We systematically investigate the mass spectrum,spatial configuration and magnetic moment of the ground and p-wave states[cu][cs]with various color-spin configurations in a multiquark color flux-tube *** results indic...
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We systematically investigate the mass spectrum,spatial configuration and magnetic moment of the ground and p-wave states[cu][cs]with various color-spin configurations in a multiquark color flux-tube *** results indicate that the state Z_(cs)(4000)^(+)can be described as the compact state[cu][cs]with 1^(3)*** main colorspin configuration is[cu]^(1)^(6c)[cs]^(1)^(6c)and its magnetic moment is 0.73μ*** state Z_(cs)(4220)^(+)can be depicted as the compact state[cu][cs]with 1^(1)P_(1)(or 1^(3)P_(1)).Its main color-spin configuration is[cu]^(0)_(3c)[cs]^(0)_(3c)(or[cu]^(0)_(3c)[cs]^(1)_(3c))and its magnetic moment is 0.12μN(or 0.64μN).The physical state should be the mixture of these two different color-spin configurations and deserves further *** addition,we also predict the properties of the states^(0)_(3c)with other quantum numbers in the model.
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...
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A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number *** the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection *** terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background *** addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production *** experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line *** Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small *** propose a small target breast mass det...
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Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small *** propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature *** the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the *** verify the performance of our model,we used public datasets to carry out the *** results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 *** detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between *** method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.
This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims to improve the dim...
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We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video *** from curren...
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We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video *** from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing *** formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and *** the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine *** the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation *** qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is *** impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its *** algorithm implementations are publicly available at https://***/zyqin19/HEVis.
Point cloud completion is crucial in point cloud processing, as it can repair and refine incomplete 3D data, ensuring more accurate models. However, current point cloud completion methods commonly face a challenge: th...
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City managers have long been committed to improving the efficiency of transportation systems and reducing congestion, which makes urban traffic flow forecasting crucial. Current forecasting methods often do not adequa...
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In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, a...
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In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther
Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence perio...
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Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
In the field of clustering algorithms, the Fuzzy C-Means algorithm stands out for its ability to deal with uncertainty by assigning membership degrees to data points. However, research on the fairness of Fuzzy C-Means...
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