Robust Vibration Output-only Structural Health Monitoring Framework Based on Multi-modal Feature Fusion and Self-learning

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness

Experimental and Probabilistic Investigations of the Effect of Fly Ash Dosage on Concrete Compressive Strength and Stress-strain Relationship

The effect of fly ash (FA) dosage on concrete’s compressive strength and stress-strain relationship is investigated in two steps in this article. First, an experimental program was conducted on concrete mixtures designed with 0% (control batch of 30 MPa mean cylinder compressive strength), 10, 20, 30, and 40% of ordinary Portland cement (OPC) mass replaced by FA, which is taken from a new source in an Asia country. The test results showed that compared to other investigated dosages, concrete using 20% FA/OPC mass-replacement gained the most improvement in the 28-day compressive strength and tensile split strength, as well as the compressive strength development. Second, a probabilistic investigation was conducted using Dropout Neural Network, Bayesian Neural Network, and Gaussian Process models. These artificial intelligence-based models were compared to other models reviewed from the literature, showing relatively good results in terms of the statistical metric R2, which are 0.92, 0.9, and 0.88, respectively. The three models were tested and validated with a dataset of 1032 experimental results on FAC collected from the literature. When testing with the experimental results obtained in the first step, a good correlation between the predicted values and the experimental results was observed within the confidence interval of (5%, 95%), showing the reliability of the proposed models. Thus, the stress-strain relationship of fly ash concrete can also be investigated in a probabilistic manner. It is proved in this study that among the proposed models, Dropout Neural Network has the best balance between performance and time complexity

Knowledge Creation And Green Entrepreneurship: A Study Of Two Vietnamese Green Firms

This paper aims to advance the understanding and practice of knowledge-based management in Vietnam by studying two Vietnamese agricultural companies. It provides illustrative examples of how knowledge-based management, pursuing a vision that fosters creativity and innovation by employees, could ultimately fulfil the profitability objective of the business and at the same time add value to the community’s quality of life. Using the SECI model as the parameter for analysis, we found that knowledge creation processes were affected by a combination of leadership, teamwork and Ba, corporate culture, and human resource management. Our conclusion emphasises the need for future research to further examine the practice of knowledge-based management in cross-industry segments in Vietnam and in other countries with similar conditions

Analysis of microalgal density estimation by using lasso and image texture features

Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively © 2023 by the authors

Determination of Reinforced Fly Ash Concrete Columns’ Resistance Using Nonlinear Models of Materials

This article introduces experimental and analytical studies on the resistance under eccentric loads of reinforced fly ash concrete (RFAC) columns, in which fly ash (FA) is used to partially replace ordinary Portland cement (OPC) with a by-mass ratio of 20%. Based on experimental results of concrete specimens with mean 28-day cylinder strength of 30 MPa, modifications on simplified bi-linear and tri-linear models of stress-strain relationships of OPC concrete specified in the Russian and Vietnamese design standards are proposed. These nonlinear deformation models are incorporated into an analytical approach to establish the resistance of RFAC columns in the form of interaction surface, associated with an assessment method for safety factor based on the principle of inverse distance weighted average (IDWA). Parameters of the proposed analytical approach are determined by test results obtained from eight RFAC column specimens having 150 × 200 (mm) rectangular cross-section, 1600 mm-height, and 4Φ14 longitudinal rebars with yield strength of 362.6 MPa. In the tests, the specimens were loaded with uniaxial eccentricities ranging from 0 to 80 mm until failed. It is shown that with ε’b1 = 0.0022 and kE = 0.91, the corresponding safety factors of bi-linear and tri-linear models validated for the tested specimens are conservative and nearest to unity, proving that the proposed analytical approach is capable of closely predicting the RFAC columns’ resistance

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch

Vietnamese version of the general medication adherence scale (Gmas):Translation, adaptation, and validation

Background: We aimed to translate, cross-culturally adapt, and validate the General Medication Adherence Scale (GMAS) into Vietnamese. Methods: We followed the guidelines of Beaton et al. during the translation and adaptation process. In Stage I, two translators translated the GMAS to Vietnamese. Stage II involved synthesizing the two translations. Stage III featured a back translation. Stage IV included an expert committee review and the creation of the pre-final version of the GMAS, and in stage V, pilot testing was conducted on 42 Vietnamese patients with type 2 diabetes. The psychometric validation process evaluated the reliability and validity of the questionnaire. The in-ternal consistency and test–retest reliability were assessed by Cronbach’s alpha and Spearman’s correlation coefficients. The construct validity was determined by an association examination between the levels of adherence and patient characteristics. The content validity was based on the opinion and assessment score by the expert committee. The Vietnamese version of the GMAS was created, in-cluding 11 items divided into three domains. There was a good equivalence between the English and the Vietnamese versions of the GMAS in all four criteria. Results: One hundred and seventy-seven patients were participating in the psychometric validation process. Cronbach’s alpha was acceptable for all questionnaire items (0.817). Spearman’s correlation coefficient of the test–retest reliability was acceptable for the GMAS (0.879). There are significant correlations between medication adherence levels and occupation, income, and the Beliefs about Medicines Questionnaire (BMQ) score regarding construct validity. Conclusions: The Vietnamese version of GMAS can be considered a reliable and valid tool for assessing medication adherence in Vietnamese patients

An efficient cuckoo-inspired meta-heuristic algorithm for multiobjective short-term hydrothermal scheduling

This paper proposes an efficient Cuckoo-Inspired Meta-Heuristic Algorithm (CIMHA) for solving multi-objective short-term hydrothermal scheduling (ST-HTS) problem. The objective is to simultaneously minimize the total cost and emission of thermal units while all constraints such as power balance, water discharge, and generation limitations must be satisfied. The proposed CIMHA is a newly developed meta-heuristic algorithm inspired by the intelligent reproduction strategy of the cuckoo bird. It is efficient for solving optimization problems with complicated objective and constraints because the method has few control parameters. The proposed method has been tested on different systems with various numbers of objective functions, and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is more efficient than many other methods for the test systems in terms of total cost, total emission, and computational time. Therefore, the proposed CIMHA can be a favorable method for solving the multi-objective ST-HTS problems

MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE

Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining proces