This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric feature...This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.展开更多
The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects...The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.展开更多
Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valle...Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya.The work involves the collection of rock and soil samples in the field,their analyses using reflectance and emittance spectroscopy,and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method.The latter method is strictly non-parametric,flexible and simple which does not require assumptions regarding the distributions of the input data.It has been successfully used in a wide range of classification problems.The DTC method successfully mapped the chert and trachyte series rocks,including clay minerals and evaporites of the area with higher overall accuracy(86%).Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data.Moreover,the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately,which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction.展开更多
Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree c...Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.展开更多
[Objective] This study aimed to improve the accuracy of remote sensing classification for Dongting Lake Wetland.[Method] Based on the TM data and ground GIS information of Donting Lake,the decision tree classification...[Objective] This study aimed to improve the accuracy of remote sensing classification for Dongting Lake Wetland.[Method] Based on the TM data and ground GIS information of Donting Lake,the decision tree classification method was established through the expert classification knowledge base.The images of Dongting Lake wetland were classified into water area,mudflat,protection forest beach,Carem spp beach,Phragmites beach,Carex beach and other water body according to decision tree layers.[Result] The accuracy of decision tree classification reached 80.29%,which was much higher than the traditional method,and the total Kappa coefficient was 0.883 9,indicating that the data accuracy of this method could fulfill the requirements of actual practice.In addition,the image classification results based on knowledge could solve some classification mistakes.[Conclusion] Compared with the traditional method,the decision tree classification based on rules could classify the images by using various conditions,which reduced the data processing time and improved the classification accuracy.展开更多
Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter da...Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.展开更多
Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional ...Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.展开更多
Asphaltenes have always been an attractive subject for researchers.However,the application of this fraction of the geochemical field has only been studied in a limited way.In other words,despite many studies on asphal...Asphaltenes have always been an attractive subject for researchers.However,the application of this fraction of the geochemical field has only been studied in a limited way.In other words,despite many studies on asphaltene structure,the application of asphaltene structures in organic geochemistry has not so far been assessed.Oil-oil correlation is a wellknown concept in geochemical studies and plays a vital role in basin modeling and the reconstruction of the burial history of basin sediments,as well as accurate characterization of the relevant petroleum system.This study aims to propose the Xray diffraction(XRD)technique as a novel method for oil-oil correlation and investigate its reliability and accuracy for different crude oils.To this end,13 crude oil samples from the Iranian sector of the Persian Gulf region,which had previously been correlated by traditional geochemical tools such as biomarker ratios and isotope values,in four distinct genetic groups,were selected and their asphaltene fractions analyzed by two prevalent methods of XRD and Fouriertransform infrared spectroscopy(FTIR).For oil-oil correlation assessment,various cross-plots,as well as principal component analysis(PCA),were conducted,based on the structural parameters of the studied asphaltenes.The results indicate that asphaltene structural parameters can also be used for oil-oil correlation purposes,their results being completely in accord with the previous classifications.The average values of distance between saturated portions(d_(r))and the distance between two aromatic layers(d_(m))of asphaltene molecules belonging to the studied oil samples are 4.69Aand 3.54A,respectively.Furthermore,the average diameter of the aromatic sheets(L_(a)),the height of the clusters(L_(c)),the number of carbons per aromatic unit(C_(au)),the number of aromatic rings per layer(R_(a)),the number of sheets in the cluster(M_(e))and aromaticity(f_(a))values of these asphaltene samples are 10.09A,34.04A,17.42A,3.78A,10.61Aand 0.26A,respectively.The results of XRD parameters indicate that plots of dr vs.d_(m),d_(r) vs.M_(e),d_(r) vs.f_(a),d_(m) vs.L_(c),L_(c) vs.L_(a),and f_(a) vs.L_(a) perform appropriately for distinguishing genetic groups.A comparison between XRD and FTIR results indicated that the XRD method is more accurate for this purpose.In addition,decision tree classification,one of the most efficacious approaches of machine learning,was employed for the geochemical groups of this study for the first time.This tree,which was constructed using XRD data,can distinguish genetic groups accurately and can also determine the characteristics of each geochemical group.In conclusion,the obtaining of structural parameters for asphaltene by the XRD technique is a novel,precise and inexpensive method,which can be deployed as a new approach for oil-oil correlation goals.The findings of this study can help in the prompt determination of genetic groups as a screening method and can also be useful for assessing oil samples affected by secondary processes.展开更多
In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a ...In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.展开更多
The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool cond...The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.展开更多
The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information i...The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating(HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities which involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.展开更多
文摘This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.
