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Orbital-angular-momentum-enhanced phase estimation using non-Gaussian states with photon loss

Physical Review A 2023年第2期108卷 022613-022613页

作者： Yong-Jian Chen Jin-Wei Gao Jin-Xuan Han Zhong-Hui Yuan Ruo-Qi Li Yong-Yuan Jiang Jie Song School of Physics Harbin Institute of Technology Harbin 150001 China School of Electronic Information Engineering China West Normal University Nanchong 637002 Sichuan China Key Laboratory of Micro-Nano Optoelectronic Information System Ministry of Industry and Information Technology Harbin 150001 China Key Laboratory of Micro-Optics and Photonic Technology of Heilongjiang Province Harbin Institute of Technology Harbin 150001 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China

This study investigates the use of orbital angular momentum (OAM) to enhance phase estimation in Mach-Zehnder interferometers by employing non-Gaussian states as input resources in the presence of noise. Our research demonstrates that non-Gaussian states, particularly the photon-subtraction-then-addition state, exhibit the best sensitivity in the presence of symmetric noise. Additionally, a higher order of the Bose operator of non-Gaussian states provides better sensitivity for symmetric noise. OAM can mitigate the deterioration of noise, making it possible to estimate small phase shifts θ→0. OAM enhances the resolution and sensitivity of all input states and mitigates the deterioration caused by photon loss. Additionally, OAM enhances the resolution and sensitivity of all input states, enabling the sensitivity to approach the 1/N limit even under significant photon loss (e.g., 50% symmetric photon loss). These results hold promise for enhancing the sensitivity and robustness of quantum metrology, particularly in the presence of significant photon loss.

关键词： Quantum information theory Quantum metrology

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Exploring Supervised Contrastive Learning for Skeleton-Based Temporal Action Segmentation

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Chen, Bowen Ji, Haoyu Ma, Hanwei Lin, Ruihan Nie, Wei Ren, Weihong Wang, Zhiyong Liu, Honghai Harbin Institute of Technology Shenzhen State Key Laboratory of Robotics and Systems Shenzhen518055 China University of Portsmouth School of Computing PortsmouthPO1 3HE United Kingdom

Skeleton-based temporal action segmentation (STAS) takes long human skeleton sequences as input and predicts action categories at the frame level. Current STAS methods primarily use frame-wise cross-entropy loss, which focuses only on the relationships among one-hot label logits and overlooks the importance of the quality of frame-wise representations. To this end, we propose a novel framework called Supervised Contrastive Skeleton-Based Temporal Action Segmentation (SCSAS) that optimizes representation learning. Specifically, our framework constructs a frame-level embedding space using a simple projection head and optimizes this space using three novel contrastive losses. These losses enhance the semantic relationships at the frame and segment levels by pulling together representations of the same activities and pushing apart those of different actions. Moreover, we introduce a confidence-based hard anchor sampling strategy to enhance the efficiency of contrastive learning. Finally, a boundary refinement module is also introduced to fully exploit the advantages of optimized representation. Our method seamlessly integrates with existing STAS methods and consistently enhances their performance across various datasets, without additional inference costs. With the incorporation of the boundary refinement branch, early STAS methods even achieve performance comparable to the state-of-the-art methods. © 2016 IEEE.

关键词： Contrastive Learning

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Channel Estimation for High-speed Mobile MIMO-OFDM systems Using Deep Learning 25

Channel Estimation for High-speed Mobile MIMO-OFDM Systems U...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Xingxing Ai Ao Deng Zeng Li Shaochuan Wu State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen Guangdong China School of Electronics and Information Engineering Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (纸本)9798400713231

In high-speed mobile channel environment characterized by fast time-varying and Doppler frequency shifts, interference between subcarriers becomes increasingly prominent. In this case, channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system faces significant challenges, including low time correlation, high interpolation errors, and Doppler frequency shifts. To address these issues, we propose a channel estimation framework that integrates attention mechanisms with an enhanced Super-Resolution Convolutional Neural Network (SRCNN). The whole network adopts the network structure of the autoencoder, embeds the channel attention module with powerful channel dynamic feature extraction ability at the encoder end, and reweights the channel features of the least squares (LS) estimation input with the learned attention map in turn, which can fully extract the features of the original data. Subsequently, the improved SRCNN reconstructs and refines the features of the dimensionally reduced data, exploiting its strong reconstruction and denoising capabilities to recover high-resolution characteristics. Finally, the decoder outputs the fully estimated channel matrix. Simulation results demonstrate that the proposed channel estimation method significantly outperforms the traditional least squares algorithm while exhibiting computational complexity slightly higher than LS but lower than the linear minimum mean square error (LMMSE) algorithm. Notably, as the number of user-transmitted symbols and subcarriers increases, the deep learning-based method shows greater advantages over the LMMSE algorithm, greatly reducing practical computational costs.

