2020

• Record 241 of
  
  Title:3.9 μm emission and energy transfer in ultra-low OH−, Ho3+ /Nd3+ co-doped fluoroindate glasses
  
  Author(s):Wang, Ruicong(1); Zhang, Jiquan(1); Zhao, Haiyan(1); Wang, Xin(1); Jia, Shijie(1); Guo, Haitao(2); Dai, Shixun(3); Zhang, Peiqing(3); Brambilla, Gilberto(4); Wang, Shunbin(1); Wang, Pengfei(1,5)
  Source: Journal of Luminescence  Volume: 225  Issue:   DOI: 10.1016/j.jlumin.2020.117363  Published: September 2020  
  Abstract:Ho3+/Nd3+ co-doped fluoroindate glass samples were prepared by melt-quenching. The absorption and emission spectra, and the differential scanning calorimetry (DSC) curve were measured and used to evaluate the spectroscopic parameters and thermal properties. An intense ~3.9 μm emission, ascribed to the transition Ho3+:5I5 →5I6, was observed under the excitation of an 808 nm laser diode and was ascribed to the efficient energy transfer process from Nd3+: 4F3/2 to Ho3+: 5I5, showing the Nd3+ role as a sensitizer. The optimal concentration ratio of Ho3+ and Nd3+ for ~3.9 μm emission was estimated to be 1:1. The spectroscopic performance suggests that the Ho3+/Nd3+ co-doped fluoroindate glass is a potential gain material for ~3.9 μm laser applications. © 2020
  Accession Number: 20202008644271
• Record 242 of
  
  Title:Siamese dilated inception hashing with intra-group correlation enhancement for image retrieval
  
  Author(s):Lu, Xiaoqiang(1); Chen, Yaxiong(1); Li, Xuelong(2)
  Source: IEEE Transactions on Neural Networks and Learning Systems  Volume: 31  Issue: 8  DOI: 10.1109/TNNLS.2019.2935118  Published: August 2020  
  Abstract:For large-scale image retrieval, hashing has been extensively explored in approximate nearest neighbor search methods due to its low storage and high computational efficiency. With the development of deep learning, deep hashing methods have made great progress in image retrieval. Most existing deep hashing methods cannot fully consider the intra-group correlation of hash codes, which leads to the correlation decrease problem of similar hash codes and ultimately affects the retrieval results. In this article, we propose an end-to-end siamese dilated inception hashing (SDIH) method that takes full advantage of multi-scale contextual information and category-level semantics to enhance the intra-group correlation of hash codes for hash codes learning. First, a novel siamese inception dilated network architecture is presented to generate hash codes with the intra-group correlation enhancement by exploiting multi-scale contextual information and category-level semantics simultaneously. Second, we propose a new regularized term, which can force the continuous values to approximate discrete values in hash codes learning and eventually reduces the discrepancy between the Hamming distance and the Euclidean distance. Finally, experimental results in five public data sets demonstrate that SDIH can outperform other state-of-the-art hashing algorithms. © 2012 IEEE.
  Accession Number: 20203709158815
• Record 243 of
  
  Title:Property-Constrained Dual Learning for Video Summarization
  
  Author(s):Zhao, Bin(1); Li, Xuelong(1); Lu, Xiaoqiang(2)
  Source: IEEE Transactions on Neural Networks and Learning Systems  Volume: 31  Issue: 10  DOI: 10.1109/TNNLS.2019.2951680  Published: October 2020  
  Abstract:Video summarization is the technique to condense large-scale videos into summaries composed of key-frames or key-shots so that the viewers can browse the video content efficiently. Recently, supervised approaches have achieved great success by taking advantages of recurrent neural networks (RNNs). Most of them focus on generating summaries by maximizing the overlap between the generated summary and the ground truth. However, they neglect the most critical principle, i.e., whether the viewer can infer the original video content from the summary. As a result, existing approaches cannot preserve the summary quality well and usually demand large amounts of training data to reduce overfitting. In our view, video summarization has two tasks, i.e., generating summaries from videos and inferring the original content from summaries. Motivated by this, we propose a dual learning framework by integrating the summary generation (primal task) and video reconstruction (dual task) together, which targets to reward the summary generator under the assistance of the video reconstructor. Moreover, to provide more guidance to the summary generator, two property models are developed to measure the representativeness and diversity of the generated summary. Practically, experiments on four popular data sets (SumMe, TVsum, OVP, and YouTube) have demonstrated that our approach, with compact RNNs as the summary generator, using less training data, and even in the unsupervised setting, can get comparable performance with those supervised ones adopting more complex summary generators and trained on more annotated data. © 2012 IEEE.
  Accession Number: 20204509445393
• Record 244 of
  
  Title:Novel Band-Edge Work Function Performance Modulation via NPT with PMOS1st/NMOS1stLaminated Stack for PMOS Low Power Target
  
