A multiple-point spatially weighted k-NN classifier for remote sensing

A novel classification method based on multiple-point statistics (MPS) is proposed in this article. The method is a modified version of the spatially weighted k-nearest neighbour (k-NN) classifier, which accounts for spatial correlation through weights applied to neighbouring pixels. The MPS characterizes the spatial correlation between multiple points of land-cover classes by learning local patterns in a training image. This rich spatial information is then converted to multiple-point probabilities and incorporated into the k-NN classifier. Experiments were conducted in two study areas, in which the proposed method for classification was tested on a WorldView-2 sub-scene of the Sichuan mountainous area and an IKONOS image of the Beijing urban area. The multiple-point weighted k-NN method (MPk-NN) was compared to several alternatives; including the traditional k-NN and two previously published spatially weighted k-NN schemes; the inverse distance weighted k-NN, and the geostatistically weighted k-NN. The classifiers using the Bayesian and Support Vector Machine (SVM) methods, and these classifiers weighted with spatial context using the Markov random field (MRF) model, were also introduced to provide a benchmark comparison with the MPk-NN method. The proposed approach increased classification accuracy significantly relative to the alternatives, and it is, thus, recommended for the identification of land-cover types with complex and diverse spatial distributions

A multiple-point spatially weighted k-NN method for object-based classification

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification

Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning

Abstract: Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)—a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis—with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures—the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems

Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning

Abstract: Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)—a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis—with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures—the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems

A compliant self-stabilization nanopositioning device with modified active–passive hybrid vibration isolation strategy

Micro/mini light-emitting diodes (LEDs) display panel inspection and repairs have a high demand for vibration isolating devices to protect industrial-level atomic force microscopes (AFM scanning head) against vibrations. The motivation of this work is to combine the advantages of both passive and active vibration isolation strategies to improve inspection performance. The developed self-stabilization device achieves this objective with a design that incorporates a suspension-type passive vibration isolation unit and integrates it with the modified active–passive hybrid (MAPH) vibration isolation strategy using piezoelectric ceramics (PZT) and voice coil motors (VCM) as compensators. First, the design, modeling, and optimization of a self-stabilization device are presented based on the MAPH vibration isolation strategy. To satisfy the requirements of vibration isolation performance and a lightweight design, a multiobjective optimization task was conducted. Next, a tailor-made double compensating PID controller was designed to allow this mechanism to run in the MAPH method to effectively isolate vibrations. Finally, a series of validation experiments, including passive vibration isolation performance tests and MAPH closed-loop tests, were applied. From 1 to 500 Hz, more than 98% frequency domain achieved a vibration isolation rate of 90%, the vibration amplification effect of the passive vibration isolation was significantly suppressed, the steady-state positioning accuracy reached ±0.1μ m, load capacity was up to 2.5 kg, the attenuation ratio of the disturbances reached up to 70%, and the heat of the VCM was effectively reduced. All results comprehensively confirmed that the developed compliant MAPH vibration isolation system has achieved a satisfactory self-stabilization function

Functional characterization of PETIOLULE-LIKE PULVINUS (PLP) gene in abscission zone development in Medicago truncatula and its application to genetic improvement of alfalfa

Alfalfa (Medicago sativa L.) is one of the most important forage crops throughout the world. Maximizing leaf retention during the haymaking process is critical for achieving superior hay quality and maintaining biomass yield. Leaf abscission process affects leaf retention. Previous studies have largely focused on the molecular mechanisms of floral organ, pedicel and seed abscission but scarcely touched on leaf and petiole abscission. This study focuses on leaf and petiole abscission in the model legume Medicago truncatula and its closely related commercial species alfalfa. By analysing the petiolule-like pulvinus (plp) mutant in M. truncatula at phenotypic level (breakstrength and shaking assays), microscopic level (scanning electron microscopy and cross-sectional analyses) and molecular level (expression level and expression pattern analyses), we discovered that the loss of function of PLP leads to an absence of abscission zone (AZ) formation and PLP plays an important role in leaflet and petiole AZ differentiation. Microarray analysis indicated that PLP affects abscission process through modulating genes involved in hormonal homeostasis, cell wall remodelling and degradation. Detailed analyses led us to propose a functional model of PLP in regulating leaflet and petiole abscission. Furthermore, we cloned the PLP gene (MsPLP) from alfalfa and produced RNAi transgenic alfalfa plants to down-regulate the endogenous MsPLP. Down-regulation of MsPLP results in altered pulvinus structure with increased leaflet breakstrength, thus offering a new approach to decrease leaf loss during alfalfa haymaking process

Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion

Since WorldView-2 (WV-2) images are widely used in various fields, there is a high demand for the use of high-quality pansharpened WV-2 images for different application purposes. With respect to the novelty of the WV-2 multispectral (MS) and panchromatic (PAN) bands, the performances of eight state-of-art pan-sharpening methods for WV-2 imagery including six datasets from three WV-2 scenes were assessed in this study using both quality indices and information indices, along with visual inspection. The normalized difference vegetation index, normalized difference water index, and morphological building index, which are widely used in applications related to land cover classification, the extraction of vegetation areas, buildings, and water bodies, were employed in this work to evaluate the performance of different pansharpening methods in terms of information presentation ability. The experimental results show that the Haze- and Ratio-based, adaptive Gram-Schmidt, Generalized Laplacian pyramids (GLP) methods using enhanced spectral distortion minimal model and enhanced context-based decision model methods are good choices for producing fused WV-2 images used for image interpretation and the extraction of urban buildings. The two GLP-based methods are better choices than the other methods, if the fused images will be used for applications related to vegetation and water-bodies

Security pattern and trend of import trade of major food in China in the context of dual circulation

[Objective] Food security is related to the national economy and people’s livelihood. Building a new development pattern with domestic circulation as the main part and domestic and international dual circulation promoting each other is an important measure to ensure food security in China’s new development stage. [Methods] This study is based on data on the import and trade of eight major foods in China from 2014 to 2022, including grains, meat, eggs and milk, aquatic products, sugars, oilseeds (mainly including soybeans), vegetables, and fruits, from the perspective of future import trade demand, used the GM (1,1) model to predict the import trade volume of major food from 2023 to 2027. From the perspective of the current situation of the import trade market, calculated the market concentration ratio (CR4 and HHI indices) and the change of import trade centers, analyzed the main factors affecting the growth rate of the import trade volume of major food through gray correlation analysis, and examined the security situation of import trade, to provide some recommendations for ensuring food security in China. [Results] (1) Currently, China’s food security for grains is absolutely guaranteed, but with the current adjustment in residents’ dietary structure and the deepening of nutrition and health concepts, the proportion of grain foods in people’s daily food consumption is continuously decreasing. The supply of non-grain foods cannot be fully met by the domestic market alone and needs long-term supplementation through the international market. However, apart from aquatic products, there are certain risks in major food import trades, which affect China’s food security and are not conducive to the operation of the “dual circulation” economic development model. (2) In China’s major food import trades, the growth rates of imports for meat, grains, and sugar showed significant fluctuations, exerting a considerable impact on the import trades of the other five major food categories. (3) There has been a significant shift in the focus of major food import trades, especially with the notable growth of Brazil in oilseeds, meat, and sugar imports, the expanding share of Russia in aquatic products and grain imports, and the increasing prominence of Southeast Asian countries such as Thailand and Myanmar in vegetable and fruit imports. These changes reflect the increasing importance of emerging markets in China’s major food imports. [Conclusion] Through the strategic adjustment of China’s food import trade toward increasing the market share of emerging markets such as the Belt and Road and BRICS countries, trade stability has improved. However, with the continued expansion of China’s food import demand in the future, more sustainable development cooperation models need to be explored to ensure the security of import trade

Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning.

Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)-a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis-with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures-the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems