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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana L. H. Oliveira J. Bautista Jesus Pinto-Lopera Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.

关键词： Software algorithms Signal processing algorithms Software Hardware Whale optimization algorithms Steady-state Mobile robots MATLAB Farming Python

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Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning

arXiv

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arXiv 2024年

作者： Masuya, Nozomu Sato, Hiroshi Yamane, Koki Kusume, Takuya Sakaino, Sho Tsuji, Toshiaki Master's Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Doctoral Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Department of Intelligent Interaction Technologies Institute of Systems and Information Engineering University of Tsukuba Japan Graduate School of Science and Engineering Saitama University Saitama Japan

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data

Proposing a Low-Rank Approximation Method with Mathematical ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hasegawa, Hiroki Yata, Kazuyoshi Okada, Yukihiko Kunimatsu, Jun University of Tsukuba Program in Service Engineering Tsukuba Japan Sciences University of Tsukuba Institute of Pure and Applied Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Medicine Tsukuba Japan

ISBN: (纸本)9798350362480

The analysis of high-dimensional tensor data presents significant challenges for traditional methods such as CANDECOMP/PARAFAC (CP) decomposition and Higher-Order Singular Value Decomposition (HOSVD) due to the size, complexity, and noise inherent in the data. Recent studies have emphasized the critical need for noise-robust approaches capable of reducing dimensionality while preserving data integrity, particularly for matrix-form data, underscoring the necessity of theoretical advancements in noise reduction. In this study, we propose a novel method, Noise-Reduced Higher-Order Singular Value Decomposition (NR-HOSVD), which integrates noise reduction techniques into the HOSVD framework to enhance dimensionality reduction for matrix data. In comparative simulations with CP decomposition and HOSVD, NR-HOSVD consistently demonstrated lower relative errors as the rank increased, particularly for high-dimensional tensors, surpassing the performance of conventional methods beyond a certain rank threshold. Furthermore, NR-HOSVD exhibited superior stability and faster convergence on large-scale datasets compared to existing approaches. These findings suggest that NR-HOSVD provides a more accurate and efficient approach to high-dimensional tensor data analysis, making it particularly well-suited for applications in big data and machine learning. In the future, NR-HOSVD is expected to serve as a pivotal method for enhancing the accuracy and efficiency of high-dimensional tensor data analysis, with broad applicability anticipated in fields such as large-scale data processing and precision medicine. © 2024 IEEE.

关键词： Data integrity

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Large Pre-Trained Models and Few-Shot FineTuning for Virtual Metrology: A Framework for Uncertainty-Driven Adaptive Process Control in Semiconductor Manufacturing

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15351-15370页

作者： Lin, Chin-Yi Tseng, Tzu-Liang Emon, Solayman Hossain Tsai, Tsung-Han University of Texas at EL Paso Department of Industrial Manufacturing and Systems Engineering Texas El PasoTX79968 United States Computational Science Program University of Texas at EL Paso Texas El PasoTX79968 United States National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan

High-precision wafer metrology poses significant cost and throughput challenges in modern semiconductor manufacturing, where frequent process changes and recipe variations demand highly adaptive and scalable solutions. In this paper, we present a Generative-FewShot-Active Virtual Metrology (GFA-VM) framework that unifies large-scale generative modeling, few-shot fine-tuning, and uncertainty-driven active sampling into a single, data-centric system. A foundational generative model, built on a hybrid architecture of Transformer networks and Variational Autoencoders (VAEs), learns diverse sensor characteristics in an offline stage without relying on extensive labeled data. During online inference, the model produces both wafer quality predictions and predictive uncertainties;samples exceeding a dynamic uncertainty threshold are selected for physical measurement and few-shot model recalibration. This selective sampling both reduces measurement costs and adapts rapidly to new process conditions (e.g., novel recipes or equipment upgrades), requiring only a handful of freshly labeled wafers. The paper further addresses the long-term stability of the system through a self-updating mechanism that adjusts the uncertainty threshold when distributional shifts occur. Empirical evaluations confirm that our GFA-VM approach achieves state-of-the-art accuracy while significantly reducing metrology overhead compared to conventional virtual metrology methods. Additionally, rigorous theoretical analyses—including proofs of convergence and label cost bounds—demonstrate the reliability of using a generative foundation plus meta-learning technique. By fostering on-demand adaptation within a closedloop framework, GFA-VM offers a comprehensive, scalable strategy for next-generation semiconductor process control. © 2004-2012 IEEE.

关键词： Semiconductor device measurement Metrology Semiconductor device modeling Semiconductor process modeling Uncertainty Adaptation models Accuracy Data models Predictive models Manufacturing

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Design of a CNN1D for Control and Monitoring of Blood Pressure

Design of a CNN1D for Control and Monitoring of Blood Pressu...

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Colombian Caribbean Conference (C3), IEEE

作者： Vanessa Susana Rodriguez Barrios Fernando Miguel Méndez Torrenegra Daladier Jabba Molinares Systems Engineering Program Universidad Simón Bolívar Barranquilla Colombia Department of Systems Engineering Universidad del Norte Barranquilla Colombia

The ability to continuously monitor the parameters associated with the state of health of an individual results in a large volume of data, which presents both challenges and opportunities for data analysis. There are several studies of convolutional neural networks (CNN) used successfully in the implementation of tools for the monitoring and analysis of a large amount of medical data, accomplishing faster and more accurate data processing for the forecasting of health-related variables. This paper will address the design of a predictive model of ECG and PPG signals to estimate blood pressure (BP) with an one dimensional convolutional neural network (CNN1D) that optimizes performance to reduce the amount of processing on devices.

