A detailed investigation was conducted about the process of alkali activation of charred rice hulls using NaOH. A carbon-rich precursor was initially prepared from the pyrolysis of rice hulls under N2 atmosphere, part of it being leached with HF to remove silica. The precursor was then mixed with NaOH, heat-treated at activation temperatures from 600 to 800 °C, and part of the product was finally washed with distilled water. Thermogravimetric curves under O2 flux showed a strong reduction in the ash content of the activated samples, indicating the consumption of silica during the activation process. From X-ray diffractometry, 29Si, and 23Na NMR spectroscopy, it was possible to identify the formation of sodium carbonate and silicates in the non-washed samples. After washing, all these compounds were removed and specific surface area measurements indicated a substantial porosity development, with larger surface area values obtained for the samples prepared from the HF-leached precursor. The use of 23Na NMR spectroscopy indicated the retention of sodium in the washed samples, in a chemical environment distinct from carbonates and silicates. The shapes and positions of the observed resonance lines pointed to a disordered environment, associated with oxygenated surface groups within the porous structure of the activated carbons. 相似文献
Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.
Automatic Image Annotation (AIA) is the task of assigning keywords to images, with the aim to describe their visual content. Recently, an unsupervised approach has been used to tackle this task. Unsupervised AIA (UAIA) methods use reference collections that consist of the textual documents containing images. The aim of the UAIA methods is to extract words from the reference collection to be assigned to images. In this regard, by using an unsupervised approach it is possible to include large vocabularies because any word could be extracted from the reference collection. However, having a greater diversity of words for labeling entails to deal with a larger number of wrong annotations, due to the increasing difficulty for assigning a correct relevance to the labels. With this problem in mind, this paper presents a general strategy for UAIA methods that reranks assigned labels. The proposed method exploits the semantic-relatedness information among labels in order to assign them an appropriate relevance for describing images. Experimental results in different benchmark datasets show the flexibility of our method to deal with assignments from free-vocabularies, and its effectiveness to improve the initial annotation performance for different UAIA methods. Moreover, we found that (1) when considering the semantic-relatedness information among the assigned labels, the initial ranking provided by a UAIA method is improved in most of the cases; and (2) the robustness of the proposed method to be applied on different UAIA methods, will allow extending capabilities of state-of-the-art UAIA methods.
Load alleviation control is highly desirable to reduce penalties associated with the added structural mass required to withstand rare load scenarios. This is particularly true for wind turbine designs incorporating long‐span blades. Implementation of compliance‐based morphing structures to modify the lift distribution passively has the potential to mitigate the impact of rare, but integrally threatening, loads on wind turbine blades while limiting the addition of actuation and sensing systems. We present a novel passive load alleviation concept based on a morphing flap exhibiting selective compliance from an embedded bistable element. A multifidelity, aeroelastic tool is used to study the shape adaptability of a morphing flap indicating that passive changes from high lift generation to load alleviation configurations can be achieved by exploiting the energy of the flow. This mechanism offers a method to reduce catastrophic peak loads potentially, thus offering the possibility to lower the overall structural weight of wind turbine blades. 相似文献
In order to reduce the cost for delivering the ever increasing broadband services, network providers need to simplify their
network architectures and have a better control of the bandwidth. In this article, we propose a simple and cost-effective
bandwidth scalable passive optical network (PON) based on orthogonal frequency division multiplexing (BSOFDM-PON). We report
performance analysis in terms of the signal-to-noise ratio (SNR), bit-error-rate (BER), and error vector magnitude (EVM) of
a PON system accommodating 32 optical network units (ONUs). Our simulations have successfully demonstrated that throughputs
of 35.5 and 53.2 Gbps can be achieved using 16 and 64 QAM, respectively, within a total distance ranging from 20 to 30 km.
It gives throughputs of 1.10 and 1.66 Gbps per ONU. 相似文献