检索结果-内蒙古大学图书馆

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and support vector machine (SVM) algorithm

引用

MICROCHEMICAL JOURNAL 2024年 207卷

作者： Cioccia, Guilherme Wenceslau, Rodrigo Ribeiro, Matheus Senesi, Giorgio S. Cabral, Jader Nicolodelli, Gustavo Cena, Cicero Marangoni, Bruno UFMS Univ Fed Mato Grosso do Sul Programa Posgrad Ciencia Mat Campo Grande MS Brazil Coordenadoria Geral Pericias Coordenador Reg Unida SEJUSP Secretaria Estado Just & Seguranca Publ Costa Rica MS Brazil CNR Ist Sci & Tecnol Plasmi ISTP Sede Bari Via Amendola 122-D I-70126 Bari Italy Univ Fed Uberlandia Inst Fis POB 593 BR-38400902 Uberlandia MG Brazil UFSC Univ Fed Santa Catarina Florianopolis SC Brazil

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The "gold standard" technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic support vector machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol's significant reduction in the likelihood of false positives.

关键词： Gunshot residues Laser-Induced Breakdown Spectroscopy Principal Component Analysis support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

引用

REMOTE SENSING 2023年第20期15卷 4946-4946页

作者： Su, Jiajia Liu, Zhihua Wang, Wenjuan Jiao, Kewei Yu, Yue Li, Kaili Lue, Qiushuang Fletcher, Tamara L. Chinese Acad Sci Inst Appl Ecol Shenyang 110016 Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100091 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130028 Peoples R China

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and support vector machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

关键词： forest fire Boosted Regression Tree model Random Forest model support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Numerical investigation and prediction of the excavation face stability for river-crossing shield tunneling: An intelligent prediction model for limit support pressure

引用

COMPUTERS AND GEOTECHNICS 2023年第1期160卷

作者： Li, Xin Xue, Yiguo Li, Zhiqiang Kong, Fanmeng Li, Guangkun Zhou, Binghua Shandong Univ Inst Marine Sci & Technol Qingdao 266000 Shandong Peoples R China China Univ Geosci Beijing Sch Engn & Technol Beijing 100084 Peoples R China Shandong Univ Geotech & Struct Res Ctr Jinan 250061 Shandong Peoples R China

The instability of the shield tunnel's excavation face, particularly underwater tunnels, is one of the most hazardous factors in subsea tunnel excavation projects. Therefore, an accurate prediction of the limit support pressure to maintain the excavation face's stability is essential to minimize the possible risk of damage. This study proposed an intelligent prediction model to address these issues. First, the analytical formula of the limit support pressure of the excavation face was established through mechanical analysis to give the mechanics criterion for active instability of the excavation face. Subsequently, the numerical simulations were conducted to reveal the evolution mechanism of the excavation face instability. The result revealed that the buried depth of the tunnel, water depth, driving speed, internal friction angle, water content, cohesion, and support safety factor were the main factors affecting the limit support pressure. Furthermore, the support vector machine (SVM) model was established for predicting the limit support pressure. The prediction results agreed well with the actual data, thereby indicating the feasibility and convenient implementation of the SVM predictor. The proposed model was validated as an effective method for predicting the limit support pressure for further engineering applications.

关键词： Excavation face instability Limit support pressure prediction Numerical simulation support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Prediction of PM2.5 Concentration Using Spatiotemporal Data with machine Learning models

引用

ATMOSPHERE 2023年第10期14卷 1517-1517页

作者： Ma, Xin Chen, Tengfei Ge, Rubing Xv, Fan Cui, Caocao Li, Junpeng North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450046 Peoples R China Chinese Acad Environm Planning Environm Protect Investment Performance Ctr Beijing 100012 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China

Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models-random forest model, ridge regression model, support vector machine model, extremely randomized trees model-were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 mu g/m(3). Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications.

关键词： data mining extremely randomized trees model PM2.5 prediction random forest model ridge regression model support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Ultra-high performance concrete design method based on machine learning model and steel slag powder

引用

CASE STUDIES IN CONSTRUCTION MATERIALS 2022年 17卷

作者： Sun, Guorui Du, Maohua Shan, Baohua Shi, Jun Qu, Yiwen Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China Harbin Inst Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Min Ind & Informat Technol Harbin 150090 Peoples R China Naval Univ Engn 717 Jiefang Rd Wuhan 430033 Peoples R China Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Natl Engn Res Ctr High Speed Railway Construct Tec Changsha Peoples R China

This paper presents a design method for green ultra-high performance concrete (UHPC) through machine learning (ML) models and steel slag (SS) powder. First, different UHPC compressive strength prediction models are developed, which include the linear regression (LR) model, back propagation artificial neural network (BP-ANN) model, genetic algorithm optimized ANN (GABP-ANN) model, random forest (RF) model and support vector machine (SVM) model. The prediction results of different ML models are compared and the SVM model is identified to have higher accuracy (R2 =0.93). Then, a design model for UHPC is proposed by the modified Andreasen & Andersen (MAA) particle packing model and SVM model. The model takes into account the particle size and the interactions of the materials, resulting in UHPC with excellent mechanical properties and a low amount of cement. Finally, green UHPC with low cement dosage and different SS powder replacement rates (substituted cement) are designed. The effects of SS on the mechanical properties, microscopic properties, hydration properties, drying shrinkage properties, toxic leaching properties and environmental properties of UHPC are investigated when the cement admixture in UHPC is low. The results indicate that the mechanical properties of UHPC decrease with the increase of SS substitution rate, but the decrease is limited. The addition of SS powder can not only inhibit the dry shrinkage and reduce the heat of hydration, but also reduce the impact on the environment.

