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Integrating Classification and Regression Tree algorithm for Operational Optimization in AC-DC Hybrid Power System Planning: A Novel Approach

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ENERGIES 2025年第4期18卷 783-783页

作者： Yang, Yuyao Zhang, Boyuan Zhang, Jun Gong, Guoxian Pan, Feng Feng, Lei Zheng, Yi Wang, Peng Metrol Ctr Guangdong Power Grid Co Ltd Qingyuan 511545 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100190 Peoples R China

The increasing demand for electricity and the imperatives of climate change have made the optimization of power system planning critical for the energy transition and grid efficiency. This study presents an innovative planning method for inter-regional AC-DC hybrid power systems, leveraging the Classification and Regression Tree (cart) algorithm to optimize the operational characteristics of direct current (DC) channels. By designing a closed-loop iteration, precise operational constraints are considered by the cart algorithm, which immerged into the planning model to achieve safe and economic optimization. Based on the empirical analysis of the HRP-38 system, this study concludes that the cart algorithm offers a constructive approach to managing the operational complexities of modern power grids. By optimizing and refining DC operational characteristics based on actual system requirements, the algorithm contributes to improvements in safety, economic efficiency, and environmental sustainability within the confines of the HRP-38 node system. Consequently, the effectiveness of the cart optimization approach could be corroborated. Meanwhile, this study also acknowledges the limitations in generalizing these results to other power grid configurations and the need for further exploration in developing environmentally conscious planning methods.

关键词： AC-DC hybrid power systems machine learning cart algorithm power system optimization

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Designing neurofuzzy system based on Icart algorithm and its application for modeling jet fuel endpoint of hydrocracking process

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2003年第1期16卷 11-19页

作者： Jia, L Li, EG Yu, JS E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

In this paper, a neuro-fuzzy system based on improved cart algorithm (Icart) is presented, in which the Icart algorithm is used to design neuro-fuzzy system. It is worth noting that Icart algorithm partitions the input space into tree structure adaptively, which avoids the curse of dimensionality (number of rules goes up exponentially with number of input variables). Moreover, it adopts density function to construct the local model for every node in order to overcome the discontinuous boundaries existed in cart algorithm. In addition, a supervised scheme is used to adjust parameters to minimize the network output error and construct more accurate fuzzy model on the basis of the Icart algorithm. Finally, to illustrate the validity of the proposed method, a simulation research and a practical application are done. The results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling, possesses high learning efficiency, and is smoother than cart algorithm. It call be successfully applied to modeling jet fuel endpoint of hydrocracking processing. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： decision trees cart algorithm Icart algorithm neurofuzzy system hydrocracking processing

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A novel student achievement prediction model based on bagging-cart machine learning algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第18期36卷 e8151-e8151页

作者： Zhang, Hong Fujian Normal Univ Sch Law Fuzhou Fujian Peoples R China Fujian Normal Univ Qishan Campus18 Wulongjiangzhong Ave Fuzhou Fujian Peoples R China

The learning effect of students is crucial for assessing teaching quality, thus playing a significant role in teaching management. Predicting student achievement is a major challenge in understanding the learning effect of students. Currently, many studies have utilized machine learning methods such as the decision tree algorithms C4.5, ID3, cart, J48, random forest, and others. However, few studies have explored the use of the Bagging algorithm in this field. Therefore, this study proposes a classification prediction method for student achievement based on the Bagging-cart algorithm. Initially, the student achievement data is preprocessed, and the Apriori method is applied to mine the strongly associated dataset. The optimal hyper-parameters are determined through grid search to train and predict the Bagging-cart algorithm. Furthermore, the cart, J48, and Bagging-cart algorithms are trained, and their evaluation indicators are compared using a confusion matrix. The results indicate that the Bagging-cart model achieves an accuracy of 98.16%, a recall rate of 91.80%, a precision of 90.83%, and an F1 score of 94.87%. In comparison, the accuracy, precision, and F1 scores are higher than those obtained with cart and J48. Although the recall rate is slightly lower than that of cart by 0.26%, it is 0.52% higher than that of J48. Consequently, this method demonstrates strong predictive capabilities and introduces a new reference method for evaluating students' learning effect.

