Gravitational-wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a space-borne instrument dedicated to monitoring high-energy transients, including Terrestrial Gamma-ray Flashes (TGFs) and Terrest...
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Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we ...
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Incomplete data clustering is a hot research topic in the field of data mining. Many methods based on incomplete clustering have two issues: (1) their models do not have non-negative constraints; (2) their model build...
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Incomplete data clustering is a hot research topic in the field of data mining. Many methods based on incomplete clustering have two issues: (1) their models do not have non-negative constraints; (2) their model building strategies do not consider combining traditional machine learning and deep learning. To address these issues, we proposed the NMF-based deep representation algorithm for incomplete data clustering (NDRIDC). First, the element-based objective function is constructed based on the known elements in the incomplete data matrix with matrix factorization principle. Then the NDRIDC deep network is constructed based on the element update gradient and nonlinear function with non-negative value domain. The learning of the NDRIDC network enables the optimization of the objective function and the filling of missing values. Further, based on the same learning framework, the NDRIDC network is employed to perform low-dimensional representation of the filled matrix. Finally, extensive experiments on regular and large-scale datasets show that our proposed algorithm has good performance.
Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies o...
Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP.
The rapid development of the Internet has brought convenience to people and has also produced the problem of 'information overload'. In view of the traditional collaborative filtering algorithm facing some bot...
The rapid development of the Internet has brought convenience to people and has also produced the problem of 'information overload'. In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method; secondly, the trust relationship is established based on the user's rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust; then find the nearest neighbor set of the target user through the fusion of user similarity and trust; finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation.
Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. This study summarized the structure and characteristics of 3608 bone scan image...
Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. This study summarized the structure and characteristics of 3608 bone scan image report text using dictionary-based information extraction method, including data cleaning, entity recognition, building dictionary and extraction rules. This method was used to obtain the structured data of bone scan image report text required for clinical research, and the effect evaluation was carried out on 1000 randomly selected report texts, with the precision rate and recall rate higher than 90%. The method proposed in this study is practical and could have good effect on structured results for bone scan imaging report text.
The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels du...
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The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build b...
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ISBN:
(数字)9781728187891
ISBN:
(纸本)9781728187907
The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.
The railway is of great significance for port cargo to achieve collection and distribution. It is necessary to evaluate the coordination of the railway collection and distribution system scientifically in port areas. ...
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The railway is of great significance for port cargo to achieve collection and distribution. It is necessary to evaluate the coordination of the railway collection and distribution system scientifically in port areas. Based on the research that analyses factors that affecting the coordination of railway collection and distribution system in port areas and constructs a comprehensive evaluation indicator system, this paper innovates a comprehensive evaluation model suitable for studies of the coordination of railway collection and distribution system in port areas by combining the cloud model algorithm with the traditional expert scoring method. Then, this paper will verify the applicability and operability of the model we proposed by representing a case study of the coordination of the railway collection and distribution system in Caofeidian Port Area.
In recent years, deep convolutional neural networks (CNNs) have demonstrated impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. However, the existing CN...
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