The difference in the statistical distribution between the training images and the test images reduces the performance of the steganalysis model, and this sample mismatch phenomenon makes it difficult to improve the accuracy of the detection model. To solve this problem, this paper proposed a deep-learning image steganalysis method based on generalized Gaussian distribution feature clustering. Firstly, a generalized Gaussian distribution is used to fit the coefficients of the image and extract statistical distribution features; Then, the extracted features are clustered to obtain corresponding category labels to achieve sample pre-classification; Finally, different subclasses of samples are used to train the network to achieve more reliable steganalysis. Experimental results show that this scheme can effectively divide images with similar statistical distributions into the same subcategory, reduce the impact of inconsistent statistical characteristics between the training image and the detection image on the steganalysis network, and improve the accuracy of steganalysis.
The operation, maintenance, and decommissioning of nuclear facilities carry high risks and are exposed to high levels of radiation. It is crucial to ensure the safety of personnel involved in nuclear facility decommissioning. When workers work in the radiation area, it is necessary to carry out radiation protection through path planning. Considering the complexity and variability of the environment in the operation process, we designed a new dose minimum path planning algorithm based on the multi-layer grid. Based on this algorithm, the computational complexity of path planning in complex scenarios is greatly reduced, ensuring the optimal dose while reducing the computational time of the algorithm. The simulation results show that the dynamic minimum dose path planning method can effectively ensure the safety of staff during the decommissioning of nuclear facilities.
The electrocardiogram (ECG) signal is vulnerable to being interfered with some unknown noises in the acquisition process due to their low frequency and amplitude, which leads to the loss of significant information in the signals. Recent deep learning models have achieved encouraging results in denoising, however, the generalization ability of the model is not robust to various noises, and the gradient difference between the denoised signal and the original signal is always ignored. In this paper, we propose a deep learning denoising method based on half instance normalization (HIN) block and gradient difference max (GDM) loss function, which includes two stages. In the first stage, we input the noisy ECG signal to obtain the denoised signal. In the second stage, we reconstruct the denoised signal by fusing the preliminary results of the first stage and correct the waveform distortion caused by the first stage denoising, to reduce the loss of information. A novel loss function is also proposed, which can consider the difference between the slope of the denoised ECG signal and the clean ECG signal. The experimental results on MIT-BIH databases show that our method reaches optimal performance both in signal-to-noise ratio (SNR) and root-mean-square error (RMSE).
For the networked linear switching system, a minimal conservative Kalman filter design method is proposed in the network transmission environment with multiple packet losses of both measurement and switching signal packets. Both the measurement signal and the switching signal transmitted in the same packet may be lost through the network. The random multiple packet dropout phenomenon is assumed to be subject to the Bernoulli random binary distribution. A novel Kalman filter is designed to estimate the state values by minimizing the filtering error covariance. Then, the desired Kalman gain matrices are derived, which is a novel switched Kalman gain matrix containing a random change of switching rate. In addition, the stability of the Kalman filter is analyzed in this paper. Finally, simulation results show the effectiveness of the proposed filter.
Traffic light detection is a part of modern intelligent driving, which affects the driving safety of smart cars. However, the object of traffic lights is small and the actual road conditions are complex. The classic object detection algorithm cannot achieve a good detection effect on small signal lights. For this situation, This paper proposes an improved yolov5 model. Because the object of the traffic light is small, the feature extracted by the P2 layer with a small receptive field is introduced. Inspired by the MOAT model, a C3_MOAT structure is proposed. At the same time, a structural re-parameterized RepConv module is added to the Head part of YOLOV5 to improve the feature extraction of the model. ability. Through the above improvements, the ability of the model to detect signal light objects is improved. Experiments show that the improved algorithm improves the map@.5 and map@.5:.95 indicators in the Bosch dataset by 2.6% and 2.7% respectively, and in the S2TLD dataset map@.5 and map@.5:.95 Indicators were increased by 2.2% and 1.6%. Compared with the YOLOV5 network, it has a good improvement.
