The thermal conductive cyanate ester/epoxy (CE/AG80) composites with hexagonal boron nitride (h-BN) reinforced were manufactured and investigated. The low and stable dielectric constant/loss of the ...composites with respect to both frequency (500 Hz-100 kHz) and temperature (50-350 °C) were discussed as functions of the content of h-BN. It was found that the low and stable dielectric constant/ loss of the composites can be maintained in the whole frequency range at room temperature, and the mutations of constant/loss at the frequency of 1 kHz occurred above 280 °C for all of the samples. Moreover, a remarkable enhancement of the thermal conductivity (0.54 W/mK a 1.8-fold enhancement) was achieved with the introduction of h-BN and significantly outperformed traditional thermal conductive fillers. The significant improvements of the thermal conductivity of the composites were analyzed by the Agari’s model and the parameters of
C
1
and
C
2
were also calculated.
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative ...optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.
Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. However, the task of deciphering ...OBI, in the current climate of the scholarship, can prove extremely challenging. Out of the 4,500 oracle bone characters excavated, only a third have been successfully identified. Therefore, leveraging the advantages of advanced AI technology to assist in the decipherment of OBI is a highly essential research topic. However, fully utilizing AI's capabilities in these matters is reliant on having a comprehensive and high-quality annotated OBI dataset at hand whereas most existing datasets are only annotated in just a single or a few dimensions, limiting the value of their potential application. For instance, the Oracle-MNIST dataset only offers 30k images classified into 10 categories. Therefore, this paper proposes an Oracle Bone Inscriptions Multi-modal Dataset(OBIMD), which includes annotation information for 10,077 pieces of oracle bones. Each piece has two modalities: pixel-level aligned rubbings and facsimiles. The dataset annotates the detection boxes, character categories, transcriptions, corresponding inscription groups, and reading sequences in the groups of each oracle bone character, providing a comprehensive and high-quality level of annotations. This dataset can be used for a variety of AI-related research tasks relevant to the field of OBI, such as OBI Character Detection and Recognition, Rubbing Denoising, Character Matching, Character Generation, Reading Sequence Prediction, Missing Characters Completion task and so on. We believe that the creation and publication of a dataset like this will help significantly advance the application of AI algorithms in the field of OBI research.
PDF