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25th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Czuni, Laszlo Kurtosi, Andras Ben Alaya, Karim Univ Pannonia Dept Informat Syst & Elect Engn Egyet U 10 Veszprem Hungary

ISBN: (纸本)9781538669792

While image segmentation is a fundamental and general task of image processing and computer vision, the segmentation of trunks in wood can be considered as a specific, ill posed, and hard problem. However, besides the lots of possible applications of it, the problems to be faced are general and can be found in other areas of open-air image analysis. In our paper we discuss these problems and propose a color clustering based approach as a feature generation step for the segmentation of trunks. The results of classification can be used as features for further segmentation techniques such as MRF. The possibility to apply for angle count based volume estimation is also discussed. The performance is shown through several tests with different types of wood.

关键词： Forest inventory Image segmentation Clustering logistic model tree k-means Angle count sampling

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Bimodal Feature-based Fusion for Real-Time Emotion Recognition in a Mobile Context

Bimodal Feature-based Fusion for Real-Time Emotion Recogniti...

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6th AAAC Affective Computing and Intelligent Interaction International Conference (ACII)

作者： Gievska, Sonja Koroveshovski, Kiril Tagasovska, Natasha George Washington Univ Dept Comp Sci Washington DC 20052 USA Univ Ss Cyril & Methodius Fac Comp Sci & Engn Skopje Macedonia Univ Lausanne Dept Informat Syst CH-1015 Lausanne Switzerland

ISBN: (纸本)9781479999538

This research explores the viability of a bimodal fusion of linguistic and acoustic cues in speech to help in real-time emotion recognition in a mobile application that steers the interaction dialogue in tune with user's emotions. For capturing affect at the language level, we have utilized both, machine learning and valence assessment of the words carrying emotional connotations. The indicative values of acoustic cues in speech are of special concern in this research and an optimized feature set is proposed. We highlight the results of both independent evaluations of the underlying linguistic and acoustic processing components. We present a study and ensuing discussion on the performance metrics of a logistic model tree that has outperformed the other classifiers considered for the fusion process. The results reinforce the notion that capturing the sound interplay between the diverse set of features is crucial for confronting the subtleties of affect in speech that so often elude the text- or acoustic-only approaches to emotion recognition.

关键词： emotion recognition linguistic data acoustic data bimodal fusion logistic model tree

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Fault Diagnosis of Ball Bearing Using Walsh–Hadamard Transform and Random tree Classifier 1st

Fault Diagnosis of Ball Bearing Using Walsh–Hadamard Transf...

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1st International and 4th National Conference on Reliability and Safety Engineering, INCRS 2018

作者： Dave, Vipul Vakharia, V. Pandit Deendayal Petroleum University Gandhinagar320007 India

ISBN: (纸本)9789811537455

Bearing failure may result in the breakdown of machinery or possibly damage the human being operating the machinery. It is therefore necessary to diagnose bearing faults at an early stage. This paper presents the application of Walsh–Hadamard transform and tree-based classifier for detecting bearing faults. An experimental was conducted from the Case Western Reserve University bearing data center. At the initial stage, coefficients from inner race fault, outer race fault, ball fault, and healthy bearing were calculated from the measured signal. Twenty-five statistical features are calculated from the acquire signals with different speeds and fault sizes. Feature vector form is used for classification purpose using various classifiers, i.e., random tree and logistic model tree. Training and testing of classifier are done, and the result revealed that 100% fault identification accuracy is obtained with random tree for both training and testing. Similarly, 98.43% training and 100% fault identification accuracy are obtained with logistic model tree. The result shows that the methodology adopted is effective to diagnose various bearing faults. © Springer Nature Singapore Pte Ltd. 2020.

