Iterative Self-organizing Data Analysis Techniques Algorithm Research Articles

A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement

ABSTRACTThis paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame and small object detection scale are improved and optimized. The YOLOv5(ISODATA) model was finally established for landslide image detection by incorporating the edge control factor and four clustering algorithms (K-means, K-means + +, k-medoid, and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to evaluate the accuracy of the detection anchor frame and add small target large-scale sampling. Three target identification models – YOLOv5, Region Convolution Neural Network (R-CNN), and Fast R-CNN – are experimentally compared in order to assess the effectiveness of the proposed method. According to the results of experiments, the proposed method outperforms the other three detection models with an AUC of 0.921, a recall of 86.14%, and an MCC of 0.887. It further demonstrates the method’s positive impact on landslide remote sensing image recognition and its ability to solve related issues.

Identification of geochemical anomalies related to mineralization: A case study from porphyry copper deposits in the Qulong-Jiama mining district of Tibet, China

Geochemical exploration data plays a vital role in mineral prospectivity mapping (MPM) for discovering unknown mineral deposits. In this study, compositional balance analysis (CoBA), unsupervised and supervised learning are collaboratively used to improve the practice of identification of geochemical anomalies related to mineralization in the Qulong-Jiama mining district of Tibet. For CoBA, five balances of geochemical elements were constructed by the sequential binary partition (SBP) technique, facilitating the interpretation of geochemical/geological processes and contrasting with geochemical anomalies generated by unsupervised and supervised learning. The iterative self-organizing data analysis techniques algorithm (ISODATA) and isolation forest (iForest) are used for preprocessing the geochemical data and therefore optimizing the appropriate training sets (e.g., positive and negative training samples) for supervised learning. Different training datasets including locations of known mineralization, randomly selected positive samples from resulting clusters by ISODATA and outliers by iForest are fed to the support vector machine (SVM) and random forests (RF) algorithm. The results of high AUC values indicate that supervised learning can effectively delineate prospective areas both with SVM and RF, however, the excessive large area of high-prospectivity and a bias toward known mineralization makes the outcomes of MPM unpractical. Nevertheless, a hybrid of the CoBA, unsupervised learning and supervised learning could alleviate such situations and provide insights into MPM using the geochemical data.

Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning

Federated learning (FL) offers a promising paradigm that empowers numerous Internet of Things (IoT) devices to implement distributed learning on the premise of ensuring user privacy and data security. However, since FL adopts a synchronous distributed training mode, the heterogeneity of participating IoT devices and limited communication resources make FL encounter serious issues of low training efficiency in actual deployment. In this article, we propose an excellent FL policy for the heterogeneous IoT-edge FL system to improve distributed training efficiency. Specifically, first, by borrowing the idea of clustering, we explore an iterative self-organizing data analysis techniques algorithm (ISODATA)-based heterogeneous-aware client scheduling strategy to alleviate the issue of low training efficiency incurred by the heterogeneity of clients. Subsequently, to tackle the challenge of limited communication resources in FL, we first analyze the characteristics of the optimal resource block allocation solution theoretically and then introduce a mixed-integer linear programming (MILP)-based strategy to judiciously allocate resource blocks for scheduled clients. Comprehensive experimental results demonstrate that, compared with benchmarking strategies, our proposed FL policy can achieve up to 55.22% accuracy improvement in a relaxed time scenario, and attain up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.62\times $ </tex-math></inline-formula> acceleration for reaching the specific expected accuracy.

Ensemble fuzzy radial basis function neural networks architecture driven with the aid of multi-optimization through clustering techniques and polynomial-based learning