  Author(s):Yao, Jiaxin(1,2); Yin, Huaxiang(1); Wu, Zhenhua(1); Tian, Jinshou(2)
  Source: ECS Journal of Solid State Science and Technology  Volume: 9  Issue: 10  DOI: 10.1149/2162-8777/abc45f  Published: October 2020  
  Abstract:In this paper, the band-edge work function performance is systematically investigated and modulated via novel nitrogen plasma treatment (NPT) with the advanced PMOS1st (TiN/TiN/TiAlC) and NMOS1st (TiN/TiN) laminated stacks for the fabricated PMOS capacitors. The basic multi-VT performance is strongly modulated by controlling NPT process. 1) Flatband voltage (VFB) shifts towards band edge are obtained as +120 mV (undiluted), +430 mV (diluted) for PMOS1st and +80 mV (undiluted), +210 mV (diluted) for NMOS1st. 2) By manipulating the NPT process from undiluted and diluted case, it can provide significant high band-edge effective work function ranging from 4.89 eV (undiluted) to 5.21 eV (diluted) for PMOS1st and 5.22 eV (undiluted) to 5.35 eV (diluted) for NMOS1st laminated stack, respectively. 3) NPT diluted with hydrogen is observed to maintain ultralow bulk trap density (1.11 1011 cm-2 for PMOS1st and nearly zero for NMOS1st) and interface trap density (3.34 1011 eV-1 cm-2 for PMOS1st and 6.45 1011 eV-1 cm-2 for NMOS1st). The significant band-edge work function modulation and very low bulk and interface trap density demonstrate the novel NPT with PMOS1st/NMOS1st laminated stack is very promising to achieve the target of PMOS low-power application in the further technology node. © 2020 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.
  Accession Number: 20204609484429
• Record 245 of
  
  Title:Time-dependent global nonsingular fixed-time terminal sliding mode control-based speed tracking of permanent magnet synchronous motor
  
  Author(s):Wu, Shaobo(1,2); Su, Xiuqin(1); Wang, Kaidi(1,2)
  Source: IEEE Access  Volume: 8  Issue:   DOI: 10.1109/ACCESS.2020.3030279  Published: 2020  
  Abstract:This paper studies global nonsingular fixed-time terminal sliding mode control (GNFTSMC) for a second-order uncertain permanent magnet synchronous motor (PMSM) system to further improve its speed tracking performance. The newly proposed GNFTSMC consists of a time-dependent terminal sliding surface and a piecewise continuous sliding mode control law. By a time-dependent function constructed from the initial conditions of the system and a predefined time, the sliding surface is always reached at the initial instant and forced to a traditional fast terminal sliding surface after the predefined time. Based on Filippov's stability principles, the globally fixed-time stability of the GNFTSMC is proved. Furthermore, a priori time independent of the initial conditions is derived to estimate the boundary of the settling time of the closed control loop. Then, the control law is analyzed to be always nonsingular. Thus, the GNFTSMC-based speed controller for the PMSM speed tracking system is developed. Finally, simulations are conducted for the proposed controller and other terminal sliding mode controllers. The results show that compared to the other controllers, the PMSM system based on GNFTSMC displays improved performance characteristics of faster speed response, smaller chattering and higher current efficiency. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
  Accession Number: 20211210122830
• Record 246 of
  
  Title:Attention Mask R-CNN for ship detection and segmentation from remote sensing images
  
  Author(s):Nie, Xuan(1); Duan, Mengyang(1); Ding, Haoxuan(2); Hu, Bingliang(3); Wong, Edward K.(4)
  Source: IEEE Access  Volume: 8  Issue:   DOI: 10.1109/ACCESS.2020.2964540  Published: 2020  
  Abstract:In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target's features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively. © 2013 IEEE.
  Accession Number: 20200508103000
• Record 247 of
  
  Title:Deep Learning Target Tracking Algorithm Based on Construction Site Scene
  
  Author(s):Ma, Shao-Xiong(1,2); Qiu, Shi(3); Tang, Ying(4); Zhang, Xiao(5)
  Source: Tien Tzu Hsueh Pao/Acta Electronica Sinica  Volume: 48  Issue: 9  DOI: 10.3969/j.issn.0372-2112.2020.09.001  Published: September 1, 2020  
  Abstract:Construction site is difficult to be effectively managed owing to its complex environment. A deep learning target tracking algorithm based on construction site scene is proposed to assist the construction progress. Firstly, according to the continuity of the target in the site scene, the enhanced group tracker is constructed to improve the successful probability of target tracking. Then, the depth detector is constructed with sliding window, stacked denoising auto encoder (SDAE) and support vector machine (SVM). Sliding window: a model is built from the gradient angle to realize window adaption. SDAE algorithm: the reverse algorithm is built to fine-tune network parameters. Optimized SVM algorithm reduces the probability of target drift and tracking failure. Finally, high precision tracking is achieved. Experiments show that the proposed algorithm can track the target effectively and realize dynamic management. © 2020, Chinese Institute of Electronics. All right reserved.
  Accession Number: 20204209348224
• Record 248 of
  
