014 and Wilcoxon rank sum tests are used to verify the superiority of the algorithm function optimization. [ABSTRACT FROM AUTHOR]

Aiming at the problems in the iterative process of seagull optimization algorithm ( SOA), such as local optimization, slow convergence speed and low optimization accuracy, this paper proposed a golden sine guide and sigmoid continuous seagull optimization algorithm( GSCSOA) . In the seagull migration stage, the algorithm used the sigmoid function as a nonlinear convergence factor to guide the seagull search process, so that the algorithm maintained a stronger global optimization ability in the early stage and converged faster in the later stage. In the seagull rushing stage, it introduced the idea of Taboo search, so that the seagulls always moved to the area with higher confidence, and the optimal position continued to change in one iteration, which improved the optimization accuracy. After that, it used the golden sine mechanism to guide the population position to update, which narrowed the search range, and improved the local optimization ability. Finally, this paper used 12 test functions and CEC2014 function set to test the improved algorithm, and the results prove that the improved seagull algorithm has better convergence speed and accuracy than original algorithm and other algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the global exploration and local development performance of the arithmetic optimization algorithm ( AOA), this paper put forward a multi-head inverse series arithmetic optimization algorithm ( SFG-AOA) with activation mechanism . The activation mechanism strategy was a probabilistic evolution mechanism based on the sigmoid function based on the operator position update level. The activation factor was order to ensure that the calculation was inherited from the parent's position information while dynamically adjusting the operator optimization step length to expand new solutions and global exploration performance. It introduced the backward learning and gray wolf information feedback mechanism based on fitness optimization to improve the algorithm development stage, and improved the optimization accuracy. It appropriately modified the nonlinear convergence curve coefficient MOP to construct a random adjustment strategy. Finally, numerical experiments show that the differences in the improvement effects of different strategies. [ABSTRACT FROM AUTHOR]

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.)

Copyright of Light Industry Machinery is the property of Light Industry Machinery 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.)

Domain Gene rate Algorithm (DGA) was frequently used by malicious services to evade domain detection.In view of the strong concealment of DGA domain, the slow detection speed and the poor real time pe rformance in existing detection methods, a DGA domain detection method based on Deep Independ ently Recurrent Neural Network (Deep-IndRNN) was proposed by using deep learning.In this method,do main was firstly vectorized by using the Bag-of-Words (Bo W) model. A lso, the inter-character features of domain were extracted by using Deep-IndRNN. Furthermore, domain was classified and exported by using Sigmoid function.As the main characteristics of the method, a multi-sequence input of Deep-IndRNN was stitched into a single vect or input, and cyclic processing was replaced by single-step p rocessing. Mean while, combined with the characteristics of Deep-IndRNN which could save longer memory, it not only effectively released system resources such as GPU and CPU occupied by deep learning, but also greatly improved training and detection speed under the premise of ensuring higher accuracy and precision.Experimental re sults show that the DGA domain det ection method based on Deep-IndRNN has high accuracy and precision in the det ection task.Compared with similar detection methods, such as DNN, CNN, LSTM, Bi LSTM and CNN-LSTM-Concat, the method proposed in this paper can significantly improve the training and detection speed,and is effective and feasible in practice. [ABSTRACT FROM AUTHOR]

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.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT 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 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 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 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.)

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.)

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.)

Impulse noise is extremely harmful to human health. Hence, great progress has been achieved in active noise control (ANC), but most is about the noise control of Gaussian stable distribution, the non-Gaussian impulse noise control research is stiil in an exploratory stage. In this paper, the trends of impulse noise active noise control algorithms developed at home and abroad are analyzed, active noise control of symmetric ɑ stable (SɑS) distribution impulsive noise is studied. An algorithm based on minimizing the squared sigmoid transformation of error signal is proposed. The convergence condition of this algorithm is also analyzed. It does not need the parameter selection and thresholds estimation. Computer simulations are carried out to validate this algorithm. Simulation results prove the effectiveness of the algorithm. Compared to the previous FXlogLMS algorithm, this algorithm has good convergence and stability. According to the Sigmoid function expression, further experimental statistical analysis about the best match relationship of m-value and the alpha value wiil be the next target. [ABSTRACT FROM AUTHOR]