Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

Towards finding the best-fit distribution for OSN data

The Journal of Supercomputing - Currently, all online social networks (OSNs) are considered to follow a power-law distribution. In this paper, the degree distribution for multiple OSNs has been...     

Reversible data hiding based on RSBEMD coding and adaptive multi-segment left and right histogram shifting

By leveraging the secret data coding using the remainder storage based exploiting modification direction (RSBEMD), and the pixel change operation recording based on multi-segment left and right histogram shifting, a novel reversible data hiding (RHD) scheme is proposed in this paper. The secret data are first encoded by some specific pixel change operations to the pixels in groups. After that, multi-segment left and right histogram shifting based on threshold manipulation is implemented for recording the pixel change operations. Furthermore, a multiple embedding policy based on chess board prediction (CBP) and threshold manipulation is put forward, and the threshold can be adjusted to achieve adaptive data hiding. Experimental results and analysis show that it is reversible and can achieve good performance in capacity and imperceptibility compared with the existing methods.     

Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Reactive pulsed laser deposited N–C codoped TiO2 thin films

TiO₂ is a large bandgap chemically stable oxide useful for several applications that involve photo-activated processes, including photocatalysis, photovoltaics, photoelectrolysis, etc. However, the large band gap renders this material not a very efficient absorber of the solar spectrum. Various schemes of cation and anion doping have been utilized that reduce this deficiency to a certain extent. In this paper we present the results of N–C codoping of TiO₂ thin films deposited by a reactive pulsed laser deposition technique. These films were compared for their optical and structural properties with undoped, N doped and C doped TiO₂ films prepared by the same technique. While all samples contained polycrystalline anatase phase, varying N₂ and CH₄ partial pressures resulted in change in TiO₂ lattice parameters due to codoping. X-ray diffraction high-resolution scans show the evidence of C incorporation into TiO₂ lattice by 2θ shift in (101) reflections due to large ionic radius of C. N doping was confirmed by XPS analyses. Direct relationship between oxygen vacancies and doping concentration was established by the deconvolution of XPS peaks. Considerable bandgap reduction occurred that was measured by using UV–vis diffuse reflectance spectroscopy. Results show that reactive pulsed laser deposition is indeed a useful method for the synthesis of codoped TiO₂ thin films as bandgap reduction of ~1.00 eV via N–C codoping was successfully achieved.     

A novel fast distance relay for series compensated transmission lines

This paper presents a fast distance relay for series compensated transmission lines based on the R–L differential-equation algorithm using the theory of equal transfer process of transmission lines. The measuring distances based on the proposed algorithm can fast approach the actual value of fault distance when a fault occurs in front of the series capacitor. When a fault occurs behind of the series capacitor, the fault loop, including the series capacitor, does not match the R–L transmission line model, so the measuring distances fluctuate severely. Based on this, the relative position of the fault with respect to the series capacitor can be judged effectively according to the fluctuation range of the measuring distances, and the accurate fault location can be obtained fast. A variety of PSCAD/EMTDC simulation tests show that the new relay has fast operating speed and high accuracy when applied to the long series compensated transmission lines.     

Fabrication,structural and vibrational properties,and physical and optical properties tailoring of nanocrystalline MoS2 films

The modification and tuning features of nanostructured films are of great interest because of controllable and distinctive inherent properties in these materials. Here, nanocrystalline MoS₂ films were fabricated on the stainless steels by a radio frequency magnetron sputtering at ambient temperature. X-ray photoelectron spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and Raman scattering spectroscopy were used to study the chemical state, chemical composition, crystal structure and vibrational properties of the fabricated MoS₂ films. The bias voltage dependent structural evolution and its influence on the optical properties of MoS₂ nanocrystalline films were systematically investigated. Besides, the residual stresses of MoS₂ nanocrystalline films were explored by employing sin²ψ approach. X-ray diffraction demonstrates that the nanocrystalline MoS₂ films have single-phase hexagonal crystal structure. All MoS₂ films are polycrystalline in nature. The bandgap values are found to be intensively dependent on bias voltage. Our findings show that the nanocrystalline MoS₂ films with different physical properties and intense quantum confinement effect can be realized through adjusting bias voltages. This work may provide deep insight for realizing transitional metal dichalcogenide-based nanostructured film optoelectronic devices with tunable physical properties through a traditional, very cost-effective, and large-scale fabrication method.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.