anexperimentalstudyonstressstrainbehaviorandconstitutivemodelofhardfillmaterial

Hardfill is a new type of artificially cemented material for dam construction works, with a wide application prospect. Its mechanical behavior lies between concrete and rockfill materials. A series of large-scale triaxial tests are performed on hardfill specimens at different ages, and the stress-strain behavior of hardfill is further discussed. The strength and stress-strain relationship of hardfill materials show both frictional mechanism and cohesive mechanism. An age-related constitutive model of hardfill is developed, which is a parallel model consisting of two components, rockfill component and cementation component. Moreover, a comparison is made between the simulated and the experimental results, which shows that the parallel model can reflect the mechanical characteristics of both rockfill-like nonlinearity and concrete-like age relativity. In addition, a simplified method for the determination of parameters is proposed

Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance

Priori Anchor Labels Supervised Scalable Multi-View Bipartite Graph Clustering

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge a certain extent. However, the unsupervised dynamic strategy usually cannot obtain the optimal anchors for MVC. The main reasons are that it does not consider the fairness of different views and lacks the priori supervised guidance. To completely solve these problems, we first propose the priori anchor graph regularization (PAGG) for scalable multi-view bipartite graph clustering, dubbed as SMGC method. Specifically, SMGC learns a few representative consensus anchors to simulate the numerous view data well, and constructs a bipartite graph to bridge the affinities between the anchors and original data points. In order to largely improve the quality of anchors, PAGG predefines prior anchor labels to constrain the anchors with discriminative cluster structure and fair view allocation, such that a better bipartite graph can be obtained for fast clustering. Experimentally, abundant of experiments are accomplished on six scalable benchmark datasets, and the experimental results fully demonstrate the effectiveness and efficiency of our SMGC

Automated Testing of Image Captioning Systems

Image captioning (IC) systems, which automatically generate a text description of the salient objects in an image (real or synthetic), have seen great progress over the past few years due to the development of deep neural networks. IC plays an indispensable role in human society, for example, labeling massive photos for scientific studies and assisting visually-impaired people in perceiving the world. However, even the top-notch IC systems, such as Microsoft Azure Cognitive Services and IBM Image Caption Generator, may return incorrect results, leading to the omission of important objects, deep misunderstanding, and threats to personal safety. To address this problem, we propose MetaIC, the \textit{first} metamorphic testing approach to validate IC systems. Our core idea is that the object names should exhibit directional changes after object insertion. Specifically, MetaIC (1) extracts objects from existing images to construct an object corpus; (2) inserts an object into an image via novel object resizing and location tuning algorithms; and (3) reports image pairs whose captions do not exhibit differences in an expected way. In our evaluation, we use MetaIC to test one widely-adopted image captioning API and five state-of-the-art (SOTA) image captioning models. Using 1,000 seeds, MetaIC successfully reports 16,825 erroneous issues with high precision (84.9\%-98.4\%). There are three kinds of errors: misclassification, omission, and incorrect quantity. We visualize the errors reported by MetaIC, which shows that flexible overlapping setting facilitates IC testing by increasing and diversifying the reported errors. In addition, MetaIC can be further generalized to detect label errors in the training dataset, which has successfully detected 151 incorrect labels in MS COCO Caption, a standard dataset in image captioning