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Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks

APPLIED SCIENCES-BASEL 2025年第8期15卷 4126-4126页

作者： Khromov, Igor Voskov, Leonid Komarov, Mikhail HSE Univ Dept Comp Engn Moscow 101000 Russia HSE Univ Grad Sch Business Moscow 101000 Russia

Intrabody communication (IBC) establishes a wireless connection between devices in a Wireless Body Area Network (WBAN) by utilizing the human body as a transmission medium. The characteristics of the IBC channel are significantly influenced by the geometric and biological features of the human body and tissues. This paper analyzes a dataset with experimental real subjects' data on signal loss in a galvanic IBC channel, models IBC identification using the K-Nearest Neighbors (knn) algorithm, and proposes a novel IBC WBAN architecture incorporating an identification function. The analysis of the dataset revealed that the IBC channel gain exhibits a wide range of variations depending on individual human body characteristics such as height, weight, body mass index, and body composition. Consequently, biometric identification can be leveraged within the IBC WBAN paradigm. Through modeling IBC identification on cleaned and labeled data, we demonstrated an identification accuracy of 99.9% based on the results of our modeling. The proposed IBC WBAN architecture with an integrated identification function is anticipated to enhance the application scope and accelerate the development of IBC WBANs.

关键词： channel gain galvanic coupling IBC identification NB-IoT knn algorithm wireless body area network (WBAN)

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Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model algorithms

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AEROSOL AND AIR QUALITY RESEARCH 2024年第12期24卷 1-19页

作者： Liu, Ruolan Yuan, Shujie Liu, Duanyang Han, Lin Zu, Fan Wu, Hong Wang, Hongbin Pengzhou Meteorol Adm Chengdu Meteorol Off Chengdu 611930 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Atmospher Sci Chengdu 610225 Sichuan Peoples R China China Meteorol Adm Nanjing Joint Inst Atmospher Sci Key Lab Transportat Meteorol Nanjing 210041 Jiangsu Peoples R China Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing 100081 Peoples R China

Low visibility will seriously affect traffic safety, and accurate prediction of low visibility can effectively reduce safety risks. This study introduces a machine learning approach for simulating visibility, utilizing the K-Nearest Neighbors algorithm and an ensemble model, which incorporate data from atmospheric boundary layer detection and conventional ground meteorological observations as simulation inputs. We developed three distinct visibility simulation schemes to identify the most effective algorithm and to assess the influence of the atmospheric boundary layer on the simulation outcomes. Our results revealed that during two separate fog events, the ensemble model consistently outperformed the knn algorithm. In the first fog event, the ensemble model achieved a more significant reduction in RMSE compared to the MAE within the same range of visibility (for VIS < 200 m, Scheme 2 reduced MAE by 33% and RMSE by 24%). Moreover, the integration of atmospheric boundary layer data notably enhanced model accuracy in both fog events, with the enhancement being particularly marked in the first event (ensemble model: for VIS < 200 m, Schemes 2 and 3 had MAEs of 20.5 m, corresponding to a relative error of less than 10.3%, and 22.9 m, corresponding to a relative error of less than 11.5%, respectively). In the second fog event, the addition of atmospheric pollutant concentration data from the boundary layer further improved results (ensemble model: for VIS < 200 m, Schemes 2 and 3 had MAEs of 20.1 m, corresponding to a relative error of less than 10.1%, and 11.4 m, corresponding to a relative error of less than 5.7%, respectively). These findings underscore the importance of incorporating atmospheric boundary layer observations in enhancing the fidelity of visibility simulations based on knn and ensemble model algorithms and their potential to significantly improve transportation safety and reduce economic losses.