This study is concerned with the concept of Ensemble Fuzzy Radial Basis Function Neural Networks (EFRBFNN) endowed with clustering techniques, polynomial-based LSE/WLSE learning as well as their optimization implemented by Multi-Objective Particle Swarm Optimization (MOPSO). The network architecture of the proposed EFRBFNN classifier is considered three types of clustering techniques and four forms of regression polynomials. The proposed classifier can not only fully capture the local distribution (feature) information contained in the data, but also choose effective network architecture for improving classification performance and compactness. Three classes of clustering techniques (such as iterative self-organizing data analysis techniques algorithm (ISODATA), affinity propagation (AP), and extended K-means) are considered to effectively produce membership degrees based on feature information extraction suitable to fit data characteristics. Least Square Error Estimation (LSE) and Weighted Least Square Error Estimation (WLSE)-based learning with four types of regression polynomials are considered to estimate to coefficients of polynomials. MOPSO is exploited for choosing the effective architecture among different clustering techniques, polynomials, and polynomial-based learning with multi-objective parametric optimization. MOPSO helps achieve a sound compromise between the preferred classification performance and the compactness realized with the aid of four objective functions such as a) classification accuracy (CA), b) complexity of clustering, c) complexity of polynomial and d) sum of squared coefficients (SSC). The improvement and effectiveness of the proposed network architecture are quantified on a basis of the comprehensive experimental results and also a comparative analysis is offered to demonstrate the superiority of the proposed classifier.

Geochemically Constrained Prospectivity Mapping Aided by Unsupervised Cluster Analysis

In this paper, we explore unsupervised cluster analysis to aid mineral prospectivity mapping (MPM) in two aspects: (1) to cluster geochemical data for MPM based on detailed analysis of evidence maps and (2) to explore coherence of spatial signatures at/around mineralized locations as well as outliers of geochemical data. To do so, a systematic procedure is proposed based on the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Through this procedure, the detailed analysis of evidence maps in Hezuo–Meiwu district, Gansu Province, China, which portray five selected geochemical elements, showed that clusters with and without mineralized locations provide insight to weighing of each evidence. Finally, through the integration of evidence maps, the favorability score map yielded high AUC (> 0.80) for delineating various mineralized locations in the study area, which proves the efficacy of unsupervised cluster analysis as an aid to MPM. Moreover, the coherence of spatial signatures of known mineralized locations, which comprise a training dataset, is vital to data-driven MPM. Groupings of mineralized locations based on the ISODATA and visual inspection supported by PCs from principal component analysis imply that different deposit types may share the same or similar spatial signature and outliers in geochemical data may be potential training samples used for data-driven MPM. Mineralized locations of the same deposit type may show significant dissimilarity. However, this provides insights into selecting mineralized/non-mineralized locations for creation of training datasets. Interestingly, in the study area, major mineralized locations in zones divided by regional fault are clustered separately into two groups. This result not only proves that cluster analysis is effective for exploring the coherence of spatial signatures at/around mineralized locations, but it also justified our previous study, whereby we performed MPM by zones using machine learning algorithms.

SDLSC-TA: Subarea Division Learning Based Task Allocation in Sparse Mobile Crowdsensing

Sparse mobile crowdsensing (Sparse MCS), a new paradigm for large-scale fine-grained urban monitoring applications, collects sensing data from relatively few areas and infers data for uncovered areas. In Sparse MCS, the task allocation problem is simplified to the area selection problem since it is typically assumed that there were enough participants across the target sensing area. However, in many real scenarios, there is no guarantee the platform can find participants to execute tasks in vital areas. In this case, additional moving costs are incurred, which is not beneficial for the MCS platform as organizers are cost-sensitive. To address this problem, we propose a novel Subarea Division Learning based Task Allocation framework in Sparse mobile Crowdsensing (SDLSC-TA) that integrates subarea division learning, task allocation, and sensing map reconstruction. Different from existing research, we design the subarea division learning module to provide guidance for a more reasonable task allocation scheme. Specifically, subarea division learning utilizes the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) to perform uneven subarea division considering historical data and spatio-temporal correlations. Based on subarea division learning results, task allocation iteratively selects the most suitable cell and participant combining sensing levels, sensing, and moving costs. Finally, sensing map reconstruction utilizes Bayesian compressive sensing (BCS) to infer missing data while ensuring high quality. Using four typical urban sensing datasets, SDLSC-TA outperforms state-of-the-art sparse MCS frameworks by 15 percent lower total costs on average and 40 percent lower average sensing map error rate.

An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning

The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping.