关键词： Ecological performance Steel slag powder support vector machine model Ultra -high performance concrete

来源：评论

学校读者我要写书评

暂无评论

Analysis of power system load forecasting based on neural networks

Analysis of power system load forecasting based on neural ne...

引用

作者： Xuhan Lang Shien-Ming Wu School of Intelligent Engineering South China University of Technology

In this research,our main goal was to improve power load forecasting accuracy by considering the impact of meteorological factors on the total power of the electrical system,examining existing load data,local weather,wind direction,and other parameters affecting total power *** divided the data from the past three years into a training dataset,comprising 75% of the data,and a testing dataset with the remaining 25%.We employed a basic machine learning technique(support vector machine) and three distinct neural network approaches(Artificial Neural Network,Convolutional Neural Network,and Long-Short Term Memory Network) to develop analytical *** experimentation,the LSTM model achieved a loss value of 0.0034 and required 1426.78 seconds of training time across *** the time expense and model complexity,we chose the LSTM model to forecast power load at 15-minute intervals for the subsequent ten days,achieving a satisfactory prediction and fitting *** results suggest that the LSTM model is a promising method for optimizing performance and reliability in electrical power systems.

关键词： power system load prediction support vector machine model Artificial Neural Network model convolutional neural network model Long-Short Term Memory model

来源：评论

学校读者我要写书评

暂无评论

Using support vector machine models for Size of Motorcycle Taxi Terminal in Bang Yai District Analysis 19

Using Support Vector Machine Models for Size of Motorcycle T...

引用

8th International Conference on Informatics, Environment, Energy and Applications (IAEA)

作者： Worachairungreung, Morakot Thanakunwutthirot, Kunyaphat Songka, Thammasak Suan Sunandha Rajabhat Univ Fac Humanities & Social Sci Bangkok Thailand Suan Sunandha Rajabhat Univ Fuculty Ind Technol Bangkok Thailand Burapha Univ Acad Serv Ctr Chon Buri Thailand

ISBN: (纸本)9781450361040

This study aims to study the factors of building usage that influence the size of the motorcycle taxi terminal. Also, as there are many factors of building usage, this research aims to study whether the modern non-parametric model, such as, the support vector machine (SVM), is able to classify the size of the motorcycle taxi terminal, using the dataset of the building usage. Based on random 50 grid cells, with the size of 500 meters. The ratio of the correct classification of the training data is 85 percents, whereas the SVM model is able to predict the size correctly. The ratio of the correct classification of the testing data is 100 percents. Even though, one of the SVM model's limitations is the Black Box problem. The Black Box prevents the model from classifying the factors that directly influence the motorcycle taxi terminal's sizes. Therefore, the author uses that dataset for the correlation test, in order to find the correlation coefficient. In this case, the factors that are highly correlated with the motorcycle taxi terminal's sizes are the number of ATMs and the bus stations.

关键词： support vector machine model Motorcycle taxi terminal Correlation Building usage

来源：评论

学校读者我要写书评

暂无评论

Data-enhanced machine recognition model of DC serial arc in electric vehicle power system

引用

IET POWER ELECTRONICS 2020年第19期13卷 4677-4684页

作者： Xia, Kun Zhang, Zihan Liu, Bangzheng Guo, Haotian Yu, Wei Xu, Jingjun Dong, Hui Univ Shanghai Sci & Technol Elect Engn Dept 516 JunGong Rd Shanghai 200093 Peoples R China Hella Shanghai Elect Co Ltd R&D Ctr Shanghai 201201 Peoples R China

Electric vehicle (EV) power system is the key to the development of EVs. If direct current (DC) arc occurs in the power system, it is difficult to extinguish at zero point. The arc fault will release a huge amount of energy and continuous sparking, which may cause spontaneous combustion or even explosion. In this study, an arc detection algorithm based on the classification of windowed Fourier transform and support vector machine (SVM) model is proposed for DC serial arc detection of EV power system. In order to optimise the arc detection algorithm, the authors use the pre-detection algorithm, which can effectively reduce the false detection rate of DC arc fault and ensure the reliability of detection algorithm. In addition, they propose an arc fault data enhancement model, which can generate arc fault current data. Finally, the experimental results show that the arc detection algorithm has a high accuracy and a false detection rate of 0%. After data enhancement, it has generalisation of the SVM classification model under the condition of high power experiment.

关键词： arcs (electric) fault diagnosis electric vehicles support vector machines fault currents Fourier transforms power engineering computing direct current arc arc detection algorithm support vector machine model DC serial arc detection EV power system pre-detection algorithm false detection rate DC arc fault arc fault data enhancement model arc fault current data SVM classification model high power experiment data-enhanced machine recognition model electric vehicle power system windowed Fourier transform classification

来源：评论

学校读者我要写书评

暂无评论

Dynamic changes of the ecological footprint in the Beijing-Tianjin-Hebei region from 1996 to 2020

引用

ECOLOGICAL INDICATORS 2020年 112卷 106142-000页

作者： Liu, Lingna Lei, Yalin China Univ Geosci Sch Humanities & Econ Management Beijing Peoples R China Minist Land & Resources Key Lab Carrying Capac Assessment Resource & Envi Beijing Peoples R China

The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region.

关键词： Ecological footprint Grey correlational model support vector machine model Energy consumption Jing-Jin-Ji region

来源：评论

学校读者我要写书评

暂无评论

Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

引用

HYDROLOGY RESEARCH 2020年第5期51卷 942-958页

作者： Li, Jianzhu Zhang, Siyao Huang, Lingmei Zhang, Ting Feng, Ping Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China Xian Univ Technol Fac Water Resources & Hydroelect Engn Xian Peoples R China

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

关键词： drought prediction integrated autoregressive moving average model random forest model standardized precipitation evaporation index support vector machine model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：