关键词： knn algorithm Ensemble model algorithm Visibility simulation Fog-Haze Machine learning

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3D modeling of the stratigraphic and structural architecture of the Crotone basin (southern Italy) using machine learning with Python

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MARINE AND PETROLEUM GEOLOGY 2024年 164卷

作者： Falsetta, Ettore Bullejos, Manuel Critelli, Salvatore Martin-Martin, Manuel Univ Calabria Dipartimento Ingn Ambiente I-87036 Arcavacata Di Rende CS Italy Univ Granada Dept Algebra Granada 18010 Spain Univ Alicante Dept Ciencias Tierra & Medio Ambiente Alicante 03080 Spain

The 3D modeling and representation of geological data have experienced significant growth within last years, due to the use of new technologies derived from advancements in land representation methods. These technologies enable interactive, intuitive and clear geological visualizations. This paper shows how, by using the open-source Python software (operable with a simple internet browser) for machine learning (linear and knn interpolations), together with Geographic Information Systems (GIS), it is possible to achieve interactive 3D visualizations of geological features in sedimentary basins. This study is performed in the onshore-offshore Crotone area (southern Italy) where a large amount of stratigraphic datasets are available from core perforation and seismic profiles due to the presence of a natural gas extraction field. Thanks to a database of 63 drilling lithologic records and 43 check points obtained from 9 interpreted seismic sections, several 3D HTML models were constructed defining three stratigraphic units (Pre-Messinian, Messinian, and Post-Messinian). An overlap of the Post-Messinian top surface and an erosional truncation of the Messinian top surface toward the N were observed, together with a rising of the Pre-Messinian top surface in the northwestern area. This stratigraphic architecture may indicate differential subsidence and/or uplifting due to syn-sedimentary fault kinematics in the whole studied area. The 3D models with the stratigraphic unit boundary surfaces obtained with knn interpolation (showing stepped and abrupt edges) allowed the interpretation in terms of structural architecture and synsedimentary fault kinematics. Three main sets of faults were deduced: N-S;NNW-SSE, and ENE-WSW. A minorly represented E-W set was added to the main sets. These faults generated a horsts-grabens structure, and in many cases a determinate set of faults caused a progressive lowering or rising of some areas with an "en echelon" arrangement. According to prev

关键词： Python libraries knn algorithm 3D stratigraphic and structural architecture Crotone basin S Italy

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Design and application research of a flexible array plantar sensor based on P(VDF-TrFE)/SnO2NPS/GR for Parkinson's disease diagnosis

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POLYMER-PLASTICS TECHNOLOGY AND MATERIALS 2024年第14期63卷 1975-1999页

作者： Luo, Yi Su, Peinan Wu, Ying Zhao, Zhidong Hangzhou DIANZI Univ Sch Elect & Informat Engn Hangzhou Peoples R China Hangzhou DIANZI Univ Sch Commun Engn Hangzhou Peoples R China Hangzhou DIANZI Univ Acad Affairs Off Hangzhou Peoples R China Hangzhou DIANZI Univ Sch Cyberspace Secur Hangzhou 310018 Peoples R China

This paper introduces a flexible array Plantar sensor fabricated through the high-voltage electrospinning process of poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) , incorporating stannic oxide nanoparticles (SnO2NPS) and graphene (GR) composite nanofilm. The surface morphology, beta-phase crystal content, piezoelectric performance, composition, and structure of the composite piezoelectric films were evaluated using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, and a vibration platform. Experimental findings reveal that P(VDF-TrFE)/SnO2NPS/GR composite films containing 5% SnO2NPS and 0.1% GR exhibit superior open-circuit voltage and short-circuit current, measuring 22.43 V and 12.95 uA, respectively. These values are approximately 1.59 times and 1.34 times higher than those of the 5% P(VDF-TrFE) composite film and about 2.37 and 2.16 times higher than those of pure P(VDF-TrFE). A flexible piezoelectric sensor was fabricated using this composite film, and the mechanical properties and electrical impedance behavior of the sensor were investigated and analyzed. A multi-channel foot pressure collection and classification system was established based on this sensor, and a Parkinson's disease machine learning model for multi-channel foot pressure collection was investigated. Various machine learning models for Parkinson's disease were compared, and a fine K Nearest Neighbors (knn) Parkinson's disease classification model with an accuracy of 97.1% was proposed. This offers a novel solution for Parkinson's disease diagnosis and holds significant reference value. [GRAPHICS] .

