A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the ...teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.
Seismic activity has complexity and randomness, and its temporal and spatial distribution has complexity, stage, level, and inheritance. The study of the temporal and spatial distribution ...characteristics of seismic activity is of great significance to the understanding of the law of seismic activity, such as the law that the time series of seismicity in the seismic belt is consistent with the complexity of geographical structure, the prediction of seismic risk, and other research related to earthquake. This article selects the seismic data catalog of the whole Eurasian seismic belt as the research object. Based on the characteristics of the seismic geological environment and tectonic environment characteristics, the multifractal analysis method is used for the seismic data of the seismic activity directory. The results show that the seismic activity of seismic zones has obvious multifractal structure of complex in time series and spatial scales, which can well reveal the seismic characteristics of seismic activity in time and space. In terms of time series, the study area
decreases significantly with time and energy before the occurrence of a large earthquake, and the time series of seismic activity in the study area is highly complex and highly correlated with the geological structure. Spatially, the spatial distribution of seismic intensity in the study area is infinite and sparse, showing the characteristics of infinite clustering. Therefore, it can reveal the basic rule of seismic activity effectively and lay a certain theoretical foundation for earthquake prevention and control in this seismic zone.
We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA’s public data platform, and ...preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study’s accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach.
As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease ...screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.
The surgical navigation system enhances surgical safety and accuracy by providing precise guidance. However, traditional pose estimation algorithms lack real-time performance and accuracy. To address ...this issue, a multi-average Long Short Term Memory (LSTM) prediction network is designed to maintain sensitivity in estimating the position of surgical instruments and track their random motion trends. Additionally, the spatial coordinates of positioning markers are applied back to the imaging plane, reducing the recognition range and improving algorithm running speed. Experimental results show that the average time of estimation is less than 1ms while ensuring the prediction effect.
Stereo matching is the operation of obtaining the parallax value between two images by matching all the corresponding image points in the two images, thus obtaining the dense parallax image between ...the two images. How to obtain accurate disparity images has always been a key point in the field of stereo vision. Presently, in the research of 3D reconstruction technology based on binocular stereo vision, the main research direction of domestic and foreign scholars is to improve the efficiency and accuracy of stereo matching, and there is research literature on soft tissues. This paper proposes an improved stereo matching algorithm based on joint similarity measures and adaptive weights. The algorithm improves the matching cost calculation based on the joint similarity measure to fit the color image of the heart soft tissue. At the same time, the algorithm uses the idea of graph cutting to improve the adaptive weight. The experimental results show that both the improved joint similarity measure and the improved adaptive weight can effectively reduce the mismatch rate. In addition, the corresponding matching effect is better than using only one of the improved joint similarity measures.
Cervical cancer (CC) is a malignant solid tumor, which is one of the main causes of morbidity and mortality in women. Persistent High-risk human papillomavirus (hrHPV) infection is closely related to ...cervical cancer and autophagy has been suggested to inhibit viral infections. miRNAs have been reported to regulate autophagy in many solid tumors with many studies implicating miR-224-3p in the regulation of autophagy. In this study, we performed a miRNA microarray analysis on CC tissues and found that a large number of miRNAs with differential expressions in hrHPV-infected tissues. We identified miR-224-3p as a candidate miRNA selectively up regulated in HPV-infected tissues and cell lines. Further analysis revealed that miR-224-3p regulates autophagy in cervical cancer tissues and cell lines. While the overexpression of miR-224-3p inhibits autophagy in HPV-infected cells, knocking down endogenous miR-224-3p increases autophagy activity in the same cells. In addition, we found that miR-224-3p directly inhibits the expression of autophagy related gene, FAK family-interacting protein of 200 kDa (FIP200). In summary, we found that miR-224-3p regulates autophagy in hrHPV-induced cervical cancer cells through targeting FIP200 expression.
Education through the online platform has gained popularity. While the online course keeps increasing, its benefit does not increase in a proportional way. Personalized recommendation systems can ...well solve this problem by mining users' interests and preferences. Aiming at the cold start problem, this paper improves the collaborative filtering algorithm by combining the user score and project attribute characteristics of the online course platform. The network communication technology is used to obtain the user ratings and project attribute data to verify the feasibility of the recommendation algorithm, adjust the parameters in the model, and compare the accuracy of the algorithm. The designed algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the platform. The improved algorithm solves the cold start problem compared with the traditional algorithm with a significantly improved prediction accuracy. The scheme can also be modified according to the changes in user preferences and can achieve good real-time recommendation effect.
Recently, the Vision Transformer (ViT) model has been used for various computer vision tasks, due to its advantages to extracting long-range features. To better integrate the long-range features ...useful for classification, the standard ViT adds a class token, in addition to patch tokens. Despite state-of-the-art results on some traditional vision tasks, the ViT model typically requires large datasets for supervised training, and thus, it still face challenges in areas where it is difficult to build large datasets, such as medical image analysis. In the ViT model, only the output corresponding to the class token is fed to a Multi-Layer Perceptron (MLP) head for classification, and the outputs corresponding to the patch tokens are exposed. In this paper, we propose an improved ViT architecture (called ViT-Patch), which adds a shared MLP head to the output of each patch token to balance the feature learning on the class and patch tokens. In addition to the primary task, which uses the output of the class token to discriminate whether the image is malignant, a secondary task is introduced, which uses the output of each patch token to determine whether the patch overlaps with the tumor area. More interestingly, due to the correlation between the primary and secondary tasks, the supervisory information added to the patch tokens help with improving the performance of the primary task on the class token. The introduction of secondary supervision information also improves the attention interaction among the class and patch tokens. And by this way, ViT reduces the demand on dataset size. The proposed ViT-Patch is validated on a publicly available dataset, and the experimental results show its effectiveness for both malignant identification and tumor localization.
The Eurasian seismic belt is the second largest seismic zone in the world. It has numerous seismic activities which have enormous impact on human’s life. It is of importance to study the ...spatio–temporal characteristics of the Eurasian seismic belt. As we can learn from previous studies, fractal and fractal dimension theories can be used to study seismic activities. In general, the temporal sequence and the spatio sequence of earthquakes both exhibit the fractal structures. When huge earthquakes occur, the fractal dimension of the temporal sequence is very low. As the days went by, the value of fractal dimension fluctuates. The seismic data from 1973 to 2014 in The Eurasian seismic belt are selected as the object of this study. Based on the single fractal model, the complex structure of the seismic spatio and temporal distribution in the seismic belt is quantitatively evaluated. Results show that over time the time interval of the seismic activity shortened, and the seismic activity on the Eurasian seismic belt has a nonlinear structure and self-similar characteristics. From the perspective of space, the fractal dimension of the Eurasian seismic belt tends to grow with time, and it also has a nonlinear structure and self-similar characteristics. When the temporal unit is set as 1 year, the accumulation and release of energy are probably periodic: the minor period might be about 8.5 years, and the major period might be about 13 years.
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