Job Shop Scheduling (JSP) and Flow Shop Scheduling (FSP) represent typical production scheduling problems. This paper delves into a JSP variant, Re-entrant Job Shop Scheduling (RJSP) with infinte buffers, introducing reentrance where certain jobs revisit machines at multiple steps. This paper devise a re-entrant job shop scheduling model with infinite buffers, aiming to minimize maximum completion time. Additionally, this paper propose an improve genetic algorithm for RJSP resolution, employing diverse initial solution generation methods and efficient crossover and mutation operators.
In this paper, we propose a low complexity method to eliminate random object motion in real time. This method is based on the radar system composed of AD8302 and phase shifter. RNN is used to predict the output voltage signal of AD8302, and the phase difference caused by motion is predicted and compensated in real time according to the relationship of phase voltage, so as to obtain vital signs. At the same time, we also modify the phase voltage curve of AD8302 to increase the accuracy of eliminating random body motion. The experiment shows that the method can eliminate the linear motion signal and retain the sinusoidal signal of the loudspeaker in the case of the combination of linear and sinusoidal motion, which greatly validates the feasibility of the method to eliminate human motion and extract vital signs.
A low-complexity FMCW-SAR motion target imaging scheme has been proposed. This scheme consists of an FMCWSAR system and a moving object detection method. The FMCW-SAR system uses an equivalent virtual array to increase the number of transceiver antenna pairs, thereby improving radar azimuth resolution and the use of a PLL structure to improve signal linearity in FMCW radar. This hardware design improves radar imaging performance while reducing complexity. The motion target detection method controls the virtual array components to monitor the motion targets in real-time during the process of motion target monitoring. In the subsequent signal processing, signal interpolation is used to fill the signal used for imaging processing. The experiment shows that this method can effectively and accurately image the detected targets and has good time resource utilization.
KEYWORDS: Video, Detection and tracking algorithms, Video surveillance, Feature extraction, Cameras, Video compression, Data conversion, Neural networks
A keyframe is a crucial image frame used to describe a shot, and the use of keyframe technology can significantly reduce the amount of data for video retrieval. For example,video-on-demand, face recognition under the camera, key lens retrieval of medical images, etc. Aiming at the problems in the current video keyframe extraction process that the extraction accuracy is low and cannot meet the real-time performance, this paper proposes a real-time video keyframe extraction algorithm CTM-NN based on the inter-frame difference method combined with clustering and neural network. The algorithm uses the inter-frame difference method based on the set threshold, HOG plus HSV first-order moment feature extraction algorithm, and uses the K-means++ clustering algorithm to finally train its own ResNet-50 model, aiming to accurately and efficiently extract real-time video Keyframes. In order to verify the algorithm proposed in this paper, experiments were carried out in the finished news video, landscape video, and real-time concrete mixing video. The experimental results show that the method proposed in this paper can meet the extraction accuracy and meet the keyframe extraction speed of the real-time video so that it can save the keyframes, automatically label while maintaining the time sequence. All in all, the CTM-NN algorithm proposed in this paper has achieved good results in the extraction and storage of real-time video keyframes
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