Nowadays, in case of an emergency, and because of the more complicated interiors of buildings, people may not be able to easily find a safe escape route within limited time. An immediately available evacuation guide can improve safety in public buildings. In this research paper, we propose an intelligent guidance system that is a combination of image recognition and Digital Signage for conducting people to safety. This system applies WSN and RFID to collect environmental information, IP cameras to monitor group movements and uses an evaluation algorithm to circulate a safe route using environmental information obtained from the system and then smartphones and digital signage to guide people to the exit. Instruction guides are displayed using the digital signage. The execution time of the proposed system was also evaluated: the overall processing time for a complex space was less than 1.1 second showing the superior efficiency of the proposed system compared with previous systems.

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Under Industry 4.0, manufacturing quality prediction has been gaining increased interest from researchers and manufacturers. From the analysis of previous studies on quality predictions using machine learning, it became clear that the high dimensionality and imbalance of data are major and common problems affecting the learning performance. This work uses a hybrid method to address this issue, applying a Synthetic Minority Oversampling Technique & TomekLinks balancing approach to create balanced data and using Random Forest as the feature selecting measurement to reduce the dimensionality of data. In addition, a Fine Gaussian Support Vector Machine (Fine Gaussian SVM) based on the representative set of features selected by the hybrid method utilized is employed in this work to predict product quality. The results of experimentation demonstrate that the hybrid method proposed in this work performs well for manufacturing quality prediction and offers a simple, quick and powerful way to address the problem of feature selection encountered by the imbalanced classification.

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Soft sensing technology is an effective way to solve the problem that important quality indicators of processing industries cannot be detected online, especially in the chemical industry. Owing to the complex working conditions, strong nonlinearity, strong coupling, and timevarying characteristics of chemical production processes, how to establish a soft sensing model with good prediction performance has become a valuable research topic. A soft sensing model based on a single-model method cannot guarantee global prediction accuracy, and the model stability is poor. A hybrid modeling method can integrate different modeling methods to describe the process characteristics of an object more comprehensively, so as to significantly improve the prediction accuracy and stability of the soft sensing model. In this paper, the key process parameter (solid-liquid ratio) in the evaporation salt (ES)-making process is taken as an example to carry out the following research. Firstly, aiming at the problems of production data obtained from the chemical industry, such as missing values, data inconsistency, high dimensions, high correlation, and time-series characteristics of features, an effective feature extraction method is proposed. On this basis, two data-driven models, the deep neural network (DNN) model for non-temporal regression prediction and the long short-term memory neural network (LSTM) model for temporal regression prediction, are established, and the regression performance of these two soft sensing models is evaluated. Secondly, another feature selection method based on prior domain knowledge, expert experience, and data mining is proposed. On this basis, a hybrid soft sensing model, the LightGBM model, is constructed for key process parameter prediction under different feature inputs, and the regression performance is evaluated. Simulation results demonstrate that introducing domain knowledge and expert experience to the modeling can enhance the interpretability of models, simplify the molding process, and further improve model performance.