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Nonlinear Value Function Approximation Method with Easy Hyperparameter Tuning and Convergence Guarantee

IEEE Access 2025年 13卷 30117-30126页

作者： Watabe, Yuuya Shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki305-8577 Japan University of Tsukuba Institute of Systems and Information Engineering Ibaraki305-8577 Japan

Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-And-error in the environment. Because this can be applicable even when the state transition function of the control object is unknown or it is difficult to create its model properly, this can reduce the designer's burden. Reinforcement learning repeats evaluating a policy and improving it based on that evaluation. An algorithm called value function approximation is used in this evaluation. Value function approximation is a general term for algorithms of approximating a function called value function, which maps an arbitrary state to how much reward can be obtained in the future when acting according to a policy from that state. Value function approximation should have high approximation accuracy and hyperparameter easy to tune. Although various value function approximation methods have been proposed so far, some of them does not have convergent guarantee and the others' hyperparameter tuning is difficult. The purpose of this paper is to propose a value function approximation method that have convergence guarantee even with nonlinear function approximator and whose hyperparameter tuning is easy and to verify its performance through numerical experiments. To this end, we focused on GTD2 method, which is one of the value function approximation methods that has convergence guarantee even with a nonlinear function approximator. GTD2 method has the drawback that its hyperparameter is difficult to tune appropriately so that the approximation does not diverge during learning. Therefore, we firstly clarified the cause of the divergence of GTD2 method. Secondly, based on this cause, we proposed Normalized and Regularized GTD2 (NRGTD2) method, that incorporates a method for suppressing divergence into GTD2 method. Finally, through numerical experiments, it was clarified that the proposed method suppresses the divergence and can achieve better approxim

关键词： Reinforcement learning

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A deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues

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Neural Computing and Applications 2025年第8期37卷 6283-6303页

作者： Pannattee, Peerawat Shimada, Shogo Yem, Vibol Nishiuchi, Nobuyuki Graduate School of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Graduate School of Science and Technology Degree Program in Systems and Information Engineering University of Tsukuba Ibaraki3058573 Japan

Effective development of virtual reality (VR) applications is heavily reliant on the evaluation of user experience (UX). However, traditional methods such as questionnaires have inherent limitations which hinder their ability to capture nuanced behavioral responses and impede the agility of VR content creation: They are time-consuming, burdensome for users, and require significant human effort for interpretation. This study introduces an automated framework aimed at addressing the limitations of questionnaire-based evaluations to assess UX in VR. Our primary focus is to validate the concept of this framework through assessment of the sense of presence (SOP), a crucial psychological perception with significant impact on VR UX. Our proposed framework utilizes a deep neural network (DNN) to analyze patterns in multimodal behavioral cues, including facial expressions, head movements, and hand movements, to predict scores from the Igroup Presence Questionnaire (IPQ). Additionally, we introduce two statistical profiles: the Visual Entropy Profile (VEP), which offers insights into visual complexity by depicting scene entropy, and the Experiential Presence Profile (EPP), which is designed to capture users’ historical SOP levels to enable personalized baseline and sensitivity estimation. The proposed framework achieves a significant correlation between actual and predicted IPQ scores, with a Spearman’s rank correlation coefficient of 0.7303, showcasing the potential of DNNs in analyzing complex behavioral signals and automating SOP assessment. This study represents a pioneering effort in leveraging DNNs for the automatic assessment of SOP and paves the way for future advances in automatically assessing VR UX and unlocking new opportunities in the field. © The Author(s) 2025.

关键词： Virtual environments

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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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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Numerical analysis of Ga gradient effects in ACIGS absorbers for high-efficiency tandem solar cells

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Optical and Quantum Electronics 2025年第3期57卷 1-19页

作者： Boukortt, Nour El I. Loureiro, Antonio Garcia Almutairi, Ohood A. Centro Singular de Investigación en Tecnoloxías Intelixentes University of Santiago de Compostela Santiago de Compostela Spain Department of Electronics and Information Systems Ghent University Technology Park 126 Zwijnaarde Belgium Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Energy Systems Program College of Engineering & Energy Abdullah Al Salem University Khaldiya Kuwait

