Application of Nonlinear Dynamical Methods for Arc Welding Quality Monitoring

Owing to its diverse, the stability of arc signals in high-powered submerged arc welding is not very salient, and weld defects are difficult to detect automatically. Aimed at this problem, this paper proposes a noise robustness algorithm for calibrating the singularity points and denoting the kinetics and stability of arc. Firstly, reconstruct a vector, which is the calculation of the approximate entropy in phase space, denotes the distortion of arc. Then, a algorithm for calculation is given based on reconstruction of chaotic time series in phase space. Finally, we apply the calculation of approximate entropy algorithm in phase space to flaw detection for arc signals, which is efficient proved by experimental results

Effect of secondary oxidation of pre-oxidized coal on early warning value for spontaneous combustion of coal

The indicative ability of a gas indicator for the spontaneous combustion of coal is affected by the secondary oxidation of oxidized coal, from old goafs, entering a new goaf through air leakages. This phenomenon can affect the accuracy of early warning systems regarding the spontaneous combustion of coal in a goaf. In this research, three kinds of coal were selected to carry out a spontaneous combustion simulation experiment in which a temperature-programmed experimental device was used to analyze the behavior of the index gas towards raw coal and oxidized coal, for which the latter was oxidized at 70 ¿C, 90 ¿C, 130 ¿C, and 150 ¿C. The results show that the chain alkane ratio in the secondary oxidation process and the trends of oxygen, CO, and C2H4 concentrations are the same as those in the primary oxidation process. On the other hand, the temperature at which C2H4 initially appears, during secondary oxidation, is lower than in primary oxidation. The CO produced in the early stage of secondary oxidation is greater than the CO produced, at the same temperature, in primary oxidation. In this regard, the usage of C2H4 concentration as an indicator with which to judge the occurrence of the spontaneous combustion of coal would allow for an earlier response. In the secondary oxidation process, the temperature of the extreme value of the alkene ratio appears higher than in primary oxidation. The presence of a higher pre-oxidation temperature and a higher proportion of secondary oxidation gas will affect an indicator’s judgement when the primary oxidation enters the severe oxidation stage. The gas produced by secondary oxidation will affect the early warning of the spontaneous combustion of coal in the coal mine goaf, which should be considered in the establishment of an early warning system.This work was supported by the National Natural Science Foundation of China [52074285].Peer ReviewedObjectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Simulating the effects of management practices on cropland soilorganic carbon changes in the Temperate Prairies Ecoregion of theUnited States from 1980 to 2012

Understanding the effects of management practices on soil organic carbon (SOC) is important for design-ing effective policies to mitigate greenhouse gas emissions in agriculture. In the Midwest United States,management practices in the croplands have been improved to increase crop production and reduce SOCloss since the 1980s. Many studies of SOC dynamics in croplands have been performed to understandthe effects of management, but the results are still not conclusive. This study quantified SOC dynam-ics in the Midwest croplands from 1980 to 2012 with the General Ensemble Biogeochemical ModellingSystem (GEMS) and available management data. Our results showed that the total SOC in the croplandsdecreased from 1190 Tg C in 1980 to 1107 TgC in 1995, and then increased to 1176 TgC in 2012. Contin-uous cropping and intensive tillage may have driven SOC loss in the early period. The increase of cropproduction and adoption of conservation tillage increased the total SOC so that the decrease in the totalSOC stock after 32 years was only 1%. The small change in average SOC did not reflect the large spatialvariations of SOC change in the region. Major SOC losses occurred in the north and south of the region,where SOC baseline values were high and cropland production was low. The SOC gains took place in thecentral part of the region where SOC baseline values were moderate and cropland production was higherthan the other areas. We simulated multiple land-use land-cover (LULC) change scenarios and analyzedthe results. The analysis showed that among all the LULC changes, agricultural technology that increasedcropland production had the greatest impact on SOC changes, followed by the tillage practices, changesin crop species, and the conversions of cropland to other land use. Information on management practiceinduced spatial variation in SOC can be useful for policy makers and farm managers to develop long-termmanagement strategies for increasing SOC sequestration in different areas

