检索结果-内蒙古大学图书馆

Mako:A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth Graph mining Formation mechanism

来源：评论

学校读者我要写书评

暂无评论

A Novel Determination of Soluble NPK Nutrient Measurement System Using K-means and SOM Methods Compared with UV Visible Spectrophotometer

A Novel Determination of Soluble NPK Nutrient Measurement Sy...

引用

International Electrical engineering Congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

Technologies are currently being used in smart farms to improve productivity. Agricultural innovation is also widespread. Therefore, the development of Internet of Things (IoTs) technologies and devices to collect data is important. Finding essential nutrients for growth such as Nitrogen (N), Phosphorus (P) and Potassium (K) are important factors. Competitiveness is the important key for growing vertically or indoors. However, soil testing can be expensive, complicated and time consuming. The development of devices that can effectively measure nutrient NPK levels could be used over time. This paper presents a suitable clustering using the K-means method and a forecasting method using the Self-Organizing Map (SOM) method to enhance the device's ability to analyze nutrients in real time. A total of 50,688 samples were tested using the K-Means clustering algorithm, and the sample data were also analyzed. A SOM was prepared to predict the accuracy of the analysis. A total of 11,486 samples were tested and analyzed, then the results of the soluble NPK nutrient monitoring system were taken. It has an efficiency of 84.15% compared to using a UV Visible spectrophotometer, allowing data to be predicted during nutrient solubility changes and to operate according to the proposed system. Therefore, the unsupervised machine learning will improve the measurement system of future cultivation systems.

关键词： Self-organizing feature maps Technological innovation Soil measurements Instruments Machine learning Time measurement Optical sensors

来源：评论

学校读者我要写书评

暂无评论

The Prediction of COVID-19 Pandemic Situation in Indonesia Using SVR and SIR Algorithm

The Prediction of COVID-19 Pandemic Situation in Indonesia U...

引用

International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Ainul Yaqin Majid Rahardi Ferian Fauzi Abdulloh Kusnawi Slamet Budiprayitno Siti Fatonah Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Department of Electrical and Automation Engineering Vocational Faculty Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The COVID-19 epidemic, which initially surfaced at the end of 2019, has since expanded to every corner of the globe and has profoundly impacted all facets of human existence. This case started to emerge in Indonesia at the end of February 2020, and until this point, there has been a spike in new patients. Researchers have run several models and projections for COVID-19 cases in Indonesia, but the results are not yet entirely reliable. Predictions produced at the national level must take into account these variations in patterns because this is likely related to the distinct patterns in each region. In this study, the prediction process will be conducted for cases of COVID-19 by using the SVR algorithm and mathematical models to predict reproduction numbers. SVR analysis to overcome the problem of nonlinearity of data in model formation. The modeling is done based on the SIR model, whose parameters are estimated based on the data. Testing result by using 3 kernels is different on each test, prediction of data cases and the level of mistake room are by using Kernel ’ RBF ‘ with a value of C = 1E3, and gamma = 0.1 with the value of MAPE and MSE respectively are 4.5% and 4.2.

关键词： COVID-19 Analytical models Sociology Prediction algorithms Mathematical models Data models Kernel

来源：评论

学校读者我要写书评

暂无评论

Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms

arXiv

引用

arXiv 2024年

作者： Zhang, Han Lin, Shengxiang Zhang, Xingyi Wang, Yu Zhang, Yangguang College of Aritificial Intelligence and Automation Hohai University Nanjing211100 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Control and Computer Engineering North China Electric Power University Beijing071003 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction. Recent advancements involve using deep learning to process calorimeter images from various sub-detectors in experiments like the Large Hadron Collider (LHC) for energy map reconstruction. This paper compares classical algorithms—MLP, CNN, U-Net, and RNN—with variants that include self-attention and 3D convolution modules to evaluate their effectiveness in reconstructing the initial energy distribution. Additionally, a test dataset of jet events is utilized to analyze and compare models’ performance in handling anomalous high-energy events. The analysis highlights the effectiveness of deep learning techniques for energy image reconstruction and explores their potential in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Hybrid Water Quality Prediction with Graph Attention and Spa...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Gao, Han Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Chinese Academy of Environmental Planning Beijing100012 China New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies;2) There are complex nonlinear relations in water quality time series;and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy. © 2022 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

The In uence of Structural Relaxation on the Holstein-Primakoff Paraprocess

The In uence of Structural Relaxation on the Holstein-Primak...

