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14th International symposium on computational intelligence and Design, ISCID 2021

作者： Li, Wei Liu, Yang Army Armored Forces Academy of PLA Bengbu Campus Bengbu China

ISBN: (纸本)9781665438568

According to the military requirement and application requirement of a communication equipment simulation training platform, the function requirement of a communication equipment simulation training platform guidance and control system(CESTPGC) is analyzed, the framework system of CESTPGC is constructed, and the key technology of the framework is analyzed. The framework design of the system has been successfully applied to the construction of CESTPGC. The results proves that the function, performance and control effect of the system fully meet the requirements of modularization and informationization. © 2021 ieee.

关键词： Informatization

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The Optimal Horizon Model Predictive control Planning for Autonomous Vehicles in Dynamic Environments

The Optimal Horizon Model Predictive Control Planning for Au...

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ieee symposium on Intelligent Vehicle

作者： Yuming Liu Shitao Chen Jiamin Shi Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xian P.R. China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

A primary challenge in autonomous driving is achieving safe and efficient trajectory planning in complex dynamic environments. This task requires adherence to traffic laws and vehicle dynamics models as well as an understanding of the spatial distributions and behavior of various traffic participants in densely populated areas. Model Predictive control (MPC) and its variants typically employ a fixed prediction horizon, which results in limited adaptability in dynamic environments. A long prediction horizon escalates computational costs, while a short prediction horizon may impact real-time performance adversely. To tackle this challenge, our study introduces an optimal horizon MPC planning approach. This method incorporates a sliding horizon window founded on reinforcement learning and interactive MPC planning, making it versatile for a variety of driving scenarios. Additionally, our approach implicitly models the spatio-temporal interactions among traffic participants, thereby enriching the information pool for effective planning. Rigorous tests and validations conducted using the real-world dataset nuPlan affirm that our proposed method delivers robust planning performance, facilitating safe and efficient trajectory planning for autonomous vehicles.

关键词： Trajectory planning Urban areas Reinforcement learning Planning computational efficiency Trajectory Vehicle dynamics

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Efficient Communication for Decentralized Federated Learning: An Energy Disaggregation Case Study

Efficient Communication for Decentralized Federated Learning...

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ieee International symposium on Power Electronics for Distributed Generation Systems (PEDG)

作者： Yusen Zhang Feng Gao Kangjia Zhou School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798350361001

ISBN: (纸本)9798350361018

The terminal devices in the energy system face limitations in communication resources, storage space, computational power, and data security, making it challenging to train and deploy computationally intensive artificial intelligence models. Therefore, employing communication-efficient federated learning to train lightweight neural network models is a suitable solution. In this paper, we propose decentralized federated learning with efficient communication achieved by cyclically broadcasting weight parameters from each device during the model training process. We explore the proposed decentralized federated learning framework using energy disaggregation as a case study. Furthermore, grouped convolution is introduced to establish lightweight models, reducing the computational and storage costs during both the training process and model deployment. We conducted extensive experiments on publicly available datasets to validate that the proposed decentralized federated learning approach exhibits significant communication efficiency and minimal impact on the performance of the global model.

关键词： Training Performance evaluation Costs Federated learning Convolution computational modeling Neural networks

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Enhancing Power Oscillation control: Comparative Analysis of Damping controllers and Hybrid computational intelligence Methods for Power System Stabilization

Enhancing Power Oscillation Control: Comparative Analysis of...

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International symposium on Artificial intelligence and Signal Processing (AISP)

作者： Aliyu Sabo Theophilus Ebuka Odoh Hossien Shahinzadeh Noor Izzri Abdul Wahab Majid Moazzami Gevork B. Gharehpetian Department of Electrical and Electronic Engineering Advanced Lightning Power and Energy Research (ALPER) Universiti Putra Malaysia Serdang Malaysia Department of Electrical Engineering Amikabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran

In this paper, Artificial ecosystem optimization (AEO) and Ant bee colony (ABC) optimization were used to design damping controllers for power oscillation damping and control. The damping controllers designed are power system stabilizer (PSS), proportional integral derivative-PSS (PID-PSS), fractional order (FO) PID-PSS (FOPID-PSS), tilt integral derivative-PSS(TID-PSS), all designed using the AEO optimization and Neurofuzzy controller (NFC) designed using the ABC optimization. The damping controllers were validated on the ieee New England power test system which is a ten-machine system. A comparison of results obtained was done based on the settling time of the rotor angle and rotor speed control of the synchronous generators (machines) in the ten-machine power test system. PID-PSS, FOPID-PSS and TID-PSS showed improved rotor angle and rotor speed control in comparison to the PSS damping controller only. NFC however, indicates a better control of the rotor angle and rotor speed than all other controllers designed.

