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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Chian Haur Jong Kai Meng Tay Yi Wen Kerk Chee Peng Lim Faculty of Engineering Data Science Centre UNIMAS Water Centre Universiti Malaysia Sarawak Kota Samarahan Malaysia Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC Australia

Fuzzy Rule Interpolation (FRI) is important for fuzzy inference systems modeling pertaining to a sparse fuzzy rule base system. The focus of this paper is on a specific class of FRI, i.e., monotone FRI (MFRI), for modeling monotone Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) in the presence of a monotone sparse fuzzy rule base. On the other hand, a function is denoted as an n-ary aggregation function for a given n-dimensional input space and an output space when both the monotone and boundary properties are satisfied. In this paper, a set of sufficient conditions derived from the principles of Ordered Weighted Averaging (OWA) and the concept of orness for TSK-FIS to obey the monotone property is firstly formulated. We show that it is necessary to have a dense fuzzy rule base, which can be obtained by interpolation of fuzzy rules in a sparse fuzzy rule base, for constructing a monotone TSK-FIS. We then devise a two-stage MFRI for establishing monotone TSK-FIS. The first stage comprises a sufficient condition, inspired from the orness concept, to generate intermediate fuzzy membership functions (FMFs). The second stage deduces the monotone consequent of each intermediate rule from the available sparse fuzzy rules. We further extend our MFRI formulation to form TSK-FIS-like n-ary aggregation functions.

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Federated Learning for Medical Image Classification: Advances, Challenges and Opportunities

SSRN

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SSRN 2023年

作者： Zhang, Xiaobo Zhao, Xiaole Wu, Yunyang Zheng, Hailong Li, Yang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Artificial Intelligence Research Institute Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Medical images are private integrations comprising private patient information owned by various hospitals and relevant research institutes, and the generated image data can be utilized to infer physical health conditions. In recent years, more and more AI researchers have used medical images as datasets for their analysis purposes, which will cause relevant personnel to worry about their privacy. As a distributed machine learning framework, federated learning can effectively solve the problem of data privacy. It participates in and completes model training without sharing personal data, with strong universality and good results. In this work, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Wilhelm Conrad Röntgen (X-Ray) as well as other typical medical imaging modes are used as research objects to summarize, analyze and look forward to the previous work related to federal learning in the field of medical image analysis. On the one hand, this survey classifies and compares the existing federal learning framework and algorithm for different medical image datasets;on the other hand, it discusses the relevant federal studies that are widely used in medical images, analyses the problems that need to be solved in the field of medical images, and looks forward to future studies. © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

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Information Technology (ACIT)

作者： Adam Bou Moghlebey Abdallah Moubayed MohammadNoor Injadat Electrical & Computer Engineering Department Lebanese American University Byblos Lebanon Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi’s Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi’s SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model.

关键词： YOLO Pedestrians Computational modeling Surveillance Pipelines Random access memory Ultra reliable low latency communication Hardware Smart transportation Sensors

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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

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Computer systems science & Engineering 2024年第3期48卷 627-643页

作者： Maha Farouk Sabir Mahmoud Ragab Adil O.Khadidos Khaled H.Alyoubi Alaa O.Khadidos Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 3Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz UniversityJeddahSaudi Arabia

Big data and information and communication technologies can be important to the effectiveness of smart *** on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability ***-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make *** to air pollution occurs a main environmental problem in smart city *** effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution ***,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart *** projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city *** achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input *** air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL *** simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

关键词： Sustainability smart cities air pollution prediction ensemble learning coot optimization algorithm

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Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

arXiv

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arXiv 2024年

作者： Altin, Mahsun Cakir, Altan Department of Computer Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Department of Physics Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Artificial Intelligence Data Science Research and Application Center Istanbul Technical University Sariyer Istanbul34467 Turkey Parton Big Data Analytics and Consulting ITU Ariteknokent Sariyer Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models’ capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300% and 650% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection. © 2024, CC BY.

关键词： Anomaly detection

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Predicting Optical Bandgaps in C60 and Functionalized Derivatives from Limited data for Renewable Energy applications

Predicting Optical Bandgaps in C60 and Functionalized Deriva...

引用

Compatibility in Power Electronics (CPE)

作者： Mehmet Tuncel Hasan Kurban Erchin Serpedin Mustafa Kurban Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Türkiye College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Prosthetics and Orthotics Ankara University Ankara Turkey

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Accurate optical bandgap prediction is essential for advancing renewable energy materials, but data scarcity and complex non-linear dependencies limit traditional approaches, especially for C 60 -based materials in organic photovoltaics. We propose a novel data-efficient machine learning framework that integrates Gaussian Process Regression (GPR) with Density Functional Theory (DFT) simulations to predict absorbance spectra and compute optical bandgaps from limited data. Our approach leverages Bayesian inference for uncertainty quantification, significantly reducing reliance on computationally expensive DFT calculations while maintaining accuracy. We demonstrate that our method generalizes across $\mathbf{C}_{60}$ and functionalized derivatives, enabling scalable and cost-effective bandgap prediction with minimal training samples.

关键词： Photovoltaic systems Renewable energy sources Accuracy Photonic band gap Discrete Fourier transforms Machine learning Gaussian processes Predictive models Density functional theory Nonlinear optics

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ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving

arXiv

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arXiv 2025年

作者： Feng, Renju Xi, Ning Chu, Duanfeng Wang, Rukang Deng, Zejian Wang, Anzheng Lu, Liping Wang, Jinxiang Huang, Yanjun Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China Department of Data and Systems Engineering The University of Hong Kong 999077 Hong Kong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Mechanical Engineering Southeast University Nanjing211189 China School of Automotive Studies Tongji University Shanghai201804 China

—This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms that inadequately capture the dynamic changes of the environment. ARTEMIS takes a different method by generating trajectory waypoints sequentially, preserves critical temporal dependencies while dynamically routing scene-specific queries to specialized expert networks. It effectively relieves trajectory quality degradation issues encountered when guidance information is ambiguous, and overcomes the inherent representational limitations of singular network architectures when processing diverse driving scenarios. Additionally, we use a lightweight batch reallocation strategy that significantly improves the training speed of the Mixture-of-Experts model. Through experiments on the NAVSIM dataset, ARTEMIS exhibits superior competitive performance, achieving 87.0 PDMS and 83.1 EPDMS with ResNet-34 backbone, demonstrates state-of-the-art performance on multiple metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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A 1D Plug-and-Play Synthetic data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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A Widespread Characteristic Estimation Analysis of Photovoltaic: Mayfly Algorithm application

Research Square

引用

research Square 2021年

作者： Kockanat, Serdar Department of Electrical and Electronics Engineering Sivas Cumhuriyet University Sivas58140 Turkey Artificial Intelligence Systems and Data Science Application and Research Center Sivas Cumhuriyet University Sivas58140 Turkey

In recent years, traditional energy sources have begun to give way to alternative and renewable energy sources such as solar energy. Therefore, the interest in the use of solar cells in energy production is increasing by researchers and practitioners. In literature, performance and control improvement studies are carried out in order to generate more energy by using solar cells effectively. To increase performance and control, several solar cell models are developed such as one diode ad double diode models. They are very effective but there is a problem of accurate and efficient estimation of parameters in these models. Recently, to overcome this problem, metaheuristic algorithms have been used in literature. In this paper, for the first time, a novel swarm based Mayfly algorithm (MA) is proposed to characteristic parameter estimation of photovoltaic models. To analyze the estimation performance of MA, single and double diode model are applied for silicon R.T.C. France, poly-crystalline Photowatt-PWP-201 and mono-crystalline STM-40/36 solar cells. Obtained results are compared with those of the well-known algorithms suggested in literature. Also, current-voltage and power-voltage characteristic are constructed using estimated parameters by MA. The results show that the MA is right choice for accurate and effective parameter estimation of photovoltaic cells. © 2021, CC BY.

关键词： Parameter estimation

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

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