Dynamic change-point detection using similarity networks

From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a simple sequential change detection procedure based on node-wise average similarity measures, and study its theoretical property. Simulation and real-data examples demonstrate such a simply stopping procedure has reasonably good performance. We further discuss the faulty sensor isolation (estimating anomalous nodes) using community detection.Comment: appeared in Asilomar Conference 201

Blockchain enabled industrial Internet of Things technology

The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past a few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper we address the integration of blockchain and IIoT from the industrial prospective. A blockchain enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain enabled IIoT

Novel research methods for estimating the impact of energy use on ecological environment: evidence from B.R.I.C.S. economies

The current study looked at the influence of fossil-fuel energy (E.U.) consumption, renewable power generation and greenhouse gas emissions in Brazil, Russia, India, China, and South Africa (B.R.I.C.S.) between 1990 and 2020. The latest study also takes into account the influence of gross domestic product (G.D.P.) and technological innovation on carbon emissions. Using cross-sectional dependence and slope heterogeneity, the order of the unit root is also determined. The findings acquired by the application of moment quantile regression. The research finds that G.D.P. and the usage of E.U. increase carbon emissions at the 25th, 50th, 75th and 90th quantiles. On the other hand, renewable energy generation and technical innovation reduce carbon emissions at the 25th, 50th, 75th and 90th quantiles. Furthermore, while implementing B.R.I.C.S. economies’ energy, environment, and growth policies based on empirical data, policymakers should analyse the asymmetry behaviour of G.D.P., E.U. consumption, renewable power output and technological innovation

Behavior-oriented numerical modeling of nearshore oceanic current and application on sea harbor

590-602The West Guangdong longshore current (WG current) is a unique oceanic current system. Plenty of field survey datasets indicated that it flows uni-directionally from north-east to south-west in the entire year even during the south-west monsoon season. At present, the natural formation mechanism of the WG current remains controversial, and the traditional process-oriented modeling method could not deal with the dilemma of the scaling mismatch between the regional ocean circulation (several thousand kilometers) and harbor structure (several hundred meters). To solve this problem, in this paper, a behavior-oriented modeling concept was developed, wherein the contribution of the WG current was considered by incorporating additional net flow flux in the hydrodynamic model to separate it from the tidal currents. Through rigorous validations according to the site observed datasets, the proposed modeling concept was found to have good precision. Using the Jida Harbor as a real-life case, the modeling results showed that after the combination of the tidal current and WG current, the westward cross-flow speed in the approach channel could exceed 0.5 m/s, and at the harbor entrance the WG current induces an intense local circulation cell while ebbing, which may bring in additional maneuver risk to the ships

Semi-Analytical Modeling of Fractured Horizontal Wells in Heterogeneous Formations Considering the Interference between Hydraulic Fractures

In this study, a semi-analytical model is developed for the pressure and rate transient analysis of multi-stage fractured horizontal wells. This model simulates the fluid flow towards a fractured horizontal well centered in an unconventional formation with considering the interferences between hydraulic fractures under various heterogeneity conditions. In this proposed model, the formation is divided into sub-systems, and each sub-system is further composed of linear flow regions. Boundaries of the linear flow regions are being updated in real-time response to the interferences between hydraulic fractures. Applicability of the proposed model in heterogeneous reservoirs is demonstrated by the comparison with the five-region model published in literature. The proposed model is applicable to the heterogeneity conditions including a fractured horizontal well having heterogeneous completions and/or the formation being heterogeneous in reservoir properties. Furthermore, the proposed model is utilized to analyze field data from fractured horizontal wells in heterogeneous conditions

Enhancement of Near-Field Radiative Heat Transfer based on High-Entropy Alloys

The enhancement of near-field radiative heat transfer (NFRHT) has now become one of the research hotspots in the fields of thermal management and imaging due to its ability to improve the performance of near-field thermoelectric devices and near-field imaging systems.  In this paper, we design three structures (multilayer structure, nanoporous structure, and nanorod structure) based on high-entropy alloys to realize the enhancement of NFRHT. By combining stochastic electrodynamics and Maxwell-Garnett's description of the effective medium, we calculate the radiative heat transfer under different parameters and find that the nanoporous structure has the largest enhancement effect on NFRHT. The near-field heat transfer factor (q) of this structure (q = 1.40×109 W/ (m2•K)) is three times higher than that of the plane structure (q = 4.6×108 W/ (m2•K)), and about two orders of magnitude higher than that of the SiO2 plate. This result provides a fresh idea for the enhancement of NFRHT and will promote the application of high-entropy alloy materials in near-field heat radiation

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.Comment: Accepted by Engineering Applications of Artificial Intelligence journal on Oct 31th. Upload the accepted clean versio

Electrocardiogram of a Silver Nanowire Based Dry Electrode: Quantitative Comparison With the Standard Ag/AgCl Gel Electrode

Novel dry electrodes have promoted the development of wearable electrocardiogram (ECG) that is collected in daily life to monitor the ambulatory activity of heart status. To evaluate the performance of a dry electrode, it is necessary to compare it with the commercial disposable silver/silver chloride (Ag/AgCl) gel electrode. In this paper, a silver nanowire (AgNW)-based dry electrode was fabricated for noninvasive and wearable ECG sensing. Signals from the AgNW electrode and the Ag/AgCl electrode were simultaneously collected in two conditions: sitting and walking. Signal quality was evaluated in terms of ECG morphology, R-peak to R-peak interval, and heart rate variability analysis. Quantitative comparisons showed that the AgNW electrode could collect acceptable ECG waveforms as the Ag/AgCl electrode in both the sitting and walking conditions. However, the baseline drift and waveform distortions existed in the AgNW electrode, likely due to electrode motion. If the skin-electrode contact is improved, the dry electrode can be a promising substitute for the Ag/AgCl electrode