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Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第5期19卷 4526-4546页

作者： Ahmad, Feezan Tang, Xiao-Wei Qiu, Jiang-Nan Wroblewski, Piotr Ahmad, Mahmood Jamil, Irfan Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116024 Peoples R China Univ Technol & Econ H Chodkowska Warsaw Fac Engn Jutrzenki 135 PL-02231 Warsaw Poland Mil Univ Technol Fac Mechatron Armament & Aerosp Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland Int Islamic Univ Malaysia Dept Civil Engn Fac Engn Jalan Gombak Selangor 50728 Malaysia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu 28100 Pakistan Univ Engn & Technol Peshawar Dept Civil Engn Peshawar 25120 Pakistan

Predicting slope stability is critical for identifying terrain that is prone to landslides and mitigating the damage caused by landslides. The relationships between factors that determine slope instability are complicated and multi-factorial, so it is sometimes difficult to mathematically characterize slope stability. In this paper, new Tree Augmented Naive-Bayes (TAN) model was developed to predict slope stability subjected to circular failures based on six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, unit weight, and slope angle. A total 87 slope stability case records obtained from published literature was used to train and test the proposed TAN model. According to the results of the performance indices-accuracy, precision, recall, F-score and Matthews correlation coefficient, the established TAN model was proven to be better at predicting slope stability with acceptable accuracy than other formerly developed empirical models in the literature. Furthermore, the slope height was revealed as the most sensitive factor in a sensitivity analysis.

关键词： machine learning algorithm Tree Augmented Naive-Bayes sensitivity analysis slope stability prediction

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Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

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REMOTE SENSING 2022年第9期14卷 2271页

作者： Luo, Lili Chang, Qinrui Gao, Yifan Jiang, Danyao Li, Fenling Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR similar to 1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra;commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R-c(2) = 0.91;R-v(2) = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R-c(2) = 0.93;R-v(2) = 0.76);compared with the FD-RF model, R-c(2) increased only slightly, but R-v(2) increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant

关键词： hyperspectral tree peony anthocyanin (Anth) unmanned aerial vehicle (UAV) spectrum transform machine learning algorithm remote sensing (RS) Shaanxi Province China ground-based spectra

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SIGNAL IMAGE AND VIDEO PROCESSING 2022年第7期16卷 1831-1839页

作者： Roy, Ganesh Bhoi, Akash Kumar Das, Subir Bhaumik, Subhasis Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Howrah W Bengal India Sikkim Manipal Univ SMU Sikkim Manipal Inst Technol SMIT Dept Comp Sci & Engn Gangtok Sikkim India Indian Inst Engn Sci & Technol Sch Mechatron & Robot Sibpur Howrah India Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Sibpur Howrah India Cent Inst Technol Dept Instrumentat Engn Kokrajhar Assam India

Brain-computer interface (BCI) and assistive robotics are the emerging topics for research in the Biomedical Engineering field. Several remarkable findings have already been observed in both the areas. But still, there are some research efforts required for improving the previous results. A robust real-time guideline for controlling an assistive robotics device through motor imagery electroencephalography (EEG) signal is still under investigation. The most challenging issue in BCI is to develop the appropriate feature extraction technique for controlling the assistive device with the EEG signals. Although several feature extraction algorithms are available, cross-correlation is famous for its noise removal capability. On the other hand, entropy-based methods are most suitable for non-stationary signal characterization. A new feature extraction method is investigated using the power of cross-correlation and spectral entropy in the present work. For classification of this feature set, machine learning algorithms viz. Linear discriminant analysis, quadratic discriminant analysis, and logistic regression are used and obtained average accuracy results of 98.67%, 98.41%, and 98.48%, respectively. The other objective of the present work is to control a lower limb exoskeleton (LLE) device with this classified signal. The LLE device has been designed using a computer-aided design model and assembled in a MATLAB Simscape simulation environment. The satisfactory motion of the LLE device has been observed after triggered through the classified signal.

