Journal of Intelligent & Fuzzy Systems最新文献

Hybrid optimization approach for energy minimization in wireless sensor networks leveraging XGBoost and random forest 利用 XGBoost 和随机森林实现无线传感器网络能源最小化的混合优化方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-234798

Ayhan Akbas, Gonca Buyrukoglu, Selim Buyrukoğlu

引用次数: 0

Coupled analysis and simulation of safety risk in subway tunnel construction by shallow-buried excavation method based on system dynamics 基于系统动力学的浅埋开挖法地铁隧道施工安全风险耦合分析与模拟

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-239674

Hongliang Yu, Zhen Peng, Zhaoliang Wu, Zirui He, Chun Huang

{"title":"Coupled analysis and simulation of safety risk in subway tunnel construction by shallow-buried excavation method based on system dynamics","authors":"Hongliang Yu, Zhen Peng, Zhaoliang Wu, Zirui He, Chun Huang","doi":"10.3233/jifs-239674","DOIUrl":"https://doi.org/10.3233/jifs-239674","url":null,"abstract":"To address the existing shortcomings in the research on the coupling of safety risk factors in subway tunnel construction using the shallow-buried excavation method, this paper conducts a coupled analysis and dynamic simulation of the safety risks associated with this construction method. Firstly, by analyzing the mechanisms and effects of risk coupling in shallow-buried excavation construction of subway tunnels, this study divides the risk system into four risk subsystems (human, material, management, and environment), establishes an evaluation index system for the coupling of safety risks, calculates the comprehensive weight values of the risk indicators using the AHP-entropy weight method, and constructs a risk coupling degree model by combining the inverse cloud model and efficacy function. Subsequently, based on the principles of system dynamics, a causal relationship diagram and a system dynamics simulation model for the coupling of “human-material” risks in construction are established using Vensim PLE software. Finally, the case study of the underground excavation section of Chengdu Metro Line 2 is employed to perform dynamic simulation using the established model. By adjusting the relevant risk coupling coefficients and simulation duration, the impact of the coupling of various risk factors on the safety risk level of the human-material coupling system is observed. The simulation results demonstrate that: 1) Heterogeneous coupling of human and material risks has a particularly significant effect on the system’s safety risks; 2) Violations by personnel and initial support structure defects are key risk coupling factors. The findings of this study provide new insights for decision-makers to assess the safety risk of shallow-buried excavation construction in subway tunnel.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"115 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003492","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

Developing interpretable machine learning model for evaluating young modulus of cemented paste backfill 开发可解释的机器学习模型，用于评估水泥浆回填土的年轻模量

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-237539

Quoc Trinh Ngo, L. Nguyen, Trung Hieu Vu, Long Khanh Nguyen, Van Quan Tran

{"title":"Developing interpretable machine learning model for evaluating young modulus of cemented paste backfill","authors":"Quoc Trinh Ngo, L. Nguyen, Trung Hieu Vu, Long Khanh Nguyen, Van Quan Tran","doi":"10.3233/jifs-237539","DOIUrl":"https://doi.org/10.3233/jifs-237539","url":null,"abstract":"Cemented paste backfill (CPB), a mixture of wet tailings, binding agent, and water, proves cost-effective and environmentally beneficial. Determining the Young modulus during CPB mix design is crucial. Utilizing machine learning (ML) tools for Young modulus evaluation and prediction streamlines the CPB mix design process. This study employed six ML models, including three shallow models Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particle Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (GB-PSO), Random Forest-Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid model exhibited superior performance (coefficient of determination R2 = 0.906, root mean square error RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dependence Plots (PDP) provided insights into component influences. Cement/Tailings ratio emerged as the most crucial factor for enhancing Young modulus in CPB. Global interpretation using SHAP values identified six essential input variables: Cement/Tailings, Curing age, Cc, solid content, Fe2O3 content, and SiO2 content.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"27 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141005618","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

Evaluating the integration of traditional gong methods in smart home environment for the recovery of health functions of stroke patients 评估传统功法在智能家居环境中的融合对中风患者健康功能恢复的影响

