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Exploring the potential of artificial intelligence techniques in prediction of the removal efficiency of vortex tube silt ejector

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International Journal of Sediment Research 2023年第4期38卷 615-627页

作者： Sanjeev Kumar Chandra Shekhar Prasad Ojha Nand Kumar Tiwari Subodh Ranjan Department of Civil Engineering Indian Institute of Technology Department of Civil Engineering National Institute of Technology

A vortex tube silt ejector is a curative hydraulic structure used to remove sediment deposits from canals and is recognized as one of the most efficient substitutes for physically removing canal sediment. The spatially varied flow in the channel and the rotational flow behavior in the tube make the silt removal process complex. It is even harder to accurately predict the silt removal efficiency by traditional models accurately. However, artificial intelligence(AI) and machine learning approaches have emerged as robust substitutes for studying complex processes. Therefore, this research makes use of AI approaches; support vector machine(SVM), random forest(RF), Random tree(RT), and multivariate adaptive regression spline(MARS) to compute the vortex tube silt ejection efficiency using the laboratory data sets. The outcomes of the artificial intelligence(AI)-based techniques also were compared with traditional models. It was found that the RT model(root mean square error, RMSE = 2.165, Nash Sutcliffe efficiency, NSE = 0.98) outperforms the other applied approaches which had relatively more significant result errors. The sensitivity analysis of the process depicts the extraction ratio as the key parameter in the computation of vortex tube silt ejector removal efficiency. The findings of the AI-based approaches discussed in the current study might be helpful for hydraulic engineers as well as researchers in the assessment of the removal efficiency of vortex tube silt ejectors.

关键词： Vortex tube silt ejector Support vector machine random forest Random tree Multivariate adaptive regression spline

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Intelligent Sound-Based Early Fault Detection System for Vehicles

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Computer Systems Science & Engineering 2023年第9期46卷 3175-3190页

作者： Fawad Nasim Sohail Masood Arfan Jaffar Usman Ahmad Muhammad Rashid Superior University LahorePakistan Intelligent Data Visual Computing Research(IDVCR) LahorePakistan National University of Technology IslamabadPakistan

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

关键词： Sound classification signal processing random forest Random tree time-frequency domain J48

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Improved Prediction of Slope Stability under Static and Dynamic Conditions Using tree-BasedModels

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 455-487页

作者： Feezan Ahmad Xiaowei Tang Jilei Hu Mahmood Ahmad Behrouz Gordan State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian116024China Key Laboratory of Geological Hazards on Three Gorges Reservoir Area Ministry of EducationChina Three Gorges UniversityYichang443002China Department of Civil Engineering Faculty of EngineeringInternational Islamic University MalaysiaJalan GombakSelangor50728Malaysia Department of Civil Engineering University of Engineering and Technology Peshawar(Bannu Campus)Bannu28100Pakistan Faculty of Civil Engineering Islamic Azad UniversityVaramin Pishva BranchTehran15914Iran

Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and Random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.

关键词： Slope stability seismic excitation static condition Random tree reduced error pruning tree

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A SRT-Based Path Planning Algorithm in Unknown Complex Environment

A SRT-Based Path Planning Algorithm in Unknown Complex Envir...

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第26届中国控制与决策会议

作者： Zou Yiping Guo Jian Zhang Ruilei Chen Qingwei School of Automation Nanjing University of Science and Technology

ISBN: (纸本)9781479937066

Aiming to the robot path planning problem in the unknown complex environment, this paper presents an improved Sensor-based Random tree（SRT） path planning algorithm. First, RRT and SRT path planning algorithms are briefly introduced. Then, an original algorithm which uses the real-time local information from robot sensors and the target tendency method to make the online planning is presented. The algorithm overcomes SRT’s redundant branches and low efficiency by combining the nonholonomic constraint conditions and the candidate viewpoint optimization, on which improves SRT’s planning efficiency, and guarantees its stability as well. Finally, simulated experiments are done on Player/Stage and MRDS platforms, and the results verified the reliability and effectiveness of the proposed algorithm.

关键词： Path Planning Optimization Strategy Random tree Nonholonomic System

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Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

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Protection and Control of Modern Power Systems 2018年第1期3卷 312-323页

作者： S.Asha Kiranmai A.Jaya Laxmi Department of Electrical Engineering University College of EngineeringOsmania UniversityHyderabadTelanganaIndia Department of Electrical and Electronics Engineering Jawaharlal Nehru Technological University Hyderabad College of EngineeringHyderabadTelanganaIndia

There is growing interest in power quality issues due to wider developments in power delivery engineering.In order to maintain good power quality,it is necessary to detect and monitor power quality problems.The power quality monitoring requires storing large amount of data for analysis.This rapid increase in the size of databases has demanded new technique such as data mining to assist in the analysis and understanding of the data.This paper presents the classification of power quality problems such as voltage sag,swell,interruption and unbalance using data mining algorithms:J48,Random tree and random Forest decision trees.These algorithms are implemented on two sets of voltage data using WEKA software.The numeric attributes in first data set include 3-phase RMS voltages at the point of common coupling.In second data set,three more numeric attributes such as minimum,maximum and average voltages,are added along with 3-phase RMS voltages.The performance of the algorithms is evaluated in both the cases to determine the best classification algorithm,and the effect of addition of the three attributes in the second case is studied,which depicts the advantages in terms of classification accuracy and training time of the decision trees.

关键词： Power quality problems Classification Data mining Decision trees J48 Random tree random forest WEKA

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Multiscale stochastic hierarchical image segmentation by spectral clustering

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Science in China(Series F) 2007年第2期50卷 198-211页

作者： LI XiaoBin TIAN Zheng Department of Applied Mathematics Northwestern Polytechnical University Xi'an 710072 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing 100080 China

This paper proposes a sampling based hierarchical approach for solving the computational demands of the spectral clustering methods when applied to the problem of image segmentation. The authors first define the distance between a pixel and a cluster, and then derive a new theorem to estimate the number of samples needed for clustering. Finally, by introducing a scale parameter into the similarity function, a novel spectral clustering based image segmentation method has been developed. An important characteristic of the approach is that in the course of image segmentation one needs not only to tune the scale parameter to merge the small size clusters or split the large size clusters but also take samples from the data set at the different scales. The multiscale and stochastic nature makes it feasible to apply the method to very large grouping problem. In addition, it also makes the segmentation compute in time that is linear in the size of the image. The experimental results on various synthetic and real world images show the effectiveness of the approach.

关键词： spectral clustering graph multiscale Random tree image segmentation

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