文摘The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.
文摘Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya.The work involves the collection of rock and soil samples in the field,their analyses using reflectance and emittance spectroscopy,and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method.The latter method is strictly non-parametric,flexible and simple which does not require assumptions regarding the distributions of the input data.It has been successfully used in a wide range of classification problems.The DTC method successfully mapped the chert and trachyte series rocks,including clay minerals and evaporites of the area with higher overall accuracy(86%).Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data.Moreover,the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately,which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction.
文摘Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.
文摘[Objective] This study aimed to improve the accuracy of remote sensing classification for Dongting Lake Wetland.[Method] Based on the TM data and ground GIS information of Donting Lake,the decision tree classification method was established through the expert classification knowledge base.The images of Dongting Lake wetland were classified into water area,mudflat,protection forest beach,Carem spp beach,Phragmites beach,Carex beach and other water body according to decision tree layers.[Result] The accuracy of decision tree classification reached 80.29%,which was much higher than the traditional method,and the total Kappa coefficient was 0.883 9,indicating that the data accuracy of this method could fulfill the requirements of actual practice.In addition,the image classification results based on knowledge could solve some classification mistakes.[Conclusion] Compared with the traditional method,the decision tree classification based on rules could classify the images by using various conditions,which reduced the data processing time and improved the classification accuracy.
基金The National Natural Science Foundation of China under contract No.42076235.
文摘Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.
文摘Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.
文摘Asphaltenes have always been an attractive subject for researchers.However,the application of this fraction of the geochemical field has only been studied in a limited way.In other words,despite many studies on asphaltene structure,the application of asphaltene structures in organic geochemistry has not so far been assessed.Oil-oil correlation is a wellknown concept in geochemical studies and plays a vital role in basin modeling and the reconstruction of the burial history of basin sediments,as well as accurate characterization of the relevant petroleum system.This study aims to propose the Xray diffraction(XRD)technique as a novel method for oil-oil correlation and investigate its reliability and accuracy for different crude oils.To this end,13 crude oil samples from the Iranian sector of the Persian Gulf region,which had previously been correlated by traditional geochemical tools such as biomarker ratios and isotope values,in four distinct genetic groups,were selected and their asphaltene fractions analyzed by two prevalent methods of XRD and Fouriertransform infrared spectroscopy(FTIR).For oil-oil correlation assessment,various cross-plots,as well as principal component analysis(PCA),were conducted,based on the structural parameters of the studied asphaltenes.The results indicate that asphaltene structural parameters can also be used for oil-oil correlation purposes,their results being completely in accord with the previous classifications.The average values of distance between saturated portions(d_(r))and the distance between two aromatic layers(d_(m))of asphaltene molecules belonging to the studied oil samples are 4.69Aand 3.54A,respectively.Furthermore,the average diameter of the aromatic sheets(L_(a)),the height of the clusters(L_(c)),the number of carbons per aromatic unit(C_(au)),the number of aromatic rings per layer(R_(a)),the number of sheets in the cluster(M_(e))and aromaticity(f_(a))values of these asphaltene samples are 10.09A,34.04A,17.42A,3.78A,10.61Aand 0.26A,respectively.The results of XRD parameters indicate that plots of dr vs.d_(m),d_(r) vs.M_(e),d_(r) vs.f_(a),d_(m) vs.L_(c),L_(c) vs.L_(a),and f_(a) vs.L_(a) perform appropriately for distinguishing genetic groups.A comparison between XRD and FTIR results indicated that the XRD method is more accurate for this purpose.In addition,decision tree classification,one of the most efficacious approaches of machine learning,was employed for the geochemical groups of this study for the first time.This tree,which was constructed using XRD data,can distinguish genetic groups accurately and can also determine the characteristics of each geochemical group.In conclusion,the obtaining of structural parameters for asphaltene by the XRD technique is a novel,precise and inexpensive method,which can be deployed as a new approach for oil-oil correlation goals.The findings of this study can help in the prompt determination of genetic groups as a screening method and can also be useful for assessing oil samples affected by secondary processes.
基金Supported by National Key R&D Program of China(2017YFD0301004)Natural Science Foundation of Shandong Province,China(ZR2016DP04)Key Project of Shandong Provincial Meteorological Bureau(2017sdqxz03)
文摘In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.
文摘The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.
文摘The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating(HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities which involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.
