In order to analyze the influence of uncertain factors on power system, PCA (polynomial chaos approximation) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. A probabilistic power flow method based on DDPCA (data driven polynomial chaos approximation) was proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow was calculated based on Gaussian samples, and the approximation coefficient was solved according to the power flow results and weights, and then the statistical characteristics of the random output variables were obtained. The proposed method was compared with the point estimation method, and the effectiveness of the proposed method was verified by the results of three examples.

Acoustic wave is one of the important means to realize the fast and accurate transmission of downhole information. The drill string serving as information transmission channel has obvious periodic pipe structure characteristics. In order to study the frequency spectrum quality of drill string channel in actual working conditions, a multilevel evaluation method of acoustic frequency spectrum based on improved radar chart was provided. The influence of axial tension stress on the frequency spectrum characteristics of acoustic wave in pipe structure was studied and analyzed by the method, and the criteria for judging the frequency spectrum characteristics were established. Practice shows that, the existence of tensile stress has a significant impact on the acoustic frequency spectrum and passband characteristics. With the increase of tensile stress, the evaluation index of frequency spectrum characteristic increases first and then decreases, and there exists a peak point with the best frequency spectrum characteristic. This method can comprehensively reflect the influence of different factors on the frequency spectrum characteristics of acoustic wave in drill string channel, and comprehensively evaluate the distortion degree of frequency spectrum characteristics from the multiple perspectives. It provides a basis for optimizing carrier frequency and designing the installation position of repeater when dealing with multi-factors interference in the field application of downhole information acoustic transmission technology.

The sedimentary environment of Cardium formation in western Canada basin is the shallow sea coastal face. The conventional oil and gas reservoir was formed from the conglomerate and sandstone deposited on the coastal muddy seafloor. The unconventional tight oil reservoir was formed from the low-permeable argillaceous sandstone around the conglomerate. Faced with the coexistence of different types of resources, the development of SINOPEC oversea blocks with benefit needs to be realized. Firstly, the member A of Cardium formation was divided into three types according to the core observation and experiment test. Then the development features of different types of reservoirs were compared based on the production dynamic data. Thirdly, the geological knowledge was verified via single well theoretical models. Finally, the development potential of different types of reservoirs in the interest block were evaluated. The development strategies of different types of reservoirs were put forward. The results show that the member A of Cardium formation can be classified into type I conglomerate reservoir, type Ⅱ conglomerate reservoir and type III tight sandstone reservoir according to the lithology differences. From type I to type Ⅲ, the reservoir physical properties gradually weaken. The development way changes from vertical well development to multistage fracturing horizontal well development. The productivity controlling factors gradually complicates. Currently, type I and II reservoir in the interest block have a limited potential, which can be further released through reperforations on some old wells. Type III reservoir has a certain potential, which can be effectively released through deployment of large numbers of horizontal wells.

To address the issues of low accuracy in rolling bearing life prediction and the difficulty of constructing health indicators, a bearing remaining life prediction model based on ASFF (adaptively spatial feature fusion) and AAKR (auto associative kernel regression) combined with CNN (convolutional neural networks) and BILSTM (bi-directional long-short term memory networks) was proposed. Firstly, the multidimensional features were extracted in the time domain, frequency domain, and time-frequency domain, and the sensitive features were screened using monotonicity and trend. Secondly, the sensitive features were feature fused using ASFF-AAKR to construct the health indicators. Finally, the health indicators were inputted into CNN and BILSTM to realize the life prediction of rolling bearings. The results show that the constructed life prediction model is better than other models, and the method has lower error and higher life prediction accuracy.

To solve the problem of large steady-state error and poor parameter robustness of MPFC system predicted by traditional model of PMSM (permanent magnet synchronous motor), a multi-voltage vector selection method based on stator flux prediction error vector analysis was proposed. Firstly, the multi-voltage vector selection criteria for determining the region where the flux error vector is located were established by dividing the sectors according to the axis in the two-phase stationary coordinate system. Then, the predicted value of stator flux and the value function of stator flux in two-phase stationary coordinate system were used to calculate the action time of each voltage vector. In addition, a discrete sliding mode stator flux observer considering the mismatch of resistance and inductance parameters was designed, which further improves the parameter robustness of the system. Finally, the effectiveness and feasibility of the proposed predictive stator flux control method are verified by simulation and experiments. The proposed method still has good steady-state performance under the condition of system parameter mismatch, and significantly reduces the stator flux and electromagnetic torque ripple.

When a train set operates at high speeds, significant vibration and noise are generated in the outer door due to external airflow and road surface excitation, which directly impacts ride stability and train operation safety. To address the significant vibration of the thin-walled backplate structure of the outer door at the first five modal frequencies, particle damping was applied to mitigate vibration and noise. Firstly, a discrete element model of the particle damper for the outer door was established to analyze system energy consumption through simulations under various filler parameters. Based on these simulation results, an optimized design of the filler parameters was conducted, and experiments were performed to assess sound insulation with each filler material. The results show that the installation of particle dampers achieves a sound insulation improvement of 5.33 dB, significantly optimizing the radiated noise from the outer doors. These findings demonstrate the effectiveness of particle damping for vibration and noise reduction in the outer door of a moving train set.

