A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.
Password-based authenticated key exchange is a popular method for secure authentication and key exchange. With the wide application of unmanned aerial vehicles, position information has also become an important factor in authentication. In this paper, we present a new key exchange protocol, which firstly realizes dual authentication for both password and position, and we propose two applicable scenarios for the PPAKE mechanism: one is unmanned aerial vehicle authentication, and the other one is authentication in the military base. By adding position authentication, the reliability of authentication has improved, and the difficulty of adversarial attacks also increases. Any arbitrary adversary who can listen, tamper, and send messages can only perform an online attack for password guessing at a specified position. Finally, we provide security proofs under the defined model.
In the present study, we used Kalamkas, which is a typical Kazakhstani oilfield, which produces wastewater with high Ca2+/Mg2+ and Fe2+ concentrations, as a case study. We investigated a method for preparing Fe2+ polymer solutions without oxygen isolation under the conditions of salinity > 110 × 103 mg/L, Ca2+/Mg2+ concentration ˃ 7000 mg/L, and Fe2+ concentration ˃ 30 mg/L. Fe2+-resistant groups were grafted onto the molecular chains of a hydrophobically associating polymer prepared using existing synthesis technology to overcome the decrease in apparent viscosity of the polymer solution due to the oxidation of Fe2+ during solution preparation. The experiments showed that PAM-IR with iron-resistant groups can be completely dissolved in the wastewater within 180 min, and can tolerate an NaCl concentration of up to 0.23 × 106 mg/L, a Ca2+ concentration of up to 10 × 103 mg/L, an Mg2+ concentration of up to 9 × 103 mg/L, and a Fe2+ concentration of up to 90 mg/L, with favorable thickening performance and resistances to NaCl, Ca2+, Mg2+, and Fe2+. PAM-IR has good injection performance and can establish a high resistance factor (FR) and residual resistance factor (FRR) to increase the sweep efficiency. Therefore, it is potentially useful for enhancing oil recovery.
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