关键词： logistic model tree Random tree Walsh–Hadamard transform

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Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units

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SENSORS 2021年第14期21卷 4625-4625页

作者： Reilly, Brian Morgan, Oliver Czanner, Gabriela Robinson, Mark A. Liverpool John Moores Univ Sch Comp Sci & Math Liverpool L3 3AF Merseyside England Liverpool John Moores Univ Sch Sport & Exercise Sci Liverpool L3 3AF Merseyside England Celt Football Club Celtic Pk Glasgow G40 3RE Lanark Scotland

Changes of direction (COD) are an important aspect of soccer match play. Understanding the physiological and biomechanical demands on players in games allows sports scientists to effectively train and rehabilitate soccer players. COD are conventionally recorded using manually annotated time-motion video analysis which is highly time consuming, so more time-efficient approaches are required. The aim was to develop an automated classification model based on multi-sensor player tracking device data to detect COD > 45 degrees. Video analysis data and individual multi-sensor player tracking data (GPS, accelerometer, gyroscopic) for 23 academy-level soccer players were used. A novel 'GPS-COD Angle' variable was developed and used in model training;along with 24 GPS-derived, gyroscope and accelerometer variables. Video annotation was the ground truth indicator of occurrence of COD > 45 degrees. The random forest classifier using the full set of features demonstrated the highest accuracy (AUROC = 0.957, 95% CI = 0.956-0.958, Sensitivity = 0.941, Specificity = 0.772. To balance sensitivity and specificity, model parameters were optimised resulting in a value of 0.889 for both metrics. Similarly high levels of accuracy were observed for random forest models trained using a reduced set of features, accelerometer-derived variables only, and gyroscope-derived variables only. These results point to the potential effectiveness of the novel methodology implemented in automatically identifying COD in soccer players.

关键词： change of direction movements Global Positioning System accelerometer gyroscope machine learning random forest logistic model tree

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Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106476-106476页

作者： Marin, Diego Bedin Ferraz, Gabriel Araujo e Silva Santana, Lucas Santos Barbosa, Brenon Diennevan Souza Barata, Rafael Alexandre Pena Osco, Lucas Prado Ramos, Ana Paula Marques Guimaraes, Paulo Henrique Sales Univ Fed Lavras Dept Agr Engn BR-37200900 Lavras MG Brazil Univ Western Sao Paulo Fac Engn & Architecture & Urbanism Rodovia Raposo TavaresKm 572 BR-19067175 Presidente Prudente SP Brazil Univ Western Sao Paulo Postgrad Program Environm & Reg Dev Rodovia Raposo TavaresKm 572 BR-19067175 Presidente Prudente SP Brazil Univ Fed Lavras Dept Stat BR-37200000 Lavras MG Brazil

Coffee leaf rust (CLR) is one of the most devastating leaf diseases in coffee plantations. By knowing the symptoms, severity, and spatial distribution of CLR, farmers can improve disease management procedures and reduce losses associated with it. Recently, Unmanned Aerial Vehicles (UAVs)-based images, in conjunction with machine learning (ML) techniques, helped solve multiple agriculture-related problems. In this sense, vegetation indices processed with ML algorithms are a promising strategy. It is still a challenge to map severity levels of CLR using remote sensing data and an ML approach. Here we propose a framework to detect CLR severity with only vegetation indices extracted from UAV imagery. For that, we based our approach on decision tree models, as they demonstrated important results in related works. We evaluated a coffee field with different infestation classes of CLR: class 1 (from 2% to 5% rust);class 2 (from 5% to 10% rust);class 3 (from 10% to 20% rust), and;class 4 (from 20% to 40% rust). We acquired data with a Sequoia camera, producing images with a spatial resolution of 10.6 cm, in four spectral bands: green (530-570 nm), red (640-680 nm), red-edge (730-740 nm), and nearinfrared (770-810 nm). A total of 63 vegetation indices was extracted from the images, and the following learners were evaluated in a cross-validation method with 10 folders: logistic model tree (LMT);J48;Extratree;REPtree;Functional trees (FT);Random tree (RT), and;Random Forest (RF). The results indicated that the LMT method contributed the most to the accurate prediction of early and several infestation classes. For these classes, LMT returned F-measure values of 0.915 and 0.875, thus being a good indicator of early CLR (2 to 5% of rust) and later stages of CLR (20 to 40% of rust). We demonstrated a valid approach to model rust in coffee plants using only vegetation indices and ML algorithms, specifically for the disease's early and later stages. We concluded that the proposed fra