关键词： EEG signal Brain-computer interface (BCI) Feature extraction machine learning algorithm Lower limb exoskeleton (LLE)

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Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and machine learning

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SENSORS 2023年第7期23卷 3416页

作者： Sun, Yi Sheng, Dongfa Liu, Dewen Southwest Forestry Univ Inst Civil Engn Kunming 650224 Peoples R China

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor netw

关键词： intelligent sensor Internet of Things application machine learning algorithm LE-LPCCA model engineering test optimization

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Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal

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SOFT COMPUTING 2021年第24期25卷 15445-15462页

作者： Satapathy, Santosh Kumar Loganathan, D. Pondicherry Engn Coll Puducherry Pondicherry India

Sleep is important part for human health and quality of life in the daily routine basis. However, numerous individuals face sleep problems due to rapid changes occurred in both social and professional lifestyles. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. machine learning methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main objective of this study is to propose a high-effective and high-accuracy based multiple sleep staging classification model based on single-channel electroencephalogram (EEG) signals using machine learning (ML) model. The proposed automated sleep staging system followed four basic stages: signal preprocessing, feature extraction and screening, classification algorithms, and performance evaluation. In this research work, a novel method is applied for signal preprocessing, feature screening and classification models. In signal preprocessing we obtain the wiener filter techniques for removing the different types of artifacts from input sleep recordings. In feature extraction, we obtain a total of 28 features based on both time and frequency domain features and non-linear features. The relevant features are screened through ReliefF weight feature selection algorithm, and eliminating the redundant features using Pearson correlation coefficients. The important contribution of this research work is establishes two layers an ensembling learning stacking model for classifying the multiple sleep stages. Three different subgroups of ISRUC-Sleep (SG-I/SG-II/SG-III) subjects sleep recordings having different health condition obtained for our proposed experimental work. Comparing with the recent contributions on sleep staging performances using single-channel EEG signals, it has found that our proposed ensemble learning stacking model was reported excellent in sleep staging classifica

关键词： Sleep stage EEG signal Feature screening machine learning algorithm Stacking model

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A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector machine Classification

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期123卷 2649-2664页

作者： Biswas, Priyajit Samanta, Tuhina Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 W Bengal India

Sensor nodes are tiny low-cost devices prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then SVM was used to classify faulty readings from normal readings. After classification, faulty readings are discarded. Here each sensor nodes periodically collects environmental features and sends them to their associated cluster heads. Each cluster head analyze collected data using the classification algorithm to detect whether any fault is present or not. Network simulator NS-2.35 and Matlab are used for evaluation of our proposed method. The fault detection algorithm was evaluated using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F-1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC). Performance evaluation shows, the proposed method performs well for high fault percentages.

关键词： Wireless sensor network machine learning algorithm Correlation coefficient SVM Measurement fault

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Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions

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ACS APPLIED MATERIALS & INTERFACES 2022年第12期14卷 14455-14465页

作者： Wang, Xiao-Lin Chen, Yantao Chu, Yingli Liu, Wen-Jun Zhang, David Wei Ding, Shi-Jin Wu, Xiaohan Fudan Univ Sch Microelectron State Key Lab ASIC & Syst Shanghai 200433 Peoples R China Zhangjiang Fudan Int Innovat Ctr Shanghai 201203 Peoples R China Natl Integrated Circuit Innovat Ctr Shanghai 201203 Peoples R China

Spectrum reconstruction with filter-free microspectrometers has attracted much attention owing to their promising potential in in situ analysis systems, on-chip spectroscopy characterizations, hyperspectral imaging, etc. Further efforts in this field can be devoted to improving the performance of microspectrometers by employing high-performance photosensitive materials and optimizing the reconstruction algorithms. In this work, we demonstrate spectrum reconstruction with a set of photodetectors based on graded-band-gap perovskite quantum dot (PQD) heterojunctions using both calculation and machine learning algorithms. The photodetectors exhibit good photosensitivities with nonlinear current-voltage curves, and the devices with different PQD band gaps show various spectral responsivities with different cutoff wavelength edges covering the entire visible range. Reconstruction performances of monochromatic spectra with the set of PQD photodetectors using two different algorithms are compared, and the machine learning method achieves relatively better accuracy. Moreover, the nonlinear current-voltage variation of the photodetectors can provide increased data diversity without redundancy, thus further improving the accuracy of the reconstructed spectra for the machine learning algorithm. A spectral resolution of 10 nm and reconstruction of multipeak spectra are also demonstrated with the filter-free photodetectors. Therefore, this study provides PQD photodetectors with the corresponding optimized algorithms for emerging flexible microspectrometer systems.

