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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tsakiridis, Nikolaos L. Samarinas, Nikiforos Kalopesa, Eleni Theocharis, John B. Zalidis, George C. Aristotle University of Thessaloniki Spectra Lab Group Laboratory of Remote Sensing Spectroscopy and Gis Department of Agriculture Thessaloniki54124 Greece Aristotle University of Thessaloniki Automation and Robotics Laboratory Electrical and Computer Engineering Thessaloniki54124 Greece

ISBN: (纸本)9798350308365

This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection. © 2024 IEEE.

关键词： Fuzzy rules

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Reinforcement Learning with Quasi-Hyperbolic Discounting (Extended Abstract)

Reinforcement Learning with Quasi-Hyperbolic Discounting (Ex...

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Indian Control Conference (ICC)

作者： S.R. Eshwar Mayank Motwani Nibedita Roy Gugan Thoppe Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (数字)9798331517212

ISBN: (纸本)9798331517229

Human decision-making is better modeled via discounting schemes such as quasi-hyperbolic and hyperbolic than classical ones such as exponential or average reward. In [3], we initiated the study of reinforcement learning with Quasi-Hyperbolic (QH) discounting and proposed the first model-free algorithm for finding a type of policy called the Markov perfect equilibrium. This paper provides a summary of that algorithm and our other findings on its convergence.

关键词： Decision making Reinforcement learning Convergence

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Adaptive dynamic programming control algorithm and simulation software design 3

Adaptive dynamic programming control algorithm and simulatio...

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2022 3rd International Conference on computer Information and Big Data Applications, CIBDA 2022

作者： Ma, Runmin Xia, Xiaonan School of Computer Sciences and Engineering Northeastern University Shenyang110169 China Department of Automation College of Information Engineering Yangzhou University Yangzhou225127 China

ISBN: (纸本)9783800758760

In this paper, an event-triggered adaptive dynamic programming tracking control scheme is investigated for a class of nonlinear systems with unmodeled dynamics based on dynamic surface control method. Due to considering the ummodeled dynamics, the feedback control consists of two controllers. One is used to compensate the unmodeled dynamics and system functions, and the other is used to optimize the system performance. Based on the control scheme, a parameter setting and real-time simulation system is developed by using Matlab GUI and M language. The system provides the functions of solving the optimal controller, tuning the controller parameters and real-time simulation of the control system. Through this software, the design parameters can be set repeatedly online and the control results can be observed in real time. The main results of algorithm design and simulation software are given in this paper. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Controllers

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Fabrication of flexible intracranial aneurysm models using stereolithography 3D printing

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Current Directions in Biomedical engineering 2023年第1期9卷 395-398页

作者： Stahl, Janneck Kassem, Leheng Berg, Philipp Behme, Daniel Klebingat, Stefan Saalfeld, Sylvia Department of Fluid Dynamics and Technical Flows Research Campus STIMULATE University of Magdeburg Germany Department of Medical Engineering Research Campus STIMULATE University of Magdeburg Magdeburg Germany Department of Neuroradiology University Hospital of Magdeburg Research Campus STIMULATE Magdeburg Germany Department of Computer Science and Automation Ilmenau University of Technology Research Campus STIMULATE Ilmenau Germany

The use of 3D printing technology for medical applications is becoming increasingly popular. Recent stereolithography (SLA)-based printing methods allow the generation of complex structures with a high surface quality. This is particularly useful for applications in the neurovascular field, where sophisticated structures are involved. In this study patient-specific intracranial aneurysm models are extracted based on medical image data und printed as thin-walled vascular phantom models. For this purpose, two commercially available 3D printers are used to print flexible vascular models with three different silicone-like elastic resins (Ultracur3D FL 300, Prusament Flex80 and Formlabs Elastic 50A) of various Shore hardness. Three aneurysm models of different size and complexity are chosen. To evaluate the geometric accuracy of the flexible models, angiographic measurements are performed for an exemplary case and morphological parameters are extracted from the generated 3D models. The printed results demonstrate a successful generation of hollow aneurysm phantoms. There are dependencies regarding the print quality from the model to platform positioning for two materials. The quantitative geometric accuracy analysis shows notable differences between the materials. The extracted morphological parameter values for all materials show a mean decrease compared to the original reference model of aneurysm volume (4.5 %) and maximum diameter (1.0 %) as well as an increase of ostium area (6.0 %) and maximum height (4.9 %). However, Formlabs Elastic 50A in particular exhibits just slight reductions with respect to the reference model, with a mean decrease for all parameters of 5.7 % as well as no dependence on printing position and resulting artifacts. The study investigates the feasibility of using SLA-based 3D printing to generate realistic flexible aneurysm phantoms. In this context, the Formlabs Elastic 50A could be identified as potentially applicable for phantom creation in

关键词： Resins

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Spatial-Temporal Data-Driven Speed Prediction for Energy Management of Battery/Supercapacitor Electric Vehicles 49

Spatial-Temporal Data-Driven Speed Prediction for Energy Man...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wu, Yue Huang, Zhiwu Che, Yunhong Wang, Zini Peng, Jun School of Automation Central South University Changsha China Aalborg University Department of Energy Technology Aalborg Denmark School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350331820

Accurate speed prediction plays a critical role in the predictive energy management of electric vehicles. This paper proposes a spatial-temporal data-driven speed prediction method for the predictive energy management of battery/supercapacitor electric vehicles. The proposed speed prediction method is performed using a long short-term memory network and validated on a real-world commuting data set in China. Different from existing prediction methods based only on speed and acceleration, we take spatial information as an additional input to improve speed prediction accuracy. The predicted future speed is then leveraged by a model predictive control-based energy management strategy to minimize the battery degradation cost. Quantitative comparisons illustrate that the proposed speed prediction method can reduce the root mean square error and mean absolute error by 10.01-19.15% compared with no spatial information prediction method. The more accurate prediction can further improve the optimality of the predictive energy management strategy, i.e., reduce the battery capacity loss and yield closer results to model predictive control with completely accurate prediction. © 2023 IEEE.