An ELM-Based Semi-Supervised Indoor Localization Technique With Clustering Analysis and Feature Extraction

In this article, a new indoor localization technique with extreme learning machine (ELM) is proposed where only a small number of received signal strength indicator (RSSI) measurements are labeled. In the off-line learning phase, the iterative self organizing data analysis techniques algorithm (ISODATA) is used to divide the RSSI measurements into some measurement data subsets. Then the multi-kernel ELM (MK-ELM) method is utilized to perform classification learning and obtain the RSSI measurement classification function. For each RSSI measurement subset, a two-stage feature extraction algorithm using the kernel principal component analysis, the deep learning network and ELM method is proposed for RSSI measurement feature extraction. At last, the position regression function of each subset is obtained by the semi-supervised regression learning. In the on-line position estimation phase, using the measurement classification and feature extraction of the received RSSI measurement, the position is estimated based on the corresponding position regression function. The field tests show that the proposed algorithm can obtain more accurate position estimation than other existing ELM based localization approaches do.

Research on Energy Management Strategies of Extended-Range Electric Vehicles Based on Driving Characteristics

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS.

Automated demarcation of the homogeneous domains of trace distribution within a rock mass based on GLCM and ISODATA

In view of the irregular trace distribution of rock discontinuities, rock mass appears as both a statistical distribution and a texture distribution in the spatial image. This paper proposes a new method on statistical texture analysis for automated demarcating the homogeneous domains of trace distribution within a rock mass. Grey-Level Co-occurrence Matrix (GLCM) is used to quantify the statistical texture features of trace distribution. Relativity, Inverse Difference Moment and Entropy are screened from ten texture parameters of GLCM using robustness analysis and using principal components analysis. The reliability of three screened texture parameters is verified by comparing the Chebyshev polynomials fitting of three screened texture parameters with Normal distribution, Fisher distribution, and Exponent distribution using χ2 testing. Automated demarcation of the homogeneous domains is implemented by means of classifying three texture parameters of Relativity, Inverse Difference Moment and Entropy in a moving window using the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The screening process of texture parameters and a case study indicates that texture parameters and automated demarcation method is so robust, reliable, and efficient that it could replace the traditional representation of the probability statistics in trace distribution and greatly save a lot of manual labor in a large-scale domain.

Semi-automatic extraction of rock discontinuities from point clouds using the ISODATA clustering algorithm and deviation from mean elevation

This paper presents a methodology for semi-automatic extraction of rock discontinuities from point clouds on the resulting 3D digital surface models of rock masses acquired by terrestrial laser scanning or photogrammetry. First, point clouds need to be pre-processed to generate a digital elevation model from a 3D digital surface model of a rock mass. Second, co-directional features of rock discontinuities are identified by classifying the standard deviation of the elevation, slope aspect and slope gradient of raster grids in a digital elevation model using the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Third, planar features of rock discontinuities are inspected by judging whether the value range of the deviation from the mean elevation (DEV) in the classified subset of raster grids is between + 1 and − 1 using the Topographic Position Index of terrain analysis. Fourth, point clouds data corresponding to the target surfaces of rock discontinuities are acquired by data post-processing using the ArcToolbox of ArcGIS. Finally, geological analysis is executed by calculating the geometric features of the extracted point clouds. A case study of a rock slope along a highway is presented using the proposed method, the results of which demonstrate that the proposed method is so rapid and accurate that it could greatly reduce the amount of manual labour, improve the efficiency of surveying, and utilize the powerful terrain analysis and visualization capabilities of ArcGIS to avoid the computational challenges associated with directly analysing point cloud data.

Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization

In this paper, a new indoor localization algorithm with received signal strength indicators (RSSI) fingerprints by a kernel-based learning technique is proposed. In the offline phase, after the training set pre-processing by the iterative self-organizing data analysis techniques algorithm (ISODATA), the measurement-label set is utilized for classification learning by the c-support vector classification approach. Moreover, each measurement-position training subset is chosen for regressing learning with hybrid kernel and cross-validation techniques. In the online phase, based on the classification result of the received RSSI measurement, the corresponding regression function is chosen for target position estimation. Compared with existing learning techniques, it can increase the training ability of a localization system dramatically. The field tests show that, without adding any extra computation complexity, the proposed algorithm can obtain more accurate position estimation than other existing learning-based localization approaches achieve.

