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Analyzing the Effect of the Intra-Pixel Position of Small PSFs for Optimizing the PL of Optical Subpixel Localization

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engineering 2023年第8期27卷 140-149页

作者： Haiyang Zhan Fei Xing Jingyu Bao Ting Sun Zhenzhen Chen Zheng You Li Yuan Department of Precision Instrument Tsinghua UniversityBeijing 100084China State Key Laboratory of Precision Measurement Technology and Instruments Tsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Integrated Circuits Tsinghua UniversityBeijing 100084China Joint International Research Laboratory of Advanced Photonics and Electronics Beijing Information Science and Technology UniversityBeijing 100192China Beijing Institute of Control Engineering Beijing 100190Chin

Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement *** unavoidable imaging noise,there is a precision limit(PL)when estimating the target positions on image sensors,which depends on the detected photon count,noise,point spread function(PSF)radius,and PSF’s intra-pixel *** studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position ***,we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small *** accurately estimate the PL in practical applications,we provide effective PSF(e PSF)modeling approaches and apply the Cramér–Rao lower *** on the characteristics of small PSFs,we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF;we then verify these equations on real ***,we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible,indicating that the optimum is ultimately limited by light ***,we apply the maximum likelihood *** combination with e PSF modeling allows us to successfully reach the PL in experiments,making the above theoretical analysis *** work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory,thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization.

关键词： Optical measurement Subpixel localization Precision limit optimization Small point spread functions Centroiding Star sensors

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FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

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Science China(information Sciences) 2024年第4期67卷 339-340页

作者： Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN School of Control and Computer Engineering North China Electric Power University School of Cyberspace Science and Technology Beijing Institute of Technology Department of Finance Operationsand Information Systems(FOIS)Brock University

In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,

关键词： Sensitive data

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Language Reasoning in Vision-Language-Action Model for Robotic Grasping

Language Reasoning in Vision-Language-Action Model for Robot...

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2024 China Automation Congress, CAC 2024

作者： Fan, Lingling Chen, Kang Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

Grasping tasks are crucial for the interaction between the robot and environment, requiring the robot to make autonomous decisions based on environmental conditions and instructions. Recently, multimodal Vision Language Models (VLMs) have demonstrated significant potential for robotic grasping. However, previous work has often been confined to high-level planning due to the gap between the text output of VLMs and the numerical predictions of robot actions, as well as the challenges of transferring rich semantic information from large models to the prediction of grasping actions. In this paper, we co-fine-tune a pre-trained VLM using the mix of large-scale visual language data and robotic grasping trajectory data, integrating model into an end-to-end robot control system to construct a Vision Language Action Model (VLA) capable of generating the robot’s underlying actions. Simultaneously, to fully leverage the prior knowledge and reasoning capabilities of the pre-trained model, we insert a language reasoning structure to bridge the mapping from high-level tasks to low-level actions. This method first deduces the relevant information regarding the target object and the end-effector motions, followed by predicting the grasping actions value with finer granularity based on the language reasoning, thereby enhancing the accuracy of action predictions. A series of grasping experiments demonstrate that our method achieves a high grasping success rate under flexible language instructions and exhibits strong generalization capabilities for unseen objects and scenes. © 2024 IEEE.

关键词： Visual languages

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Reinforcement Learning for Exploration vs. Exploitation Problems in Medical Exercise Sessions

Reinforcement Learning for Exploration vs. Exploitation Prob...

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control Conference (ANZCC), Australian and New Zealand

作者： Roxanne R. Jackson Damiano Varagnolo Steffi Knorn Department of Control Engineering Technical University of Berlin Germany Department of Engineering Cybernetics Norwegian University of Science and Technology Norway Department of Information Engineering University of Padova Italy

Biofeedback in gamified medical exercise sessions has proven to be an effective technique for adapting patient behaviours to improve health outcomes. In this paper, we formulate a method for designing optimal training sessions with two conflicting goals: maximising the desired exercise effect and sufficiently exciting the system for identification in order to update the personalised patient model. We exploit the flexibility of model-free reinforcement learning to obtain an optimal controller, which is robust to uncertainty in the system parameters. We compare the controller from reinforcement learning to a standard dual control formulation in simulation on an illustrative case study of building pelvic floor muscular strength and tone while performing Kegel exercises. The results indicate that the reinforcement learning method attains a better exercise effect while improving the parameter estimates compared to a standard dual controller. However, due to the trial-and-error nature of reinforcement learning, this comes at the expense of computational time.

