Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Abstract: Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Abstract: This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

High-Performance Electron Acceptor with Thienyl Side Chains for Organic Photovoltaics.

Abstract: We develop an efficient fused-ring electron acceptor (ITIC-Th) based on indacenodithieno[3,2-b]thiophene core and thienyl side-chains for organic solar cells (OSCs). Relative to its counterpart with phenyl side-chains (ITIC), ITIC-Th shows lower energy levels (ITIC-Th: HOMO = −5.66 eV, LUMO = −3.93 eV; ITIC: HOMO = −5.48 eV, LUMO = −3.83 eV) due to the σ-inductive effect of thienyl side-chains, which can match with high-performance narrow-band-gap polymer donors and wide-band-gap polymer donors. ITIC-Th has higher electron mobility (6.1 × 10–4 cm2 V–1 s–1) than ITIC (2.6 × 10–4 cm2 V–1 s–1) due to enhanced intermolecular interaction induced by sulfur–sulfur interaction. We fabricate OSCs by blending ITIC-Th acceptor with two different low-band-gap and wide-band-gap polymer donors. In one case, a power conversion efficiency of 9.6% was observed, which rivals some of the highest efficiencies for single junction OSCs based on fullerene acceptors.

Improved particle swarm optimization combined with chaos

Abstract: As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.

Bioinspired Interfaces with Superwettability: From Materials to Chemistry

Abstract: Superwettability is a special case of the wetting phenomenon among liquids, gases, and solids. The superhydrophobic/superhydrophilic effect discovered initially has undergone a century of development based on materials science and biomimetics. With the rapid development of research on anti-wetting materials, superoleophobic/superoleophilic surfaces have been fabricated to repel organic liquids besides water. Further studies of underwater superoleophobic/superoleophilic/superaerophobic/superaerophilic materials provide an alternative way to fabricate anti-wetting surfaces rather than lowering the surface energy. Owing to a series of efforts on the studying of extreme wettabilities, a mature superwettability system gradually evolved and has since become a vibrant area of active research, covering topics of superhydrophobicity/superhydrophilicity, superoleophobicity/superoleophilicity in gas or under liquid, superaerophobicity/superaerophilicity under liquid, and combinations of these states. The kinetic stu...