Applying computational intelligence to musical acoustics by Bożena KOSTEK

“…The presented research studies involved using artificial neural networks, rough set method, fuzzy logic, genetic algorithms and other soft computing techniques. The investigated problems are related to classification of musical instrument sounds, musical phrases recognition, intelligent music processing, computer control of classical pipe organ instruments, and quality assessment.…”
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High-Accuracy Parallel Neural Networks with Hard Constraints for a Mixed Stokes/Darcy Model by Zhulian Lu, Junyang Zhang, Xiaohong Zhu

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Magnetic Modeling of Magnetic Levitation Systems Based on a Fast Finite Difference Method by Yibo Wang, Wei Pang, Xianze Xu, Fengqiu Xu

“…An accurate magnetic force model is crucial for the high-precision control of magnetic levitation systems. Soft magnetic materials, renowned for their excellent magnetic properties, are commonly employed in these systems. …”
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A novel anthropometric method to accurately evaluate tissue deformation by Chongyang Ye, Xiaolu Li, Haiyan Song, Yu Shi, Ruixin Liang, Jun Zhang, Ka Po Lee, Zhaolong Chen, Beibei Zhou, Raymond Kai-Yu Tong, Kit-Lun Yick, Sun-Pui Ng, Joanne Yip

“…IntroductionBiomechanical imaging through body scanning can provide a more comprehensive understanding of the soft tissue deformation exerted by compression sportswear, which is crucial in sports science research and functional sportswear design. …”
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Extracting Knowledge From Scientific Texts on Patient-Derived Cancer Models Using Large Language Models: Algorithm Development and Validation Study by Jiarui Yao, Zinaida Perova, Tushar Mandloi, Elizabeth Lewis, Helen Parkinson, Guergana Savova

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Artificial intelligence algorithms for the diagnosis of signs of diabetic retinopathy, diabetic macular edema, age-related macular degeneration, vitreomacular interface abnormaliti... by E.A. Katalevskaya, A.Yu. Sizov, M.I. Tyurikov, Yu.V. Vladimirova

“…For fundus images analysis algorithms accuracy, sensitivity, specificity, AUROC were calculated for the following structures: microaneurysms, intraretinal hemorrhages, hard exudates, soft exudates, retinal and optic disc neovascularization, preretinal hemorrhages, epiretinal fibrosis, laser coagulates. …”
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Evaluation method of e-government audit information based on big data analysis by Jingui He, Hansi Ya

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Research on somatic interactive exhibition design of lacquer artwork based on multi-source data analysis by Ke Ni

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Comparative Evaluation of Reinforcement Learning Algorithms for Multi-Agent Unmanned Aerial Vehicle Path Planning in 2D and 3D Environments by Mirza Aqib Ali, Adnan Maqsood, Usama Athar, Hasan Raza Khanzada

“…In the second phase, we transition this comparison to a physics-based 3D simulation, incorporating realistic UAV (fixed wing) dynamics and checkpoint-based navigation. We compared five algorithms, namely, Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Multi–Agent DDPG (MADDPG), in various scenarios. …”
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Blind recognition of primitive BCH code based on average cosine conformity by Zhaojun WU, Limin ZHANG, Zhaogen ZHONG, Yufeng LONG

“…In order to overcome the poor performance of existing algorithms for recognition of BCH code in low signal-to-noise ratio (SNR),a recognition algorithm based on average cosine conformity was proposed.Firstly,by traversing the possible values of code length and m-level primitive polynomial fields,the code length was identified by matching the initial code roots.Secondly,on the premise of recognizing the code length,the GF(2^m) domain was traversed under the m-level primitive polynomial and the primitive polynomial with the strongest error-correcting ability was the generator polynomial for the domain.Finally,the minimum common multiple corresponding to the minimum polynomial of code roots was obtained,and the BCH code generator polynomial was recognized.In checking matching,the statistic of average cosine conformity was introduced.The optimal threshold was solved based on the minimum error decision criterion and distribution of the statistic to realize the fast identification of the BCH.The simulation results show that the deduced statistical characteristics are consistent with the actual situation,and the proposed algorithm can achieve reliable recognition under SNR of 5 dB and code length of 511.Comparing with existing algorithms,the performance of the proposed algorithm is better than that of the existing soft-decision algorithm and 1~3.5 dB better than that of the hard-decision algorithms.…”
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Blind recognition of primitive BCH code based on average cosine conformity by Zhaojun WU, Limin ZHANG, Zhaogen ZHONG, Yufeng LONG

“…In order to overcome the poor performance of existing algorithms for recognition of BCH code in low signal-to-noise ratio (SNR),a recognition algorithm based on average cosine conformity was proposed.Firstly,by traversing the possible values of code length and m-level primitive polynomial fields,the code length was identified by matching the initial code roots.Secondly,on the premise of recognizing the code length,the GF(2^m) domain was traversed under the m-level primitive polynomial and the primitive polynomial with the strongest error-correcting ability was the generator polynomial for the domain.Finally,the minimum common multiple corresponding to the minimum polynomial of code roots was obtained,and the BCH code generator polynomial was recognized.In checking matching,the statistic of average cosine conformity was introduced.The optimal threshold was solved based on the minimum error decision criterion and distribution of the statistic to realize the fast identification of the BCH.The simulation results show that the deduced statistical characteristics are consistent with the actual situation,and the proposed algorithm can achieve reliable recognition under SNR of 5 dB and code length of 511.Comparing with existing algorithms,the performance of the proposed algorithm is better than that of the existing soft-decision algorithm and 1~3.5 dB better than that of the hard-decision algorithms.…”
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European sovereign debt control through reinforcement learning by Tato Khundadze, Willi Semmler, Willi Semmler, Willi Semmler

“…We demonstrate that the Soft Actor-Critic algorithm provides comparable or, in some cases, better solutions to multi-objective macroeconomic optimization problems, in comparison to Nonlinear Model Predictive Control (NMPC) algorithm.…”
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Adaptive-clustered image-block hierarchical DCT hybrid digital-analog wireless transmission method by Shu TANG, Peng YANG, Xianzhong XIE, Guangyi ZHOU, Jiaqing LI, Yu ZHAO

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TANS: A Tolerance-Aware Neighborhood Search Method for Workflow Scheduling with Uncertainties in Cloud Manufacturing by Haiyan Xu, Fanhao Ma, Long Chen

“…In this paper, we consider the workflow scheduling problem with soft deadlines and fuzzy time uncertainties in cloud manufacturing environments. …”
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Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 by Xianghe Meng, Fei Yuan, Dexiang Zhang

“…Abstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. …”
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Stage-induced learning-based cooperative target hunting strategy for multiple unmanned surface vehicles by Xingru QU, Yuze JIANG, Feifei LONG, Rubo ZHANG, Ying GAO

“…This is integrated with the multi-agent soft actor-critic (MASAC) algorithm for cooperative hunting training. …”
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Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications by Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, Fèlix Llovell

“…Subsequently, data from the molecular characterization via polar soft-SAFT is used as output targets to train a machine learning algorithm based on artificial neural networks, enabling the prediction of mixture saturation properties out of the training dataset's scope. …”
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Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong, Lirong Xiang

“…The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. …”
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Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production by Feiyang Ou, Julius Suherman, Chao Zhang, Henrik Wang, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

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Reward shaping-based deep reinforcement learning for look-ahead dispatch with rolling-horizon by Hongsheng Xu, Yungui Xu, Ke Wang, Yaping Li, Abdullah Al Ahad

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