An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems by Jiahao Zhang, Kaiqiang Feng, Jie Li, Chunxing Zhang, Xiaokai Wei

“…Aimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter (AUKF) algorithm based on the maximum versoria criterion (MVC) is developed. …”
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Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets by Xu Gu, Xi Yang, Hong Liu, Dong Yang

“…Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. …”
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A Precise Oxide Film Thickness Measurement Method Based on Swept Frequency and Transmission Cable Impedance Correction by Yifan Li, Qi Xiao, Lisha Peng, Songling Huang, Chaofeng Ye

“…A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…This study confirms the effectiveness of objective assessment methods based on formant analysis in speech acquisition research, provides a theoretical basis for algorithm optimization in speech evaluation systems, and holds significant application value for the development of Computer-Assisted Language Learning (CALL) systems and the improvement of multi-ethnic Mandarin speech recognition technology.…”
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Machine-learning-derived prediction models of outcomes for patients with pseudomyxoma peritonei: development and validation in two retrospective cohorts by Yu-Run Cui, Xin-Li Liang, Yan Li

“…Recent advancements have shown that machine learning (ML) algorithms hold great promise in developing predictive models within the medical field. …”
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Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs by Venkatesh Chebolu, Sadananda Behera, Goutam Das

“…Next, we employ our SBPP-IA algorithm for dynamic traffic scenario and compare it with existing algorithms in terms of different QoT parameters. …”
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Joint Caching and Computation in UAV-Assisted Vehicle Networks via Multi-Agent Deep Reinforcement Learning by Yuhua Wu, Yuchao Huang, Ziyou Wang, Changming Xu

“…To address these challenges, this paper proposes a MADRL-based joint optimization approach. We precisely model the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and adopt the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which follows the Centralized Training Decentralized Execution (CTDE) paradigm. …”
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Flexible hardware for teaching and research in renewable energy and off-grid microgrids by Cristian Escudero-Quintero, Juan Pablo Guzman-Rodriguez, Juan Pablo Villegas-Ceballos, Elkin Edilberto Henao-Bravo, Daniel Gonzalez-Montoya

“…Microgrids are an active research field due to their integration of renewable and non-renewable energy sources alongside energy storage systems to supply both DC and AC loads. While existing studies extensively explore hierarchical control structures, focusing on algorithm design and optimization, practical implementation remains challenging due to system complexity. …”
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Texture mapping of warp knitted shoe upper based on ARAP parameterization method by Guilong Cheng, Aijun Zhang, Yanting Zhang

“…Additionally, discrete harmonic and parametric optimization unfolding algorithms are employed to enhance texture mapping quality. …”
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Quantum reinforcement learning in continuous action space by Shaojun Wu, Shan Jin, Dingding Wen, Donghong Han, Xiaoting Wang

“…Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. …”
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A Gain-Computation Enhancements Resource Allocation for Heterogeneous Service Flows in IEEE 802.16 m Mobile Networks by Wafa Ben Hassen, Meriem Afif

“…This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. …”
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Resource Allocation in User-Centric Cell-Free Massive MIMO URLLC Systems With Network Slicing by Han Lu, Weiwei Xia, Weiwei Miao, Mingxuan Zhang, Feng Yan, Lianfeng Shen

“…The user-slice association problem is addressed using the simulated annealing algorithm. For the resource allocation problem, a successive rate lower bound maximization algorithm is employed to obtain the convex lower bound of the data rate under short packet transmission, and the successive convex approximation (SCA) method is used to determine the optimal bandwidth and frequency allocation. …”
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FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models by Woohong Byun, Jongseok Woo, Saibal Mukhopadhyay

“…Coupled with the proposed algorithm and dataflow optimization, it enables on-chip storage of all necessary parameters, minimizing off-chip memory access. …”
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Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning by Sangwoo Park, Osvaldo Simeone

“…Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. …”
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Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County) by Majid Pishnamaz Ahmadi, keyvan Mohammadzadeh, seyed Asadolah Hejazi

“…For this purpose, the TM Landsat 5 (1990), the ETM + Landsat 7 (2000 and 2010) and the OLI Landsat 8 image for 2017 were used. In this study, ESP algorithm was used to optimize the scale in order to improve the image segmentation results and subsequently increase the accuracy of classification results. …”
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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

“…Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. …”
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Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Abstract Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. …”
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Dynamic characteristics of a semi-active fractional-order inerter-based suspension with acceleration-velocity switch control by Yong Wang, Jiachen Li, Mingzhu Ji, Xiwen Qiao, Yang Wang

“…The optimized SA-FOIB suspension further improves the dynamic performance of the suspension dynamic deflection and wheel dynamic load, which the corresponding RMS values are smaller than the unoptimized SA-FOIB suspension, while deteriorates the vehicle body acceleration.…”
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Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making by Xin Chen, Ping Lu, Yong Liu, Fei-Ping Hong

“…This study introduces AutoSDM-DDPG, an automated SDM framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Using an Actor-Critic network, the framework dynamically optimizes negotiation strategies to address multidimensional demands and resolve preference inconsistencies in treatment planning. …”
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Enhancing molecular property prediction with quantized GNN models by Areen Rasool, Jamshaid Ul Rahman, Rongin Uwitije

“…This paper presents a systematic approach to molecular networks by integrating GNN models with the DoReFa-Net quantization algorithm. The proposed method aims to enhance computational efficiency while maintaining predictive performance, enabling lightweight yet effective models suitable for molecular task. …”
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