关键词： Flexible array sensor high-voltage electrospinning knn algorithm nanoelectrospun piezoelectric film P(VDF-TrFE)

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New business management model of enterprises based on data-driven

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International Journal of Information Technology and Management 2025年第1-2期24卷 1-12页

作者： Zhang, Xiaofeng Zhongyuan Trust Co. Ltd. Henan Zhengzhou450000 China

In the current enterprise management mode, there are problems such as low efficiency of enterprise management data processing and reducing the economic benefits of enterprises, which affect the rapid development of enterprises. In order to solve this problem, this paper studies the new business management model of enterprises based on data-driven. Build a data-driven enterprise management mode framework, integrate enterprise management data with knn algorithm, and calculate user access trust and reliability values with trust management model to improve data processing efficiency and data security. Based on the digital processing of enterprise management data, the development strategy of new business management mode is given. The experimental results show that after applying the management mode designed in this paper, the maximum profit of the enterprise can reach 20.5 million yuan, and the maximum value of the enterprise data processing time is only 6.03 s. This proves that the designed management mode is more efficient for the enterprise management data processing, and can effectively improve the enterprise economic income, and has certain practical application value. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： business management model data integration data-driven development strategy knn algorithm trust management model

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C-QoSRP: Cluster Based QOS-Routing Protocol for VANET in Highway Environment

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2024年第3期58卷 313-325页

作者： Boulfekhar, Samra Aissani, Djamil Bejaia Univ Res Unit LaMOS Modeling & Optimizat Syst Targa Ouzemmour 06000 Bejaia Algeria

This paper presents a new cluster-based quality of service routing protocol (C-QoSRP) designed for VANET in a highway setting, offering the benefit of wide-range communication facilitated by road-side units (RSUs). The protocol incorporates the K-nearest neighbor (knn) algorithm to optimize cluster stability by combining mobility and link quality metrics. A noteworthy aspect of this approach is that in the event of a cluster head (CH) failure, C-QoSRP employs RSUs as temporary cluster heads (TCHs). The TCH assumes the CH's responsibilities to ensure reliable communication within the cluster. Unlike traditional protocols that primarily focus on CH stability, C-QoSRP aims to minimize transmission delays and improve overall network stability. Simulation results demonstrate that C-QoSRP protocol surpasses existing ones in terms of QoS parameters such as packet delivery ratio, end-to-end delay, throughput, overhead and Cluster stability.

关键词： VANET routing QoS clustering knn algorithm

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An Enhanced Frequency Analysis and Machine Learning Based Approach for Open Circuit Failures in PV Systems

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IEEE ACCESS 2024年 12卷 96342-96357页

作者： Lavador-Osorio, Mauricio Zuniga-Reyes, Marco-Antonio Alvarez-Alvarado, Jose M. Sevilla-Camacho, Perla-Yazmin Garduno-Aparicio, Mariano Rodriguez-Resendiz, Juvenal Univ Autonoma Queretaro Fac Informat Juriquilla 76230 Queretaro Mexico Tecnol Nacl Mex IT Tuxtla Gutierrez Tuxtla Gutierrez 29050 Chiapas Mexico Univ Autonoma Queretaro Fac Ingn Juriquilla 76010 Queretaro Mexico Univ Politecn Chiapas Programa Posgrad Energias Renovables Suchiapa 29150 Mexico

Over the last decades, the accelerated implementation of photovoltaic systems (PVS) has led to the creation of open circuit fault detection systems based on measurements made in completed facilities, growing by making the volume of data to be analyzed with each new installation, improving fault detection and location systems with various methods. In this article, an electronic adaptive device was developed that operates under a method based on the spectral analysis of signals using the Discrete Fourier Transform (DFT) and a classifier based on the k-Nearest Neighbor (k-NN) machine learning algorithm (ML) for the detection of Open Circuit Faults (OCF). The contribution of this work is that the entire photovoltaic array operated in conditions of radiance less than 10 (W)/(m)2 overnight with a red LED pulsed light applied on the photovoltaic array module furthest from the inverter. Under these operating conditions, the presence of an open circuit fault alters the variability in the impedances of the photovoltaic array under different fault locations in the systems compared to healthy systems without an open circuit fault, revealing that the predictability of the methodology shows values from 90% to 93% as the size of the photovoltaic system increases, concluding the effectiveness of the procedure.