引用

作者： Nabiaek, M. Postawa, P. Pietrusiewicz, P. Razak, K.A. Walters, S. Stambuaa, S. Jez, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Czestochowa42-200 Poland Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Czestochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis TamanMuhibbah Arau Perlis02600 Malaysia Advanced Engineering Centre University of Brighton BrightonBN2 4GJ United Kingdom

Structural relaxations play a large role in forming the properties of magnetic materials. In amorphous alloys, which are metastable materials, obtaining a relatively stable structure requires a properly designed thermal treatment process. Samples should be annealed above the Curie temperature and below the crystallization temperature. With the process designed in such a way, the domain structure is not interfered with and the formation of crystal grains does not occur. By analyzing the so-called Holstein-Primakoff paraprocess, it is possible to determine the in uence of magnetic atoms on the magnetization process in strong magnetic elds above the eld of effective anisotropy. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

来源：评论

学校读者我要写书评

暂无评论

Effect of loads on temperature distribution characteristics of oil-immersed transformer winding

引用

Distributed Generation and Alternative Energy Journal 2022年第2期37卷 237-254页

作者： Zhao, Zhengang Jiang, Zhangnan Li, Yang Li, Chuan Zhang, Dacheng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China Yunnan Key Laboratory of Computer Technology Applications Kunming650000 China

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively. © 2021 River Publishers

关键词： Temperature distribution

来源：评论

学校读者我要写书评

暂无评论

Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

引用

Acta Mechanica Sinica 2022年第8期38卷 132-148页

作者： Shitong Fang Naser Padar Mohammad Javad Mirzaei Shengxi Zhou Wei-Hsin Liao Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong999077China Department of Electrical Engineering Amirkabir University of TechnologyTehran15875-4413Iran Faculty of Electrical and Computer Engineering University of TabrizTabriz516661-6471Iran Research&Development Institute in Shenzhen Northwestern Polytechnical UniversityShenzhen518057China

Tristable energy harvesters(TEHs)have been proposed to achieve broad frequency bandwidth and superior low-frequency energy harvesting ***,due to the coexistence of three potential wells and the sensitivity to system conditions and external disturbances,the desired high-amplitude inter-well oscillation in the TEHs may be replaced by the chaotic or intra-well oscillations with inferior energy ***,the chaos has an unpredictable trajectory and may cause system damages,lessen the structural durability as well as require a more complicated circuit for power ***,in this paper,we firstly propose an adaptive finite-time disturbance observer(AFTDO)for performance enhancement of TEHs by detecting the external disturbances that induce the chaos,and reject them for the recovery of the desired inter-well *** proposed AFTDO eliminates the need to know in advance the upper bounds of imposed perturbations in conventional observers by means of the proposed adaptive protocols,leading to the higher efficacy of *** mathematical model of the piezoelectric TEH system and the AFTDO is *** demonstrate the effectiveness of the AFTDO,a series of numerical simulations have been *** show that for both cases with sinusoidal and impulsive disturbances,the AFTDO can successfully track the trajectories of the disturbance signals with the adaptive gain,and reject the disturbance to enable the TEH to sustain the periodic inter-well oscillation with effective energy harvesting performance.

关键词： Tristable energy harvesters Finite-time disturbance observer Sinusoidal and impulsive disturbances Chaos Inter-well oscillation

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based segmentation of breast masses using convolutional neural networks 12

Deep learning-based segmentation of breast masses using conv...

引用

12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Anghelache Nastase, I.-N. Moldovanu, S. Moraru, L. The Modelling & Simulation Laboratory Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Emil Racovita Theoretical Highschool 12-14 Regiment 11 Siret Street Galati800332 Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Department of Chemistry Physics & Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.61

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

High Step-down Converters for Battery Charging in Photovoltaic Applications 35

High Step-down Converters for Battery Charging in Photovolta...

引用

35th International Conference on Power Electronics and Electrical Drives, EDPE 2021

作者： Malović, Zvonimir Vuger, Nikola Šunde, Viktor Ban, Željko Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

This paper describes non-isolated high step-down converters used to connect photovoltaic modules to the battery storage of a bicycle charging station. Due to the high voltage difference between input and output, some problems may occur with conventional non-isolated DC-DC buck converters. The characteristics of alternative topologies of high step-down converters have been explored. Ideal converter models were created in the PLECS simulation software package based on which the voltage and current stress of the semiconductor switches were determined. Based on measured voltage and current relations, commercial switches were selected. Based on catalogue data, electro-thermal models of the switches were created. A comparative analysis of converters based on conduction and switching losses was performed. Converters were also compared based on the number of components and complexity of control. ©2021 IEEE.

关键词： Batteries Conduction losses Duty cycle Photovoltaic module Step-down converters Switching losses

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：