To meet the needs of the guidance of algorithm search in a complex environment, the optimality of global path in a static environment, and the security of real-time obstacle avoidance in a dynamic environment for path planning of cultural and tourism service robots, an algorithm based on the fusion of improved A * algorithm and Dynamic Window Approach is proposed. Firstly, based on the traditional A * algorithm, evaluation function are improved to improve the algorithm's search directionality to a certain extent; Secondly, the concept of safe distance is introduced, and a cubic broken line optimization method is proposed, which eliminates redundant nodes and inflection points, and only retains necessary key path points, greatly reducing the number of inflection points and improving the smoothness of the path. Subsequently, a dynamic obstacle vertical distance cost function was added to the evaluation function of the dynamic window approach to effectively reduce conflicts and collision risks between robots and dynamic obstacles. Finally, the improved A * algorithm is integrated with the dynamic window approach, selecting critical path points as temporary target points for the dynamic window approach. The dynamic window approach is used in segments for local real-time path correction, ensuring the optimal global path and avoiding collisions with unknown obstacles, ultimately achieving a safe and fast destination.
The assembly process is one of the most important spots in the process of product production. A reasonable assembly sequence can not only reduce the assembly workload but also reduce the manufacturing cost of products. Therefore, this paper proposes a product assembly sequence planning method based on the semantic process. First of all, a research framework of product assembly sequence planning based on semantic process knowledge was constructed by analyzing the requirement of product assembly sequence planning for assembly process semantic knowledge. Secondly, an assembly semantic process knowledge information model was proposed. Based on this, assembly sequence planning ontology and SWRL (Semantic Web Rule Language) assembly semantic rules were constructed in Protege software. Thirdly, an iterative modification method for inferring the sequence of an atypical sub-assembly group was designed. Finally, taking the assembly sequence planning process of the drive axle of construction machinery as an example, the construction method of the assembly semantic process knowledge information model and the assembly sequence planning decision technology in this thesis were applied to verify the reliability of the information model.
The molded case circuit breaker(MCCB)is an indispensable basic electrical component within industrial power systems.In order to enhance the visual recognition efficiency of the operational status information of MCCB,an improved design method for the open/close symbols of MCCB was proposed.The analysis of the open/close symbol design could lead to the summarization of reasons for the limited visualization of operational status information.By conducting eye-tracking experiments and utilizing evaluation indicators such as first entry time,first fixation duration,total fixation duration,hot spot distribution,fixation progression,and subjective questionnaires,the design issues of the current operational status information of MCCB were comprehensively analyzed.Based on the experimental conclusions,the key points for improving the size and color of the open/close symbols were proposed.The superiority of the improved open/close symbol design was verified through a comparative analysis of eye-tracking experimental data before the improvement.Furthermore,the research on the improvement of open/close symbols could serve as a reference for enhancing the visualization effect of operational status information in similar electrical products.Eye-tracking experiments could also provide key technical support for the analysis and enhancement of operational status information visualization in MCCB.
Addressing the absence of an early warning mechanism for image privacy leakage in current social media platforms,an optimization scheme for image privacy target detection based on the YOLOv8 model was proposed,thus reducing the risk of privacy leakage when users share images.Building upon the YOLOv8 baseline model,the bottleneck transformer(BoT)module was integrated into the backbone network,capturing global contextual information and modeling long-range dependencies of targets.Concurrently,the bidirectional feature pyramid network(BiFPN)structure was introduced to improve the neck network,facilitating the deep fusion of multi-scale features.On this basis,based on the deep transfer learning method,the YOLOv8 pre-training model was fine-tuned and trained to achieve automatic detection of image privacy.A privacy image dataset was constructed using the LabelImg annotation tool,and the common YOLO series model was compared with the improved YOLOv8 in the transfer learning mode.The results demonstrated that YOLOv8 exhibited strong performance in the baseline model,while the F1 and mAP@.5 values of the improved model proposed in this study reached 0.885 and 0.927,respectively,reflecting a 4.0%and 3.4%increase compared with YOLOv8.This significantly enhanced detection accuracy was well-suited for image privacy detection in various application scenarios.
With the continuous increase of motor vehicles,the road traffic environment has become increasingly complex,particularly due to changes in light conditions and complex backgrounds that can interfere with the accuracy and precision of target detection algorithms.Meanwhile,the diverse shapes of targets in road scenes can pose challenges to the detection task.In response to these challenges,a method named YOLOv8n_T was proposed.Building on the YOLOv8 skeleton network,it incorporated a D_C2f block utilizing deformable convolution to enhance feature learning for targets under complex backgrounds,making it more adaptable to the diverse and complex scenarios of road targets.Furthermore,the model incorporated a dual routing attention module to query adaptively and remove irrelevant regions,retaining only the most relevant regions.For small targets such as pedestrians and traffic lights on the road,a small target detection layer was added.Experimental results demonstrated that the proposed YOLOv8n_T could significantly enhance the precision of target detection in road scenarios,with an average precision increase of 6.8 percentage points compared to the original YOLOv8n and 11.2 percentage points compared to YOLOv5n on the BDD100K dataset.
