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Validation of a two-wheel vehicle model using Genetic Algorithm

Validation of a two-wheel vehicle model using Genetic Algori...

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International symposium on computational intelligence and Informatics

作者： Attila Widner Balázs Varga Dániel Medgyesi Tamás Tettamanti Dept. of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Fac. of Transportation Engineering and Vehicle Engineering Budapest Hungary Dept. of Innovative Vehicles and Materials John von Neumann University GAMF Fac. of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350398830

Vehicle dynamics simulation is widely used in the automotive industry. The usability of each model depends on how well it can replicate the behaviour of the real vehicle. Each simulation model must go through a thorough investigation process- model validation- before use. Accordingly, the research introduces a comprehensive methodological framework for vehicle dynamics model validation using a sophisticated vehicle dynamics measurement system and a machine learning algorithm. The validation process of a two-wheel vehicle model (bicycle model) is presented where the authors’ previously proposed work is exploited as a base and further developed, i.e. by using a Genetic Algorithm (GA) the vehicle parameters (yaw inertia, axle cornering stiffness) are estimated in an automatized fashion.

关键词： Industries Computer science Mechatronics Machine learning algorithms computational modeling Vehicle dynamics Usability

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Learn to climb: teaching a reinforcement learning agent the single rope ascending technique

Learn to climb: teaching a reinforcement learning agent the ...

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International symposium on computational intelligence and Informatics

作者： Balázs Varga Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9798350398830

Single rope ascending technique is used in industrial alpinism, forestry, or various leisure activities. This paper presents a multi-body model of this technique involving an actuated 3D model of a humanoid, the climbing gear, and the rope, modeled as a finite-element object. This model serves as a training ground for reinforcement learning agents trying to mimic humans in rope climbing. To demonstrate the environment, an agent with a state-of-the-art reinforcement learning algorithm (Soft Actor-Critic) was trained. Results suggest that the agent can learn how to ascend the rope with speed comparable to real humans. However, the learned technique is not human-like: the artificial agent uses its arms excessively to climb, which would be too tiring for a human. That is because the environment only rewards ascension and does not penalize the energy used. The presented learning environment is developed for humanoid robots in mind that can perform complex tasks while on the rope and can carry much heavier payloads compared to climbing robots in the literature.

关键词： Training Solid modeling Three-dimensional displays Mechatronics Gears Humanoid robots Reinforcement learning

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Deep learning based object detection for agricultural machinery

Deep learning based object detection for agricultural machin...

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International symposium on computational intelligence and Informatics

作者： Sándor Tihamér Brassai András NÉMeth Attila Hammas Szabolcs László GÁBor Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary Intelligent Control Systems Sapientia Hungarian University of Transylvania Corunca Romania Ph.D. in Military Engineering National University of Public Service Budapest Hungary

ISBN: (纸本)9798350398830

Drone imagery based object supervising has become more and more widespread. In the paper the Single shot Alignment Network is used to classify and localize the objects. The images were acquired by using two types of drones, DJI Tello and Zll SG906 Pro 2 in about thirty classes, and about nineteen were processed and detailed in the paper. The objects labeling was realized in CVAT labeling tool. For neural network management the mmdetection framework was used, and the obtained results were detailed on s2a-net. The paper focuses on the preparation of the neural network system to be used for agricultural machine detection. The network was trained on a PC with reduced processing capabilities. The dataset was cut in smaller tasks. An architecture is proposed to be used in future for dataset management during the training process.

关键词： Training Mechatronics Neural networks Object detection Agricultural machinery Labeling Task analysis

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Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

Proposing a New Model for Estimation of Oil Rate Passing Thr...

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International symposium on computational intelligence and Informatics

作者： Omid Hazbeh Hamzeh Ghorbani Omid Molaei Milad Shayanmanesh Sahar Lajmorak Rituraj Rituraj Samaneh Bahrami Faculty of Earth sciences Shahid Chamran University Ahwaz Iran Faculty of General Medicine University of Traditional Medicine of Armenia Yerevan Armenia Department of Petroleum Engineering Omidiyeh Branch Islamic Azad University Omidiyeh Iran Department of Petroleum Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Earth Sciences Institute for Advanced Studies in Basic Sciences (IASBS) 444 Prof. Yousef Sobouti Blvd. Zanjan Iran Obudai University Budapest Hungary Faculty of English Litreture Hafez Institute of Higher Education Shiraz Iran

ISBN: (纸本)9798350398830

One of the main duties of production engineers is to maintain the reservoir productivity and keep it at a desirable level during the production time by preventing extra production and controlling the production through wellhead chokes. Wellhead chokes are tools that are installed in flowing pipes to resist the pressure, limit and control the production, prevent water and gas coning, and control the pressure in order to maintain the wellhead equipment in good working order. Wellhead chokes, which are installed in well flow, are divided into two main groups: positive or fixed chokes and adjustable or variable chokes. Passing flow rate through wellhead chokes is a function of wellhead pressure, choke diameter, before choke temperature, and water production rate. The objective of this work is to propose a new model for the estimation of the oil rate passing through wellhead chokes. In this study, 180 actual tested data for 5 wells from a heavy crude oil field were used to develop a new model for estimating oil rate passing through wellhead chokes. The proposed model has an average relative error of about 5.8%.

关键词： Productivity Mechatronics Oils computational modeling Estimation Production Resists

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Automatic Difficulty Balance in Two-Player Games with Deep Reinforcement Learning

Automatic Difficulty Balance in Two-Player Games with Deep R...