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration

The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference. Common video encoding configurations (e.g., resolution and frame rate) have been identified with significant impacts on striking the balance between bandwidth consumption and inference accuracy and therefore their adaption scheme has been a focus of optimization. However, previous profiling-based solutions suffer from high profiling cost, while existing deep reinforcement learning (DRL) based solutions may achieve poor performance due to the usage of fixed reward function for training the agent, which fails to craft the application goals in various scenarios. In this paper, we propose ILCAS, the first imitation learning (IL) based configuration-adaptive VA streaming system. Unlike DRL-based solutions, ILCAS trains the agent with demonstrations collected from the expert which is designed as an offline optimal policy that solves the configuration adaption problem through dynamic programming. To tackle the challenge of video content dynamics, ILCAS derives motion feature maps based on motion vectors which allow ILCAS to visually ``perceive'' video content changes. Moreover, ILCAS incorporates a cross-camera collaboration scheme to exploit the spatio-temporal correlations of cameras for more proper configuration selection. Extensive experiments confirm the superiority of ILCAS compared with state-of-the-art solutions, with 2-20.9% improvement of mean accuracy and 19.9-85.3% reduction of chunk upload lag.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

MetaLoc: Learning to Learn Wireless Localization

The existing indoor fingerprinting-based localization methods are rather accurate after intensive offline calibrations for a specific environment, and they are built based either on the received signal strength (RSS) or the channel state information (CSI). However, a well-calibrated localization method (which can be a pure statistical signal processing method or an emerging data-driven method) will present poor generalization abilities in changing environments, which results in large losses in knowledge and human effort. To break the environment-specific localization bottleneck, we propose a novel data-driven fingerprinting-based localization framework empowered by the model-agnostic meta-learning (MAML), named MetaLoc. Specifically, MetaLoc is characterized by its ability to rapidly adapt itself to a new, possibly unseen, environment with very little calibration. The underlying data-driven localization model is a deep neural network, and we leverage historical data previously collected from various well-calibrated environments to train an optimal set of meta-parameters as an initialization to the new environments. Furthermore, we develop two MetaLoc paradigms in the proposed MetaLoc based on the different ways of obtaining meta-parameters. The centralized paradigm using vanilla MAML is much easier to implement, while the distributed paradigm incorporates domain shifts into the vanilla MAML to accelerate the convergence speed of the training process. The experimental results obtained for both synthetic- and real datasets demonstrate MetaLoc's strengthes in terms of localization error, robustness and cost-effectiveness compared with various baseline methods

Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors

Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q2 = 0.823, r2 = 0.979; CoMSIA model: q2 = 0.804, r2 = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors

Efficient View Synthesis with Neural Radiance Distribution Field

Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to the need for multiple network forwardings to render a single pixel. Existing methods to improve NeRF either reduce the number of required samples or optimize the implementation to accelerate the network forwarding. Despite these efforts, the problem of multiple sampling persists due to the intrinsic representation of radiance fields. In contrast, Neural Light Fields (NeLF) reduce the computation cost of NeRF by querying only one single network forwarding per pixel. To achieve a close visual quality to NeRF, existing NeLF methods require significantly larger network capacities which limits their rendering efficiency in practice. In this work, we propose a new representation called Neural Radiance Distribution Field (NeRDF) that targets efficient view synthesis in real-time. Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF. The key is to model the radiance distribution along each ray with frequency basis and predict frequency weights using the network. Pixel values are then computed via volume rendering on radiance distributions. Experiments show that our proposed method offers a better trade-off among speed, quality, and network size than existing methods: we achieve a ~254x speed-up over NeRF with similar network size, with only a marginal performance decline. Our project page is at yushuang-wu.github.io/NeRDF.Comment: Accepted by ICCV202

Chromosomal DNA deletion confers phage resistance to Pseudomonas aeruginosa.

Bacteria develop a broad range of phage resistance mechanisms, such as prevention of phage adsorption and CRISPR/Cas system, to survive phage predation. In this study, Pseudomonas aeruginosa PA1 strain was infected with lytic phage PaP1, and phage-resistant mutants were selected. A high percentage (~30%) of these mutants displayed red pigmentation phenotype (Red mutant). Through comparative genomic analysis, one Red mutant PA1r was found to have a 219.6 kb genomic fragment deletion, which contains two key genes hmgA and galU related to the observed phenotypes. Deletion of hmgA resulted in the accumulation of a red compound homogentisic acid; while A galU mutant is devoid of O-antigen, which is required for phage adsorption. Intriguingly, while the loss of galU conferred phage resistance, it significantly attenuated PA1r in a mouse infection experiment. Our study revealed a novel phage resistance mechanism via chromosomal DNA deletion in P. aeruginosa