中国人均灰水生态足迹变化驱动效应测度及时空分异

PDF HTML阅读 XML下载 导出引用 引用提醒 中国人均灰水生态足迹变化驱动效应测度及时空分异 DOI: 10.5846/stxb201706221136 作者: 作者单位: 辽宁师范大学城市与环境学院,辽宁师范大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家社会科学基金重点项目（16AJY009） Driving effect measurements and spatial-temporal variation of the per capita gray water ecological footprint in China Author: Affiliation: College of Urban and Environment,Liaoning Normal University,College of Urban and Environment,Liaoning Normal University Fund Project: Key Projects of National Social Sci-ence Fund, No. 16AJY009. 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:将传统灰水足迹和水生态足迹方法相结合，运用扩展的Kaya恒等式和LMDI指数分解方法对中国各省市的人均灰水生态足迹变化的驱动因素进行测度分析，充分考虑了资本和劳动力因素，选取经济活度效应，资本深化效应，资本效率效应，足迹强度效应，环境效率效应5个效应对人均灰水生态足迹变化的影响，结合ISODATA聚类模型对各效应进行空间聚类，从而分析各效应的空间特征。结果显示：中国人均灰水生态足迹产出变化是这5种因素共同作用的结果，资本深化效应和经济活度效应具有增量效应特点，而环境效率效应、足迹强度效应、资本效率效应呈减量效应特点；在各驱动效应的的强弱对比中，资本深化效应和足迹强度效应的特征较为明显。经济发展带动了科技进步也使得用水效率不断提高是足迹强度效应呈减量效应的主要原因；而工业化阶段经济向资本密集型转变是资本效率下降的主要原因。研究对中国灰水生态变化与资本要素之间的关系进行了探讨，对环保政策的调整及水资源可持续利用研究具有一定的参考价值。 Abstract:In this study, the authors apply water ecological footprint measurement methods to the field of gray water footprint research and provide a method for calculating gray water ecological footprints. Based on the extended Kaya identity and the Logarithmic Mean Divisia Index (LMDI) technique decomposition methods, we calculated the gray water ecological footprint of 31 provinces and the per capita gray water ecological footprint in China from 2000 to 2014. We then measured and decomposed the values of the driving effects of per capita gray water ecological footprint change. From these results, we selected five of the more important economic and environmental factors to apply as capital factors to gray water research. This method included measurements of the effects of the working population, capital stocks, capital output coefficients, water ecological footprint intensity, and gray water ecological footprint emission coefficients. The five driving effects were economic activity, capital deepening, capital efficiency, footprint intensity, and environmental efficiency. The values of the driving effects on changes during the time period are discussed and analyzed based on the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering model for spatial clustering of the effects. The results showed that the Chinese per capita gray water ecological footprint output changed because of the interaction of these five factors. Among them, the incremental capital deepening effect was obvious, as a large amount of capital has been invested to promote rapid development of the regional economy and to employ a rapidly growing population. Economic activity can also promote the characteristics and the size of regional economies as population growth will increase regional demands for water. The other capital deepening effect is a decline in the output efficiency of capital. With the rapid development of its economy, China is in a period of industrialization and urbanization. The economic structure has gradually changed from a population-intensive to a capital-intensive structure. Industry, especially heavy industry, had a higher initial investment and a lower output efficiency, which is why the effect of capital efficiency continues to decrease. The most obvious effect on footprint intensity was the effect of per capita gray water ecological footprint reduction. With the improvement of economic activity and advances in science and technology, the water ecological footprint intensity has been greatly reduced. As a result of the effective control of pollutant emissions, the per capita gray water ecological footprint in most provinces and cities has shown a decreasing trend. A reduction in environmental efficiency is an inevitable consequence of a decrease in the water ecological footprint intensity. With a decrease in water ecological footprint intensity, more water is reused and we can improve water efficiency in most provinces to reduce the gray water ecological footprint. This study discusses the relationship between gray water ecological changes and capital factors; the reported results have value as a reference for the adjustment of environmental policies and the sustainable utilization of water resources. 参考文献 相似文献 引证文献

