Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

A Hybrid Surrogate Model for the Prediction of Solitary Wave Forces on the Coastal Bridge Decks

To facilitate the establishment of the probabilistic model for quantifying the vulnerability of coastal bridges to natural hazards and support the associated risk assessment and mitigation activities, it is imperative to develop an accurate and efficient method for wave forces prediction. With the fast development of computer science, surrogate modeling techniques have been commonly used as an effective alternative to computational fluid dynamics for the establishment of a predictive model in coastal engineering. In this paper, a hybrid surrogate model is proposed for the efficient and accurate prediction of the solitary wave forces acting on coastal bridge decks. The underlying idea of the proposed method is to enhance the prediction capability of the constructed model by introducing an additional surrogate to correct the errors made by the main predictor. Specifically, the regression-type polynomial chaos expansion (PCE) is employed as the main predictor to capture the global feature of the computational model, whereas the interpolation-type Kriging is adopted to learn the local variations of the prediction error from the PCE. An engineering case is employed to validate the effectiveness of the hybrid model, and it is observed that the prediction performance (in terms of residual mean square error and correlation coefficient) of the hybrid model is superior to the optimal PCE and artificial neural network (ANN) for both horizontal and vertical wave forces, albeit the maximum PCE degrees used in the hybrid model are lower than the optimal degrees identified in the pure PCE model. Moreover, the proposed hybrid model also enables the extraction of explicit predictive equations for the parameters of interest. It is expected that the hybrid model could be extended to more complex wave conditions and structural shapes to facilitate the life-cycle structural design and analysis of coastal bridges.

Applicability of Neural Networks for the Fermentation of Propionic Acid by Propionibacterium acidipropionici

According to our best knowledge, this is the first report applying Artificial neural networks (ANN) for simulation of batch propionic acid (PA) fermentation. Therefore, the main focus of this research was to investigate the applicability of ANN on PA fermentations. To demonstrate this, we used the results of 40 Propionibacterium acidipropionici fermentations (ca 2,000 data points) to build up the ANN, and additional two independent fermentations to demonstrate the prediction capability of the observed ANN. Analyzing the predicted output parameters we observed, that ratio of propionic acid to acetic acid (PA/AA) variables can only be used for ANN after normalization. Finally, the fit of the ANN model to the measured data was fine (average correlation coefficients over 0.9). A special feature was also tested: fermentation time was also used as an input parameter, thus making the ANN suitable to predict time course of PA fermentations as well which was also satisfying.

Machine Learning Approach to predict Metro Ridership based on Land Use Densities

Predicting metro ridership is an essential requirement for efficient metro operation and management. The dependence of metro ridership on the land use densities entails a need for an accurate predictive model. To this end, the current study is aimed to develop a novel machine learning (ML) based model to predict the metro station ridership utilizing the land use densities near metro stations. The ridership data was obtained from Qatar Rail, and the land use data were obtained from the Ministry of Municipality and Environment in Qatar. The land use densities in the catchment area of 800 m around the metro stations have been considered in this study. The non-linear relationship between the metro ridership and land use densities has been captured through different ensemble ML models including random forests, extremely randomized trees, and gradient tree boosting. Results showed that the ML models, once meticulously optimized and trained are capable of producing an accurate prediction for metro ridership. Among the ML models, gradient tree boosting showed the highest prediction capability. The authors concluded that the proposed prediction model can be utilized by both urban and transport planners in their processes to plan the land use around metro stations, predict the transit demand from those plans, and ultimately achieve the optimal use of the transit system i.e., Transit-Oriented Developments.

An Empirical Analysis of Software Fault Proneness Using Factor Analysis with Regression

During the early stages of the life cycle development process for software, the developer mainly makes use of the fault prediction process for the development of different modules. These modules help in detecting faulty modules and classes. Further, this process also helps in determining the modules which require a high level of refactoring during the maintenance stage. The objective of this research is to classify faults and to explore the usability of Factor Analysis with Regression (FAWR) which drastically ameliorate the system performance. A review of recent studies performed that uses the different fault prediction techniques. To direct this research, two research questions (RQ) are defined, one related to the integration of techniques to enhance the development of fault prediction model, and another is to check the technique to overcome the limitations of old methods. To answer these RQs, FAWR techniques are used for predicting faults. To assess the quality of the technique, two experiments were conducted. Results show that FAWR is the better performing method among the two prediction methods investigated. The results proved that the prediction capability of FAWR technique is significantly better. Factorization method is able to classify a module whether it is fault-prone or not. The constructed models use to estimate the proneness of faults surpass the standard regression models. The system evaluations indicate that the reduction of terms results in the betterment of outcomes. Moreover, the consideration of FAWR is a significant technique for the prediction of faults in software.

Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.