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Motivated by the global economy that is greatly shaped by the landscape changes in energy and manufacturing where more and more devices and systems are interconnected, intelligent manufacturing in which data mining is of great importance is studied. In this article, an energy monitoring platform for small- and medium-sized enterprises developed by the point energy team (www.pointenergy.org) is first introduced, which monitors and records the energy consumption of manufacturing processes at various levels of granularity. In processing the collected data, the incompleteness in the data due to various factors needs to be addressed first otherwise it may lead to the inaccurate portrayal of the system and poor generalization of the resultant model trained by the data. Hence, a novel orthogonal-least-square-based autoencoder is proposed to generate new samples for the imputation of missing values. This approach is to learn the representative code from the original samples by constructing an improved encoder network in which the hidden neurons are orthogonal with each other. The new samples are then generated through the decoder network. The proposed approach selects the hidden neurons one by one based on the OLS estimation until an adequate network is built. The classical techniques and other generative models are compared to verify the effectiveness of the proposed algorithm. For these methods, the optimal parameters are estimated based on the performance metric of the cross-validation mean square error. In the experiment, two real industrial datasets from a baking process and a polymer extrusion process are adopted and the percentage of missing values varies from 0.02 to 0.25. The experimental results confirm that the proposed method offers stable performance in the presence of different missing ratios, and it outperforms significantly alternative approaches while the missing ratio is greater than 0.05. [ABSTRACT FROM AUTHOR]

Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long-term prediction. The experimental results show that the developed model is in good agreement with the real-life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system. [ABSTRACT FROM PUBLISHER]