关键词： Channel estimation

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Pognotis and Health Mnagement of Reactor Equipment Based on Cloud-Edge-Terminal Architecture

Pognotis and Health Mnagement of Reactor Equipment Based on ...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Yisong Liu Chengning Zhou Jihong Yan Xiaofeng Wang Caixue Liu School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China State Key Laboratory of Advanced Nuclear Energy Technology Nuclear Power Institute of China Chengdu China SMEE Harbin Institute of Technology Harbin China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

With the rapid development of cloud computing technology and the increasing intelligence of various reactor equipment in nuclear power plants, fault prediction and health management have become more and more important. Generally, traditional machine learning methods face the problem of difficulty in adequately handling massive data, which poses a serious challenge to health management. However, the Cloud-Edge-Terminal framework, which tightly integrates cloud computing, edge computing, and terminal devices for efficient data processing and business services, provides new ideas to solve the above. Firstly, this paper describes the importance of reactor equipment fault diagnosis, then reviews the development of fault diagnosis at various stages, then integrates the cloud-edge-end architecture into reactor critical equipment health management, and focuses on the technological approaches involved in reactor equipment prognostics and health management under this model, and finally discusses future research directions and development trends.

关键词： Fault diagnosis Training Deep learning Cloud computing Protocols Transfer learning Computer architecture Data models Inductors Prognostics and health management

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Hysteresis Feedforward Compensation of Reluctance Actuator: A Neural Network Approach Using Stochastic Configuration Network 11

Hysteresis Feedforward Compensation of Reluctance Actuator: ...

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11th IEEE Data Driven Control and Learning systems Conference, DDCLS 2022

作者： Liu, Yang Miao, Qian Lili Harbin Institute of Technology Ministry of Industry and Information Technology Key Laboratory of Ultra-Precision Intelligent Instrumentation Harbin150001 China Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Since the current voice coil motor cannot break through the physical limit, the next-generation ultra-precision motion stage cannot meet the needs of high speed, high acceleration and high precision. To solve this problem, a new type of actuator must be developed and reluctance actuator has been proposed as one of the most potential candidates. However, the strong nonlinearity of the reluctance motor limits its application in ultra-precision. For example, the hysteresis effect, the relationship between force and current, and force and displacement, and so on. In this paper, a combined control strategy composed of feedforward compensation and PID is introduced to the reluctance actuator system, and the BP neural network and stochastic configuration network (SCN) are applied to the construction of the hysteresis inverse model. According to the simulation results, it can be concluded that the accuracy of the inverse model constructed by the SCN method is higher than that of the inverse model constructed by the BP neural network. It is concluded that the Stochastic Configuration Network method is effective for the hysteresis nonlinearity compensation of the reluctance actuators, and promising in precision stage control. © 2022 IEEE.

关键词： Hysteresis

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3D Reconstruction for Scenes with Noticeable Differences in Elevation based on Few-view Satellite Images

3D Reconstruction for Scenes with Noticeable Differences in ...

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Information systems and Computing technology (ISCTech), International Conference on

作者： Xinqi Liu Maosheng Wei Luyun Tian Yiming Yan Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University HEU Harbin China Harbin Space Star Data System Science and Technology Company Ltd. Harbin China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information Engineering Beijing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Due to the difficulty of acquiring a sufficient amount of satellite images in a short period of time, three-dimensional (3D) reconstruction with few-view satellite images has a wide range of application areas. However, based on few-view satellite images, most existing algorithms, including dense matching and the neural radiation fields, lose their stability when facing scenes with noticeable differences in elevation which is abbreviated as NDE-scenes. The greater the elevation difference, the more challenging the reconstruction becomes. To solve the problem of poor reconstruction in NDE-scenes, we propose a new framework based on neural radiation fields in this paper, along with two improvement methods: the signed distance function (SDF) field generation network (SDFGN) optimization strategy and the shadow prior module. It enables accurate and robust reconstruction of NDE-scenes with fewer views. Comparison and ablation experiments on the DFC2019 dataset verify the advancements of the proposed methods and the effectiveness of each module.

关键词： Point cloud compression Computers Three-dimensional displays Accuracy Lighting Satellite images Image reconstruction Optimization Information systems

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DC FAULT RIDE-THROUGH for WIND POWER INTEGRATION VIA MMC-HVDC BASED on PRECISE CONTROL of ENERGY STORAGE CONFIGURED INSIDE WIND FARM

DC FAULT RIDE-THROUGH for WIND POWER INTEGRATION VIA MMC-HVD...

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Renewable Power Generation and Future Power systems Conference 2023, RPG 2023 UK

作者： Sun, Yinfeng Guo, Yuhang Fan, Fulin Fan, Jiabin Li, Guoqing Wang, Zhenhao Wu, Xueguang Jilin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom State Grid Smart Grid Research Institute Beijing China Beijing Key Laboratory of DC Power Grid Technology and Simulation Beijing China

ISBN: (纸本)9781839539848

The modular multilevel converter-based high voltage direct current (MMC-HVDC) overhead lines transmission scheme is the preferred option for wind power integration, though it is susceptible to DC faults. To ensure the connection of a wind farm (WF) to the grid during DC faults, this paper proposes a coordinated control strategy for DC fault ride-Through (FRT) based on the energy storage system configured inside the WF for wind power integration through a bipolar MMC-HVDC. In the case of an instantaneous DC fault, the non-fault pole is regulated to operate at full power state to maximise wind power transmission. The exceedance of available wind power outputs over the transmission capacity of the non-fault pole is then accurately allocated to the internal energy storage units (ESUs) of wind generators (WGs) in order to ensure the power balance of the system and reduce the state of charge variance of ESUs as soon as possible. In order to reduce the energy capacities of ESUs required to store the excessive wind power in the event of a permanent DC fault, the method of step-by-step removing WGs is adopted to ensure the system power remaining balanced when the internal ESUs of the disconnected WGs are out of operation. The model is developed and carried out on the PSCAD/EMTDC simulation platform to verify the effectiveness of the proposed control strategies. © The Institution of Engineering & technology 2023.

