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This study prepared an SiC thin film by using the ratio frequency magnetron sputtering method, investigated the effects of different sputtering powers on the SiC material and analysed the changes in crystal morphology and photoluminescence characteristics caused by changes in the growth conditions used. It was considered that there was 6H-SiC crystal morphologies in the SiC thin film under the experimental conditions prevailing in this study. The SiC morphologies with small grain sizes intermingled and therefore formed anSiC thin film. The analyses of the photoluminescence spectra and Scanning Electron Microscope indicated that the SiC thin film materials with preferable crystal compositions could be prepared under appropriate power inputs.

Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack accurate, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle forest disturbances. This study presents a new approach called ‘Stochastic Continuous Change Detection (S-CCD)’ using a dense Landsat data time series. S-CCD improves upon the ‘COntinuous monitoring of Land Disturbance (COLD)’ approach by incorporating a mathematical tool called the ‘state space model’, which treats trends and seasonality as stochastic processes, allowing for modeling temporal dynamics of satellite observations in a recursive way. The quantitative accuracy assessment is evaluated based on 3782 Landsat-based disturbance reference plots (30 m) from a probability sampling distributed throughout the Conterminous United States. Validation results show that the overall accuracy (best F1 score) of S-CCD is 0.793 with 20% omission error and 21% commission error, slightly higher than that of COLD (0.789). Two disturbance sites respectively associated with wildfire and insect disturbances are used for qualitative map-based analysis. Both quantitative and qualitative analyses suggest that S-CCD achieves fewer omission errors than COLD for detecting those disturbances with subtle/gradual spectral change. In addition, S-CCD facilitates a better real-time monitoring, benefited by its complete recursive manner and a shorter lag for confirming disturbance than COLD (126 days vs. 166 days for alerting 50% disturbance events), and reached up to ~4.4 times speedup for computation. This research addresses the need for near-real-time monitoring and large-scale mapping of forest health and offers a new approach for operationally performing change detection tasks from dense Landsat-based time series.

Object-based image analysis (OBIA) has gained widespread popularity for creating maps from remotely sensed data. Researchers routinely claim that OBIA procedures outperform pixel-based procedures; however, it is not immediately obvious how to evaluate the degree to which an OBIA map compares to reference information in a manner that accounts for the fact that the OBIA map consists of objects that vary in size and shape. Our study reviews 209 journal articles concerning OBIA published between 2003 and 2017. We focus on the three stages of accuracy assessment: (1) sampling design, (2) response design and (3) accuracy analysis. First, we report the literature’s overall characteristics concerning OBIA accuracy assessment. Simple random sampling was the most used method among probability sampling strategies, slightly more than stratified sampling. Office interpreted remotely sensed data was the dominant reference source. The literature reported accuracies ranging from 42% to 96%, with an average of 85%. A third of the articles failed to give sufficient information concerning accuracy methodology such as sampling scheme and sample size. We found few studies that focused specifically on the accuracy of the segmentation. Second, we identify a recent increase of OBIA articles in using per-polygon approaches compared to per-pixel approaches for accuracy assessment. We clarify the impacts of the per-pixel versus the per-polygon approaches respectively on sampling, response design and accuracy analysis. Our review defines the technical and methodological needs in the current per-polygon approaches, such as polygon-based sampling, analysis of mixed polygons, matching of mapped with reference polygons and assessment of segmentation accuracy. Our review summarizes and discusses the current issues in object-based accuracy assessment to provide guidance for improved accuracy assessments for OBIA.

The expansion of tree plantations in tropical forests for commercial rubber cultivation threatens biodiversity which may affect ecosystem services, and hinders ecosystem productivity, causing net carbon emission. Numerous studies refer to the challenge of reliably distinguishing rubber plantations from natural forest, using satellite data, due to their similar spectral signatures, even when phenology is incorporated into an analysis. This study presents a novel approach for monitoring the establishment and expansion of rubber plantations in Seima Protection Forest (SPF), Cambodia (1995–2015), by detecting and analyzing the ‘shapelet’ structure in a Landsat-NDVI time series. This paper introduces a new classification procedure consisting of two steps: (1) an exhaustive-searching algorithm to detect shapelets that represent a period for relatively low NDVI values within an image time series; and (2) a t-test used to determine if NDVI values of detected shapelets are significantly different than their non-shapelet trend, thereby indicating the presence of rubber plantations. Using this approach, historical rubber plantation events were mapped over the twenty-year timespan. The shapelet algorithm produced two types of information: (1) year of rubber plantation establishment; and (2) pre-conversion land-cover type (i.e., agriculture, or natural forest). The overall accuracy of the rubber plantation map for the year of 2015 was 89%. The multi-temporal map products reveal that more than half of the rubber planting activity (57%) took place in 2010 and 2011, following the granting of numerous rubber concessions two years prior. Seventy-three percent of the rubber plantations were converted from natural forest and twenty-three percent were established on non-forest land-cover. The shapelet approach developed here can be used reliably to improve our understanding of the expansion of rubber production beyond Seima Protection Forest of Cambodia, and likely elsewhere in the tropics.

