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Quantum Engineering 2020年第2期2卷

作者： Jin, Rui-Bo Cai, Wu-Hao Ding, Chunling Mei, Feng Deng, Guang-Wei Shimizu, Ryosuke Zhou, Qiang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Laser Spectroscopy Shanxi University Taiyuan China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan China Institute of Fundamental and Frontier Sciences and School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China The University of Electro-Communications Chofu Japan

Spectrally intrinsically uncorrelated biphoton states generated from nonlinear crystals are very important but rare resources for quantum photonics and quantum information applications. Previously, such biphoton states were generated from several kinds of crystals, however, their wavelength ranges and nonlinear efficiencies were still limited for various applications. In order to explore new crystal for wider wavelength range and higher nonlinear efficiency, here we theoretically study the generation of spectrally uncorrelated biphoton states from 14 crystals in the "BBO family," including BBO, CLBO, KABO, KBBF, RBBF, CBBF, BABF, BiBO, LBO, CBO, LRB4, LCB, YCOB, and GdCOB. They satisfy three kinds of group-velocity matching condition from near-infrared to telecom wavelengths. Furthermore, heralded single photons can be generated with a purity as high as 0.98, which is achieved without any narrow filtering. The indistinguishability of photons from independent sources is examined by the Hong-Ou-Mandel interference, which results in a visibility of 98% also without any further filtering, that is, photons from different heralded single-photon sources are highly indistinguishable. Our study may provide single-photon sources with good performance for quantum information processing at near-infrared and telecom wavelengths. © 2020 John Wiley & Sons, Ltd.

关键词： Photons

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Real-time exciton-manipulation of plexcitonic coupling in a ws2-ag nanocavity

arXiv

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arXiv 2020年

作者： Han, Xiaobo Wang, Kai Jiang, Yanan Xing, Xiangyuan Li, Shujin Li, Fang Liu, Weiwei Wang, Bing Lu, Peixiang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Wuhan National Laboratory for Optoelectronics School of Physics Huazhong University of Science and Technology Wuhan430074 China CAS Center for Excellence in Ultra-intense Laser Science Shanghai201800 China

We demonstrate the real-time exciton-manipulation of plexcitonic coupling in monolayer WS2 coupled to a plasmonic nanocavity by immersing into a mixed solution of dichloromethane (DCM) and ethanol. By adjusting the mixture ratio, a continuous tuning of the Rabi splitting energy ranged from 178 meV (in ethanol) to 266 meV (in DCM) is achieved. The results are mainly attributed to the remarkable increase of the neutral exciton density in monolayer WS2 as the concentration of DCM is increased. It offers an important stepping stone towards a further study on plexcitonic coupling in layered materials, along with potential applications in quantum information processing and nonlinear optical materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Tungsten compounds

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MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

arXiv

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arXiv 2020年

作者： Liu, Jun Li, Qing Cao, Rui Tang, Wenming Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham United Kingdom

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to unsupervised learning to obtain monocular depth. However, most unsupervised learning methods capable of achieving high depth prediction accuracy will require a deep network architecture which will be too heavy and complex to run on embedded devices with limited storage and memory spaces. To address this issue, we propose a new powerful network with a recurrent module to achieve the capability of a deep network while at the same time maintaining an extremely lightweight size for real-time high performance unsupervised monocular depth prediction from video sequences. Besides, a novel efficient upsample block is proposed to fuse the features from the associated encoder layer and recover the spatial size of features with the small number of model parameters. We validate the effectiveness of our approach via extensive experiments on the KITTI dataset. Our new model can run at a speed of about 110 frames per second (fps) on a single GPU, 37 fps on a single CPU, and 2 fps on a Raspberry Pi 3. Moreover, it achieves higher depth accuracy with nearly 33 times fewer model parameters than state-of-the-art models. To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Unsupervised learning

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A Simple Analysis of Multimodal Data Fusion

A Simple Analysis of Multimodal Data Fusion

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Jiangchang Cheng Yinglong Dai Yao Yuan Hongli Zhu College of Information Science and Engineering Hunan Normal University Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Liberal Arts and Sciences National University of Defense Technology Changsha China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Multimodal data fusion has a long research history since audio-visual speech recognition, which is inspired by the McGurk effect. Because of the limited model capacity of the traditional methods, multimodal data fusion researches are not so popular for a period. Recently, the advances of deep learning techniques open up new opportunities for the multimodal data fusion field. However, there is still a great gap in multimodal data processing ability between artificial intelligence and human beings. Many problems in multimodal data processing are still necessary to be researched. In this work, we propose to gain an insight into the information fusion level and apply different information fusion strategy to different situations. We analyze the different situations of the multimodal data fusion process and divide them into two categories, including consistent information fusion and contradictory information fusion. We demonstrate some toy examples of the different cases of the multimodal data fusion process.

关键词： Deep learning Data privacy Toy manufacturing industry Data integration Speech recognition Security History

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Speech Loss Compensation by Generative Adversarial Networks

Speech Loss Compensation by Generative Adversarial Networks

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Asia-Pacific Signal and information processing Association Annual Summit and Conference (APSIPA)

作者： Yupeng Shi Nengheng Zheng Yuyong Kang Weicong Rong The Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen University China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Speech loss, including frequency loss and packet loss, can lead to significant speech distortion in many Internet-based speech communication services. In this study, a generative adversarial networks (GANs) structure, which takes deep convolutional neural networks (CNN) as the generator and discriminator components, is adopted as a general framework for speech loss compensation. Network settings are modified for real-time communications. A set of experiments are conducted to evaluate the performance of the GANs-based framework for both bandwidth expansion (BWE) and packet loss concealment (PLC) at several simulated loss conditions. Experimental results demonstrate that the proposed system achieves better performance, with respective to 4 objective metrics, in both BWE and PLC compared to the baseline systems.