Manual grinding with hand-held tools generates a lot of dust, currently, it is common to use negative pressure trapping device to limit the dust in the confined space, and make the dusty airflow directed through the dust removal device to be purified to solve the problem of dust dissemination. In order to solve the problem of ineffective ventilation and protection facilities in an aluminum grinding workshop, which led to the uncontrolled spread of dust between operations, a structural design of a “U-shaped slit” dust collection device for the grinding process of small aluminum parts was proposed.Finite element simulation and analysis method was used to investigate the dust prevention and control effect of the device. The results show that: when the device on both sides of the slit width is 2 cm, the rear side of the slit width is 5 cm, the air volume is 3 600 m³/h, and the internal airflow channel structure is an asymmetric slanting closure structure, the control surface of the wind speed is more uniform, average wind speed of personnel breathing zone reaches 1.3 m/s, in the same time, the noise of the internal airflow channel is relatively small and the dust particle trapping effect is better. It is concluded that the size of the device's “U-shaped slit” and the internal airflow channel structure design can effectively capture the dust generated during the sanding process, reduce the concentration of dust in the workplace, and provide a reference for the protection of dust in the sanding place.

Road feeling simulation is one of the key technologies for the development of the steer-by-wire system. In order to improve the quality of the road feeling simulation, the road feeling torque of the steer-by-wire system was designed based on the principle of steering resisting torque generation of the traditional steering system and the method of calculating the dynamics model. In order to reduce the torque pulsation of the road feeling motor caused by uncertain system parameters and sensor noise, a vector control strategy for the road feeling motor based on the active disturbance rejection control algorithm was designed, and the control parameters in the active disturbance rejection control algorithm were optimized using the particle swarm optimization algorithm. Based on the steering wheel middle position maneuvering stability test and the steering-effort test, the road feeling simulation effect was verified. The results show that the active disturbance rejection control based on particle swarm optimization algorithm has strong adaptability, and can effectively realize the accurate simulation of road feeling. It can effectively reduce the burden on the driver. The relevant research can provide a reference for the design of the steer-by-wire.

BCI (brain computer interface) is one of the important research methods in the fields of brain cognition, brain medicine and brain-like research,where the precise implantation of microelectrodes is a necessary prerequisite and important guarantee. With the rapid development of robotics, machine vision and artificial intelligence, surgical robots are gradually used in brain-computer interface implantation surgery. To meet the demand for precise implantation of microelectrodes in the somatosensory and cerebral motor cortex of SD(Sprague-Dawley) rats, a vision-guided precision implantation system for brain-computer interface microelectrodes was presented. Based on the method of machine vision to identify the key points of the rat skull, the coordinate system was established to obtain the point cloud information of the rat skull, to realize the high-precision identification and localization of the target points, and to guide the actuator to complete the electrode implantation operation. Through model analysis and animal experiments, it has been demonstrated that the implantation system can accurately identify surgical targets on the subjects, guide the actuator to swiftly penetrate the skull, and accurately and stably implant the electrodes into the target area, which effectively improves the accuracy of microelectrode implantation.

In order to address issues such as high noise, low brightness, and blurred details in low-light conditions, a new algorithm named UMDCEAD-NET, integrating zero-reference depth curves for low-light image enhancement and denoising, was developed. The algorithm's design was initially centered around a feature extraction network, employing a U-Net architecture as the backbone network. To enhance the feature extraction capabilities and preserve more detailed image information, Mobile-Net was integrated into the downsampling phase of the U-Net backbone. Subsequently, to address the issue of inadequate lighting and pixel-level image degradation, the extracted features underwent iteration using depth curve estimation (LE-curve), in conjunction with depth separable convolution, which served to reduce the network model's parameter count. Furthermore, five non-reference loss functions were engineered to bolster the algorithm's generalization capabilities and its retention of detail under varying lighting conditions. The enhanced image was then subjected to noise reduction in tandem with AD-NET(attentional denoising network), thereby diminishing the noise and aligning the image more closely with human visual perception. Experimental outcomes demonstrated that the proposed algorithm achieved an average PSNR (peak signal-to-noise ratio) of 22.29 on the public dataset Zero-DCE, which exceeded the performance of the Zero-DCE++ algorithm by 32%. Additionally, on the public dataset LOL, the algorithm attained an average PSNR of 21.15, outperforming the SGZ algorithm by 3%. These results indicate that the algorithm effectively mitigates noise in enhanced images, enriching the detail information in both dark and bright regions, and significantly improving image quality compared to other mainstream algorithms.