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The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding.

The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results.

The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status.

This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA).

To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies.

The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design.

A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.

This paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

This paper aims to study the effect of ultraviolet (UV) light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environment.

The effect of UV light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environments were investigated by the corrosion weight gain experiment, in situ electrochemical noise, scanning electron microscope and X-ray diffraction.

UV light accelerated the corrosion process of BC550 weathering steel in the simulated marine atmospheric environment during the first 168 h. The maximum influence factor of UV light was 0.32, and it was only 0.08 after 168 h of corrosion process.

As the extension of corrosion time, the thickness and density of the corrosion product layer increased, which weakened the acceleration effect of UV light.

This chapter offers a case study of the distinctive political activism in Taiwanese metal by analysing the intertextuality of Just Not Meant to Be (還君明珠) (2015) by Crescent Lament (恆月三途). From the perspective of a cultural insider, the author examine the socio-cultural dynamics underlying this activism and explain how Taiwanese metal attempts to tackle the troubled past of Taiwan. The author brings attention to Just Not Meant to Be's commentary about the activism it takes part in, and reflect on problematics inherent to political activism in Taiwanese metal. Finally, the author explicates the problematics in the context of metal subculture in general. Pivotal throughout this chapter are the questions: Why does Taiwanese metal replicate forms of domination it seeks to counter? What can metal subculture in general learn from Taiwanese metal and its political activism?

The purpose of this paper is to examine whether the institutions play a role in tourism development and international recognition, specifically the influence of marketization on the international tourists’ inbound arrivals in different Chinese provinces.

This paper constructs a demand model of tourism and empirically analyzes the relationship between marketization and inbound tourism demand with the panel data of the provinces of China and NERI Index of Marketization.

Marketization does have an influence on inbound tourism demand of China. Specially, the relationship between government and market, the development of product market, the market intermediary organizations and the legal system environment can increase the demand of the foreign tourists to visit China, although the magnitudes are different.

This paper argues that the qualities of marketization intuitions are important in increasing inbound tourism, given that it can bring better tourism experience and improve the international recognition. Strengthening the legislation and protecting the legitimate rights and interests of consumers can attract more international travelers to China. Market distribution of competitive economic resources, reducing political intervention into corporate activities and relieving tax burdens of enterprises can improve the competitiveness and the service qualities of Chinese domestic tourism firms.

This paper leads the discussions of institutions and tourism. It combines the consumer theory and uses static and dynamic panel data models to analyze the influencing factors of Chinese tourism. It argues that Chinese inbound tourism shall develop with the systemic marketization progress in China.

This study examines the causes of flood disasters in Jianghan Plain, China and provides practical solutions to mitigate them. Results from this study indicate that both historical archives and more recent recorded data point to an increasing frequency in flood disasters since 1961. Furthermore, damage and losses from flood disasters have also increased significantly in the region. By analyzing the physical geographic factors and human activities, this study found that the main causative factors contributing to increasing flood disasters are landform/topography, climate elements, reduced drainage capacity of rivers in contrast to increased flood discharge, and human activities. Finally, the study examines various practical solutions to mitigate flood disasters in the Jianghan Plain.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The growing demand of efficiency and economy has led to a dramatic increase of the operating speed of the journal bearing, with a higher temperature distribution. This paper aims to investigate the three-dimensional temperature distribution of journal bearings.

A thermo-hydrodynamic lubrication model of a journal bearing was established based on the full 3D CFD method. A two-sided wall was used to include the conjugate heat transfer effect. The temperature-dependent characteristics of lubrication and cavitation impact were also included. The simulation results well agreed with the experimental results. Based on this method, the three-dimensional temperature distribution was analyzed under different operating conditions.

The temperature distribution in the radial direction had a difference. An increase of speed and de-crease of inlet temperature promoted temperature differences in the higher temperature zone and the increasing temperature zone, respectively. However, the inlet pressure had less influence on these differences. The temperature distribution was basically the same at a lower bearing conductivity. As the conductivity increased, the radial temperature difference was increased.

The temperature distribution in the radial direction was found under different operating conditions, and the present research provides references to understand the three-dimensional temperature distribution of journal bearings.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.