The introduction of Industry 4.0 and rapid development of Manufacturing Cyber-Physical Systems (MCPS), as well as the increasing demand for multi-variety, small batch and personalized customization, pose a huge challenge to the traditional manufacturing systems. In order to meet the production requirements for fast iteration and realize agile and efficient manufacturing resource allocation, this paper proposes an ontology-based resource reconfiguration method from the perspective of resource utilization. First, an intelligent device ontology that describes the intelligent manufacturing resource is established using the Web Ontology Language (OWL). On this basis, the relational database is associated with the ontology of manufacturing system, which makes the manufacturing resources be mapped to the model instances. Finally, we analyze the equipment reconfiguration of intelligent manipulator as an application case, which explains the proposed method for resource reconfiguration based on ontology, and verifies its feasibility in manufacturing. Lastly, this study provides a new method for reconfigurable research of manufacturing resources.

Electrochemical impedance spectroscopy (EIS) was used to study the initial discharge/charge process in a sulfur cathode with different potentials. In the second discharge region (2.00–1.70 V), where soluble polysulfides are reduced to Li₂S, the EIS spectra exhibit three semicircles/arcs as the frequency decreased. An appropriate equivalent circuit is proposed to fit the experimental EIS data. Based on detailed analysis of the change in kinetic parameters obtained from simulating the experimental EIS data as functions of potential, the high-frequency, middle-frequency and low-frequency semicircles/arcs can be attributed to the Schottky contact reflecting the electronic properties of materials, the charge transfer step and the formation of Li₂S respectively. The inclined line arises from the diffusion process in the detectable potentials and frequency range. Several important electrochemical reactions also have been verified by cyclic voltammetry curves.

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.