关键词： association rules bagging algorithm cart algorithm prediction student achievement analysis

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Vehicle classification with single multi-functional magnetic sensor and optimal MNS-based cart

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MEASUREMENT 2014年 55卷 142-152页

作者： Li, Haijian Dong, Honghui Jia, Limin Ren, Moyu Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

To take advantages of magnetic sensor technology in terms of cost, size, weight, power consumption and wireless communication, a wireless multi-functional magnetic sensor was designed and developed. Then, a novel method with single multi-functional magnetic sensor and optimal Minimum Number of Split-sample (MNS)-based Classification and Regression Tree (cart) algorithm was proposed in this paper to classify on-road vehicles. The sensor was deployed on the road to acquire real-time vehicle waveform data. The decision tree model based on cart algorithm was used to execute on-line vehicle classification in the sensor node. Eight speed-independent time-domain waveform features were extracted as the model inputs. This paper trained the decision tree model by using vehicle samples derived from the multi-functional magnetic sensor and pruned the optimal decision tree with a Minimum Error Pruning (MEP) rule to obtain an optimal pruning tree which is more robust to new samples. Some experiments were implemented by different sample sets and classification methods. The results showed that the proposed method achieved on-line vehicle classification in the sensor node. For the field sample sets with two vehicle classes, the average accuracy rates of test samples were 88.9% and 94.4% in the original samples and swapping samples respectively. Besides higher accuracy, the method also has a better sample robustness, which is easy to classify new samples. The comparison results of current methods also showed that the proposed method has some advantages in aspects of accuracy rate, sample robustness and execution time. (C) 2014 Published by Elsevier Ltd.

关键词： Vehicle classification Multi-functional magnetic sensor cart algorithm Decision tree

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An expert system for control chart pattern recognition

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期62卷 291-301页

作者： Bag, Monark Gauri, Susanta Kumar Chakraborty, Shankar Jadavpur Univ Dept Prod Engn Kolkata 700032 W Bengal India Indian Inst Informat Technol Allahabad 211012 Uttar Pradesh India Indian Stat Inst SQC & OR Unit Kolkata 700108 India

This paper focuses on the design and development of an expert system for on-line detection of various control chart patterns so as to enable the quality control practitioners to initiate prompt corrective actions for an out-of-control manufacturing process. Using this expert system developed in Visual BASIC 6, all the nine most commonly observed control chart patterns, e.g., normal, stratification, systematic, increasing trend, decreasing trend, upward shift, downward shift, cyclic, and mixture can be recognized well, employing an optimal set of seven shape features. Based on an observation window of 32 data points, it can plot the control chart, compute the control limits, identify the control chart pattern, calculate the process capability index, determine the maximum run length, and identify the starting point of the maximum run length. After pattern recognition, it can also inform the users about various root assignable causes associated with a particular pattern along with the necessary pre-emptive actions. It opens up wide opportunities for quality improvement and real-time applications in diverse manufacturing processes. This developed expert system is built for a vertical drilling process and its recognition performance is tested using simulated process data.

关键词： Control chart pattern Pattern recognition Shape feature cart algorithm Expert system

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Soft computing techniques in prediction gas sensor based 2D material

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ORGANIC ELECTRONICS 2018年 62卷 181-188页

作者： Akbari, Elnaz Nilashi, Mehrbakhsh Alizadeh, Azar Buntat, Zolkafle Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Calif Sch Engn Merced CA USA Univ Teknol Malaysia Fac Elect Engn Inst High Voltage & High Current Johor Baharu 81310 Malaysia

Two-dimensional materials may be the key to unlock the next technological advancement. There have been many other two-dimensional materials discovered after the discovery of graphene, all with their own unique and interesting properties. One type of two-dimensional materials that were discovered has attracted quite a bit of attention, Phosphorene. Phosphorene is a natural semiconductor with a variable band gap and similar structure to carbon-based graphene. The Field Effect Transistor (FET) structure is employed in this project, where phosphorene has been proposed as the channel material between drain and source. An analytical model is developed assuming that gate voltage variations are proportional to nitrogen dioxide (NO2) gas concentration changes. Furthermore, to obtain other models for the current-voltage(I-V) characteristic, Artificial Neural Network (ANN) and Classification and Regression Trees (cart) algorithms have been employed. Finally, the developed models were compared with the data extracted from experimental work by A. Abbas, which show satisfactory agreement and validity of the proposed models.

关键词： Phosphorene FET I-v Gas sensor ANN cart algorithm

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Fracture toughness analysis of nano steel fibre reinforced concrete based on improved locust algorithm

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2023年第1期67卷 66-81页

作者： Chen, Yanmin Zheng, Lina Miao, Pu Shangqiu Inst Technol Sch Civil Engn Shangqiu 476000 Peoples R China

A fracture toughness analysis method of nano steel fibre reinforced concrete based on improved locust algorithm was proposed in this paper. Firstly, P-CMOD curve is introduced to analyse the fracture mechanism of nano steel fibre reinforced concrete. Then, the minimum cart tree is trained by cart algorithm, and the objective function of fracture toughness is obtained by iterative training. Finally, the improved locust algorithm is introduced to calculate the fracture energy coefficient of nano steel fibre reinforced concrete, and the softening curve is modified by this algorithm to achieve the fracture toughness analysis of nano steel fibre reinforced concrete. The experimental results show that the concrete fracture toughness of this method is 0.56 Mpa center dot m(-1/2), and the critical crack opening displacement is 0.240 mm. The analytical values are consistent with the actual values. This method improves the analysis effect of fracture toughness and critical crack opening displacement of nano steel fibre reinforced concrete.