  Title:An Obstacle Avoidance Algorithm for Manipulators Based on Six-Order Polynomial Trajectory Planning
  
  Author(s):Ma, Yuhao(1,2); Liang, Yanbing(1)
  Source: Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University  Volume: 38  Issue: 2  DOI: 10.1051/jnwpu/20203820392  Published: April 1, 2020  
  Abstract:Aiming at a series of requirements of obstacle avoidance trajectory planning of manipulators, a new algorithm based on six-order polynomial trajectory planning is proposed. Firstly, the six-order polynomial is used for the trajectory planning of the manipulator. Assuming that the coefficients of the sixth order term in the curve equation are undetermined parameters, by adjusting these parameters, the shape of the curve can be changed to make manipulators avoid the obstacle and to optimize performance indicators of the trajectory simultaneously. Thus, the obstacle avoidance trajectory planning of manipulators is transformed into a multi-objective optimization problem. Secondly, combining collision detection results and kinematics indexes, a fitness function is defined by the weighting coefficient method. At last, an ideal collision-free trajectory that is collaborative optimized in kinematics, trajectory length and rotation angle is planned in the joint space through genetic algorithm optimization. Additionally, the algorithm is validated by simulation experiments with MATLAB, the results show that the method of this study can effectively plan obstacle-free trajectories satisfying the performance requirements of the manipulator. © 2020 Journal of Northwestern Polytechnical University.
  Accession Number: 20203008969333
• Record 249 of
  
  Title:Spatial heterodyne spectroscopy for long-wave infrared: Optical design and laboratory performance
  
  Author(s):Han, Bin(1,2); Feng, Yutao(1); Zhang, Zhaohui(1); Bai, Qinglan(1); Wu, Junqiang(1); Wu, Yang(1,2); Chang, Chenguang(1); Sun, Jian(1)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 11566  Issue:   DOI: 10.1117/12.2580379  Published: 2020  
  Abstract:Spatial heterodyne spectroscopy for long-wave infrared identifies an ozone line near 1133 cm-1(about 8.8 μm) as a suitable target line, the Doppler shifts of which are used to retrieve stratosphere wind and ozone concentration. The basic principle of Spatial Heterodyne Spectroscopy (SHS) is elaborated. Theoretical analyses for the optical parameters of spatial heterodyne spectroscopy are deduced. The optical system is designed to work at 160 K and to maximize the field of view (FOV). The optical design and simulation is carried on to fulfill the requirement. The principle prototype was built and a frequency-stable laser was used to conduct the experiment. Result shows that the designed interferometer can meet the requirement of spectral resolution (0.1 cm-1) and that the spatial frequency of fringe pattern is consistent with the theoretical value at normal temperature and pressure. © 2020 SPIE. All rights reserved.
  Accession Number: 20204909589258
• Record 250 of
  
  Title:A novel S-scheme MoS2/CdIn2S4 flower-like heterojunctions with enhanced photocatalytic degradation and H2 evolution activity
  
  Author(s):Zhang, Bin(1); Shi, Huanxian(1); Hu, Xiaoyun(2); Wang, Yishan(3); Liu, Enzhou(1); Fan, Jun(1)
  Source: Journal of Physics D: Applied Physics  Volume: 53  Issue: 20  DOI: 10.1088/1361-6463/ab7563  Published: May 13, 2020  
  Abstract:A novel flower-like MoS2/CdIn2S4 composite was designed and synthesized via a simple in-situ hydrothermal method, for the first time. Under visible light irradiation, the 10% MoS2/CdIn2S4 hybrid exhibited the strongest photocatalytic activities for both degradation of dye (Rhodamine B) and hydrogen generation. The RhB (10 mg L-1) can be almost degraded in 30 min, and the degradation rate constant (k) of 10% MoS2/CdIn2S4 can up to 0.13595 min-1, which is about 2.6 and 73.1 times to CdIn2S4 (0.05311 min-1) and MoS2 (0.00186 min-1). Under simulated sunlight irradiation, the hydrogen evolution rate of 10% MS/CIS can reach to 1868.19 μmol•g-1•h-1, which is 2.26 and 6.2 times higher than that of the pure CdIn2S4 (827.09 μmol•g-1•h-1) and MoS2 (303.1 μmol•g-1•h-1), respectively. Additionally, the 10% MS/CIS exhibits a superior stability in the recycling experiment. The enhanced photocatalytic performance can be attributed to that the in-situ loading of MoS2 on the CdIn2S4 can provide the larger surface area, strengthen the visible-light response range and accelerate the charge separation. A conceivable S-scheme charge transfer mechanism was proposed to reveal the photocatalytic reaction process in this system. © 2020 IOP Publishing Ltd.
  Accession Number: 20201508399354
• Record 251 of
  
