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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Ahmed, Syed Thouheed Guthur, Amogh S. Reddy, K.L. Shreyan Ahmed, Affaan Reddy, T. Vishal School of Computing and Information Technology REVA University Bangalore India

ISBN: (纸本)9798350325188

The design of in-car navigation systems is a critical factor in determining driver stress levels and workload. This study seeks to collect information on drivers' experiences with in-car navigation systems, specifically their stress and workload levels. By analyzing the data collected, researchers can identify design features that contribute to both positive and negative user experiences, informing future navigation system design and development. Furthermore, results of this study could be used for improving existing systems' user experiences (UX), relieving driver stress, and encouraging safer driving practices on the road. The increasing demand for in-car navigation systems has led to the proliferation of these devices in vehicles. While these devices provide useful information for drivers, they can also contribute to driver stress and workload. The complexity of these systems, along with their frequent use during driving, can cause distraction and increase mental workload, which can ultimately lead to accidents. Based on the findings, in-car navigation system accuracy rates were highest for individuals within the age range 25-45 and range 18-24 with accuracy rates at 89.54095 respectively, while its accuracy rate for 46-68 individuals ranged between 64.3802-63.50802. These findings hold significant ramifications for the design and development of in-car navigation systems, particularly with respect to relieving driver stress and workload associated with their usage. © 2023 IEEE.

关键词： Effects Machine Learning Navigation System Stress and Workload svm algorithm

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Support Vector Machine algorithm for human fall recognition Kinect-Based skeletal data

Support Vector Machine algorithm for human fall recognition ...

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2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS 2015)

作者： Trinh Hoai An Nguyen Thanh Hai Truong Quang Phuc Tran Thanh Mai HCMC Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam HCMC Univ Technol & Educ Fac Elect Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781467366403

Falls are the major reason of injury and accidental death for older people. It is important to recognize falls early for assistance and treatment. In this paper, a Support Vector Machine (svm) algorithm for recognition falls and other activities based on skeletal data is proposed. Skeletal data, which will be extracted from capturing human body using a Kinect camera system, are obtained on three persons. In order to distinguish falling states such as lying, sitting and standing, the svm will be applied for training and testing to validate the obtained data. There are three experiments were performed to recognize three circumstances of fall and non-fall, fall and standing, fall and sitting. Experimental results show with the high accuracy of recognition activities to illustrate the effectiveness of the proposed approach.

关键词： Skeletal data data acquisition falling classification svm algorithm

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Fall detection system based on infrared array sensor and multi-dimensional feature fusion

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MEASUREMENT 2022年 192卷 110870-110870页

作者： Yang, Yi Yang, Honglei Liu, Zhixin Yuan, Yazhou Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualties. In order to obtain a high precision and low cost fall detection system for the elderly, a fall detection system based on infrared array sensor and multi-dimensional feature fusion is proposed in this paper. First, we propose a new data acquisition method using infrared array sensor, which effectively enlarges the detection area. Then the personnel positioning is performed before fall detection, which can ensure real-time detection while reducing computational complexity. In addition, a sliding window algorithm is developed and four representative features of a fall are extracted from the collected data, which is fitful to the online detection. Among them, the four characteristics include the change in the center of mass of the falling process, the change in the speed, the change in the area of the person, and the change in variance. Finally, based on the refined features, the support vector machine (svm) classifier is introduced to identify falls and improve the classification accuracy. The experimental results validate that the proposed fall detection system shows good fall detection accuracy and great practicability.

关键词： Fall detection Infrared array sensor Feature extraction svm algorithm

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Assessment of WorldView-3 Data for Lithological Mapping

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REMOTE SENSING 2017年第11期9卷 1132-1132页

作者： Ye, Bei Tian, Shufang Ge, Jia Sun, Yaqin China Univ Geosci Beijing Sch Earth Sci & Resources Beijing 100083 Peoples R China China Geol Survey Oil & Gas Survey Beijing 100083 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr AG Beijing 100083 Peoples R China

The WorldView-3 (WV-3) satellite is a new sensor with high spectral resolution, which equips eight multispectral bands in the visible and near-infrared (VNIR) and additional eight bands in the shortwave infrared (SWIR). In order to meet the requirements of large-scale geological mapping, this paper assessed WV-3 data for lithological mapping in comparison with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Operational Land Imager (OLI/Landsat-8) data. The study area is located in the Pobei area of the Xinjiang Uygur Autonomous Region, where bedrock outcrops are widely distributed. The whole experiment was divided into six steps: data pre-processing, visual interpretation of various lithological units, samples procedure, lithological mapping by a support vector machine algorithm (svm), accuracy evaluation, and assessment. The results showed that the classification accuracy of WV-3 data was 87%, which kept 17% higher than that of ASTER data, 14% higher than that of OLI/Landsat-8 data, indicated that WV-3 data contained more diagnostic absorption features mainly thanks to its SWIR bands, and benefited by its high spatial resolution, as well. However, it also confirmed that there were some considerable flaws, such as the confusing identification of biotite-quartz schist. Overall, the WV-3 data is still the most promising data for geological applications currently.