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-238267

Baoliang Wang, Hongping Su, Xiaoqian Luo, Luqiang Yin

{"title":"Evaluating the integration of traditional gong methods in smart home environment for the recovery of health functions of stroke patients","authors":"Baoliang Wang, Hongping Su, Xiaoqian Luo, Luqiang Yin","doi":"10.3233/jifs-238267","DOIUrl":"https://doi.org/10.3233/jifs-238267","url":null,"abstract":"Since the 21st century, network and mobile communication technology are gradually entering the medical and health services field. Combining body area networks, broad-generation mobile communications, and cloud platforms has made various medical applications for large-scale populations possible. The development of digital medical technology, especially digital telemedicine, is increasingly proving to be an important means of significantly reducing the cost of medical care and access, changing the distribution of medical resources, and improving the overall level of care. To observe the effects of traditional Chinese medicine gongfu combined with rehabilitation therapy on mild depression, anxiety, and functional recovery of activities of daily living (ADL) in patients recovering from stroke, and to provide new treatment methods to improve the function and daily living ability of the group who develop mild depression and anxiety after stroke. In this paper, the digital medical engineering application combining information technology and medical treatment integrates various high-end information technologies such as body domain network and cloud computing to solve the difficulties in the current application one by one, to provide the national people with the system provides “timely”, “local” and “bottomless” remote digital health services to the Chinese people.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"116 s436","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002719","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

Research on application of multimodal data fusion in intelligent building environment perception 多模态数据融合在智能建筑环境感知中的应用研究

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-241252

Xi Wang, Rong Guo

{"title":"Research on application of multimodal data fusion in intelligent building environment perception","authors":"Xi Wang, Rong Guo","doi":"10.3233/jifs-241252","DOIUrl":"https://doi.org/10.3233/jifs-241252","url":null,"abstract":"With the rapid development of the building industry, intelligent buildings benefit from its safety, energy saving, environmental protection and integration and other advantages have been widely loved by people, most operators also realize the importance of intelligent buildings to bring people humanized and customized services, and in order to realize the personalized service of the building, multi-modal data fusion is an effective method. On the other hand, in today’s Internet of Things society, many practical applications need to deploy a large number of sensing equipment for data collection and processing, so as to carry out high-quality monitoring of the physical world, but due to the inherent limitations of these hardware equipment and the influence of factors such as the environment, single mode data often cannot be completely and comprehensively monitored to the physical world’s changing characteristics. In this development context, multi-modal data fusion has become a research hotspot in the field of machine learning. Based on this, this paper proposes a one-stage fast object detection model with multi-level fusion of multi-modal features and end-to-end characteristics for building indoor environment perception, and conducts experimental analysis on the performance of the model. The verification results show that the accuracy of the proposed method is 50.7% and the running speed is 0.107 s, which has better performance than the existing detection methods.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004186","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

Uncertainty model-based edge detection technology in badminton 基于不确定性模型的羽毛球边缘检测技术

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-240271

Mingyuan Liu

{"title":"Uncertainty model-based edge detection technology in badminton","authors":"Mingyuan Liu","doi":"10.3233/jifs-240271","DOIUrl":"https://doi.org/10.3233/jifs-240271","url":null,"abstract":"As virtual reality technology develops, the analysis and processing of video content have become hot spots in the field of computer vision. Video Action Detection aims to locate features in network video, and its research spans many fields, such as computer vision and spatial prediction. In view of the problem of low-efficiency classification models and inaccurate localization of small-scale targets in complex scenes, we propose a novel method to generate candidate intervals for action detection. The action recognition model is adopted to generate the action score sequence on the video time series. We also propose the uncertainty model of the descending pose detection algorithm. The pre-reaction phase generates a candidate list in the form of concatenated videos containing exactly the same pose to detect action poses that are not identical and of non-maximum duration. Experiments with traditional target detection and multiple deep learning models show that the proposed Non-Maximum Suppression algorithm has a strong ability to extract neural network features. Furthermore, compared with traditional ATSS and Faster R-CNN methods, the detection quality and performance are improved by more than 15.2% and 7.8%, respectively. Our method can fully utilize perception information to improve the quality of decision planning and plays a connecting role between perception fusion and decision planning.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"72 s307","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002506","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

Machine learning-based model for predicting arrival time of container ships 基于机器学习的集装箱船抵达时间预测模型

