Journal of Intelligent & Fuzzy Systems最新文献_第3页

Machine learning prediction of concrete frost resistance and optimization design of mix proportions 混凝土抗冻性的机器学习预测和混合比例的优化设计

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-236703

Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, Qicai Wang, Jie He

{"title":"Machine learning prediction of concrete frost resistance and optimization design of mix proportions","authors":"Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, Qicai Wang, Jie He","doi":"10.3233/jifs-236703","DOIUrl":"https://doi.org/10.3233/jifs-236703","url":null,"abstract":"This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2, MSE, MAE and RMSE, it is found that the nonlinear model has better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TrumpetNet: A Convolutional Neural Network with Self-Attention Mechanisms for visual detection of trumpet fingering 小号网络：具有自注意力机制的卷积神经网络，用于小号指法的视觉检测

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-219342

José E. Valdez-Rodríguez, Nahum Rangel, M. Moreno-Armendáriz

引用次数: 0

Identification of horizontal gene transference by means of anomaly detection and natural language-inspired interpretable embeddings 通过异常检测和自然语言启发的可解释嵌入识别水平基因转移

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-219337

V. Lomas-Barrie, Michelle Reyes-Camacho, Antonio Neme

{"title":"Identification of horizontal gene transference by means of anomaly detection and natural language-inspired interpretable embeddings","authors":"V. Lomas-Barrie, Michelle Reyes-Camacho, Antonio Neme","doi":"10.3233/jifs-219337","DOIUrl":"https://doi.org/10.3233/jifs-219337","url":null,"abstract":"Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140215899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy convolutional neural network model applied to classification problems 应用于分类问题的模糊卷积神经网络模型

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-22 DOI: 10.3233/jifs-219369

Claudia I. Gonzalez, Cesar Torres

引用次数: 0

A novel rolling bearing fault diagnosis method for limited data 针对有限数据的新型滚动轴承故障诊断方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-21 DOI: 10.3233/jifs-236787

Haibin Sun, Wenbo Zhang

{"title":"A novel rolling bearing fault diagnosis method for limited data","authors":"Haibin Sun, Wenbo Zhang","doi":"10.3233/jifs-236787","DOIUrl":"https://doi.org/10.3233/jifs-236787","url":null,"abstract":"The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCS-YOLOv5s: A cattle detection and counting method for complex breeding environment SCS-YOLOv5s：适用于复杂繁殖环境的牛群检测和计数方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-21 DOI: 10.3233/jifs-237231

Zhi Weng, Rongfei Bai, Zhiqiang Zheng

{"title":"SCS-YOLOv5s: A cattle detection and counting method for complex breeding environment","authors":"Zhi Weng, Rongfei Bai, Zhiqiang Zheng","doi":"10.3233/jifs-237231","DOIUrl":"https://doi.org/10.3233/jifs-237231","url":null,"abstract":"Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, mAP@0.5 is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ERRRT-A*: A fast path planning algorithm suitable for large-scale scenes ERRRT-A*：适用于大规模场景的快速路径规划算法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-21 DOI: 10.3233/jifs-238695

Lixin Zhang, Hongtao Yin, Ang Li, Longbiao Hu

引用次数: 0

Multi-objective optimization scheduling method considering flexible load integration for distribution network 考虑配电网灵活负载集成的多目标优化调度方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-21 DOI: 10.3233/jifs-238367

Yingjie Li, Rongrong Sun, Guangrong Huang, Yuanbin Deng, Haixuan Zhang, Delong Zhang

引用次数: 0

Optimized TOPSIS technique for trajectory selection of self-driving vehicles on highways 高速公路上自动驾驶车辆轨迹选择的优化 TOPSIS 技术

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-21 DOI: 10.3233/jifs-219365

Andrés Antonio Arenas Muñiz, Dante Mújica-Vargas, Arturo Rendón Castro, Antonio Luna-Álvarez, Virna V. Vela-Rincón

引用次数: 0

Research on flexible job-shop scheduling problem based on variation-reinforcement learning 基于变异-强化学习的灵活工作车间调度问题研究

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-236981

Changshun Shao, Zhenglin Yu, Jianyin Tang, Zheng Li, Bin Zhou, Di Wu, Jingsong Duan

引用次数: 0

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献