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Review of Reinforcement Learning-Based control Algorithms in Artificial Pancreas Systems for Diabetes Mellitus Management

Review of Reinforcement Learning-Based Control Algorithms in...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Lehel Dénes-Fazakas Győző Dénes Fazakas György Eigner Levente Kovács László Szilágyi University Research and Innovation Center Physiological Controls Research Center Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Biomatics and Applied Artificial Intelligence Institute Óbuda University Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Óbuda University Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

The escalating prevalence of diabetes mellitus presents a pressing global health challenge, with projections indicating a further rise in both type 1 and type 2 diabetes cases. While type 1 diabetes necessitates external insulin administration due to pancreatic insufficiency, type 2 diabetes may also require insulin supplementation, albeit in lesser amounts. Traditional insulin delivery methods, involving multiple daily injections, can be burdensome for patients, particularly those with type 1 diabetes, which requires higher insulin doses. To mitigate these challenges, researchers have developed artificial pancreas systems―a closed-loop approach capable of automatically measuring blood glucose levels and administering insulin. These systems integrate insulin pumps with continuous glucose monitors (CGMs) to maintain glycemic control. However, the effectiveness of these systems heavily depends on the control algorithm employed. While conventional control engineering algorithms have been predominant, recent advancements in artificial intelligence (AI) have led to the exploration of AI-based controllers, particularly those leveraging reinforcement learning (RL). RL-based algorithms, categorized as semi-supervised learning, demonstrate promise in adapting to individual patient dynamics without the need for labeled data. This review investigates the utilization of various mathematical models, such as UVA/PADOVA, Bergman's minimal, and Hovorka models, in conjunction with RL-based algorithms in diabetes management. By examining existing research findings, we elucidate the efficacy of RL-based approaches in patient-specific learning and offer insights into future directions for optimizing diabetes management strategies.

关键词： Reviews Heuristic algorithms computational modeling Sociology Reinforcement learning Diabetes Glucose

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Digital Twin System for Home Service Robot Based on Motion Simulation

Digital Twin System for Home Service Robot Based on Motion S...

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ieee symposium Series on computational intelligence (SSCI)

作者： Zhengsong Jiang Guohui Tian Yongcheng Cui Tiantian Liu Yu Gu Yifei Wang School of Control Science and Engineering Shandong University Jinan China School of Information University of California Berkeley USA

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical.

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Application of Fuzzy-Based Uncertainty in Cardiac MRI Segmentation

Application of Fuzzy-Based Uncertainty in Cardiac MRI Segmen...

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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Qiao Lin Xin Chen Chao Chen Nikesh Jathanna Peter P Swoboda Shahnaz Jamil-Copley Jonathan M. Garibaldi School of Computer Science University of Nottingham Ningbo China School of Computer Science University of Nottingham UK Nottingham University Hospitals NHS Trust UK Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds UK

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Cardiac magnetic resonance imaging (MRI) is pivotal in diagnosing cardiac-related diseases. Tissue segmentation from cardiac MRI images is the initial and most crucial step in downstream analyses. However, most current research focuses on designing segmentation models on cardiac MRI to produce segmentation results, without considering the reliability of these outputs. This paper novelty applied fuzzy-based uncertainty to assess the reliability of cardiac MRI segmentation results without access to ground truth images. Experimental results show that class-level uncertainty has a strong linear negative relationship (PE=-0.92) with the true segmentation quality (measured by Dice). This suggests that when ground truth images are unavailable, uncertainty can effectively estimate the cardiac MRI segmentation quality. Furthermore, qualitative analysis with clinicians is conducted to explore the clinical application of fuzzy-based uncertainty. Based on their feedback and comments, class-level uncertainty provides more detailed information and is more suitable to infer the segmentation quality in comparison to other uncertainties. As for the representation of uncertainty, the predicted Dice is the best option compared to other representation methods and is straightforward for clinicians to understand.