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FPGA Based High Throughput LK Optical Flow Acceleration Design 3

FPGA Based High Throughput LK Optical Flow Acceleration Desi...

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3rd International Conference on Electronic information engineering and Data Processing, EIEDP 2024

作者： Liang, Wei Sun, Ting Xing, Fei Dong, Xinran Xiao, Heng Li, Lin School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China Department of Precision Instrument Tsinghua University Beijing100084 China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China

ISBN: (纸本)9781510680531

In the field of machine vision, algorithms for estimating optical flow in sequential images have been proposed for many years. However, due to the advancements in image resolution and video frame rates of sensor, the optical flow algorithm running on a CPU or a GPU may struggle to meet real-time processing and low power consumption requirements. This paper presents an FPGA-based accelerated method for high-speed LK optical flow estimation. We implemented a serial input and multi-window output buffer structure to enhance the algorithm's parallel processing capability and improve processing speed. Additionally, we designed a multi-clock convolution structure to reduce resource consumption. Tested on the Middlebury dataset, the FPGA-based LK optical flow algorithm developed in this paper can achieve a processing speed of 330 frames per second for image sequences with a resolution of 640x480. It also maintains a relatively high level of accuracy and has low power consumption. © 2024 SPIE.

关键词： Optical flows

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Transformer-Based Sequence Modeling with Action Discretization for Robotic Grasping

Transformer-Based Sequence Modeling with Action Discretizati...

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2024 China Automation Congress, CAC 2024

作者： Hou, Yaokun Zheng, Song Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

The powerful representational and sequential modeling capabilities of Transformer have been validated in various fields such as natural language processing. However, how to better address sequential decision-making problems like robotic grasping through the Transformer architecture and achieve autonomous control of robotic grasping remains a longstanding challenge. Models that directly output continuous decision actions for robotic arms often rely on mean squared error (MSE) loss for training, which is unable to effectively handle the randomness and diversity of actions. This limitation leads to the generation of relatively singular actions in certain grasping scenarios, reducing the success rate of grasping. To address this issue, this paper proposes a Transformer-based method for discretizing robotic arm actions, where actions are discretized by dimension to enable autoregressive modeling. Experimental results demonstrate that this method effectively improves the grasping performance of robotic arms in simulation environments. © 2024 IEEE.

关键词： Mean square error

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Secure Recursive State Estimation of Networked Systems Against Eavesdropping: A Partial-Encryption-Decryption Method

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IEEE Transactions on Automatic control 2024年第6期70卷 3681-3694页

作者： Zou, Lei Wang, Zidong Shen, Bo Dong, Hongli Donghua University College of Information Science and Technology Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai201620 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Northeast Petroleum University Artificial Intelligence Energy Research Institute Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

This paper addresses the problem of secure recursive state estimation for a networked linear system, which may be vulnerable to interception of transmitted measurement data by eavesdroppers. To effectively protect information security, an encryption-decryption-based communication scheme can be used, but encrypting all the measurement data from sensors can result in significant computational costs. To address this issue, a partial-encryption-decryption (PED) mechanism is proposed to enhance information security with relatively low computational costs. In this mechanism, only part of the transmitted measurement signals are encrypted, and the remaining signals are transmitted directly to the estimator. A Jordan-canonical-form-based approach is developed to select the appropriate parameter for the PED mechanism, and recursive formulas for the state estimator are designed based on the principle of minimum mean squared error. Sufficient conditions are derived to guarantee the ultimate boundedness of the estimation error variance matrix. Finally, the proposed PED-based recursive state estimation scheme is evaluated through two simulation examples to demonstrate its effectiveness. © 1963-2012 IEEE.

关键词： Mean square error

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

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Particle filter-based perception method for obstacles in dynamic environment of a mobile robot 25

Particle filter-based perception method for obstacles in dyn...

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25th International Conference on Methods and Models in Automation and Robotics, MMAR 2021

作者： Gyenes, Zoltan Szadeczky-Kardoss, Emese Gincsaine Budapest University of Technology and Economics Department of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9781728173801

The state perception problem for obstacles occurring in the workspace of a mobile robot is a challenging task. The main goal of this work is to introduce a novel concept of the Particle filter algorithm where not only the positions but also the velocities of static and moving obstacles can be perceived using measurement data of an onboard LiDAR sensor. Next to the well-known resampling methods, a novel resampling algorithm was also introduced where the effective number of the particles can be considered. The introduced algorithm can generate an appropriate solution for both the position and velocity perception task. © 2021 IEEE.

关键词： Mobile robots

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