A Clustering-Based Evidence Reasoning Method

Aiming at the counterintuitive phenomena of the Dempster–Shafer method in combining the highly conflictive evidences, a combination method of evidences based on the clustering analysis is proposed in this paper. At first, the cause of conflicts is disclosed from the point of view of the internal and external contradiction. And then, a new similarity measure based on it is proposed by comprehensively considering the Pignistic distance and the sequence according to the size of the basic belief assignments over focal elements. This measure is used to calculate the commonality function of evidences to amend the evidence sources; Meanwhile, the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) method based on the new measure is used for clustering according to the clustering characters of the original evidences. The Dempster rule is applied to combining all the evidences in each clustering into an evidential representative, and the reliability is calculated based on the commonality and the occurrence frequency of the evidences in the clustering. At last, Murphy's method is used to combine these evidential representatives of the different clusterings. The experimental results through a series of numeric examples show that the method proposed in this paper is more effective and superior to others.

An Adaptive Weighted Fuzzy C-means Clustering Algorithm for Remote Sensing Image Classification

The Fuzzy C-means (FCM) clustering algorithm is a well-known tool for pattern and image classification, while FCM is showing unstable behaviors with different fuzzy indexes. The New Weighted Fuzzy C-means (NW-FCM) algorithm, based on the weighted mean concept, was proposed to improve the performance of FCM, however, NW-FCM needs man-machine interaction and can not classify by itself. In this paper, an Adaptive Weighted Fuzzy Clustering Algorithm (AWFCM), which integrates the weighted mean concept with the mechanism of splitting and lumping from the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA), is proposed for remote sensing image classification. At the same time, a statistical method is introduced into the algorithm. The AWFCM does not need a priori knowledge about the number of clusters and their centers, and can automatically estimate an initial number of clusters and their centers, also the optimum final number of clusters. Experimental results demonstrate that the AWFCM has better performance than those of the K-means, ISODATA and FCM algorithms.

Tone recognition based on biomimetic pattern recognition theory

Pitch frequency trajectories can more truly reflect the tone characteristics of Mandarin.It is a better way to identify tone by identifying different pitch trajectories.According to the theory of biomimetic pattern recognition,an improved tone recognition algorithm was proposed.It used Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to find centers of coverage areas,and multi-weight neural network to achieve coverage for each cluster center.The experimental results show that in the case of a small sample,the proposed algorithm can get a higher recognition rate than that of Hidden Markov model (HMM) and Support Vector Machine (SVM).

MC-7 A new time-resolved immunofluorometric assay of thyroxine binding globulin in serum: A. Tan, M.J. Khosravi, E.P. Diamandis (CyberFluor Inc., 179 John Street, Toronto, Ontario M5T 1X4)

This paper presents a methodology for semi-automatic extraction of rock discontinuities from point clouds on the resulting 3D digital surface models of rock masses acquired by terrestrial laser scanning or photogrammetry. First, point clouds need to be pre-processed to generate a digital elevation model from a 3D digital surface model of a rock mass. Second, co-directional features of rock discontinuities are identified by classifying the standard deviation of the elevation, slope aspect and slope gradient of raster grids in a digital elevation model using the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Third, planar features of rock discontinuities are inspected by judging whether the value range of the deviation from the mean elevation (DEV) in the classified subset of raster grids is between + 1 and − 1 using the Topographic Position Index of terrain analysis. Fourth, point clouds data corresponding to the target surfaces of rock discontinuities are acquired by data post-processing using the ArcToolbox of ArcGIS. Finally, geological analysis is executed by calculating the geometric features of the extracted point clouds. A case study of a rock slope along a highway is presented using the proposed method, the results of which demonstrate that the proposed method is so rapid and accurate that it could greatly reduce the amount of manual labour, improve the efficiency of surveying, and utilize the powerful terrain analysis and visualization capabilities of ArcGIS to avoid the computational challenges associated with directly analysing point cloud data.