关键词： Image segmentation Visualization Uncertainty Hospitals Magnetic resonance imaging Measurement uncertainty Quality control Prediction algorithms Reliability engineering Diseases

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Speed Gain in Elastic Joint Robots: An Energy Conversion-Based Approach

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ieee ROBOTICS AND automation LETTERS 2021年第3期6卷 4600-4607页

作者： Mansfeld, Nico Keppler, Manuel Haddadin, Sami Tech Univ Munich TUM Munich Sch Robot & Machine Intelligence Chair Robot Sci & Syst Intelligence D-80797 Munich Germany German Aerosp Ctr DLR Inst Robot & Mechatron D-82234 Wessling Germany

Like humans or animals, robots with compliant joints are capable of performing explosive or cyclic motions by making systematic use of energy storage and release, and it has been shown that they can outperform their rigid counterparts in terms of peak velocity. For rigid joint robots, there exist well-established, computationally inexpensive tools to compute the maximum achievable Cartesian endpoint velocity, which is an important performance and safety characteristic for robot designs. For elastic joint robots, optimal control is usually employed to determine the maximum possible link velocity together with the associated trajectory, which is time consuming and computationally costly for most systems. In this letter, we propose methods to obtain estimates of the maximum achievable Cartesian endpoint velocities of gravity-free elastic joint robots that have computational requirements close to the rigid joint robot case. We formulate an optimal control problem to verify the methods and provide results for a planar 3R robot. Furthermore, we compare the results of our approach with those from real-world throwing experiments which were previously conducted on the elastic DLR David system. Finally, we apply the methods to derive and quantitatively compare the safety properties of DLR David and a hypothetically rigid version of this robot in terms of the Safety Map framework proposed in our previous work.

关键词： Robots Safety Robot sensing systems Springs Torque Trajectory Optimal control Compliant joints and mechanisms methods and tools for robot system design human-centered robotics physical human-robot interaction robot safety

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Sign Diversity: A Method for Measuring Diversity in Base Learner Selection for Ensemble Regression

Sign Diversity: A Method for Measuring Diversity in Base Lea...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Farzad Rezazadeh Emad Olfatbakhsh Andreas Kroll Department of Measurement & Control University of Kassel Kassel Germany Mercedes-Benz Tech Innovation GmbH Ulm Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Data-driven modeling is essential for predicting the behavior of complex industrial systems, but such systems often challenge machine learning models due to their intricacies. Ensemble learning enhances predictive performance by combining multiple base learners (BLs), where diversity among BLs is a critical factor. Existing diversity measures typically rely on general statistical metrics not tailored for ensemble learning and do not address the challenge of determining an appropriate ensemble size. This study introduces the Weighted Sign Disagreement Metric (WSDM) and its extension, which are methods specifically designed for measuring diversity among BLs in stacking ensemble regression tasks. Both methods incorporate the concept of sign diversity by considering the signs of BL predictions relative to target values. These methods introduce an adjustable hyperparameter to balance the trade-off between accuracy and diversity among BLs. The extension of WSDM specifically addresses the challenge of determining an appropriate ensemble size, enhancing the flexibility and effectiveness of ensemble design. Experimental results on six synthetic datasets demonstrate that WSDM and its extension significantly improve predictive accuracy and generalization compared to traditional diversity measures. By addressing the deficiencies of existing methods and exploring the trade-off between accuracy and diversity, these methods offer a robust framework for heterogeneous stacking ensemble regression.

关键词： Data-driven modeling Weight measurement Accuracy Diversity reception Stacking Diversity methods Predictive models Size measurement Ensemble learning Synthetic data

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Panicle Segmentation on UAV Captured Multispectral Paddy Crop Imagery

Panicle Segmentation on UAV Captured Multispectral Paddy Cro...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： N Tejasri S Praneela P Rajalakshmi M Balram Uday B Desai Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology PJTSAU Hyderabad India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

automation of crop yield estimation is crucial to cultivate efficient breeding techniques to fulfill the increasing population demands and adapt to climate change. The panicle count of paddy is directly associated with the yield of crop. In this work, we implement computer vision-based image segmentation methods for segmenting panicles by utilizing Unmanned Aerial Vehicle (UAV) captured multispectral imagery. The developed algorithms in this study could be used as a reference for panicle counting models during the vegetative and heading stages of the paddy crop. In addition, we introduce a semi-automatic image annotation method for effortless creation of labeled paddy crop datasets. With this method, our developed tool collaborates with an annotation tool to offer preliminary image annotations, which users can subsequently refine the annotations.

关键词： Image segmentation Climate change Annotations computational modeling Image annotation Estimation Autonomous aerial vehicles

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