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ieee symposium on computational intelligence and Games, CIG

作者： Simão Reis Rita Novais Luís Paulo Reis Nuno Lau Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Aveiro Portugal

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

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The True Online Continuous Learning automation (TOCLA) in a continuous control benchmarking of actor-critic algorithms

The True Online Continuous Learning Automation (TOCLA) in a ...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Frost, Gordon Vallejo, Marta Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781728125473

Reinforcement learning problems are often discretised, use linear function approximation, or perform batch updates. However, many applications that can benefit from reinforcement learning contain continuous variables and are inherently non-linear, for example, the control of aerospace or maritime robotic vehicles. Recent work has brought focus onto online temporal difference methods, specifically for using non-linear function approximation. In this paper, we evaluate the Forward Actor-Critic against the regular Actor-Critic, and Continuous Actor-Critic Learning automation. We also propose and evaluate a new algorithm called True Online Continuous Learning automation (TOCLA) which combines these two approaches. The chosen benchmark problem was the MountainCarContinuous-v0 environment from OpenAI Gym, which represents a further step in complexity over the benchmark used to test the Forward Actor Critic in previous works. Our results demonstrate the superiority of TOCLA in terms of its sensitivity to hyper-parameter selection compared with the Forward Actor Critic, Continuous Actor-Critic Learning automation, and Actor Critic algorithms.

关键词： Reinforcement Learning Actor-Critic TOCLA CACLA Forward Actor-Critic Nonlinear Function Approximation

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V-Net Based Liver and Tumor Segmentation Using 3D ConditionalRandom Fields and Graph Cuts Method 14

V-Net Based Liver and Tumor Segmentation Using 3D Conditiona...

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

作者： Ramzan, Zaki Li, Wei Zeeshan Raza, Hafiz Muhammad Sabir, Muhammad Waheed School of Electrical and Control Engineering SUST Department of Automation Xi'an China Genomics and Computational Biology Lab COMSATS University Islamabad Sahiwal Campus Pakistan Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781665438568

Automatic liver segmentation from 3D biomedical images is an essential task for many clinical applications and also helpful to radiologists and doctors to analyze and segment the liver tumor faster and accurately. However, it is still a challenging task due to the complex background of the liver, fuzzy boundary, and different appearance of liver and tumor. The present study was performed to propose more efficient deep learning based approach for liver and tumor segmentation from 3D CT scans using V-Net and two different post-processing methods: Conditional Random Field (CRF) and Graph Cuts. In propose approach, using 3-slice input and ResNet as base network outer performs by applying CRF as a post-processing step. Results are evaluated using different metrics that are used for biomedical images processing and with different combinations of parameters such as 3-slice input using ResNet with CRF and Graph Cuts. Results were significant by the proposed approach as compared to other state-of-the- art approaches. Tumor segmentation part are better than other approaches having 96.60%, 5.10%, -1.5%, 0.96mm, and 12.76mm for DSC, VOE, RVD, ASD, and MSD respectively for liver segmentation and 83.91%, 38.92%, -2.6%, 1.13mm and 5.76mm for DSC, VOE, RVD, ASD and MSD respectively for tumor segmentation. © 2021 ieee.

关键词： Computerized tomography

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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

作者： Sanchita Das Gouranga Maity Diptarka Mandal Dmitrii Kaplun Alexander Voznesensky Ram Sarkar Dept. of CSE Jadavpur University Kolkata India Department of Applied Mathematics Defence Instituite of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Dept. of Automation and Control Process St. Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net.

关键词： Image segmentation Accuracy Microscopy computational modeling Biological system modeling Source coding Computer architecture Entropy Robustness Medical diagnostic imaging

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An Earth Observation Satellite Mission Planning Method Based on Deep Q-Learning 6

An Earth Observation Satellite Mission Planning Method Based...

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6th International symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Wu, Peng University of Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9781665441377

With the increasing number of earth observation satellites and observation demands, satellite mission planning is facing huge challenges. For traditional satellite mission planning methods, there are problems such as difficulty in modeling, high computational complexity, and low scalability. This paper proposes a satellite mission planning method based on Deep Q-Learning. Firstly, a hybrid satellite mission planning model is established that combines the constraint-satisfaction model, the 0-1 knapsack model and the Markov decision model. Then, the decision algorithm based on Deep Q-Learning is used to solve the problem, so that the satellite can make full use of the past mission planning experience to learn the optimal planning strategy. Finally, several traditional satellite mission planning methods-branch and bound algorithm, genetic algorithm, simulated annealing algorithm are compared, and various performances of the algorithms are tested and analyzed through simulation experiments. The superiority of this method is verified. © 2021 ieee.

关键词： Satellites

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An Adaptive Model Management Strategy: Balancing Exploration and Exploitation

An Adaptive Model Management Strategy: Balancing Exploration...

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ieee symposium Series on computational intelligence (SSCI)

作者： Caie Hu Sanyou Zeng Changhe Li Mechanical Engineering and Electronic Information China University of Geoscience Wuhan China Automation and Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geoscience Wuhan China

ISBN: (纸本)9781728190495

Model management is a critical component in data-driven surrogate-assisted evolutionary optimization. It is mainly used for selecting candidate samples to be evaluated using exact functions to balance the exploration and exploitation of algorithms. However, there are some potential limitations for typical infill sampling criteria. An adaptive model management strategy is proposed to overcome the limitations. It adaptively adjusts the selection probability of candidate samples, and avoids potentially unreliability and the burden of selecting parameters of typical infill sampling criteria. The comparison results on benchmark problems demonstrate the competitiveness of the strategy in solving data-driven optimization problems.

关键词： Adaptation models Toy manufacturing industry Reinforcement learning Evolutionary computation Benchmark testing Linear programming Optimization

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