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Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy.

The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN.

CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%.

This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

The purpose of this paper is to present a study of the effect of different types of annealing schedules for a ribonucleic acid (RNA) secondary structure prediction algorithm based on simulated annealing (SA).

An RNA folding algorithm was implemented that assembles the final structure from potential substructures (helixes). Structures are encoded as a permutation of helixes. An SA searches this space of permutations. Parameters and annealing schedules were studied and fine-tuned to optimize algorithm performance.

In comparing with mfold, the SA algorithm shows comparable results (in terms of F-measure) even with a less sophisticated thermodynamic model. In terms of average specificity, the SA algorithm has provided surpassing results.

Most of the underlying thermodynamic models are too simplistic and incomplete to accurately model the free energy for larger structures. This is the largest limitation of free energy-based RNA folding algorithms in general.

The algorithm offers a different approach that can be used in practice to fold RNA sequences quickly.

The algorithm is one of only two SA-based RNA folding algorithms. The authors use a very different encoding, based on permutation of candidate helixes. The in depth study of annealing schedules and other parameters makes the algorithm a strong contender. Another benefit is that new thermodynamic models can be incorporated with relative ease (which is not the case for algorithms based on dynamic programming).

The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns.

To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States.

The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction.

A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model.

The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic algorithm (GA) is presented.

Via the algorithm, the deformations of a five-degree-of-freedom (5-DOF) hybrid robot TriMule800 at a limited number of positions are taken as the training set. The current position of the robot and the axial force it is subjected to are used as the input; the deformation of the robot is taken as the output to construct a BPNN; and an adaptive GA is adopted to optimize the weights and thresholds of the BPNN.

This algorithm can quickly predict the deformation of a robot at any point in the workspace. In this study, a force-deformation experiment bench is built, and the experiment proves that the correspondence between the simulated and actual deformations is as high as 98%; therefore, the simulation data can be used as the actual deformation. Finally, 40 sets of data are taken as examples for the prediction, the errors of predicted and simulated deformations are calculated and the accuracy of the prediction algorithm is verified.

The entire algorithm is verified by the laboratory-developed 5-DOF hybrid robot, and it can be applied to other hybrid robots as well.

Robots have been widely used in FSW. Traditional series robots cannot bear the large axial force during welding, and the deformation of the robot will affect the machining quality. In some research studies, hybrid robots have been used in FSW. However, the deformation of a hybrid robot in thick-plate welding applications cannot be ignored. Presently, there is no research on the deformation of hybrid robots in FSW, let alone the analysis and prediction of their deformation. This research provides a feasible methodology for analysing the deformation and compensation of hybrid robots in FSW. This makes it possible to calculate the deformation of the hybrid robot in FSW without external sensors.

THE importance of achieving a low structural weight is illustrated by simple estimates of the large decreases in aircraft gross weight and size made possible by conscientious weight saving in structural design. A brief review is then made of the many variables in aircraft design which affect the weight of the structure. The review is made chiefly to emphasize the close interplay in project work between the structural and aerodynamic effects of changes of layout. Finally some remarks are made about comparative structural design efficiency. It is concluded that good weight prediction formulae are at present the best means by which the structural design efficiencies of different aircraft may be readily compared.

The purpose of this paper is to predict the population of Anhui province. The authors analyze the trend of the main demographic indicators.

On the basis of the main methods of statistics, this paper studies the tendency of the population of Anhui province. It mainly analyzes the sex structure and the age structure of the current population. Based on the GM(1,1) model, this paper forecasts the total population, the population sex structure, and the population age structure of Anhui province in the next ten years.

The results show that the total population was controlled well, but there have been many problems of the population structure, such as the aging population, high sex ratio, heavy social dependency burden, and the declining labor force.

This paper forecasts the main indexes of the population of Anhui province and provides policy recommendations for the government and the relevant departments.

This paper utilizes data analysis method and the grey forecasting model to study the tendency of the population problems in Anhui province.