Spectral unmixing has been widely used by researchers in quantitative remote sensing due to the prevalence of mixed pixels in low-or middle-resolution images. In this article, six linear and nonlinear unmixing approaches – fully constrained least squares FCLS, bilinear-Fan model BFM, polynomial post-nonlinear model PPNM, supervised fuzzy c-means SFCM, Support Vector Machine SVM, and artificial neural network ANN – are applied with multispectral Landsat Thematic Mapper TM data in order to systematically compare their performance under different scenarios. In addition, a strategy of band selection was proposed for solving the endmember variability issue. The unmixing results were analysed in terms of the overall performance, pure and mixed data set, sub-scenes with different mixture proportions by calculating the accuracy indices: root mean square error RMSE and the Pearson correlation coefficient r. Nonlinear approaches can generate a closer abundance fraction map to reference, and have a higher overall accuracy than the linear approach. Nevertheless, the performance of nonlinear approaches differed dramatically with the increased proportion of mixed pixels in different study areas. SVM, SFCM, BFM, and PPNM depicted a scenario better when the proportion of mixed pixels was high, whereas ANN worked more effectively when processing large amounts of relatively pure pixels or mixed pixels with large/extreme proportions. The linear approach, in contrast, performed more consistently for various areas. Overall, our study indicates that nonlinear approaches are more effective than the linear one, especially for a study area consisting of different small parcels. The performance of nonlinear approaches is more sensitive to the proportion change of mixed pixels in a study area. The linear approach, however, is more appropriate for a rough estimation, particularly with little prior knowledge of the study area.

Multi-order inertial system is a common controlled object in industrial field. The conventional modeling method is least squares identification algorithm. However, it is necessary to determine the order of the object model before using the excitation data to identify it. This requires prior knowledge, and the identified model is prone to large deviations. This paper presents an intelligent modeling method for multi-order inertial objects. Firstly, the third-order inertial objects are discretized and the DNN deep learning network is embedded in the discrete structure to establish the intelligent model structure. Secondly, in order to ensure that the third-order inertial system can contain the object’s inertial time, three third-order inertial systems are set up in the intelligent model to estimate the inertial time of the controlled object, and the inertial time of the intermediate inertial system is set to be close to the object’s inertial time. At the same time, a margin of (± 50%) is given and set in the other two inertial systems. Finally, the pseudo-random excitation signal is added to the open-loop or closed-loop system of the object, and the input and output data are sent to the intelligent model for training, so the accurate identification model can be obtained. The validity of the intelligent model is verified by testing the simulation data. This method has great practical significance for the design and application of intelligent model.

The dynamic characteristics of long-span suspension bridges are complex. The natural vibration frequency is changed with different structural parameters, and the sensitivity to different parameters is different. In order to solve this problem, the spatial model of a long-span suspension bridge was established by using finite element software, and the first 20 natural vibration periods, natural vibration frequencies and vibration modes were analyzed and calculated. The accuracy of the obtained natural vibration frequency data was verified through field tests. Finally, based on the model, the stiffness of structural components is studied by one -factor-at-one-time, and the influence of various variables on the frequency and mode of a certain mode is studied by one-factor-at-one-time method. The results show that different structural parameters have different effects on the vibration frequency. When the stiffness of stiffening girder and main tower is changed, with the increase of stiffness, the variation of frequency mostly presents an upward trend, and the range is large. With the change of the secondary dead load, most of the frequencies decrease first and then tend to be stable. It can be seen from the field test results that the vibration shapes and frequencies measured by numerical simulation and test are close to each other, which can meet the requirements of engineering precision. The stiffness of the main cable and the main tower has a great influence on the modes and periods corresponding to them. The increase of the secondary dead load can reduce the natural vibration frequency of the suspension bridge, but it is not unlimited to increase the secondary dead load to reduce the frequency. The stiffness of the stiffening girder has a great influence on the frequency of the suspension bridge. When the bending stiffness of the stiffening girder increases to 3 times of the original one, the order of vibration modes of the structure will change. The research results can provide references for structural design and dynamic parameter adjustment of long-span suspension bridge.

This study developed a novel error compensation method aimed at eliminating placement error caused by hand–eye calibration and pick-and-place tool motions in automatic stiffener bonder for flexible printed circuit. Using the transformation of homogeneous coordinates to develop an error model of the system describing the coupling of errors among various coordinate systems, the least squares method is used to calculate the unknown model parameters. The experiment results demonstrate that this error compensation method reduced placement error by an order of magnitude. The mounting precision throughout the entire work area was ±0.046 mm at 3sigma, and for flexible printed circuit products with a specification limit of 0.1 mm, the process capability index of the automatic stiffener bonder in this study was 2.19. This represents that the system is capable of fully satisfying the precision requirements of flexible printed circuit stiffener bonding. The proposed system employing a vibrating feeder bowl and machine vision–aided target positioning is applicable to a variety of stiffeners, which enhances production flexibility. The proposed error model considers the complex coupling effect of the errors among multiple coordinate systems in hand–eye calibration, without the need of detecting and calculating the calibration error item by item, and takes into account the errors produced by the rotation and downward pressing motions of the pick-and-place tool.

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as “targeted change detection”. Based on a one-class classifier “Support Vector Domain Description (SVDD)”, a novel algorithm named “Three-layer SVDD Fusion (TLSF)” is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.