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arXiv 2024年

作者： Peng, Yanhong Zhang, Ceng Hu, Chenlong Mao, Zebing Department of Information and Communication Engineering Graduate School of Engineering Nagoya University Nagoya4648601 Japan College of Mechanical Engineering Chongqing University of Technology Chongqing400054 China Department of Mechanical Engineering National University of Singapore Singapore119077 Singapore Department of Computer Science and Technology Tsinghua University Beijing100084 China Department of Mechanical Engineering Tokyo Institute of Technology Tokyo152-8550 Japan

This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems. We propose a novel framework that employs fine-tuned LLMs to interpret natural language commands and convert them into specific operational instructions for EHD pump control. This approach aims to enhance user interaction with complex hardware systems, making it more intuitive and efficient. The methodology involves four key steps: fine-tuning the language model with a dataset of color specifications and corresponding Arduino code, developing a natural language processing interface, translating user inputs into executable Arduino code, and controlling EHD pumps for accurate color mixing. Conceptual experiment results, based on theoretical assumptions, indicate a high potential for accurate color synthesis, efficient language model interpretation, and reliable EHD pump operation. This research extends the application of LLMs beyond text-based tasks, demonstrating their potential in industrial automation and control systems. While highlighting the limitations and the need for real-world testing, this study opens new avenues for AI applications in physical system control and sets a foundation for future advancements in AI-driven automation technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Color

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A novel design of layered recurrent neural networks for fractional order Caputo-Fabrizio stiff electric circuit models

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Modern Physics Letters B 2024年第2期39卷

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Shoaib, Muhammad Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI Center Yuan Ze University Taoyuan 320 Taiwan

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo-Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo-Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor-Capacitor (RC) and fractal Resistor-Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg-Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10^-9 to 10^-19 and absolute error (AE) within 10^-6 to 10^-8. The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures. © World Scientific Publishing Company.

关键词： Caputo-Fabrizio electric circuit model integrated circuits optimization RC circuits Recurrent neural network RL circuits soft computing stiff model

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Integrating Physical Wheelchairs and Virtual Basketball for Enhanced Access and Enjoyment in Wheelchair Basketball

Integrating Physical Wheelchairs and Virtual Basketball for ...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Yu-Hung Hsu Iuan-Kai Fang Chun Wang Jui-Chien Chen Min-Chun Hu Department of Computer Science National Tsing Hua University Hsinchu Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan School of Information University of Texas at Austin USA

Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice.

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Joint Block-Sparse Recovery Using Simultaneous BOMP/BOLS

arXiv

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arXiv 2023年

作者： Lu, Liyang Wang, Zhaocheng Chen, Sheng The Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China The Tsinghua Shenzhen International Graduate School Shenzhen518055 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

We consider the greedy algorithms for the joint recovery of high-dimensional sparse signals based on the block multiple measurement vector (BMMV) model in compressed sensing (CS). To this end, we first put forth two versions of simultaneous block orthogonal least squares (S-BOLS) as the baseline for the OLS framework. Their cornerstone is to sequentially check and select the support block to minimize the residual power. Then, parallel performance analysis for the existing simultaneous block orthogonal matching pursuit (S-BOMP) and the two proposed S-BOLS algorithms is developed. It indicates that under the conditions based on the mutual incoherence property (MIP) and the decaying magnitude structure of the nonzero blocks of the signal, the algorithms select all the significant blocks before possibly choosing incorrect ones. In addition, we further consider the problem of sufficient data volume for reliable recovery, and provide its MIP-based bounds in closed-form. These results together highlight the key role of the block characteristic in addressing the weak-sparse issue, i.e., the scenario where the overall sparsity is too large. The derived theoretical results are also universally valid for conventional block-greedy algorithms and non-block algorithms by setting the number of measurement vectors and the block length to 1, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Compressed sensing

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Splitter-Aware Multiterminal Routing with Length-Matching Constraint for RSFQ Circuits

Splitter-Aware Multiterminal Routing with Length-Matching Co...

