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arXiv 2022年

作者： Tian, Haoyue Gao, Pan Wei, Ran Paul, Manoranjan Nanjing University of Aeronautics and Astronautics China Science and Technology on Electro-Optic Control Laboratory China School of Computing and Mathematics Charles Sturt University Australia

Motion estimation and motion compensation are indispensable parts of inter prediction in video coding. Since the motion vector of objects is mostly in fractional pixel units, original reference pictures may not accurately provide a suitable reference for motion compensation. In this paper, we propose a deep reference picture generator which can create a picture that is more relevant to the current encoding frame, thereby further reducing temporal redundancy and improving video compression efficiency. Inspired by the recent progress of Convolutional Neural Network(CNN), this paper proposes to use a dilated CNN to build the generator. Moreover, we insert the generated deep picture into Versatile Video Coding(VVC) as a reference picture and perform a comprehensive set of experiments to evaluate the effectiveness of our network on the latest VVC Test Model-VTM. The experimental results demonstrate that our proposed method achieves on average 9.7% bit saving compared with VVC under low-delay P configuration. Copyright © 2022, The Authors. All rights reserved.

关键词： Video signal processing

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A Novel Method for Analysis of Key Causes of Aviation Insecurity Events Based on Mutual Information

A Novel Method for Analysis of Key Causes of Aviation Insecu...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Bo, Ren Tao, Xue Jianguang, Zhu ZengHang Lijie, Cui Hao, Liu Equipment Management and UAV Engineering College Air Force Engineering University Xi’an Shaanxi710051 China Zhengzhou Regional Military Representative Office of Air Force Zhengzhou450000 China Equipment Department Beijing100076 China Science and Technology On Electro-Optic Control Laboratory Luo yang710052 China

ISBN: (纸本)9789811581540

Based on mutual information, this paper proposes a method of aviation safety dimension reduction, which is used to construct the causation model of aviation accidents under uncertain environment. First, based on the Bow-tie model, a comprehensive m comprehensively considering many factors such as the cause of the accident, possible consequences, and control measures. Aiming at the problem of high-dimensional modeling of causal factors, a mutual information measure was proposed to measure the causal strength between causal factors and aviation safety, and screen out causal variables that have a greater impact on aviation safety. For mutual information needed to solve the problem of multidimensional variables cause of joint integration it is proposed based on sampling in conjunction with low deterministic kernel density estimation method for solving joint integration, convergence and high accuracy. Taking aviation safety as an example, the correctness and scientificity of the method in this paper are verified. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Statistics

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Target Recognition Method for Carrier Aircraft Fleet based on Improved D-S Evidence Theory

Target Recognition Method for Carrier Aircraft Fleet based o...

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第40届中国控制会议

作者： Lifan Sun Jie Zou Zhe Yang School of Information Engineering Henan University of Science and Technology Technology on Electro-optic Control Laboratory

In modern naval warfare,the battlefield environment is extremely complex and *** inaccuracy of information obtained through various radar,ESM,photoelectric,and other detection equipment leads to a high degree of conflict in *** way to effectively integrate this information and accurately identify the enemy targets is the key to victory in *** serve the matter,this paper proposes a carrier aircraft fleet target recognition method based on improved D-S evidence ***,the probability assignment of sensor evidence is transformed by an improved probability assignment *** the transformation of probability is done,and appropriate evidence of the high degree of conflict is collected,the appropriate conflict coefficient is used to calculatethe degree of evidence ***,the target recognition results are obtained through the fusion of the improved evidence theory *** simulation example demonstrates that the method proposed in this paper can effectively solve the conflict problem in target recognition and achieve high-quality evidence combination results to obtain high-speed identification targets,which can be widely used in all kinds of decisions.

关键词： Target recognition multi-sensor evidence theory decision fusion

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Helmet Pose Measurement Method Based on Feature Learning

Helmet Pose Measurement Method Based on Feature Learning

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Gaobo Fu Xinbo Zhao Xiaochun Zou Linjie Yan Baoshang Zhang School of Software Northwestern Polytechnical University Xi'an China Science and Technology on Electro-optic Control Laboratory Luoyang China School of computer Science Northwestern Polytechnical University Xi'an China School of Electronic Information Northwestern Polytechnical University Xi'an China