关键词： improved locust algorithm machine learning fracture toughness softening curve cart algorithm

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A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

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NEURAL COMPUTING & APPLICATIONS 2018年第11期29卷 991-1004页

作者： Alauthaman, Mohammad Aslam, Nauman Zhang, Li Alasem, Rafe Hossain, M. A. Northumbria Univ Fac Engn & Environm Dept Comp Sci & Digital Technol Newcastle Upon Tyne NE1 8ST Tyne & Wear England Imam Mohammad Ibn Saud Islamic Univ Dept Elect Engn Fac Engn Riyadh Saudi Arabia Anglia Ruskin Univ Informat Technol Inst Bishop Lane Chelmsford CM1 1SQ Essex England

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

关键词： P2P Bot Multilayer neural network cart algorithm TCP protocol C&C Resilient back-propagation

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A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods

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METEOROLOGICAL APPLICATIONS 2016年第4期23卷 587-593页

作者： Geng, Huantong Shi, Dawei Zhang, Wei Huang, Chao Nanjing Univ Informat Sci & Technol Coll Atmospher Sci Nanjing Jiangsu Peoples R China Meteorol Bur Lianyungang City Meteorol Observ Lianyungang Peoples R China

This study examines the landfalling tropical cyclones (TCs) over China using state-of-the-art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (cart)). Using the 1951-2012 TC best track dataset released by the Shanghai Typhoon Institute of the Chinese Meteorological Administration, the tracks of TCs landfalling over the Chinese coast were classified into three clusters through an FMM. Several climate indices were analysed using the cart algorithm for the three clusters. The prediction model built by cart for summer track frequency was based on a random sampling of the data for 46 years (about 75% of the total years) as the training set with a training accuracy of 100% (Cluster-1), 89.96% (Cluster-2) and 100% (Cluster-3). Data for the remaining 16 years (about 25%) were used for testing with a prediction accuracy of 87.5% (Cluster-1), 62.5% (Cluster-2) and 68.75% (Cluster-3). This study focuses on Cluster-1 of summer TCs landfalling over China for its high frequency, strong intensity, severe impacts and long lifespan. Furthermore, it suggests that the FMM algorithm is effective for track classification of TCs landing over China. In addition, the cart algorithm, which was used to build the prediction model of Cluster-1 for the classification of track frequency, showed high accuracy and its results can be explained and understood easily. It provides a novel framework for forecasting the frequency of TCs landfalling over China.

关键词： Cluster-1 TC track FMM algorithm cart algorithm

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Using combined clustering and tree algorithms for investigation and prediction of groundwater depth changes within irrigation network of Abyek plain, Iran

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第1期20卷 671-682页

作者： Mirhashemi, S. H. Jou, P. Haghighat Panahi, M. Univ Zabol Fac Water & Soil Dept Water Engn Zabol Iran Univ Zanjan Fac Agr Dept Water Engn Zanjan Iran

Using a clustering and tree method combination, this research looked at the prediction of changes in irrigation network groundwater depth in the Abyek plain. Groundwater depth variations in various plain regions were examined initially, utilizing the K-means technique for geographic grouping and aquifer depth changes. It was then applied to a tree algorithm using K-means findings. A tree method was then used to forecast changes in aquifer depth across all clusters. There were five clusters of groundwater alterations based on the K-means algorithm findings, and aquifer decline increased from cluster 1 to 5. Clusters 1 and 5 showed the greatest increases in aquifer depth and the greatest decreases. K-means and classification and regression tree findings show that in locations where the most aquifer decline was recorded, human causes were successful, while in regions where the highest groundwater depth rise was found, natural factors were effective. Factors of precipitation, agricultural water demand (million cubic meters), and water delivered to irrigation network (million cubic meters) in regions with high aquifer drawdown and factors of volume of precipitation, water delivered to irrigation network, and air humidity percentage in regions with increased groundwater depth had the greatest impact. For varied variations in the Abyek plain groundwater depth, rainfall volume was evident in most tree diagrams.

关键词： Spatial clustering cart algorithm K-means algorithm Agricultural well Volume of precipitation

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