关键词： Poles

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Characterization of plasma discharge channel in EDM using photography and spectroscopy 15

Characterization of plasma discharge channel in EDM using ph...

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15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2021

作者： Qin, Ling Liu, Chen Zhang, Ruixue Zhang, Ya-Ou Yang, Xiaodong Zhao, Wansheng State Key Laboratory of Mechanical System and Vibration China School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Department of Mechanical Engineering and Automation Harbin Institute of Technology Harbin150001 China

The plasma discharge channel of electrical discharge machining (EDM) brings a temperature of several thousands of degrees Celsius into the gap between the anode and cathode, resulting in the material removal by means of melting and evaporating. To investigate the characteristics of the plasma discharge channel, high-speed imaging technology is adopted to observe the dimension of the plasma. A spectrometer is used to calibrate the temperature of the plasma as well. Then the particle-in-cell with Monte Carlo collision (PIC-MCC) simulation is conducted to analyze the variation and distribution of the plasma and heat flux. The experimental results demonstrate that the discharge channels' morphology obtained is stable after a few microseconds of expansion after breakdown, and the diameter of discharge channel and the current density increases with the increase of discharge current. Besides, the emission intensity of plasma increases with the increase of electrical parameters. However, the temperature of plasma decreases after high current, which means debris particles make machining condition deteriorated. Finally, the simulation results demonstrate that despite the current density increases, the maximum heat flux at the center of electrode reduces rapidly with the expansion of the discharge channel. The distribution of heat flux upon electrode evolves towards "wider and flatter", leading to larger debris particles than exceptional size. © 2022 The Author(s).

关键词： High speed photography

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A 6-PTRT Parallel Robot using Parallel Guide Wires 7

A 6-PTRT Parallel Robot using Parallel Guide Wires

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2021 7th International Conference on Mechanical Engineering, Materials and Automation technology, MMEAT 2021

作者： Yang, Xian Zhuo, Beichen Jia, Haozhe Rong, Weibin Liu, Chun Gao, Wenxiang Li, Shijie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Chengdu Aircraft Industry Group Co. Ltd Chengdu China

In this paper, a parallel guide 6-PTRT parallel robot with 6 degrees of freedom is designed for drilling and riveting in the narrow space inside the nacelle, and its kinematics analysis is performed, including the solution of inverse kinematics solution, Jacobian matrix, workspace results and the forward kinematics solution. The major structure parameters of the parallel robot are optimized by integrating the global performance index and the size of the workspace, and a set of parallel robot size parameters with good comprehensive performance are obtained. They are layout angles θ 1 = 17.5 , θ 2 = 15 , radius of movable platform r = 130mm, radius of fixed platform R = 220mm, length of branch chain L = 220mm. These parameters meet the given size of the space available for the robot and the mounting dimensions of the drilling tool © Published under licence by IOP Publishing Ltd.

关键词： Fixed platforms

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Sound Source Reconstruction with Gibbs Sampler from Non-Synchronous Wind Tunnel Measurements 6

Sound Source Reconstruction with Gibbs Sampler from Non-Sync...

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Zeng, Fanchang Xu, Lingji Yu, Liang Bao, Anyu Chen, Bao Chen, Xinzhe School of Ocean Engineering and Technology Sun Yat-sen University Guangdong China Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai China Aerodynamic Research Institute Aviation Industry Corporation of China Harbin China

ISBN: (纸本)9798350339994

In conventional array signal processing, the resolution of acoustic imaging and the working frequency of the array are directly limited by the aperture and density of the microphone array due to Rayleigh limit. One solution is to sequentially scan the object through a prototype array to synthesize a large aperture or higher-density array, which is referred to as non-synchronous measurements (NSM). A fundamental problem in the NSM is the recovery of the missing phase relations between consecutive measurements compared to synchronous measurements (SM), and this problem is deemed as the matrix completion problem of a block diagonal spectral matrix. In this work, acoustic source reconstruction from NSM is considered as the problem of solving a system of equations (inverse acoustical problem). Bayesian inference based on Gibbs sampling is proposed to solve the ill-posed inverse problem. Sources strength distribution and noise are modeled as a Bayesian hierarchical framework solved by sampling in the posterior probability density functions (PDF). The proposed method is validated effectively by numerical simulations and wind tunnel test experiments. © 2023 IEEE.

关键词： Gibbs sampler Inverse acoustical problem Non-synchronous measurements Wind tunnel testing

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