关键词： Gallium nitride Packet loss Training Speech enhancement Generative adversarial networks Bandwidth

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Robustness of the planning algorithm for ocean observation tasks

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International Journal of Performability Engineering 2020年第4期16卷 629-638页

作者： Zheng, Yuting Li, Dongcheng Wang, Liyu Zhao, Man Ying, Wen China University of Geosciences Wuhan430074 China Department of Computer Science University of Texas at Dallas RichardsonTX75082 United States Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China China Academy of Electronic Lingshui572427 China

Problems in ocean observation tasks rely on manual planning and simple strategic planning. These problems include planning difficulties, high computational costs, and low resource utilization. To solve such problems, this paper proposes three robust indicators: the task completion rate, task resource consumption, and multi-modality collaborative observation rate. The paper also fully considers various constraints in terms of the marine environment, energy, communications, and other aspects. The paper establishes a robust planning model for ocean observation tasks and presents the design for a robust planning algorithm for these tasks based on the classic multi-objective optimization algorithm - the ant colony algorithm. The results of simulation experiments show that, when the number of observation tasks and amount of available observation resources exceed the capability of manual calculation, the robust planning scheme derived using the proposed method completed more tasks with lower costs, a higher resource use rate, and better algorithm performance than planning schemes generated by simple strategies. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Multiobjective optimization

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A novel singer identification method using GMM-UBM 6th

A novel singer identification method using GMM-UBM

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6th Conference on Sound and Music Technology, CSMT 2018

作者： Zhang, Xulong Jiang, Yiliang Deng, Jin Li, Juanjuan Tian, Mi Li, Wei School of Computer Science Fudan University Shanghai201203 China Centre for Digital Music Queen Mary University of London London United Kingdom Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai201203 China

ISBN: (纸本)9789811387067

This paper presents a novel method for singer identification from polyphonic music audio signals. It is based on the universal background model (UBM), which is a singer-independent Gaussian mixture model (GMM) trained on many songs to model the singer characteristics. For our model, singing voice separation on a polyphonic signal is used to cope with the negative influences caused by background accompaniment. Then, we construct UBM for each singer trained with the Mel-frequency Cepstral Coefficients (MFCCs) feature, using the maximum a posterior (MAP) estimation. Singer identification is realized by matching test samples to the obtained UBMs for individual singers. Another major contribution of our work is to present two new large singer identification databases with over 100 singers. The proposed system is evaluated on two public datasets and two new ones. Results indicate that UBM can build more accurate statistical models of the singer’s voice than conventional methods. The evaluation carried out on the public dataset shows that our method achieves 16% improvement in accuracy compared with the state-of-the-art singer identification system. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Gaussian distribution

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Video Captioning With Temporal And Region Graph Convolution Network

Video Captioning With Temporal And Region Graph Convolution ...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xinlong Xiao Yuejie Zhang Rui Feng Tao Zhang Shang Gao Weiguo Fan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Managerment and Engineering Shanghai University of Finance and Econnomics Shanghai China School of Information Technology Deakin University Victoria Australia Department of Business Analytics University of Iowa Iowa USA

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Video captioning aims to generate a natural language description for a given video clip that includes not only spatial information but also temporal information. To better exploit such spatial-temporal information attached to videos, we propose a novel video captioning framework with Temporal Graph Network (TGN) and Region Graph Network (RGN). TGN mainly focuses on utilizing the sequential information of frames that most of existing methods ignore. RGN is designed to explore the relationships among salient objects. Different from previous work, we introduce Graph Convolution Network (GCN) to encode frames with their sequential information and build a region graph for utilizing object information. We also particularly adopt a stack GRU decoder with a coarse-to-fine structure for caption generation. Very promising experimental results on two benchmark datasets (MSVD and MSR-VTT) show the effectiveness of our model.

关键词： Decoding Feature extraction Convolution Task analysis Vocabulary Electron tubes Computational modeling

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Control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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A differential evolution-based optimization tool for interplanetary transfer trajectory design

arXiv

引用

arXiv 2020年

作者： Zuo, Mingcheng Dai, Guangming Peng, Lei Tang, Zhe No. 388 LuMo Road Hongshan District Wuhan China Department of Industrial Engineering University of Bologna Via Fontanelle 40 ForlìI-47121 Italy Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Key Laboratory of Geological Survey and Evaluation of Ministry of Education

The extremely sensitive and highly nonlinear search space of interplanetary transfer trajectory design bring about big challenges on global optimization. As a representative, the current known best solution of the global trajectory optimization problem (GTOP) designed by the European space agency (ESA) is very hard to be found. To deal with this difficulty, a powerful differential evolution-based optimization tool named COoperative Differential Evolution (CODE) is proposed in this paper. CODE employs a two-stage evolutionary process, which concentrates on learning global structure in the earlier process, and tends to self-adaptively learn the structures of different local spaces. Besides, considering the spatial distribution of global optimum on different problems and the gradient information on different variables, a multiple boundary check technique has been employed. Also, Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) is used as a local optimizer. The previous studies have shown that a specific swarm intelligent optimization algorithm usually can solve only one or two GTOP problems. However, the experimental test results show that CODE can find the current known best solutions of Cassini1 and Sagas directly, and the cooperation with CMA-ES can solve Cassini2, GTOC1, Messenger (reduced) and Rosetta. For the most complicated Messenger (full) problem, even though CODE cannot find the current known best solution, the found best solution with objective function equaling to 3.38 km/s is still a level that other swarm intelligent algorithms cannot easily reach. Copyright © 2020, The Authors. All rights reserved.

关键词： Global optimization

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