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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Lizhen Peng Shuangliang Tian Qiqi Lu School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χ ai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers.

关键词： Computer science Color

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Existence of periodic and S-asymptotically periodic solutions to fractional diffusion equations with analytic semigroups

Existence of periodic and S-asymptotically periodic solution...

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作者： Mu, Jia Nan, Jiecuo Zhou, Yong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Xiangtan University Hunan411105 China Nonlinear Analysis and Applied Mathematics Research Group Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-Liouville fractional derivatives. Even so, in this paper, periodic (S-asymptotically periodic) mild or classical solutions for FDE with Weyl-Liouville fractional derivatives could be obtained in various fractional power spaces. In addition, a numerical simulation example and a specific example of fractional diffusion equation are given to verify the main theoretical results. © 2019 John Wiley & Sons, Ltd.

关键词： Diffusion

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Segmentation of bone metastases based on attention mechanism

Segmentation of bone metastases based on attention mechanism

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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Guoyi Che Yongchun Cao Ao Zhu Qiang Lin Zhengxing Man Haijun Wang Key Laborator of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Gansu Provincial Hospital Lanzhou China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, but also different diagnosis results will be caused by the different diagnosis experience of doctors. In view of the above problems, this paper uses U-Net network as the basic model, and at the same time conducts model performance optimization research. Based on the U-Net network, the attention mechanism is integrated to segment the bone metastases in the pelvic area. Introducing the attention mechanism into the U-Net network can help improve the correlation of the pelvic region and reduce the interference caused by problems such as uneven brightness and low contrast to the model. Through multiple sets of experimental demonstrations, the U-Net network integrated with the attention mechanism can better segment bone metastases in the pelvic region based on SPECT images, and the model indicators have been significantly improved.

关键词： Image segmentation Medical services Interference Bones Single photon emission computed tomography Optimization Diseases

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Coordination Evaluation Research of Railway Collection and Distribution System in Port Areas Based on Cloud Model

Coordination Evaluation Research of Railway Collection and D...

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作者： Kai Zeng Haihong Xie Liuyi Wu Yongbin Xu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Institute of Computing Technology China Academy of Railway Sciences

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.

关键词： Railway in port areas collection and distribution system coordination evaluation cloud model

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Corrigendum to “Existence ofSolutions to Non-Coercivity Quasilinear Elliptic Problem”

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Journal of Function Spaces 2021年第1期2021卷

作者： Jimao Xiawu Shuibo Huang Yingyuan Mi Maoji Ri School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu 730030 *** Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou Gansu 730030 ***

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SVM-based deep stacking networks

arXiv

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arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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On Star Coloring of Several Corona Graphs

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Jin Cai Shuangliang Tian Zhuomo An School of Mathematics and Computer Science Northwest for Minzu University Lanzhou 730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou 730030 China

Let G be a simple graph with vertex set V(G) and edge set E(G). A vertex coloring of G is called a star coloring of G if any of the paths of 4 order are bicolored. The minimum number of colors required for a star coloring of G is denoted by χs(G). The corona product of simple graphs G of order m and H of order n is graph G ∘ H with vertex set V(G ∘ H) = {vi|i = 1,2,⋯m}∪{vij|i = 1,2,⋯m, j = 1,2,⋯n}, in which vi is adjacent to every vertex of Hi if and only if, vi ∈ V(G), vij ∈ V(Hi). According to the existing graph dyeing literature, it has become a very important technical means to study the graph dyeing problem by using the graph structure operation. Therefore, it is of great significance to study the star coloring of graphs for studying the acyclic coloring and distance coloring of graphs, the study has strong application background and great theoretical value for computing graphs. In this paper, we find the upper bound of χs(G ∘ H) and the exact values of χs(G ∘ H) of the corona product G ∘ H of two graphs G and H as: χs(G ∘ H) ≤ χs(G) + χs(H); χs(Pm ∘ H) = χs(H) + 2; χs(K1,m ∘ H) = χs(H) + 2; χs(Cn ∘ H) = χs(H) + 2, where n ≠ 5.

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SPECT bone imaging thyroid lesion segmentation

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Journal of Physics: Conference Series 2021年第1期1994卷

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou 730030 Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou 730124 School of Mathematics and Computer Science Northwest Minzu University Lanzhou 730030

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.

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Deep learning SPECT lung perfusion image classification method based on attention mechanism

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Sitao Zeng Yongchun Cao Qiang Lin Zhengxing Man Tao Deng Rong Wang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou 730030 Gansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou 730030 Gansu China Department of Nuclear Medicine Gansu Provincial Hospital Lanzhou 730030 Gansu China

SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image; secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion; then, in order to improve the model's feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model; finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model.

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