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作者： Kou, Mingyang Cheng, Pei-Yi Zeng, Jun Ho, Tsung-Yi Takagi, Kazuyoshi Yao, Hailong Department of Computer Science and Technology Tsinghua University Beijing China Realtek Semiconductor Corporation Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Graduate School of Engineering Mie University Tsu Japan Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Aided by the advancement of super-conductive materials, rapid single flux quantum (RSFQ) digital circuits are emerging as a promising complement or even replacement of the traditional CMOS digital integrated circuits. RSFQ digital circuits typically work at a low temperature of around 4.2 K, i.e., around -268.95 °C. Nevertheless, the operating frequency of RSFQ digital circuits reaches up to 770 GHz, which is orders of magnitudes faster than contemporary CMOS digital circuits. The high operating frequency causes critical design challenges especially for the clock networks and data path signals, where relative skew on wires need to be observed for achieving the correct functionality. Therefore, for designing a timing-variability-aware SFQ layout, it is necessary to match the PTL delays that are proportional to their respective lengths. And the matching of PTL delays should be carried out by extensions in PTL lengths. To meet the above-mentioned critical timing requirements, it is necessary to incorporate length-matching constraints into a routing problem, which is transformed from the timing requirements of matching the PTL delays during the logical synthesis stage. However, existing routing algorithms are inherently limited by preallocated splitters (SPLs), which complicates the subsequent routing stage under length-matching constraints. In this article, in order to effectively address the length-matching constraints, we reallocate SPLs to fully utilize routing resources. We propose the first multiterminal routing algorithm for RSFQ circuits, which integrates SPL reallocation into the routing stage and achieves 100% routing completion in the tested benchmarks. Compared with the state-of-the-art method, the proposed multiterminal routing algorithm reduces the required area by 17% and the runtime by 7%. © 1982-2012 IEEE.

关键词： Timing circuits

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Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation

Fuzzy Positive Learning for Semi-Supervised Semantic Segment...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Pengchong Qiao Zhidan Wei Yu Wang Zhennan Wang Guoli Song Fan Xu Xiangyang Ji Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Automation and BNRist Tsinghua University Beijing China

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are provided in https://***/qpc1611094/FPL.

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Novel nonlinear fractional order Parkinson's disease model for brain electrical activity rhythms: Intelligent adaptive Bayesian networks

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Chaos, Solitons and Fractals 2024年 180卷

作者： Mukhtar, Roshana Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Chaudhary, Naveed Ishtiaq Shu, Chi-Min Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In this study, a novel investigation in developing intelligent adaptive Bayesian networks (IABN) is carried out to solve the fractional order Parkinson's disease model (FOPDM) represented with a system of three fractional differential equations reflecting the brain's electrical activity rhythms at different cerebral cortex positions. The IABN are developed by using the competency of the multi-layer architecture of neural networks with backpropagation through Bayesian regularization optimization scheme. The reference dataset for IABN is created through Grunwald-Letnikov fractional derivative-based numerical solver for FOPDM in case of varying sensor locations on the cerebral cortex, as well as, considering different fractional orders in FOPDM. The proposed IABN is applied to the created datasets arbitrarily partitioned into training and testing sets by optimizing the fitness criterion based on mean square error (MSE) metric. The detailed analyses of the proposed IABN through extensive simulations and comparison with the reference numerical solutions of FOPDM in terms of MSE training/testing plots, absolute error, training/testing regression plots, and error histogram results, endorse the worth of the proposed intelligent scheme for different fractional orders. © 2024 Elsevier Ltd

关键词： Bayesian networks

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Analysis of the musculoskeletal effects of lower limb slippage when indoor dogs walk on the wooden floor

Analysis of the musculoskeletal effects of lower limb slippa...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Daisuke Chugo Miyabi Igarashi Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Uemura Takashi Hiroaki Kamishina Yoshiharu Hata Takayuki Yamada Takahiro Uchida Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan KyotoAR Animal Advanced Medical Center Kuse Kyoto Japan Rinrei Co. Ltd. Chuo Tokyo Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

In recent years, dogs are commonly kept indoors. Dogs, as animals that inherently walk on grippy outdoor ground, sometimes slip when walking on indoor wooden flooring, which can lead to injuries and arthritis. We have therefore developed a method to increase the coefficient of friction by applying a special coating to the wooden flooring to make it less slippery when the dog walks on it. The objective of this paper is to investigate whether the friction coefficient of the flooring can prevent dogs’ lower limbs from slipping when they walk, and to analyze the effects of lower limb slippage on the musculoskeletal system of dogs when they walk on the flooring. In the measurement experiment, walking movements were performed with and without paint coating on the wooden flooring, the test dogs were applied to a 3D musculoskeletal system simulation software model, and the amount of muscular force exerted was calculated through a simulation experiment with measured data. In addition, the floor reaction forces of the lower limbs during walking were measured and compared, and the effects of the friction coefficient of the floor environment on walking behavior were discussed.

关键词： Legged locomotion Solid modeling Force measurement Three-dimensional displays Friction Force Dogs

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

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