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-234552

Manh Hung Nguyen, Hong Van Nguyen, Van Quan Tran

{"title":"Machine learning-based model for predicting arrival time of container ships","authors":"Manh Hung Nguyen, Hong Van Nguyen, Van Quan Tran","doi":"10.3233/jifs-234552","DOIUrl":"https://doi.org/10.3233/jifs-234552","url":null,"abstract":"Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO > XGB > RF > RF-PSO > GB > KNN > SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"74 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004249","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

A multimodal transfer learning framework for the classification of disaster-related social media images 用于灾害相关社交媒体图像分类的多模态迁移学习框架

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-241271

Saima Saleem, Anuradha Khattar, Monica Mehrotra

{"title":"A multimodal transfer learning framework for the classification of disaster-related social media images","authors":"Saima Saleem, Anuradha Khattar, Monica Mehrotra","doi":"10.3233/jifs-241271","DOIUrl":"https://doi.org/10.3233/jifs-241271","url":null,"abstract":"Rapidly classifying disaster-related social media (SM) images during a catastrophe event is critical for enhancing disaster response efforts. However, the biggest challenge lies in acquiring labeled data for an ongoing (target) disaster to train supervised learning-based models, given that the labeling process is both time-consuming and costly. In this study, we address this challenge by proposing a new multimodal transfer learning framework for the real-time classification of SM images of the target disaster. The proposed framework is based on Contrastive Language-Image Pretraining (CLIP) model, jointly pretrained on a dataset of image-text pairs via contrastive learning. We propose two distinct methods to design our classification framework (1) Zero-Shot CLIP: it learns visual representations from images paired with natural language descriptions of classes. By utilizing the vision and language capabilities of CLIP, we extract meaningful features from unlabeled target disaster images and map them to semantically related textual class descriptions, enabling image classification without training on disaster-specific data. (2) Linear-Probe CLIP: it further enhances the performance and involves training a linear classifier on top of the pretrained CLIP model’s features, specifically tailored to the disaster image classification task. By optimizing the linear-probe classifier, we improve the model’s ability to discriminate between different classes and achieve higher performance without the need for labeled data of the target disaster. Both methods are evaluated on a benchmark X (formerly Twitter) dataset comprising images of seven real-world disaster events. The experimental outcomes showcase the efficacy of the proposed methods, with Linear-Probe CLIP achieving a remarkable 7% improvement in average F1-score relative to the state-of-the-art methods.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"10 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004642","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

Research on economic growth in smart cities based on wireless sensor networks 基于无线传感器网络的智慧城市经济增长研究

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-242195

Chao Yuan, Ziqi Zhao

{"title":"Research on economic growth in smart cities based on wireless sensor networks","authors":"Chao Yuan, Ziqi Zhao","doi":"10.3233/jifs-242195","DOIUrl":"https://doi.org/10.3233/jifs-242195","url":null,"abstract":"With the acceleration of urbanization, the concept of smart city is rising gradually. Wireless sensor network as an important technical support of smart city, its application in environmental monitoring and water resources management has a profound impact on economic growth. Water resource is one of the most dependent resources for human beings. With the growth of world population and the rapid development of economy, water resource crisis is constant, water pollution, water shortage and water waste coexist. How to build a perfect water resource economic policy is a worldwide problem at present. At present, the formulation of water resources policies is often based on experience or the knowledge system of decision makers. Due to the dynamic nature of water resources utilization and the incomplete information of decision makers, there are often policy failures, which affect economic growth. Based on this, this paper uses system dynamics model to study the mechanism of water resources management policies affecting economic growth by taking Gansu, Tianjin and Zhejiang as three qualitatively representative arid areas, transitional areas and water-rich areas. The research results show that under the same water resources policy coupling, different regions also have different eco-economic effects. The effect of coupled water resources policy is better than that of single water resources management policy.","PeriodicalId":194936,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003459","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

A consensus reaching model for expert behavior-driven adjustment of expert weights based on picture fuzzy sets 基于图片模糊集的专家行为驱动的专家权重调整共识达成模型

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-05-07 DOI: 10.3233/jifs-238151

Meiqin Wu, Linyuan Ma, Jianping Fan

引用次数: 0

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献