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To study the difference of volatile substances and their relative contents in different fermentation conditions, the optimal fermentation conditions were determined according to the important aroma volatile compounds profiling. The fermentation time, the amounts of SO2, and yeast addition were used as fermentation conditions, using the headspace solid phase microextraction (HS‐SPME) combined with gas chromatography–mass spectrometry (GC–MS) analysis, a total of 182 volatile substances were identified in nine treatments. In nine treatments, a total of 182 volatile compounds were detected. However, the majority of detected volatile compounds were esters, with a total number of 37. Each treatment had the maximum quantity and relative concentration of esters; moreover, five esters (ethyl acetate, isobutyl acetate, and others) were discovered in all treatments. PCA results showed a large positive relation between esters and fermentation treatments (85.79%). The addition of 40 mg/L SO2, 100 mg/L yeast, and a fermentation time of 2 days (T7) were recognized as the optimum fermentation conditions, where 14 ester compounds were detected and a relative ester content of 65.65% was achieved. Ultimately, the results indicate that with the optimized fermentation conditions of this study, grape wines with more fruity, sweet, milky, and better ester concentrations can be produced and could also provide a certain reference for the production of other fruit wines. Novelty impact statement: A total of 182 volatile substances were identified in nine treatments. The number and relative content of esters were the highest in each treatment. Five esters (ethyl acetate, isobutyl acetate, and others) were detected in all the treatments. [ABSTRACT FROM AUTHOR]

This paper presents a new paradigm of explicitly modeling and harnessing the data structure to address the inverse problems in structural dynamics, identification, and data-driven health monitoring. In particular, it is shown that the structural dynamic features and damage information, intrinsic within the structural vibration response measurement data, possesses sparse and low-rank structure, which can be effectively modeled and processed by emerging mathematical tools such as sparse representation and compressed sensing, low-rank matrix decomposition and completion, as well as the unsupervised multivariate blind source separation. It is also discussed that explicitly modeling and harnessing the sparse and low-rank data structure could benefit future work in developing data-driven approaches toward rapid, unsupervised, and effective system identification, damage detection, as well as massive SHM data sensing and management. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Digital cameras are cost-effective vision sensors and able to directly provide two-dimensional information of structural condition in monitoring and assessment applications. For example, digital cameras are essential components of unmanned aerial vehicles (UAVs) and robotic agents for mobile sensing and inspection of pipelines, buildings, transportation infrastructure, etc, especially in post-natural disaster and man-made extreme events assessment. Additionally, while surveillance cameras have been widely used for transportation systems (e.g., traffic monitoring), if appropriately mounted on the large-scale structures such as the bridges, they can continuously monitor the structural condition under operational loads and hazards, complementing the regular visual inspection and assessment conducted by experts. In these or other applications, efficiently and reliably transferring the structural images or videos, which are as such large-scale, are important and challenging, especially in wireless platform that is either required (e.g., UAVs and robotic agents) or more suitable (e.g., camera monitoring networks) with only limited power and communication resources. This paper studies the computational algorithms for efficient and reliable transmission of the structural monitoring images; in particular, the compressed sensing (CS) technique is explored for robust data transmission and recovery. The sparse representation or data structure of the structural images is exploited, leading to the CS based central strategy: on some sparse domain, randomly encode large-scale image data into few relevant coefficients, which are then transferred (robust to random data loss) and recovered (in base station) for subsequent structural health diagnosis. Image data of bench scale pipe structure, concrete structure and full scale stay cable are employed for validation of the CS based method. Its performance is also compared with traditional transform coding and low-dimensional encoding (sampling), and their advantages and drawbacks are discussed. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Structural vibration responses themselves contain rich dynamic information, exploiting which can lead to tackling the challenging problem: simultaneous denoising of both gross errors (outliers) and dense noise that are not uncommon in the data acquisition of SHM systems. This paper explicitly takes advantage of the fact that typically only few modes are active in the vibration responses; as such, it is proposed to re-stack the response data matrix to guarantee a low-rank representation, through which even heavy gross and dense noises can be efficiently removed via a new technique termed principal component pursuit (PCP), without the assumption that sensor numbers exceed mode numbers that used to be made in traditional methods. It is found that PCP works extremely well under broad conditions with the simple but effective strategy no more than reshaping the data matrix for a low-rank representation. The proposed PCP denoising algorithm overcomes the traditional PCA (or SVD) and low-pass filter denoising algorithms, which can only handle dense (Gaussian) noise. The application of PCP on the health monitoring data of the New Guangzhou TV Tower (Canton Tower) shows its potential for practical usage. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Tension force is a crucial indicator in reflecting the stressing state of old cables during the cable replacement process. Even though the vibration‐based method is popular in the cable force identification due to its simple calculation process and low cost, the frequency is hard to be recognized with both high time and frequency resolutions attributed to the Heisenberg uncertainty principle, which hinders its application in identifying time‐varying cable force. In this paper, a novel quasi‐real‐time cable force identification method is presented based on a quasi‐ideal time‐frequency analysis method called multi‐synchrosqueezing transform (MSST), by which the cable frequencies can be identified with appreciable time‐frequency resolution. To achieve the identification in a real‐time manner, an Automatic Frequency Order Identification (AFOI) algorithm is developed to recognize the frequency order automatically depending on the MSST result, in which the interference of fake modes and omitted modes to the identification of the actual frequency order is eliminated to a large extent. The performance of the proposed AFOI algorithm and the quasi‐real‐time cable force identification method is evaluated on a practical cable replacement engineering case. Results show that the correct orders of the multiple frequencies received from MSST can be identified along the time domain, which demonstrates the effectiveness of the proposed method. The variation of the tension force of not only the replaced cable but also its neighbor cables is estimated with desired time‐frequency resolution, which promotes the safety state assessment of a cable in a real‐time manner during the replacement process. [ABSTRACT FROM AUTHOR]