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Examining global warming factors using self-organizing map and Granger causality network:a case from South Korea

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Ecological Processes 2023年第1期12卷 140-150页

作者： Thakur Dhakal Tae-Su Kim Do-Hun Lee Gab-Sue Jang Department of Life Science Yeungnam UniversityGyeongsan 38541Republic of Korea National Institute of Ecology Seocheon 33657Republic of Korea

Background Understanding and patterning the possible causal variables of global warming is attributed to the development of effective prevention and mitigation strategies for climate ***,we aimed to pattern and visualize the possible causal variables of global warming and measure the causality between *** We patterned and visualized the time series(103 years,from 1918 to 2020)of global surface temperature(GTemp)data with the gross domestic product(GDP)per capita,human population(Pop),and carbon dioxide(CO_(2))emissions of South Korea using a self-organizing map(SOM)and examined the causable local feature of global warming using the Granger causality(GC)*** time-series data were trained and mapped in 4×4 SOM grids,and causality networks between variables were examined using multivariate Granger test *** SOM patterned 103 years of data,and a dominant cluster contained continuous time-series data from 2007 to ***,the CO_(2)emissions of South Korea were obtained as a predictable unidirectional causal variable for GTemp from GC *** on data from the past 34 years,significant causality(p-value=0.01)was observed with the integrated effect of Pop,GDP,and CO_(2)on *** This study patterned the time-series data using SOM and examined the causal relationship between variables using the GC *** study framework could be used as a reference by future scholars,ecologists,and the United Nations Sustainable Development Goals.

关键词： Climate change Ecology self-organizing map Sustainable development goal Greenhouse gas

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Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

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Journal of Central South University 2015年第2期22卷 601-609页

作者：宋羽姜庆超颜学峰 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology)

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical *** with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

关键词： statistic pattern framework self-organizing map fault diagnosis process monitoring

Patterns of upper layer circulation variability in the South China Sea from satellite altimetry using the self-organizing map

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Acta Oceanologica Sinica 2008年第z1期27卷 129-144页

作者： WEISBERG Robert H College of Marine Science University of South Florida

Patterns of the South China Sea (SCS) circulation variability are extracted from merged satellite altimetry data from October 1992 through August 2004 by using the self-organizing map (SOM). The annual cycle, seasonal and inter-annual variations of the SCS surface circulation are identified through the evolution of the characteristic circulation *** annual cycle of the SCS general circulation patterns is described as a change between two opposite basin-scale SW-NE oriented gyres embedded with eddies: low sea surface height anomaly (SSHA) (cyclonic) in winter and high SSHA (anticyclonic) in summer half year. The transition starts from July—August (January—February) with a high (low) SSHA tongue east of Vietnam around 12°~14° N, which develops into a big anticyclonic (cyclonic) gyre while moving eastward to the deep basin. During the transitions, a dipole structure, cyclonic (anticyclonic) in the north and anticyclonic (cyclonic) in the south, may be formed southeast off Vietnam with a strong zonal jet around 10°~12° N. The seasonal variation is modulated by the interannual variations. Besides the strong 1997/1998 event in response to the peak Pacific El Nio in 1997, the overall SCS sea level is found to have a significant rise during 1999~2001, however, in summer 2004 the overall SCS sea level is lower and the basin-wide anticyclonic gyre becomes weaker than the other years.

关键词： circulation patterns self-organizing map satellite altimetry annual cycle inter-annual variation South China Sea

Seasonal variability of Kuroshio intrusion northeast of Taiwan Island as revealed by self-organizing map

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Chinese Journal of Oceanology and Limnology 2014年第6期32卷 1435-1442页

作者：殷玉齐林霄沛李宜振曾相明 Physical Oceanography Laboratory Ocean University of China Department of Marine Earthand Atmospheric SciencesNorth Carolina State University

The self-organizing map method is applied to satellite-derived sea-level anomaly fields of1993-2012 to study variations of the Kuroshio intrusion northeast of Taiwan *** major features are revealed,showing significant seasonal variability of the *** general,the intrusion increases(decreases) with a high(low) sea-level anomaly at the edge of the East China Sea shelf in winter(summer).Open-ocean mesoscale eddies play an additional role in modulating the seasonal variation of the *** analyses are needed to study eddy-Kuroshio interaction dynamics.

关键词： Kuroshio intrusion self-organizing map mesoscale eddies

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

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Journal of Arid Land 2023年第3期15卷 310-326页

作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao College of Earth Science Chengdu University of TechnologyChengdu 610059China Beijing Supermap Software Co. Ltd.Beijing 100015China Sichuan Real Estate Registration Center Chengdu 610014China Chengdu Institute of Mountain Land and Disasters Chinese Academy of SciencesChengdu 610041China School of Earth Systems and Sustainability Southern Illinois University CarbondaleCarbondaleIL 62901United States of America Environmental Resources and Policy Southern Illinois University CarbondaleCarbondaleIL 62901United States of America Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention Chengdu 610081China

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and ***,traditional methods for studying gravels are low-efficiency and have many *** study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan ***,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were *** results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated *** regions were mainly distributed in high mountainous areas with large topographic *** central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller *** component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and *** study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

关键词： self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring

Outlier Detection in Near Infra-Red Spectra with self-organizing map

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Transactions of Tianjin University 2005年第2期11卷 129-132页

作者：李晓霞李刚林凌刘玉良王焱李健杜江 School of Precision Instruments and Opto-Electronics Engineering Tianjin University Tianjin 300072 China School of Electrical and Automatic Engineering Hebei University of Technology Tianjin 300130 China 3.School of Mechanical Engineering Tianjin University of Technology Tianjin 300191 China School of Mechanical Engineering Tianjin University of Technology Tianjin 300191 China

A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e.g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research.

关键词： outlier near infra-red spectra minimum subsets resampling self-organizing map

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

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Frontiers of Chemical Science and Engineering 2023年第6期17卷 759-771页

作者： Jiannan Zhu Vladimir Mahalec Chen Fan Minglei Yang Feng Qian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Chemical Engineering McMaster UniversityHamiltonOntario L8S 4L8Canada Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation *** model is comprised of self-organizing-map and the neural network *** self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network *** the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted *** of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the *** results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker ***,the MISR model has smoother error convergence than the previous *** operating conditions have been determined via multi-round-particle-swarm and differential evolution *** experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

Distinguishing volcanic lithology using self-organizing map

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Global Geology 2007年第1期10卷 74-77页

作者： Ping ZHANG Baozhi PAN College of Geoexploration Science and Technology Jilin University Changdzun 130026 China

self-organizing map is an unsupervised learning *** has the ability of self-organization,self-learning and side associative *** on the principle it can identified the complex volcanic *** to the logging data of the volcanic rock samples,the SOM will be trained,The SOM training results were analyzed in order to choose optimally parameters of the *** identifying the logging data of volcanic formations,the result shows that the map can achieve good application effects.

关键词： self-organizing map volcanic rock lithology recognition logging data

Visual Sensing Algorithm for Soft Manipulator based on self-organizing map

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Visual Sensing Algorithm for Soft Manipulator based on Self-...