Capital Normal University

About: Capital Normal University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Terahertz radiation & Quantum entanglement. The organization has 11441 authors who have published 11988 publications receiving 159071 citations. The organization is also known as: Shǒudū Shīfàn Dàxué.

Enhanced Room‐Temperature Phosphorescence through Intermolecular Halogen/Hydrogen Bonding

Abstract: Room-temperature phosphorescence (RTP) materials with high efficiency have attracted much attention because they have unique characteristics that cannot be realized in conventional fluorescent materials. Unfortunately, efficient RTP in metal-free organic materials is very rare and it has traditionally been considered as the feature to divide purely organic compounds from organometallic and inorganic compounds. There has been increasing research interest in the design and preparation of metal-free organic RTP materials in recent years. It has been reported that intermolecular interactions make a big difference to the photophysical behavior of organic molecules. In this regard, herein, the parameters that affect RTP efficiency are discussed, and a brief review of recent intermolecular halogen-/hydrogen-bonding strategies for efficient RTP in metal-free organic materials are provided. The opportunities and challenges are finally elaborated in the hope of guiding promising directions for the design and application of RTP materials.

Nonlocality of orthogonal product-basis quantum states

Abstract: We study the local indistinguishability of mutually orthogonal product basis quantum states in the high-dimensional quantum system. In the quantum system of ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}$, where $d$ is odd, Zhang et al. [Z.-C. Zhang et al., Phys. Rev. A 90, 022313 (2014)] have constructed ${d}^{2}$ orthogonal product basis quantum states that are locally indistinguishable. We find a subset that contains $6d\ensuremath{-}9$ orthogonal product states that are still locally indistinguishable. We generalize our method to an arbitrary bipartite quantum system ${\mathbb{C}}^{m}\ensuremath{\bigotimes}{\mathbb{C}}^{n}$. We present a small set with only $3(m+n)\ensuremath{-}9$ orthogonal product states and prove that these states are local operations and classical communication (LOCC) indistinguishable. Even though these $3(m+n)\ensuremath{-}9$ product states are LOCC indistinguishable, they can be distinguished by separable measurements. This shows that separable operations are strictly stronger than the local operations and classical communication.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

Abstract: Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.