Addressing the challenges posed by high throughput of raw cotton and long impurity inspection duration in cotton mills， an improved YOLOv7 model incorporating lightweight modifications was proposed for impurity detection in raw cotton. Initially， redundant convolutional layers within YOLOv7 model were pruned， thereby increasing detection speed. Following this， FasterNet convolutional layer was integrated into the primary network to mitigate model computational load， diminish redundancy in feature maps， and consequently realized real-time detection. Ultimately， CSP-RepFPN （Cross Stage Partial networks with Replicated Feature Pyramid Network） was used within neck network to facilitate the reconstruction of feature pyramid， augment flow of feature information， minimize feature loss， and elevate the detection precision. Experimental results show that， the improved YOLOv7 model achieves a detection mean Average Precison of 96.0%， coupled with a 37.5% reduction in detection time on self-made raw cotton impurity dataset； and achieves a detection accuracy of 82.5% with a detection time of only 29.8 ms on publicly DWC （Drinking Waste Classification） dataset. This improved YOLOv7 model provides a lightweight approach for real-time detection， recognition and classification of impurities in raw cotton， yielding substantial time savings.

Knowledge Tracing （KT） is a fundamental and challenging task in online education， and it involves the establishment of learner knowledge state model based on the learning history； by which learners can better understand their knowledge states， while teachers can better understand the learning situation of learners. The KT research for learners of online education was summarized. Firstly， the main tasks and historical progress of KT were introduced. Subsequently， traditional KT models and deep learning KT models were explained. Furthermore， relevant datasets and evaluation metrics were summarized， alongside a compilation of the applications of KT. In conclusion， the current status of knowledge tracing was summarized， and the limitations and future prospects for KT were discussed.

With the continuous development of remote sensing technology， there has been a significant increase in the volume of remote sensing data. Providing accurate and timely remote sensing information recommendation services has become an urgent problem to solve. Existing remote sensing image recommendation algorithms mainly focus on user portrait， overlooking the influence of image content semantics on recommendation results. To address these issues， a remote sensing image recommendation method based on content interpretation was proposed. Firstly， an object extraction module based on YOLOv3 was used to extract objects from remote sensing images. Then， the location distribution vectors of key objects were integrated as image content information. Additionally， a multi-element user interest portrait was constructed and dynamically adjusted based on the user’s active search history to enhance the personality of recommendation results. Finally， the image content information was matched with the inherent attribute information of image and the user profile model to achieve accurate and intelligent recommendations of remote sensing data. Comparative experiments were conducted on real order data， to compare the proposed method with the newer recommendation method based solely on image attribute information. Experimental results show that the proposed method achieves a 70% improvement in the discrimination of positive and negative samples on the experimental data compared to the recommendation method considering user portrait. When using 10% training data with similar consumption time， the recommendation error rate decreases by 4.0 - 5.6 percentage points compared to the comparison method. When using 100% training data， the recommendation error rate decreases by 0.6 - 1.0 percentage points. These results validate the feasibility and effectiveness of the proposed method.

Medical image registration models aim to establish the correspondence of anatomical positions between images. The traditional image registration method obtains the deformation field through continuous iteration， which is time-consuming and has low accuracy. The deep neural networks not only achieve end-to-end generation of deformation fields， thereby speeding up the generation of deformation fields， but also further improve the accuracy of image registration. However， all of the current deep learning registration models use single Convolutional Neural Network （CNN） or Transformer architecture， and have the problems such as the inability to fully utilize the advantages of the combination of CNN and Transformer， resulting in insufficient registration accuracy， and the inability to maintain the original topology effectively after image registration. To solve these problems， a parallel medical image registration model based on CNN and Transformer — PPCTNet （Parallel Processing of CNN and Transformer Network） was proposed. Firstly， the model was constructed using Swin Transformer， which currently has the excellent registration accuracy， and LOCV-Net （Lightweight attentiOn-based ConVolutional Network）， a very lightweight CNN. Then， the feature information extracted by Swin Transformer and LOCV-Net were fully integrated by designing a fusion strategy， so that the model not only had the local feature extraction capability of CNN and the long-distance dependency capability of Transformer， but also had the advantage of being lightweight. Finally， based on the brain Magnetic Resonance Imaging （MRI） dataset， PPCTNet was compared with 10 classical image alignment models. The results show that compared to the currently excellent registration model TransMorph （hybrid Transformer-ConvNet network for image registration）， PPCTNet has the highest registration accuracy 0.5 percentage points higher， and the folding rate of deformation field 1.56 percentage points reduced， maintaining the topological structures of the registered images. Besides， compared with TransMorph， PPCTNet has the parameters reduced by 10.39×10⁶， and the computational cost reduced by 278×10⁹， which reflects the lightweight advantage of PPCTNet.

When the Unmanned Aerial Vehicle （UAV） cluster attacks ground targets， it will be divided into two formations： a strike UAV cluster that attacks the targets and a auxiliary UAV cluster that pins down the enemy. When auxiliary UAVs choose the action strategy of aggressive attack or saving strength， the mission scenario is similar to a public goods game where the benefits to the cooperator are less than those to the betrayer. Based on this， a decision method for cooperative combat of UAV clusters based on deep reinforcement learning was proposed. First， by building a public goods game based UAV cluster combat model， the interest conflict problem between individual and group in cooperation of intelligent UAV clusters was simulated. Then， Muti-Agent Deep Deterministic Policy Gradient （MADDPG） algorithm was used to solve the most reasonable combat decision of the auxiliary UAV cluster to achieve cluster victory with minimum loss cost. Training and experiments were performed under conditions of different numbers of UAV. The results show that compared to the training effects of two algorithms — IDQN （Independent Deep Q-Network） and ID3QN （Imitative Dueling Double Deep Q-Network）， the proposed algorithm has the best convergence， its winning rate can reach 100% with four auxiliary UAVs， and it also significantly outperforms the comparison algorithms with other UAV numbers.

Concerning the efficiency and security issues of automatic door, an safety and energy saving detection technology of automatic door based on Omni-Directional Vision Sensor (ODVS) was proposed. Firstly, on-site 360° panorama image around automatic door was collected timely by ODVS and preprocessed according to the detection requirements. Secondly, moving target was detected and tracked through Motion History or Energy Images (MHoEI) algorithm. Then, the behavior of pedestrian was analyzed according to the direction of motion and spatial position of the foreground object. Finally, in order to make automatic door secure, energy-saving and comfortable, the automatic door was controlled to open or close according to the behavior and state of pedestrian. At the same time, the number of people passing through the automatic door can also be worked out accurately. This technology can be directly used in intelligent monitoring and business survey. The experimental results indicate that the detection technology of automatic door can recognize the behavior of pedestrian around the automatic door, avoid the security risks of automatic door and improve the accuracy of people counting.

Abstract: A seam tracking algorithm based on multi-information sensor of vision and arc is proposed. The algorithm is applied to the automatic control process of MIG pulse welding quality. This paper captures different description information for the effective feature extraction and transmission with the vision sensor and the arc sensor, and can be used in multi-sensor information fusion algorithm for seam tracking. The vision sensor obtains image information by industrial CCD for the lateral deviation control of the weld torch, the arc sensor obtains current information by data acquisition card for the height deviation control of the weld torch, The two kinds of sensor information in a complementary way to integrate for the lateral and height corrective control of the welding process. Experimental results show that the proposed algorithm can improve the welding quality, and thus verify the algorithm efficiency and rationality.