Ag-alloyed Cu(In,Ga)Se₂ (ACIGS) is a promising material for the bottom subcell in tandem solar cell structures, offering a tunable bandgap that spans the optimal range for maximum efficiency;however, the bandgap widening of ACIGS thin films poses a significant challenge in enhancing their photocurrent. This paper investigates the impact of different grading profiles, specifically focusing on the 'hockey stick'/V-like Ga gradient–induced (GGI) profiles, on the performance of thin-film ACIGS cells. To enhance the physical understanding of these grading profiles we have used Silvaco TCAD tools to analyze how these profiles' depths, notch position, and slope affect the power conversion efficiency (PCE). The optimized ACIGS cell achieves a PCE of 25.80%, compared to 23.60% for the reference device. For the tandem configuration, we pair the optimized ACIGS bottom cell (double grading profile) with a perovskite top cell (band gap ~ 1.66 eV), achieving an overall efficiency of 30.26% under AM1.5G illumination. The obtained results highlight the critical role of Ga gradient profiles in enhancing the bottom cell's performance and stability. Additionally, we compare our results with existing studies to evaluate the broader impact of Ga compositional optimization on ACIGS-based solar cells. This work provides valuable insights into advanced grading strategies and helps us understand the physical behaviour of these high-performance tandem solar cells. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Silver alloys

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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System for Automating Medication Orders to the Hospital Pharmacy by Nurses in Peru Through Voice Recognition

System for Automating Medication Orders to the Hospital Phar...

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Software engineering and Computer Science (CSECS), International Conference on

作者： Conrado Moreno-Fernández Fabian Colque-Fernández José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The management of nurses' orders to a Peruvian hospital pharmacy faces efficiency challenges, which affects time spent on patient care. This context highlights the need for a system that optimizes this process. In this sense, a web-based system is proposed for the automation of orders from the nurse's cell phone, which uses a customized model for automatic speech recognition. The development of the system was divided into three stages: first, data collection with 21 orders placed by three nurses and 978 audio recordings to validate the automatic speech recognition model; second, implementation of the system allowing agile order processing; and finally, evaluation of results in terms of time and accuracy. The findings indicate that the processing time ranged from 37.70 to 60.15 seconds, representing a 97%-time reduction in operational tasks. In addition, the Word Error Rate was 10.51%, significantly lower than the 25.05% of Whisper Large V2. These results demonstrate the system's potential for adoption in various hospital contexts.

关键词： Automation Accuracy Hospitals Error analysis Terminology Transforms Time measurement Artificial intelligence Automatic speech recognition Software engineering

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Model for Endometriosis Detection Using Machine Learning Algorithms

Model for Endometriosis Detection Using Machine Learning Alg...

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Software engineering and Computer Science (CSECS), International Conference on

作者： Alexis Bautista Jahir Tardillo José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four Machine Learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

关键词： Support vector machines Measurement Training Machine learning algorithms Accuracy Software algorithms Predictive models Bayes methods Random forests Software engineering

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Classification Of Ornamental Fish Types Using CNN Method With VGG-16 And U-NET Architecture

Classification Of Ornamental Fish Types Using CNN Method Wit...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ni Luh Wiwik Sri Rahayu Ginantra Christina Purnama Yati I Gusti Ayu Agung Diatri Indradewi I Komang Agus Yudiawan Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia Department of Informatics Engineering Information Systems Study Program Faculty of Engineering and Vocational Studies Universitas Pendidikan Ganesha (UNDIKSHA) Denpasar Indonesia

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Ornamental fish are a type of fish that live in freshwater or seawater habitats and are typically maintained for aesthetic purposes, often in ornamental ponds or as household pets. The diversity of colors, shapes, and patterns observed in ornamental fish presents a challenge in their classification. Therefore, a method capable of accurately recognizing the visual characteristics of these fish is required. The objective of this study is to evaluate the performance of two Convolutional Neural Network (CNN) architectures, specifically VGG-16 and U-Net, using five distinct types of ornamental fish, namely Angel Fish, Betta Fish, Clown Fish, Gold Fish, and Guppy Fish. The dataset comprises 3.240 images that have been processed using TensorFlow and Keras on Google Collaboratory. The results demonstrated that U-Net demonstrated superior performance in segmentation, with an Average Intersection over Union (IoU) of 58.91 % and an Average Dice Coefficient of 73.72%. In contrast, VGG-16 exhibited signs of overfitting, as evidenced by lower evaluation metrics. To enhance the efficacy of the transfer learning model, further optimization is recommended.

关键词： Measurement Image segmentation Visualization Shape Transfer learning Fish Internet Convolutional neural networks Optimization Overfitting

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