In this paper,we propose an efficient method to construct energy-minimizing B-spline curves by using discrete mask method.The linear relations between control points are firstly derived for different energy-minimizati...In this paper,we propose an efficient method to construct energy-minimizing B-spline curves by using discrete mask method.The linear relations between control points are firstly derived for different energy-minimization problems,then the construction of B-spline curve with minimal internal energy can be addressed by solving a sparse linear system.The existence and uniqueness of the solution for the linear system are also proved.Experimental results show the efficiency of the proposed approach,and its application in 1 G blending curve construction is also presented.展开更多
为解决BERT(bidirectional encoder representations from transformers)编码器在掩码过程中人为引入噪音、掩码比例过小难以掩盖短交互序列中的项目以及掩码比例过大导致模型难以训练3个问题,提出一种更改BERT编码器掩码方式的对比学...为解决BERT(bidirectional encoder representations from transformers)编码器在掩码过程中人为引入噪音、掩码比例过小难以掩盖短交互序列中的项目以及掩码比例过大导致模型难以训练3个问题,提出一种更改BERT编码器掩码方式的对比学习方法,为模型提供3类学习样本,使模型在训练过程中模仿人类学习进程,从而取得较好的结果。提出的算法在3个公开数据集上进行对比试验,性能基本优于基线模型,其中,在MovieLens-1M数据集上HR@5和NDCG@5指标分别提高9.68%和10.55%。由此可见,更改BERT编码器的掩码方式以及新的对比学习方法能够有效提高BERT编码器的编码准确性,从而提高推荐的正确率。展开更多
With the recent increase in the utilization of logistics and courier services,it is time for research on logistics systems fused with the fourth industry sector.Algorithm studies related to object recognition have bee...With the recent increase in the utilization of logistics and courier services,it is time for research on logistics systems fused with the fourth industry sector.Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field,but so far,algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development.In this study,the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study,and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance.The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once)and Mask R-CNN(Regions with Convolutional Neuron Networks).Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second)and a recognition rate of 95%.In addition,there were significant improvements in the range of actual box recognition.The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.展开更多
基金Thanks for the reviewers’comments to improve the paper.This research was supported by the National Nature Science Foundation of China under Grant Nos.61772163,61761136010,61472111,Zhejiang Provincial Natural Science Foundation of China under Grant Nos.LR16F020003,LQ16F020005.
文摘In this paper,we propose an efficient method to construct energy-minimizing B-spline curves by using discrete mask method.The linear relations between control points are firstly derived for different energy-minimization problems,then the construction of B-spline curve with minimal internal energy can be addressed by solving a sparse linear system.The existence and uniqueness of the solution for the linear system are also proved.Experimental results show the efficiency of the proposed approach,and its application in 1 G blending curve construction is also presented.
文摘为解决BERT(bidirectional encoder representations from transformers)编码器在掩码过程中人为引入噪音、掩码比例过小难以掩盖短交互序列中的项目以及掩码比例过大导致模型难以训练3个问题,提出一种更改BERT编码器掩码方式的对比学习方法,为模型提供3类学习样本,使模型在训练过程中模仿人类学习进程,从而取得较好的结果。提出的算法在3个公开数据集上进行对比试验,性能基本优于基线模型,其中,在MovieLens-1M数据集上HR@5和NDCG@5指标分别提高9.68%和10.55%。由此可见,更改BERT编码器的掩码方式以及新的对比学习方法能够有效提高BERT编码器的编码准确性,从而提高推荐的正确率。
基金This work was supported by a grant from R&D program of the Korea Evaluation Institute of Industrial Technology(20015047).
文摘With the recent increase in the utilization of logistics and courier services,it is time for research on logistics systems fused with the fourth industry sector.Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field,but so far,algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development.In this study,the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study,and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance.The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once)and Mask R-CNN(Regions with Convolutional Neuron Networks).Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second)and a recognition rate of 95%.In addition,there were significant improvements in the range of actual box recognition.The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.
