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 & Computer science. 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.

Study of the Partial Ag-to-Zn Cation Exchange in AgInS2/ZnS Nanocrystals

Abstract: AgInS2–ZnS (AIZS) nanocrystals (NCs) were synthesized using a simple one-step approach by heating a Ag/In/Zn/S solution to 210 °C providing highly tunable photoluminescence (PL). The incorporation of Zn even at low temperatures (∼150 °C) and the increased cation exchange of silver by zinc at higher temperatures strongly influence the optical properties of the resulting NCs. The correlation between synthesis parameters and resulting optical properties provided insights on the growth and stability of ternary and quaternary semiconductors. Systematic investigation with time-resolved spectroscopy showed distinguishable PL behaviors between developing and fully grown AIZS NCs. Attempts to coat as-prepared AgInS2 NCs resulted in the same PL behavior as the one-step reaction product indicating that Zn readily exchanges with Ag ions even when not directly incorporated in the initial reaction mixture. Even with a low amount of zinc, the fully grown AIZS NCs showed improved PL QYs and single exponential decay behav...

A Near-Optimal UAV-Aided Radio Coverage Strategy for Dense Urban Areas

Abstract: Unmanned aerial vehicles (UAVs) may be used for providing seamless network coverage in urban areas for improving the performance of conventional cellular networks. Given the predominantly line-of-sight channel of drones, UAV-aided seamless coverage becomes particularly beneficial in case of emergency situations. However, a single UAV having a limited cruising capability is unable to provide seamless long-term coverage, multiple drones relying on sophisticated recharging and reshuffling schemes are necessary. In this context, both the positioning and the flight strategy directly affect the efficiency of the system. Hence, we first introduce a novel UAV energy consumption model, based on which an energy-efficiency-based objective function is derived. Second, we propose an energy-efficient rechargeable UAV deployment strategy optimized under a seamless coverage constraint. Explicitly, a two-stage joint optimization algorithm is conceived for solving both the optimal UAV deployment and the cyclic UAV recharging and reshuffling strategy. Our simulation results quantify the efficiency of our proposed algorithm.

An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network

Abstract: An enhanced intelligent diagnosis method for rotary equipment is proposed based on multi-sensor data-fusion and an improved deep convolutional neural network (CNN) models An improved CNN based on LeNet-5 is constructed which can enhance the features of the samples by stacking bottleneck layers without changing the size of the samples A new conversion approaches are also proposed for converting multi-sensor vibration signals into color images, and it can refine features and enlarge the differences between different types of fault signals by the fused images transformed in red–green–blue (RGB) color space In the last stage of network learning, visual clustering is realized with t-distributed stochastic neighbor embedding (t-SNE) to evaluate the performance of the network To verify the effectiveness of the proposed method, examples in practice such as the diagnosis for the wind power test rigs and industrial fan system are provided with the prediction accuracies of 9989% and 9977%, respectively In addition, the efficiency of other comparative baseline approaches such as the deep belief network and support vector machine (SVM) is evaluated In conclusion, the proposed intelligent diagnosis method based on multi-sensor data-fusion and CNN shows higher prediction accuracy and more obvious visualization clustering effects