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A Framework of Risk Profile in Public Healthcare System Development: A Literature Review 2nd

A Framework of Risk Profile in Public Healthcare System Deve...

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2nd International Symposium on Disaster Resilience and Sustainable Development, 2021

作者： Krutphong, Kodchakorn Tang, Jing Leelawat, Natt Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789811947148

Risk profile has been utilized in a variety of knowledge fields beyond disaster management or risk management it also plays as the tools in assessment process before the planning both in operation and *** public healthcare system is considered as one of the most critical stakeholders during crises such as disasters and *** risk profile in the public healthcare system requires experts to help developing it according to its *** to a limited number of reviews in the field, this review study focuses on the public healthcare system risk profile’s guideline and development from well-known research *** study also provides the SWOT analysis to realize the strengths, weaknesses, opportunities, and threats of each specific guideline for further *** research results found the ratio of the research in the keywords of the risk profile mostly in the field of medicine and rising number since 2011–*** order to develop the risk profile in such a specific area, it is necessary to use the strength part which suits with the appropriate area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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The Banking Industry is Undergoing a Digital Transformation as Part of Industrial 4.0 10

The Banking Industry is Undergoing a Digital Transformation ...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sardjono, Wahyu Pranata, Dewi Sagita Ramdhana, Aditya Agung Dwi BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Mathematical & Engineering Sciences King's College London Cyber Security Studi Program Faculty of Natural Department of Informatics West Minster Strands WC2R 2LS United Kingdom University of Airlangga Master of Management Faculty of Economics and Business Surabaya60286 Indonesia

ISBN: (纸本)9798350363104

In the current day, digital transformation is imperative;both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. Successful transformation, however, does not happen by accident;in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment. © 2024 IEEE.

关键词： Commerce

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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Action Robust Reinforcement Learning with Highly Expressive Policy

Action Robust Reinforcement Learning with Highly Expressive ...

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IEEE International Conference on systems, Man and Cybernetics

作者： SeongIn Kim Takeshi Shibuya Doctor's Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In traditional reinforcement learning, there can be a degradation in the control performance of the policy when the environmental parameters differ between the training and application phase. The policy that minimizes this degradation is referred to as a robust policy. A framework called Noisy action Robust Markov Decision Process (NR-MDP) was proposed for training robust policies, and the Action Robust Deep Determin-istic Policy Gradient (AR-DDPG) algorithm was introduced as a method for solving NR-MDP. The optimal policy in NR-MDP includes policies following various probability distributions, whereas AR-DDPG is restricted to deterministic policies. We propose a new robust reinforcement learning method called Action Robust Q-Learning (AR-QL) that enables the training of optimal policies in NR-MDP by leveraging various sampling techniques to extend the representational capacity of policies, targeting an improvement in policy robustness. To validate this, we confirmed that AR-QL can acquire the optimal policy for a simple NR-MDP problem, for which AR-DDPG fails to obtain the optimal policy. Furthermore, we confirmed that the robust performance of policy trained by AR-QL in the OpenAI's InvertedPendulum environment surpasses that of policy trained by AR-DDPG.

关键词： Training Degradation Q-learning Markov decision processes Toy manufacturing industry Probability distribution Robustness Computational efficiency Noise measurement Cybernetics

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Characterization of Fluoride-added Hydroxyapatite Derived from Eggshells for Dental Application 15

Characterization of Fluoride-added Hydroxyapatite Derived fr...

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15th Biomedical engineering International Conference, BMEiCON 2023

作者： Uthayaphamornwat, Vilasinee Khantachawana, Anak Kitiwan, Mettaya Srirussamee, Kasama Tunthawiroon, Phacharaphon School of Engineering Department of Biomedical Engineering Bangkok10520 Thailand Faculty of Engineering Department of Mechanical Engineering Bangkok10140 Thailand Biological Engineering Program Faculty of Engineering Bangkok10140 Thailand School of Science Department of Physics Bangkok10520 Thailand Devices and Systems for Energy and Environment Research Unit School of Science Bangkok10520 Thailand School of Engineering Department of Industrial Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350345247

Hydroxyapatite (HA) is one of the potential biomaterials used for treating dental defects. It can be incorporated with fluoride to enhance its anti-caries properties, though other properties could be dependent on processing technique. Hence, this study aims to characterize the influence of sintering on the properties of fluoride-added HA derived from eggshells. FTIR and XRD techniques are used to characterize the chemical and crystal structures of the samples, whilst dissolution test is carried out to characterize material degradation in the acidic environment. The results show that sintering increased the crystallinity in both HA and fluoride-add HA (F-HA) samples, which corresponds with the enhanced stability during the dissolution test. Moreover, it is also found that the addition of fluoride could further stabilize the material structure, though the impurities found in all the samples still need to be minimized. These findings suggest that sintering and fluoride addition could potentially enhance anti-caries properties of eggshell-derived HA, which could be beneficial for dental application in future subject to further process optimization and analysis. © 2023 IEEE.

关键词： Hydroxyapatite

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