Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

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.

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

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.

Spectral indices derived,non-parametric Decision Tree Classification approach to lithological mapping in the Lake Magadi area,Kenya

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.

Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

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.

Introducing a Novel Approach for Oil-Oil Correlation based on Asphaltene Structure: X-ray Diffraction

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.

Extraction of Planting Information of Winter Wheat in a Province Based on GF-1/WFV Images

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.