  Title:Application of Deep Neural Network in Quantitative Analysis of VOCs by Infrared Spectroscopy
  
  Author(s):Zhang, Qiang(1,2); Wei, Ru-Yi(1); Yan, Qiang-Qiang(1); Zhao, Yu-Di(1); Zhang, Xue-Min(1); Yu, Tao(1)
  Source: Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis  Volume: 40  Issue: 4  DOI: 10.3964/j.issn.1000-0593(2020)04-1099-08  Published: April 1, 2020  
  Abstract:In view of the fact that shallow artificial neural networks (ANNs) rely on prior knowledge for artificial extraction of features, while shallower network structures limit the ability of neural networks to learn complex nonlinear relationships, this paper applies deep neural networks (DNN) to the study of inversion of multi-component volatile organic compounds (VOCs) by leaf-transformed infrared spectroscopy (FTIR), and the effectiveness of the algorithm was verified by simulation experiments. Eight VOCs including benzene, toluene, 1, 3-butadiene, ethylbenzene, styrene, o-xylene, m-xylene, and p-xylene were selected from the US Environmental Protection Agency (EPA) database. In the wavelength range of 8~12 μm, each gas has four different concentration lines, and the absorbance spectrum at one concentration is selected from each VOCs gas according to Beer-Lambert's law to obtain 65 536 different kinds. Samples of VOCs mixed gas absorbance spectra. The absorbance spectra of 5 000 groups of mixed gases were randomly selected, of which 4 000 were used as training samples and 1000 were used as prediction samples. The dimensional reduction of the spectral matrix was performed by integral extraction and principal component extraction, and the spectral dimension was reduced from 3457 to 30 dimensions. The new matrix obtained by preprocessing the spectral matrix was used as the network input, and the concentration matrix of the eight VOCs was used as the output. A deep neural network regression prediction model of 30-25-15-10-8 was established, and multiple groups were realized by using spectral data. Inversion of VOCs concentration, the root mean square error of the sample obtained by inversion was 0.002 7×10-6, which was obvious compared with the accuracy of previous methods using nonlinear partial least squares fitting and artificial neural network. improve. The root mean square error of each VOCs gas does not exceed 0.005×10-6, and the root mean square error of each sample does not exceed 0.006×10-6, which proves that the deep neural network prediction model has good nonlinear fitting ability. And good stability. When the training sample is insufficient (typical value: less than 500), the deep neural network cannot fully learn, the network error is larger, and the accuracy is lower than that of the single hidden layer artificial neural network, but as the number of training samples increases, the deep neural network accuracy is continuously improved. When the number of training samples is sufficient, the deep neural network has stronger nonlinear relation learning ability than the shallow artificial neural network, and the prediction accuracy is higher and the model is more stable. At the same time, due to the dimensionality reduction of the spectral matrix before training, the complexity of the algorithm is greatly reduced, and the inversion efficiency is effectively improved. The analysis shows that the deep neural network prediction model has good nonlinear fitting ability and good stability. It can fully learn the data features without manual extraction of features, and at the same time, the concentration inversion of multi-component VOCs can achieve higher precision. © 2020, Peking University Press. All right reserved.
  Accession Number: 20202208742435
• Record 252 of
  
  Title:Dissipative soliton operation of a diode-pumped Yb:KGW solid-state laser in the all-positive-dispersion regime
  
  Author(s):Li, Guangying(1,2); Lou, Rui(1); Wang, Xu(1); Sun, Zhe(1); Wang, Yishan(1); Xie, Xiaoping(1,2); Zhang, Guodong(3); Cheng, Guanghua(3)
  Source: Optical Engineering  Volume: 59  Issue: 6  DOI: 10.1117/1.OE.59.6.066105  Published: June 1, 2020  
  Abstract:We report on the dissipative soliton operation of a diode-pumped single-crystal bulk Yb:KGW laser oscillator in the all-positive-dispersion regime. Stable passively mode-locked pulses with strong positive chirp and steep spectral edges are obtained. The spectral centering at 1038.6 nm has a bandwidth of about 6.9 nm, and the chirped pulses have a pulse duration of 4.317 ps. The maximum average power can be up to 2.07 W when pumped by absorbed pump power of 5.3 W. The mode-locked slope efficiency and optical-optical conversion efficiency are shown to be 62% and 39%, respectively. Considering the pulse repetition rate with a value of 52 MHz, the corresponding pulse energy is estimated to be 39.8 nJ. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).
  Accession Number: 20203409067185