Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity be...
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Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform(SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.
Compared with conventional object detection, remote sensing images are taken from the air. The angle of view is not fixed and the object direction, scale which compared with conventional object detection algorithm are...
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Compared with conventional object detection, remote sensing images are taken from the air. The angle of view is not fixed and the object direction, scale which compared with conventional object detection algorithm are quite different. These factors lead to the object detection in remote sensing images difficult. To solve the above problems, this paper proposes an improved remote sensing object detection method based on Faster-RCNN algorithm. Using online difficult example mining technology,feature pyramid structure, Soft-NMS technology, and RoI-Align technology to enhance the capabilities of Faster-RCNN in small object detection task in remote sensing images. The algorithm in this paper was evaluated on the RSOD-Dataset, compared with the original Faster-RCNN algorithm, the proposed algorithm improves the detection accuracy and training convergence speed,which shows that these improvements are of great significance to the object detection algorithm of remote sensing images.
This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systemscontrolled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional（LKF） with more i...
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This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systemscontrolled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional（LKF） with more information of time-varying delay is constructed. Then, by applying the Wirtinger-based integral inequality and the extended reciprocally convex combination lemma（RCCL）, a new synchronization criterion for time-varying delay is obtained, and a less conservatism corollary for the constant delay is established by weakening some terms of LKF. Finally, a numerical example is given to show the better performance of the proposed criteria.
In the field of face recognition and analysis, eye state detection is an essential step, which is the prerequisite and breakthrough of drowsiness estimation and auxiliary driving. This paper presents an eye state dete...
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In the field of face recognition and analysis, eye state detection is an essential step, which is the prerequisite and breakthrough of drowsiness estimation and auxiliary driving. This paper presents an eye state detection method based on Weight Binarization Convolution Neural Network（WBCNN）. The weight of the network is constrained by binarization, which can limit the weight to 1 or-1, reducing the power dissipation and internal storage considerably. The human eye state features which can be extracted by convolution neural network effectively, and binary network not only contributes to reducing the storage size of the model, but also accelerates the computation. Experiments on eye state detection were conducted on the Closed Eyes in the wild（CEW） and FER2013 Databases, from which the results show that our method achieved average test accuracy of 97.41%on CEW. We used the FER2013 facial expression database for pre-training, which can make up for the lack of CEW training samples. The computational speed of non-binary is slower than binary network. Moreover, less storage capacity is required by our method.
In this paper, the object detection technology based on deep learning is applied to the assembly process of space power station simulation, which can provide assistance for the attitude adjustment and navigation of th...
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In this paper, the object detection technology based on deep learning is applied to the assembly process of space power station simulation, which can provide assistance for the attitude adjustment and navigation of the aircraft through the detection of some components. Firstly, the 3 D modeling and rendering of the space power station are carried out, on which the image dataset is collected and established. Then, based on the YOLOv3 network, we improve the structure of feature *** fusing the information of shallow and deep features, we can improve the detection ability of the network for different scale *** and quantitative experimental results show that the improved YOLOv3 network can accurately and effectively detect the key components of the Space solar power station.
With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to stu...
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With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to study this issue. These methods capture partial information of single-cell sequencing data, and obtain the different performance on the same data set. Combining these different results into one can improve the accuracy and validity. Here, we proposed ECBN, Ensemble Clustering based on B ayesian Network. ECBN can ensemble several different results of state-of-the-art single cell clustering methods, such as Seurat, CIDR, SC3 and t-SNE+k-means, and generate a more optimal clustering result through Bayesian network. Experiments are carried on the 5 single cell data sets and compared with 4 individual single cell clustering methods and 3 integrative *** size of experiment data sets ranges from 822 to 3605 and the results show that our method can achieve good ***, ECBN can also use the graphical regularization to lighten the limitation which is generated by the different basis results.
Inconsistent feeder impedances in microgrids easily lead to uneven reactive power output of the inverter. This paper proposes an improved current-based droop control strategy for this problem. The droop coefficient is...
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Inconsistent feeder impedances in microgrids easily lead to uneven reactive power output of the inverter. This paper proposes an improved current-based droop control strategy for this problem. The droop coefficient is adjusted by the inverter capacity ratio, and the reactive current measured at the point of common coupling（PCC） is used as a reference value for improved differential control to compensate the voltage and control its reactive power output. It is found that the improved droop control has good adaptive ability and stability. The simulation results also prove the correctness and feasibility of the proposed strategy.
In order to solve the problem that ultrasonic ranging is difficult to obtain the first wave, an ultrasonic ranging system based on cross-correlation method is designed. In this system, the signal generating unit is ma...
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In order to solve the problem that ultrasonic ranging is difficult to obtain the first wave, an ultrasonic ranging system based on cross-correlation method is designed. In this system, the signal generating unit is mainly responsible for transmitting high frequency sound waves, and the data acquisition unit is mainly responsible for collecting and storing the sound waves. Then,according to the time delay between the collected acoustic signals, the time difference between the signals received by the signal acquisition unit is obtained, so as to measure the distance.
The regenerative chatter during milling seriously affects the stability of the *** paper proposes a method based on Lyapunov-Krasovski functional analysis for the stability of the milling ***,the mechanism analysis of...
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The regenerative chatter during milling seriously affects the stability of the *** paper proposes a method based on Lyapunov-Krasovski functional analysis for the stability of the milling ***,the mechanism analysis of the milling process is performed,then the state-space equation of the time-varying delay system caused by the regeneration effect is ***,based on the model of a time-delay system,a stability criterion is developed by constructing an augmented LyapunovKrasovski functional(LKF) and using auxiliary function inequality with reciprocally convex combination ***,the validity of the method is verified through an example,and the milling stability domain lobe diagram with a parameter combination of spindle speed-cutting depth is obtained which provides operational guidelines to guarantee a stable vibration-free process.
This paper studies the dissipative problem of neural networks with time-varying delay and external disturbance. A suitable augmented Lyapunov-Krasovskii functional(LKF) is constructed by taking full advantage of the d...
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This paper studies the dissipative problem of neural networks with time-varying delay and external disturbance. A suitable augmented Lyapunov-Krasovskii functional(LKF) is constructed by taking full advantage of the delay information of the system and the conditions of the excitation function. Then by employing auxiliary function inequalities, the reciprocally convex combination and a vector zero-value method to deal with the derivative of the LKF, a less conservative delay-dependent dissipative criterion is obtained. Finally, a numerical example is given to show the effectiveness of this criterion.
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