Wyniki wyszukiwania - Biblioteka Nauki

1

Simulation of Reliability Prediction Based on Multiple Factors for Spinning Machine

100%

Li C. , Liu R. , Pan F.

Autex Research Journal

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2020

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tom Vol. 20, no. 1

17--23

EN

Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.

2

Gastric histopathology image segmentation using a hierarchical conditional random field

63%

Sun C. , Li C. , Zhang J. , Rahaman M. M. , Ai S. , Chen H. , Kulwa F. , Li Y. , Li X. , Jiang T.

Biocybernetics and Biomedical Engineering

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2020

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tom Vol. 40, no. 4

1535--1555

EN

For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, conditional random field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel hierarchical conditional random field (HCRF) based gastric histopathology image segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 :2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.

3

SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis

51%

Chen A. , Li C. , Zou S. , Rahaman M. M. , Yao Y. , Chen H. , Yang H. , Zhao P. , Hu W. , Liu W. , Grzegorzek M.

Biocybernetics and Biomedical Engineering

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2022

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tom Vol. 42, no. 1

204--214

EN

Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset-A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional kNN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: .https://github.com/Demozsj/Detection-Sperm.

4

Evaluation of the diference in water quality between urban and suburban rivers based on self organizing map

51%

Jiang Y. , Gui H. , Li C. , Chen J. , Chen C. , Wang C. , Zhao H. , Guo Y. , Xu J. , Li J. , Qiu H.

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tom Vol. 69, no. 5

1855--1864

EN

Urban rivers play an important role in maintaining the urban aquatic ecological environment, and there are bound to be differences in the water environment quality and pollution sources due to different locations of urban rivers. Therefore, this paper selects the urban river (Tuo River) and the suburban river (Bian River) in Suzhou City, Anhui, China, as the research objects. Based on the understanding of the hydrogeochemical characteristics of these two rivers, the self-organizing map is used to identify the main control factors that affect the water quality of the two rivers. The results showed that both the Bian river and Tuo river were weakly alkaline. The average content of conventional ions in Tuo river is less than that of Bian river (except HCO3 −); the water of Bian river was of Na–SO4–Cl type, and the water of Tuo river was mainly of Na–HCO3 type, with the minority was of Na–SO4–Cl type; Silicate weathering is an important source of conventional ions in the water of these two rivers; agricultural non-point source pollution is the main source of pollutants in Bian river, while Tuo river was mainly affected by natural factors, and human activities had little impact.