Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Laser. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network

Abstract: A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Tuning local chemistry of P2 layered-oxide cathode for high energy and long cycles of sodium-ion battery.

Abstract: Layered transition-metal oxides have attracted intensive interest for cathode materials of sodium-ion batteries. However, they are hindered by the limited capacity and inferior phase transition due to the gliding of transition-metal layers upon Na+ extraction and insertion in the cathode materials. Here, we report that the large-sized K+ is riveted in the prismatic Na+ sites of P2-Na0.612K0.056MnO2 to enable more thermodynamically favorable Na+ vacancies. The Mn-O bonds are reinforced to reduce phase transition during charge and discharge. 0.901 Na+ per formula are reversibly extracted and inserted, in which only the two-phase transition of P2 ↔ P'2 occurs at low voltages. It exhibits the highest specific capacity of 240.5 mAh g-1 and energy density of 654 Wh kg-1 based on the redox of Mn3+/Mn4+, and a capacity retention of 98.2% after 100 cycles. This investigation will shed lights on the tuneable chemical environments of transition-metal oxides for advanced cathode materials and promote the development of sodium-ion batteries.

Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Abstract: Rolling bearings are the key components of rotating machinery. Thus, the prediction of remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper proposes a new method for RUL prediction of bearings based on time-varying Kalman filter, which can automatically match different degradation stages of bearings and effectively realize the prediction of RUL. The evolution of monitoring data in normal and slow degradation stages is a linear trend, and the evolution in accelerated degradation stage is nonlinear. Therefore, Kalman filter models based on linear and quadratic functions are established. Meanwhile, a sliding window relative error is constructed to adaptively judge the bearing degradation stages. It can automatically switch filter models to process monitoring data at different stages. Then, the RUL can be predicted effectively. Two groups of bearing run-to-failure data sets are utilized to demonstrate the feasibility and validity of the proposed method.