关键词： Photovoltaic systems Voltage measurement Fault detection Electrical fault detection Temperature measurement Inverters Current measurement Discrete Fourier transforms Nearest neighbor methods Machine learning Circuit faults Solar power generation Heuristic algorithms Fault detection in photovoltaic systems open circuit fault detection discrete Fourier transform knn algorithm dynamic impedance in photovoltaic systems

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Identifying New Persons in the context of the Robocup@Home competition using knn 20

Identifying New Persons in the context of the Robocup@Home c...

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20th Latin American Robotics Symposium (LARS) / 15th Brazilian Symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Segalle, David Armenio, Gustavo Fardo Lewin, Gustavo Fontes Silva, Everson de Souza Fabro, Joao Alberto UTFPR Fed Univ Technol Parana Comp Engn Course Curitiba PR Brazil UTFPR Comp Engn Course Curitiba Parana Brazil UTFPR Elet Engn Course Curitiba PR Brazil UTFPR PPGCA Grad Program Appl Informat Curitiba PR Brazil

ISBN: (纸本)9798350315387

Robocup@Home proposes a challenge related to Person Recognition: after presented, a new 'operator' should become 'immediately' recognizable by the robot. The presentation procedure may require the operator to correctly interact with the robot, following a certain procedure, as instructed by the robot itself (for example, staying in front of the robot, so that the robot can take pictures of this person). In this paper, we propose the use of the knn (K-Nearest Neighbor) supervised machine learning algorithm to include a new 'operator' in a database of persons recognizable by the robot. This algorithm uses information taken from an image segmentation of the face of the operator. The experiment evaluates how long it takes to include a new operator if the robot has from 1 to 12 current operators, evaluating also how long it takes to include this operator based on 1, 2 or more images of the new operator, taken from slightly different points of view. The results confirm that knn can be used to 'present to the robot' up to 13 new operators, with up to 15 images for each operator, in less than 60 seconds.

关键词： knn algorithm person recognition face recognition online training

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A Study on Base Station Traffic Classification Based on K-means and Improved knn Models 6

A Study on Base Station Traffic Classification Based on K-me...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Sun, Jiaqi Wang, Yuan Yan, Shaochen Sun, Yuchen Zhejiang University Hangzhou China Hebei Agricultural University Baoding China Northwestern Polytechnical University Xi'an China Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9798350305623

In this paper, time series data features are extracted for classification based on HTRN Dataset, and 30,000 data objects are randomly selected as the training test sample set for preliminary cluster analysis. Then, based on the improved knn algorithm, the remaining samples are categorized into the categories with the closest distance. The original data are preprocessed and randomly sampled to obtain the test samples to divide the dataset;the tsfresh tool is used to extract the statistical features, entropy features and segmentation features of the time series as the corresponding feature vectors for clustering, and the feature vector F is obtained;at the same time, the importance of the individual features of the constituent feature vectors is analyzed based on the Random Forest method. The optimal number of clusters is obtained according to the contour coefficient and the elbow rule, and then the k-means method is used for feature-based cluster analysis;the improved knn model is applied to classify the non-tested sample plots. The final results show that the above method can effectively improve the processing efficiency and accuracy, and has certain practical feasibility, in order to provide certain reference for research in other fields. © 2023 IEEE.

关键词： knn algorithm Random Forest tsfresh tool

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WEB-based Collaborative Platform for College English Teaching

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第2期14卷 790-800页

作者： Zhang, Yuwan Henan Polytech Inst Sch Culture Tourism & Int Educ Nanyang 473000 Peoples R China

At present, colleges and universities are trying to apply online education. The online college English course teaching cooperation platform is an important part of college English teaching. At present, teachers' scoring method for students' online examination on this kind of platform is mainly human scoring, which has a low efficiency. In view of this, based on the characteristics of web, this paper constructs an English test paper scoring algorithm based on text matching degree algorithm and improved knn algorithm. The data analysis type of the algorithm is mainly prescriptive analysis that is, judging whether to give points according to the characteristics of the data. The automation and high efficiency of the algorithm can save a lot of human costs in the field of online education. The experimental results show that the recall rate of the improved knn scoring algorithm for specific semantic topics is up to 0.9, and only 7.3% of students report that the algorithm misjudges their grades. The results indicate that the algorithm has the potential to be applied to the Web-based college English course teaching collaboration platform and reduce the workload of teachers and improve their efficiency.

关键词： Web text similarity knn algorithm vocabulary matching network teaching

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