ALBERT

Description: Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.

Description: The quality of Computed Tomography (CT) images crucially depends on the precise knowledge of the scanner geometry. Therefore, it is necessary to estimate and calibrate the misalignments before image acquisition. In this paper, a Two-Piece-Ball (TPB) phantom is used to estimate a set of parameters that describe the geometry of a cone-beam CT system. Only multiple projections of the TPB phantom at one position are required, which can avoid the rotation errors when acquiring multi-angle projections. Also, a corresponding algorithm is derived. The performance of the method is evaluated through simulation and experimental data. The results demonstrated that the proposed method is valid and easy to implement. Furthermore, the experimental results from the Micro-CT system demonstrate the ability to reduce artifacts and improve image quality through geometric parameter calibration.

Description: Paraffin fuel has been found to have a regression rate that is higher than conventional HTPB (hydroxyl-terminated polybutadiene) fuel and, thus, presents itself as an ideal energy source for a hybrid rocket engine. The energy characteristics of paraffin-based fuel and HTPB fuel have been calculated by the method of minimum free energy. The thermal degradation characteristics were measured for paraffin, pretreated paraffin, HTPB and paraffin-based fuel in different working conditions by the using differential scanning calorimetry (DSC) and a thermogravimetric analyzer (TGA). The regression rates of paraffin-based fuel and HTPB fuel were tested by a rectangular solid-gas hybrid engine. The research findings showed that: the specific impulse of paraffin-based fuel is almost the same as that of HTPB fuel; the decomposition temperature of pretreated paraffin is higher than that of the unprocessed paraffin, but lower than that of HTPB; with the increase of paraffin, the initial reaction exothermic peak of paraffin-based fuel is reached in advance, and the initial reaction heat release also increases; the regression rate of paraffin-based fuel is higher than the common HTPB fuel under the same conditions; with the increase of oxidizer mass flow rate, the regression rate of solid fuel increases accordingly for the same fuel formulation.

Description: The cumulative error at a point in a LiDAR-derived DEM consists of three components: propagated LiDAR-sensor error, propagated ground error, and interpolation error. To combine these error components so as to assess the vertical accuracy of a LiDAR-derived DEM, statistical methods based on the error propagation theory are often used. Due to the existence of systematic error, statistical methods are only effective if a large number of checkpoints are available, which may not be affordable in many practical applications. This paper presents approximation theory as an alternative methodology that departs from error propagation theory in fundamental ways. Using approximation theory, an error bound of the cumulative error at any point in the study site can be obtained, thus informing users conservatively of the spatial variation of DEM accuracy and pointing out the weakly determined areas. The new method is illustrated from DEM users’ perspective by assessing whether a publicly available LiDAR-derived DEM meets FEMA’s accuracy standard for flood risk mapping. The paper calls for a change in the existing methods of assessing and reporting the errors in a LiDAR-derived DEM, in particular those introduced during the ground filtering process.

Description: With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Description: Ethylene as an indicator for evaluating fruit ripening can be measured by very sensitive electrochemical gas sensors based on a high-resolution current produced by a bias potential applied to the electrodes. For this purpose, a measurement system for monitoring ethylene gas concentrations to evaluate fruit ripening by using the electrochemical ethylene sensor was successfully developed. Before the electrochemical ethylene sensor was used to measure the ethylene gas concentrations released from fruits, a calibration curve was established by the standard ethylene gases at concentrations of 2.99 ppm, 4.99 ppm, 8.01 ppm and 10 ppm, respectively, with a flow rate of 0.4 L·min−1. From the calibration curve, the linear relationship between the responses and concentrations of ethylene gas was obtained in the range of 0–10 ppm with the correlation coefficient R2 of 0.9976. The micropump and a novel signal conditioning circuit were implemented in this measurement, resulting in a rapid response in detecting ethylene concentrations down to 0.1 ppm in air and in under 50 s. In this experiment, three kinds of fruits—apples, pears and kiwifruits—were studied at a low concentration (under 0.8 ppm) of trace ethylene content in the air exhaled by fruits. The experimental results showed that a low cost, compact measurement system constructed by using an electrochemical ethylene sensor has a high sensitivity of 0.3907 V·ppm−1 with a theoretical detection limit of 0.413 ppm, and is non-invasive and highly portable.

Description: Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

Description: The aim of this review was to assess the current viable technologies for wireless power delivery and data transmission through metal barriers. Using such technologies sensors enclosed in hermetical metal containers can be powered and communicate through exterior power sources without penetration of the metal wall for wire feed-throughs. In this review, we first discuss the significant and essential requirements for through-metal-wall power delivery and data transmission and then we: (1) describe three electromagnetic coupling based techniques reported in the literature, which include inductive coupling, capacitive coupling, and magnetic resonance coupling; (2) present a detailed review of wireless ultrasonic through-metal-wall power delivery and/or data transmission methods; (3) compare various ultrasonic through-metal-wall systems in modeling, transducer configuration and communication mode with sensors; (4) summarize the characteristics of electromagnetic-based and ultrasound-based systems, evaluate the challenges and development trends. We conclude that electromagnetic coupling methods are suitable for through thin non-ferromagnetic metal wall power delivery and data transmission at a relatively low data rate; piezoelectric transducer-based ultrasonic systems are particularly advantageous in achieving high power transfer efficiency and high data rates; the combination of more than one single technique may provide a more practical and reliable solution for long term operation.

Description: By exploiting the statistical analysis method, human dynamics provides new insights to the research of human behavior. In this paper, we analyze the characteristics of the computer operating behavior through a modified multiscale entropy algorithm with both the interval time series and the number series of individuals’ operating behavior been investigated. We also discuss the activity of individuals’ behavior from the three groups denoted as the retiree group, the student group and the worker group based on the nature of their jobs. We find that the operating behavior of the retiree group exhibits more complex dynamics than the other two groups and further present a reasonable explanation for this phenomenon. Our findings offer new insights for the further understanding of individual behavior at different time scales.

Description: To determine the phenotypic variation in 700 ten-year grafted Chinese fir collected from six provinces in southern China, 10 phenotypic traits were investigated: tree height, diameter at breast height, bark thickness, volume of timber, heartwood ratio, density of wood, hygroscopicity, tracheid length, tracheid diameter, and ratio of tracheid length to tracheid diameter. Abundant phenotypic variation was found among the six populations; the phenotypic variation coefficients all exceeded 10%, and the largest was for volume of timber. Significant variation (p 〈 0.01 or 0.05) in traits was found among the populations, except for diameter at breast height, heartwood ratio, and tracheid diameter, while all traits differed significantly (p 〈 0.01) within populations. The high value of repeatability (broad-sense heritability) suggested moderate genetic control of the traits. The 10 traits were strongly correlated within the entire population; strong positive correlations (p 〈 0.01) were observed between growth traits, and significant negative correlations (p 〈 0.01 or 0.05) were found between the density of wood and most other characteristics, except for heartwood ratio and ratio of tracheid length to tracheid diameter. Using diameter at breast height and density of wood as criteria, 98 relatively fast-growing genotypes with relatively high wood basic density were identified.