A Finite Queue Model Analysis of PMRC-based Wireless Sensor networks

In our previous work, a highly scalable and fault- tolerant network architecture, the Progressive Multi-hop Rotational Clustered (PMRC) structure, is proposed for constructing large-scale wireless sensor networks. Further, the overlapped scheme is proposed to solve the bottleneck problem in PMRC-based sensor networks. As buffer space is often scarce in sensor nodes, in this paper, we focus on studying the queuing performance of cluster heads in PMRC-based sensor networks. We develop a finite queuing model to analyze the queuing performance of cluster heads for both non-overlapped and overlapped PMRC-based sensor network. The average queue length and average queue delay of cluster head in different layers are derived. To validate the analysis results, simulations have been conducted with different loads for both non- overlapped and overlapped PMRC-based sensor networks. Simulation results match with the analysis results in general and confirm the advantage of selecting two cluster heads over selecting single cluster head in terms of the improved queuing performance

Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

Macro-mesoscopic perspective damage characteristics and energy-damage constitutive model of coal-rock composite structures subjected to cyclic loading

During deep coal mining processes, periodic mining disturbances cause the neighboring coal strata to bear the effects of cyclic loading and unloading, making it essential to study the mechanical responses and macro-micro failure characteristics of the coal-rock composite structures under different cyclic loads. In this study, three different loading rates were selected to perform uniaxial cyclic compression tests (with simultaneous acoustic emission signal measurement) under two types of cyclic loads, investigating the damage characteristics of coal-rock composites. Based on the principle of energy dissipation, an energy-damage constitutive model for the cyclic loading of composites was constructed and validated with experimental data. The results indicate that the loading rate is directly proportional to the peak strength of the composite specimen, where the peak stress increased by 22.44% and 28.89% for the gradual cyclic loading and unloading path (path I) and the cyclic loading and unloading path (Path II) respectively. The higher the loading rate, the faster the internal crack extension in the specimen, the crushing degree of the coal component in the coal-rock composite specimen is intensified, and the fractal dimension increases subsequently, and the faster the internal crack extension in the specimen becomes. With the increase of the loading rate, the damage along the matrix in the coal fraction increases. The paths with a large span of cyclic gradation (Path I) contribute to stress transfer within the specimen and provide favorable conditions for the development of cracks within the specimen, leading to a higher degree of damage in the corresponding specimen. The consistency between the test curves and the energy-damage constitutive model curves is relatively high, indicating that the proposed energy-damage constitutive model can well describe the deformation behavior of the coal-rock composite specimens during cyclic loading and unloading processes

Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

Load-Similar Node Distribution for Prolonging Network Lifetime in PMRC-Based Wireless Sensor Networks

In this paper, the energy hole problem in Progressive Multi-hop Rotational Clustered (PMRC)-based wireless sensor networks (WSNs) is studied. We first analyze the traffic load distribution in PMRC-based WSNs. Based on the analysis, we propose a novel load-similar node distribution strategy combined with the Minimum Overlapping Layers (MOL) scheme to solve the energy hole problem in PMRC-based WSNs. Simulation results demonstrate that the load-similar node distribution strategy significantly prolongs network lifetime than uniform node distribution and an existing nonuniform node distribution strategies. The analysis model and the proposed load-similar node distribution strategy have the potential to be applied to other multi-hop WSN structures

Interactive visual cluster analysis by contrastive dimensionality reduction

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions