Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

A comparison of land unit delineation techniques for land evaluation in the Western Cape, South Africa

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay $$\tau $$ź and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.

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.

Based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm and kernel online sequential extreme learning machine (KOSELM) algorithm, a new hybrid short-term traffic flow prediction model (ICEEMDAN-KOSELM-ARIMA) for signalized intersections is proposed according to the current and historical traffic flow data. First, traffic flow historical time series are decomposed by ICEEMDAN algorithm for the purpose of improving the prediction accuracy. Several intrinsic mode functions could be obtained by the decomposition process. Then, permutation entropy algorithm is employed to analyze the random properties of intrinsic mode function components. According to the different random properties of intrinsic mode functions, different prediction models can be built. On this basis, KOSELM prediction models are established for the intrinsic mode function components with big randomness. And auto-regressive integrated moving average (ARIMA) prediction models are built for the intrinsic mode function components with small randomness. Finally, an actual signalized intersection is selected to verify the effect and performance of the hybrid prediction model proposed in this article. Results show that compared with other models, the new proposed hybrid prediction model can effectively improve prediction accuracy, of which prediction errors are the lowest and fitting effect with actual values is the best.

In order to extract the vibration feature of remanufactured engines, the vibration signal is decomposed by ensemble empirical mode decomposition(EEMD), and this method is applied to study vibration mode of remanufactured engines. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF(Intrinsic Mode Function) components and original signal, and sensitive factors of IMF components are calculated using correlation coefficients. Hilbert transform is made with sensitive IMF components. The study results show that vibration feature of remanufactured engines is include in IMF components decomposed by EEMD, the vibration modes of remanufactured engines can be differentiated by Hilbert-Huang spectrum and marginal spectrum based on sensitive IMF components. According to vibration feature, the vibration state of remanufactured engines is divided into cylinder wall, cylinder cover and crankshaft vibration modes, which are important to improve the remanufacture level of engines.

Blood cell analysis is becoming more and more attainable and worthy for blood diseases diagnosis. And blood cell segmentation is an important step for automated blood cells analysis based on image processing. This paper presented an improved support vector machine algorithm for blood cell segmentation for molecular hyperspectral images processing. This algorithm combined SVM with iterative self-organizing data analysis techniques algorithm to correct the miss-identified pixels by putting them into the minimum Euclidean distance cluster. Satisfactory performance can be seen from the experimental results that the proposed algorithm is superior to ISODATA and SVM algorithms in blood cell segmentation, because the new algorithm combines spatial and spectral information of blood cells.

Two main challenges introduced in current voice conversion are the dependence on parallel training data and the trade-off between speaker similarity and speech quality. To tackle the latter problem, this paper proposes a novel conversion method based on Iterative Self-organizing DATA Analysis Techniques Algorithm (ISODATA) clustering algorithm. Specially, we use ISODATA during the training of Gaussian mixture model, the optimized mixture number can guarantee the validity and accuracy of the GMM model, which can acquire speaker's identity effectively related to speaker similarity between original target speech and converted speech, Next, we combine improved GMM and bilinear frequency warping for the conversion stage, which can get a good balance between speaker similarity and speech quality. Theory analysis and experimental results demonstrate that the proposed algorithm can achieve higher quality and similarity compared with other two methods.

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

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.

The atomic stress tensor at a given continuum point is a spatial average value of some volume near the point. Recent progresses in multiscale modeling include the dealing of the optimal number and the size of these volumes. In this paper, we motivate the application of Iterative self-organizing data analysis technique algorithm to estimate volume numbers. The size of these space averaging volumes then could be got using Gaussian mixture model. Reduced computation complexity is offered by this method. Atomistic simulations are conducted to analyze the stress of a stone-wales defect graphene sheet to validate the method. Other multiscale values could also be determined using this method.

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.

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

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