In modern aviation systems, the helmet sight (HMS) assists the pilot to cooperate with the warplane to quickly complete the target tracking and aiming, and the helmet pose measurement is the core technology of the helmet sight. Since the helmet pose measurement technology involved in aviation safety, this technology is kept confidential at home and abroad, and there is a lack of open source helmet data set and high-precision helmet posture measurement algorithm. For this purpose, this paper produced the first helmet dataset, Helment 8K, and proposed the CSPDarkNet-Ht helmet pose measurement algorithm based on feature learning. The target is to form the helmet dataset image on the helmet model according to the unique feature points of the helmet model design. Helment 8K data set is made by building a helmet model with 3dmax and then rendering 8282 helmet images at different angles with targets. The CSPDarkNet-Ht algorithm divides the yaw angle and pitch angle of the helmet pose angle into two sub-tasks for classification and regression. Firstly, The algorithm uses CSPDarkNet to extract the low, medium and high semantic information of the target features for the backbone network. Then, different semantic feature layers are classified from different angles and regression of the highest semantic feature layer is performed to obtain the predicted pose angles. Finally, the loss function was optimized to include the cross-entropy loss of the five coarse and fine categories as the total loss. Moreover, the average absolute error of the angular prediction on the Helment 8K dataset is 0.5, and the prediction accuracy is 91.09% within 1 degree error, which is consistent with the accurate measurement of the helmet pose.

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Spin-density wave and superconductivity in La4Ni3O10 under ambient pressure

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Physical Review B 2025年第14期111卷 144502-144502页

作者： Ming Zhang Hongyi Sun Yu-Bo Liu Qihang Liu Wei-Qiang Chen Fan Yang Zhejiang Key Laboratory of Quantum State Control and Optical Field Manipulation Department of Physics Zhejiang Sci-Tech University Hangzhou 310018 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China International Quantum Academy Shenzhen 518048 China Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Physics State key laboratory of quantum functional materials and Guangdong Basic Research Center of Excellence for Quantum Science Southern University of Science and Technology Shenzhen 518055 China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen 518045 China School of Physics Beijing Institute of Technology Beijing 100081 China

High-pressure studies have revealed superconductivity in La4Ni3O10, sparking interest in its ambient-pressure properties and the underlying electronic correlations. Motivated by experimental observations of an incommensurate spin-density wave (SDW) at ambient pressure, we investigate the SDW characteristics and possible superconductivity in La4Ni3O10 using a multiorbital random-phase approximation (RPA). Starting with a 12-orbital tight-binding model derived from density functional theory (DFT) calculations, we include Hubbard interactions to explore the interplay between electronic correlations and magnetic instabilities. Our analysis reveals a stripe-like SDW with a wave vector Q≈(±0.7π,0), suggesting a possible density wave instability in agreement with experiments. This configuration is driven by nesting between the α1 pocket, primarily contributed by the outer-layer Ni dz2 orbitals, and the β1 pocket, contributed by both the dz2 and dx2−y2 orbitals of the outer layer. It exhibits interlayer antiferromagnetic ordering between the top and bottom NiO layers, with the magnetic moment of the middle layer being nearly zero. We demonstrate that the Hund coupling JH is the primary driver of the observed SDW and determine the specific criterion: JH>0.16U. Building upon our findings on the SDW mechanism, we further demonstrate that hole doping (δ=−0.4) enhances Fermi surface nesting, leading to the emergence of a superconducting state with a gap structure similar to that of the high-pressure phase.

关键词： Spin density waves

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Efficient and Accurate Design of Infrared and Laser-Compatible Stealth Metasurface Using Bidirectional Artificial Neural Network

Efficient and Accurate Design of Infrared and Laser-Compatib...

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International Symposium on Micromechatronics and Human science (MHS)

作者： Pengfei Zhang Xiong Cheng Yao Ma Jun Liu Liyan Zhu Daying Sun Xiaodong Huang Key Laboratory of MEMS of the Ministry of Education School of Integrated Circuits Southeast University Nanjing China School of Integrated Circuits Nanjing University of Science and Technology Nanjing China National Key Laboratory of Electromagnetic Environmental Effects and Electro-optical Engineering Army Engineering University Nanjing China

This work introduces a bidirectional artificial neural network (ANN) aimed at designing infrared and laser-compatible stealth metasurfaces, which can effectively overcome the time and computational costs occurring in the traditional electromagnetic (EM) simulation method. The forward network predicts optical response of given metasurface geometries, while the inverse network outputs metasurface geometries from the desired optical response. A tandem network resolves the non-uniqueness problem inherent in the inverse network, ensuring high predictive accuracy. The ANN approach reduced the computational time from approximately 15 minutes based on the EM simulation to 2 milliseconds, thus greatly improving the efficiency of the design process. This novel application of ANN presents a promising approach to the design of stealth metasurfaces.