关键词： Multispectral imagery Precision agriculture Plant disease logistic model tree

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Optimization of Computational Intelligence models for Landslide Susceptibility Evaluation

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REMOTE SENSING 2020年第14期12卷 2180-2180页

作者： Zhao, Xia Chen, Wei Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Util Xian 710021 Peoples R China

This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209;30%: 89) as training and validation datasets. Then, based on the EBF method, theBelvalues of 16 conditioning factors related to landslide occurrence were calculated, and theseBelvalues were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning.

关键词： evidential belief function function tree logistic regression logistic model tree Nanchuan District

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STATISTICAL PATTERN RECOGNITION TECHNIQUE FOR IMPROVED REAL-TIME MYOELECTRIC SIGNAL CLASSIFICATION

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2013年第2期25卷

作者： Geethanjali, P. Ray, K. K. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India Techno Global Balurghat Dept Elect Engn Balurghat W Bengal India

The authors in this paper propose a statistical technique for pattern recognition of electromyogram (EMG) signals along with effective feature ensemble to achieve an improved classification performance with less processing time and memory space. In this study, EMG signals from 10 healthy subjects and two transradial amputees for six motions of hand and wrist is considered for identification of the intended motion. From four channels myoelectric signals, the extracted time domain features are grouped into three ensembles to identify the effectiveness of feature ensemble in classification. The three feature ensembles obtained from multichannel continuous EMG signals are applied to the new classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree (DT), logistic model tree (LMT) and feature subspace ensemble using k-nearest neighbor (kNN). Novel classifiers SLR, DT and LMT, select only the dominant features during training to develop the model for pattern recognition. This selection of features reduces the processing time as well as memory space of the controller for real-time application. The performance of SLR, DT, LMT and feature subspace ensemble using kNN classifiers are compared with other conventional classifiers, such as neural network (NN), simple kNN and linear discriminant analysis (LDA). The average classification accuracy with SLR is found to be better with feature ensemble-1 compared to the other classifiers. Also, the statistical Kruscal-Wallis test shows, the classification performance of SLR is not only better but also takes less time and memory space compared to other classifiers for classification. Also the performance of the classifier is tested in real-time with transradial amputees for actuation of drive for two intended motions with TMS320F28335eZdsp controller. The experimental results show that the SLR classifier improves the controller response in real-time.

关键词： Electromyogram (EMG) Feature extraction Time domain features Auto-regression (AR) coefficients Simple logistic regression Decision tree logistic model tree Digital signal processor (DSP) processor

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Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm – A Machine learning approach

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Materials Today: Proceedings 2021年 46卷 1174-1180页

作者： K. Balachandar R. Jegadeeshwaran School of Mechanical Engineering Vellore Institute of Technology Chennai Tamil Nadu 600127 India Department of Mechanical Engineering Prathyusha Engineering College Thiruvallur Tamil Nadu 602025 India

Friction stir welding (FSW) is a new kind of solid-state welding technique. Rigid and reliable joints in intricate shapes are possible with FSW. This type of welding process is frequently used in many commercial applications like automobile, shipbuilding, aerospace, and many more. The monitoring of the welding tool condition is essential for the inline process to identify and avoid the early defect of the workpiece and the breakdown due to the increase of the ideal condition of the machinery. Condition monitoring of the FSW tool is an advanced and novel predictive maintenance technique, in which the real-time vibration data are collected from the FSW machine under different operating conditions using an accelerometer sensor. The acquired vibration signals were analyzed using the machine learning approaches through feature extraction and feature classification. This paper aims the vibration analysis based on FSW tool condition monitoring using machine learning algorithms such as decision tree, logistic model tree (LMT), Hoeffeding, and Random forest. Among all classifiers, Random Forest gives better results.

关键词： Friction stir welding logistic model tree Statistical features Random forest Confusion matrix

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