关键词： Energy management

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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SF_(6)decomposition and insulation condition monitoring of GIE:A review

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High Voltage 2021年第6期6卷 955-966页

作者： Fuping Zeng Haotian Li Hongtu Cheng Ju Tang Yilu Liu School of Electrical Engineering and Automation Wuhan UniversityWuhanChina Department of Electrical Engineering and Computer Science USA Oak Ridge National Laboratory KnoxvilleTennesseeUSA

The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage *** reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of *** key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still *** the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of *** decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and *** decomposition characteristics of SF6 are closely related to the property of insulation *** present,this area is attracting attention from the power industry and research *** study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.

关键词： insulation fault diagnosis

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Possibility Analysis of Preserving the ST Segment Shift when Approximated by Gaussian Pulses 4

Possibility Analysis of Preserving the ST Segment Shift when...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Mospan, Denis Kukharenko, Dmitriy Nevliudova, Viktoriia Yurko, Alexey Gladkyi, Viktor Kremenchuk Mykhailo Ostrohradskyi National University Computer Engineering and Electronics Department Kremenchuk Ukraine Kharkiv National University of Radioelectronics Computer-Integrated Technologies Department Automation and Mechatronics Kharkiv Ukraine

ISBN: (纸本)9798350346831

In this paper, a virtual device for automatic determination of parameters of approximate asymmetric Gaussian curves in the Labview software environment for analysis of electrocardiographic signals is improved. The introduction of piecewise linear functions in the sections of individual waves prevents the disappearance of horizontal sections that have a vertical offset relative to the isoline. As a result, the possibility of preserving the displacement of the horizontal section of the ST segment, which carries diagnostically important information, is shown. © 2022 IEEE.

关键词： Piecewise linear techniques

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Reinforcement Learning with Quasi-Hyperbolic Discounting

Reinforcement Learning with Quasi-Hyperbolic Discounting

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IEEE Conference on Decision and Control

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Reinforcement learning has traditionally been studied with exponential discounting or the average reward setup, mainly due to their mathematical tractability. However, such frameworks fall short of accurately capturing human behavior, which has a bias towards immediate gratification. Quasi-Hyperbolic ($\mathbf{Q H}$) discounting is a simple alternative for modeling this bias. Unlike in traditional discounting, though, the optimal QH-policy, starting from some time $t_{1}$, can be different to the one starting from $t_{2}$. Hence, the future self of an agent, if it is naive or impatient, can deviate from the policy that is optimal at the start, leading to sub-optimal overall returns. To prevent this behavior, an alternative is to work with a policy anchored in a Markov Perfect Equilibrium (MPE). In this work, we propose the first model-free algorithm for finding an MPE. Using a two-timescale analysis, we show that, if our algorithm converges, then the limit must be an MPE. We also validate this claim numerically for the standard inventory system with stochastic demands. Our work significantly advances the practical application of reinforcement learning.

关键词： Stochastic processes Reinforcement learning Mathematical models Numerical models Behavioral sciences Standards

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An Approach Based on Knowledge Distillation for Lightweight Defect Classification of Green Plums

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 213-223页

作者： Wang, Jinhai Wang, Wei Liao, Lan Luo, Lufeng Lin, Xuemin Zeng, Xinan Foshan University School of Computer Science and Artificial Intelligence Foshan528000 China Foshan University Department of Food Science Foshan528000 China Foshan University Guangdong Key Laboratory of Intelligent Food Manufacturing Foshan528225 China Foshan University School of Mechatronic Engineering and Automation Foshan528000 China

During the cultivation and growth of green plums, various defects frequently occur, potentially affecting their overall quality and economic value. Accurate classification and identification of these defects have become essential components of the harvesting process, particularly when employing smart agricultural equipment. These defects pose significant challenges to the yield and quality of green plums, making their precise detection crucial for ensuring optimal output and economic efficiency. However, most contemporary research on fruit defect classification and grading using artificial intelligence techniques primarily focuses on accuracy, often neglecting the constraints imposed by limited resources. This study addresses the aforementioned challenges by employing knowledge distillation techniques to optimize the performance of a lightweight model. Specifically, during the knowledge distillation process, the vision transformer model, known for its robust recognition capabilities, was selected as the teacher model. The lightweight MobileNetv3 model, chosen for its ease of deployment, served as the student model and was trained using the Lion optimizer. In addition, the dual guidance learning module was designed to enhance knowledge transfer between the teacher and student models, thereby improving the overall capability of the student model. Experimental validation demonstrated that the proposed method excels in the green plum defect recognition task, with the student model, MobileNetv3, achieving an accuracy of 99.17% and exhibiting high performance in key metrics such as precision, recall, and F1-score. Notably, MobileNetv3 not only delivers exceptional performance but also features a low parameter count and computational complexity, facilitating its efficient deployment in practical applications. This study provides an effective and practical solution for the automatic identification and sorting of green plum defects, significantly advancing the development an

关键词： Teaching

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