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Reconfigurable Metagrating Enabled by Shape Memory Polymers

Reconfigurable Metagrating Enabled by Shape Memory Polymers

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Sidi Wang Kai Feng Jianjia Yi Key Laboratory of Integrated Services Network Xidian University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

We propose and demonstrate a shape-controlled metagrating with adjustable energy distribution based on shape memory Polymers (SMPs). The design consists of a capacitance printed on the top of the FR4 substrate, SMPs substrate (VeroWhitePlus) in the middle layer, and a metallic ground at the bottom. Using the perfect diffraction theory to design reconfigurable metagratings based on shape memory polymers is. Furthermore, the functionalities of the designed shape-controlled tunable metagrating are investigated by full-wave simulations. Simulation results show that the reconfigurable metagrating can achieve energy bisection of +1, -1 order and the energy trisection of +1, 0, -1 order. And the proposed reconfigurable metagrating is experimentally demonstrated. The design breaks through the limitation that the traditional electrically tunable reconfigurable metagratings need external power supply, and provides a new design idea for the dynamic control of energy distribution of the reconfigurable metagratings.

关键词： Radio frequency Shape Diffraction Simulation Switched mode power supplies Process control Holography

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A Bytecode Service Composition Engine for Embedded Services

A Bytecode Service Composition Engine for Embedded Services

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Siao Wang Chenglie Du Jinchao Chen Zuo Zhao Ying Zhang Northwestern Polytechnical University China Science and Technology on Electro-iptic Control Laboratory China

ISBN: (纸本)9781665426565

With the development of embedded systems, people tend to abstract the capacity of an embedded equipment as a service in order to simplify the development, deployment, management and maintenance of the embedded software. By aggregating abilities of individual embedded devices via service composition, people can easily build a more reliable and efficient system. Although the service composition problem has been extensively studied in the field of Web, the adoptation of service composition technique to embedded devices stumps due to the resource limitation and platform heterogeneity of embedded systems. To address these problems, this paper builds a service composition engine for embedded systems that comprises three main works: First, this paper provides a uniform method to represent the embedded service composition problem. Second, this paper designs a compiling method based on topological sorting to convert the unified composition information into the service composition file that represents the way to implement the composite service. Third, this paper devises a bytecode virtual machine to execute the service composition file, and implements the composite service in a resource-friendly way. At last, a carefully devised experiment is conducted, and the result shows our devised engine provides a lightweight, reliable and well-performed way to realize the service composition technique on embedded devices.

关键词： Embedded systems Instruction sets Optimization methods Parallel processing Maintenance engineering Virtual machining Software reliability

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Efficient Multi-Stage Network with Pixel-Wise Degradation Prediction for Real-Time Motion Deblurring

SSRN

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SSRN 2022年

作者： Hao, Zeyu Wang, Hang Zhang, Xuchong Li, Yuhai Sun, Hongbin Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Microelectronics Xi’an JiaoTong University Shaanxi Xi’an710049 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

Motion deblurring is an indispensable task in perceptual systems because motion blur can seriously influence the visual effect and the quality of subsequent perception tasks. In practical application, efficiency and effect of motion deblurring are both important. However, among the existing methods, there is no design that can meet the real-time and visual quality requirements at the same time. Thus, an efficient motion deblurring network is proposed by leveraging multi-stage pixel-wise degradation prediction. Specifically, some lightweight modules are designed to accelerate processing while attention and multi-scale mechanism are introduced to maintain quality. In addition, a pixel-wise degradation prediction module and a spatial-channel compensation module are further employed to improve the deblurring quality, such as the distortion of moving objects in the restoration images. Extensive experimental results show that the proposed network can achieve the same PSNR level as the SOTA lightweight deblurring methods and is far faster (5.3 times for DMPHN, 6.7 times for IFI-RNN). Therefore, the proposed design achieves a balance between quality and speed compared with the existing methods. © 2022, The Authors. All rights reserved.

关键词： Pixels

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Fault Diagnosis Strategy for Few Shot Industrial Process Based on Data Augmentation and Depth Information Extraction

SSRN

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SSRN 2022年

作者： Tian, Ying Xiang, Xin Peng, Xin Zhong, Yin Zhang, Wei Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Data based intelligent fault diagnosis method is an important tool for ensuring the stability of industrial process. However, in the actual industrial process, due to the difficulty of feature extraction and the lack of labeled fault data, it is hard to form a fault diagnosis model with good performance. To address this problem, the Self-Attention embedded Generative Adversarial Networks combined with the Residual Network(SAGAN-ResNet) is proposed in this study. Firstly, to handle the lack of the fault data, the data enhancement method which consists of Self-Attention embedded generator and discriminator is adopted. Then, to extract the feature for better diagnosis performance, based on the augmented training dataset, Residual Network (ResNet) is introduced. Finally, the proposed method is compared with others, the results show that proposed method has advantages in the case of complex process fault diagnosis with few shot industrial data. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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