关键词： spectrum reconstruction machine learning algorithm photodetectors perovskite quantum dots graded band gap

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Rapid discrimination of glycogen particles originated from different eukaryotic organisms

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2022年第PartA期222卷 1027-1036页

作者： Tang, Jia-Wei Qiao, Rui Xiong, Xue-Song Tang, Bing-Xin He, You-Wei Yang, Ying-Ying Ju, Pei Wen, Peng-Bo Zhang, Xiao Wang, Liang Xuzhou Med Univ Sch Med Informat & Engn Dept Intelligent Med Engn Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Dept Clin Pharm Xuzhou Jiangsu Peoples R China Fifth Peoples Hosp Huaian Lab Med Huaian Jiangsu Peoples R China Xuzhou Med Univ Med Technol Sch Dept Lab Med Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Life Sci Xuzhou Jiangsu Peoples R China Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Lab Med Guangzhou Guangdong Peoples R China

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

关键词： Glycogen SERS machine learning algorithm

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INVESTIGATION OF THE EFFECT OF DEPOSIT LAYER ON HEAT TRANSFER IN THE TRIGA MARK-II NUCLEAR RESEARCH REACTOR COOLING SYSTEM

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THERMAL SCIENCE 2022年第5期26卷 3987-4001页

作者： Akay, Orhan Erdal Das, Mehmet Kahramanmaras Sutcuimam Univ Fac Engn & Architecture Mech Engn Dept Kahramanmaras Turkey Firat Univ Engn Fac Mechatron Engn Dept Elazig Turkey

In this presented study, the cooling problem of the I.T.U. Triga Mark-II reactor has been handled and analyzed, and solutions were proposed. First of all, a thermal model of the reactor, heat exchanger, and cooling tower trio was established in the reactor. With this model, which was obtained with the help of experimental data, the parameters affecting the change of reactor water temperature over time were determined, and significant findings were obtained by investigating the pos-sibilities of increasing the cooling power of the existing system. Then, using these mathematical equations, the effects of parameters that can affect the power of the reactor cooling system are investigated. The parameters affecting the cooling pow-er are the cooling water flow rates in the second cooling circuits and the depos-ited layer that may exist as a result of numerical calculations. Different models have been created with machine learning algorithms (page regression, decision tree) to estimate the effect of the deposit layer. The mathematical and predictive models obtained with the experimental data for the heat transfer coefficient of the deposit layer, hbd, were compared. The pace regression algorithm modeled the hbd values with the least error rate (RMSE: 1.66) among the models. It has been cal-culated that the average tank water temperature will decrease by approximately 3.5 degrees C if the deposits layer is cleared.

关键词： Triga Mark-II heat exchanger deposit layer equation derivation heat transfer coefficient machine learning algorithm

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Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers

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Nano-Micro Letters 2025年第1期17卷 361-377页

作者： Feng He Sitong Chen Ruili Zhou Hanyu Diao Yangyang Han Xiaodong Wu School of Mechanical Engineering Sichuan UniversityChengdu 610065People’s Republic of China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065People’s Republic of China State Key Laboratory of Polymer Materials Engineering Sichuan UniversityChengdu 610065People’s Republic of China

Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced *** to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static ***,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic ***,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential *** controllable and reversible polarization process of the conjugated polymers is fully in situ ***,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference *** with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior *** demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning *** tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.

关键词： Passive tactile sensors Reversible polarization of conjugated polymers Tactile perception machine learning algorithm Object recognition

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