为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of a...为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。展开更多
Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper,...Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network(PNN) where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.展开更多
The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The exi...The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The existing instru-mental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires reg-ular manual conduct.Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition.In this perspective,the Machine Learning(ML)approach acts appropriate as it exhi-bits comparison of specific performance in the past with present,intended for predicting the same in near future.The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle.In order to examine the vibration response of a wheel hub,the in-house design&development of DAQ(Data Acquisition System)is described.Micro Electro-Mechanical Scheme(MEMS)built accelerometer is incorporated with open source hardware and software to collect and store the data.This framework is easy to develop,monitor and can be retrofitted in two wheeled vehicle.For various pressure conditions,the change in response of wheel hub vibration with respect to time is collected.The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters.The classification of different conditions of tyre pressure is carried out using ML classifiers.展开更多
With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working...With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.展开更多
At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The exist...At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.展开更多
文摘为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。
文摘Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network(PNN) where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
文摘The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The existing instru-mental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires reg-ular manual conduct.Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition.In this perspective,the Machine Learning(ML)approach acts appropriate as it exhi-bits comparison of specific performance in the past with present,intended for predicting the same in near future.The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle.In order to examine the vibration response of a wheel hub,the in-house design&development of DAQ(Data Acquisition System)is described.Micro Electro-Mechanical Scheme(MEMS)built accelerometer is incorporated with open source hardware and software to collect and store the data.This framework is easy to develop,monitor and can be retrofitted in two wheeled vehicle.For various pressure conditions,the change in response of wheel hub vibration with respect to time is collected.The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters.The classification of different conditions of tyre pressure is carried out using ML classifiers.
文摘With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(No.52025071)the Joint Funds of the National Natural Science Foundation of China(No.U1866205)。
文摘At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.