关键词： WorldView-3 lithological mapping spectral and spatial assessment svm algorithm

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Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf algorithm

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SENSORS 2023年第14期23卷 6645-6645页

作者： Shen, Weijie Xiao, Maohua Wang, Zhenyu Song, Xinmin Zhejiang Tech Inst Econ Hangzhou 310018 Peoples R China Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China

This study targets the low accuracy and efficiency of the support vector machine (svm) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of svm and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor & alpha;. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-svm was 98.75%. Then, the IGWO-svm model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-svm (particle swarm optimization) and GWO-svm diagnosis models. Results showed that the IGWO-svm model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

关键词： rolling bearing fault diagnosis IGWO algorithm svm algorithm

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Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)

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REMOTE SENSING 2022年第7期14卷 1692-1692页

作者： Santarsiero, Valentina Nole, Gabriele Lanorte, Antonio Tucci, Biagio Cillis, Giuseppe Murgante, Beniamino CNR IMAA I-85050 Potenza Italy Univ Basilicata Sch Engn 10 Viale Ateneo Lucano I-85100 Potenza Italy

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM-OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (svm) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time.

关键词： land take remote sensing svm algorithm change detection analysis geographic information system

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Research on Personal Credit Risk Assessment based on Machine Learning algorithm

World Scientific Research Journal

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World Scientific Research Journal 2020年第11期6卷 146-156页

作者： Keting Ye

The main purpose of this paper is to use machine learning algorithm to establish different classification models to evaluate and predict personal credit risk. In this paper, we take the data of give me some credit in the kaggle competition as an example. We take the seriousdlqin2yrs variable which is overdue for more than 90 days or worse as the target variable, and take other characteristic variables of the data as independent variables for modeling and analysis. In the stage of data preprocessing, this paper first uses the k-nearest neighbor method to fill in the missing values in the data, then processes the outliers in the data, and tests the multicollinearity among variables. In the process of model construction, logistic regression model is constructed by step-by-step screening method, decision tree model is constructed by cart algorithm, classification prediction model is constructed by svm algorithm, and integrated model is constructed based on three algorithms. With the help of AUC value and ROC curve, by comparing the prediction effect of different models in training data set and test data set, it is found that the integrated learning model performs better, has higher classification effect and has stability.

关键词： Credit evaluation Logistic model Decision tree model svm algorithm Integrated learning

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Support Vector Machines Principles and Actually Example

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Procedia Computer Science 2024年 243卷 2-11页

作者： Xingyu Chen Zewen Yin Hao Tian College of Science China Agricultural University Beijing 100083 China International College China agricultural university Beijing 100083 China

With the development of big data technology, it is necessary to quickly and accurately mine the information contained in data. In this paper, we study the selection of kernel function types and the selection of kernel function parameters for support vector machines under classification and regression problems, and experimentally verify their regression prediction performance and classification performance on scientific datasets. Firstly, the scientific data mining model is constructed, the model design principle is clarified, and then linear kernel function, Gaussian kernel function, and polynomial kernel function are selected for the comparison experiments, and the performance classification and regression prediction of the data are finally obtained.

关键词： Support Vector Machine svm algorithm

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3-Phase Induction motor speed control based space vector modulation technique for direct matrix converter under unbalanced conditions

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JOURNAL OF ELECTRICAL SYSTEMS 2022年第2期18卷 246-259页

作者： Boukhili, F. Zaafouri, A. Dami, M. A. ENSIT LISIER Lab ISET Jendouba Tunisia ENSIT LISIER Lab Tunis Tunisia

In this work, the performance analysis and discussion of a space vector modulation technique used for a three-phase direct matrix converter feeding a three-phase induction motor is presented. For this, the induction motor models and the method of the space vector modulation technique are developed. Then, the overall electrical drive system is simulated using MatLab/Simulik software. Results obtained from simulation are the harmonic spectrum analysis of the motor current and the total harmonic distortion for unbalanced input voltages. These results show the good performance of the control system strategy.

关键词： Direct matrix converter Induction-motor svm algorithm Harmonics Distortion

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Smart Machine Health Prediction Based on Machine Learning in Industry Environment

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INFORMATION 2023年第3期14卷 181页

作者： Yeruva, Sagar Gunuganti, Jeshmitha Kalva, Sravani Salkuti, Surender Reddy Kim, Seong-Cheol VNR Vignana Jyothi Inst Engn & Technol Dept CSE AIML & IoT Hyderabad 500090 India VNR Vignana Jyothi Inst Engn & Technol Dept CSE Hyderabad 500090 India Woosong Univ Dept Railroad & Elect Engn Daejeon 34606 South Korea

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, svm, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health.

关键词： Decision Tree algorithm IoT KNN algorithm machine health prediction predictive maintenance svm algorithm XGBoost algorithm

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