The localization of sensing nodes is most pronounced in the application of wireless sensor networks. To address this issue, a node localization algorithm called the DMA is proposed in this paper. This algorithm identifies the node position by using the estimation matrix and distance matrix together with the optimized linear transforming function. With the integration of GA, the position of the node can be accurately determined. The conducted simulation outcomes and the corresponding analysis verify the high accuracy and low energy consumption of the proposed algorithm, which can outperform other widely used approaches. This study designs and deploys the proposed algorithm and shows its sensor node localization theory, which makes it a promising basis for the realization of positioning in WSNs.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Inertia-dominated droplet impact transfers momentum to a dry flat target within a short span of time t characterized by (droplet diameter D)/(impact speed U). We investigate experimentally how impact force dynamics change when a droplet hits a thin liquid film of thickness H, less than or approximately equal to the droplet diameter, atop the flat target. Impact force and morphology are recorded simultaneously by piezoelectric force sensing and high-speed video imaging. Compared with a dry surface, the force of droplet impact on a thin liquid film is found to follow the same initial [Formula: see text] scaling and reach a slightly higher peak value, but at a significantly later time. Modeling the impact process as a perfect inelastic collision between the droplet and a liquid column of height equal to the film thickness yields the proper timescale [Formula: see text] to characterize temporal evolution of the impact force near the inertial peak and through its subsequent exponential decay. The impact crater penetration depth developing within the thin film over the same time span is also found to collapse to a self-similar form based on this characteristic timescale, which attests to the validity of the inelastic collision model in capturing the underlying impact flow physics.
Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets
Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.
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