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Load level forwarding work element analysis based on automatic follow-up data

SILVA FENNICA 2016年第3期50卷

作者： Manner, Jussi Palmroth, Lauri Nordfjell, Tomas Lindroos, Ola Forestry Res Inst Sweden Skogforsk Uppsala Sci Pk SE-75183 Uppsala Sweden John Deere Forestry POB 472 FI-33101 Tampere Finland Swedish Univ Agr Sci Dept Forest Biomat & Technol SE-90183 Umea Sweden

Recent developments in on-board technology have enabled automatic collection of follow-up data on forwarder work. The objective of this study was to exploit this possibility to obtain highly representative information on time consumption of specific work elements (including overlapping crane work and driving), with one load as unit of observation, for large forwarders in final felling operations. The data used were collected by the John Deere TimberLink system as nine operators forwarded 8868 loads, in total, at sites in mid-Sweden. Load-sizes were not available. For the average and median extraction distances (219 and 174 m, respectively), Loading, Unloading, Driving empty, Driving loaded and Other time effective work (PM) accounted for ca. 45, 19, 8.5, 7.5 and 14% of total forwarding time consumption, respectively. The average and median total time consumptions were 45.8 and 42.1 minutes/load, respectively. The developed models explained large proportions of the variation of time consumption for the work elements Driving empty and Driving loaded, but minor proportions for the work elements Loading and Unloading. Based on the means, the crane was used during 74.8% of Loading PM time, the driving speed was nonzero during 31.9% of the Loading PM time, and Simultaneous crane work and driving occurred during 6.7% of the Loading PM time. Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.

关键词： haulage cut-to-length logging harvesting data gathering automatic recording classification algorithm Hidden Markov Models

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Data-driven prediction-control system modeling for magnetorheological damping force

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2023年第2期34卷 155-167页

作者： Lv, Hongzhan Sun, Qi Ma, Weiguang Donghua Univ Coll Mech Engn 2999 North Renmin RdSongjiang Campus Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Text Equipments Shanghai Peoples R China

As a new type of intelligent damper, the magnetorheological damper has been widely used in robot, automobile NVH, and intelligent structure. However, for the intelligent response control from the structural excitation, it is the challenge to realize the intelligent control of the magnetorheological damping system. In this paper, the prediction-control mechanism of the magnetorheological damping system is modeled by a data-driven method, such as neural network and classification algorithm. The NARX (Nonlinear autoregressive with external input) neural network is used to predict the desired damping force required for the structural system in the forward direction, and the decision tree classification algorithm is used to reversely-control the desired current of the magnetorheological damping system in instant response to the structural system's damping force requirement. The analysis results show that the prediction-control method is feasible to realize the intelligent control of the damper based on the state data of the damped system, which provides a new idea for the intelligent control of the magnetorheological damper system.

关键词： Data-driven magnetorheological damping force prediction-control neural network classification algorithm

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Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture classification

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SENSORS 2024年第23期24卷 7705页

作者： Liu, Zhuofu Shu, Zihao Cascioli, Vincenzo Mccarthy, Peter W. Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Harbin 150080 Peoples R China Murdoch Univ Chiropract Clin Murdoch 6150 Australia Univ South Wales Fac Life Sci & Educ Pontypridd CF37 1DL Wales Durban Univ Technol Fac Hlth Sci ZA-1334 Durban South Africa

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training;0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.

关键词： sitting posture force-sensitive resistor triaxial accelerometers classification algorithm sensor verification accuracy computational efficiency

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Rapid phase stability calculations in fluid flow simulation using simple discriminating functions

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COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 112-127页

作者： Gaganis, Vassilis Tech Univ Crete Mineral Resources Engn Dept Khania 73100 Greece

This paper presents a new phase stability method that is applicable when repeated phase behavior calculations are needed as it is the case with multiphase fluid flow compositional simulation in upstream petroleum engineering. Two discriminating functions act as classifiers in such a way that a positive value of one of the two functions determines the stability state of the mixture. The two functions are generated off line, prior to the simulation, and their expressions are very simple so that they can be evaluated rapidly in a non-iterative way for every discretization block and at each timestep during the simulation. The CPU time required for phase stability calculations is dramatically reduced while still obtaining correct classification results corresponding to the global minimum of the system Gibbs energy function. The method can be applied to any chemical engineering problem where the class of several objects needs to be determined repeatedly and quickly. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multiphase fluid flow simulation Phase stability analysis Upstream petroleum engineering classification algorithm

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An Improved Count-Based Classifier for Categorical Data

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IEEE ACCESS 2024年 12卷 125427-125445页

作者： Singh, Sanskriti Sanjay Kumar Chauhan, Alok Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

The classification of categorical data is a fundamental task in machine learning, with numerous algorithms and techniques available. However, existing approaches often face challenges related to interpretability, scalability, and handling sparse or imbalanced datasets. This study presents an optimized version of the Count-Based Classifier, a novel approach that leverages the simplicity of counting occurrences to perform classification on categorical data. The optimized algorithm addresses the limitations of the original Count-Based Classifier, improving its computational efficiency, robustness, and overall performance. Through a comprehensive evaluation across 21 diverse categorical datasets, the Optimized Count-Based Classifier demonstrates competitive performance, consistently matching or surpassing established classifiers such as Decision Trees, Support Vector Machines, etc. The classifier's inherent interpretability, stemming from its reliance on counting operations, is a valuable asset, particularly in domains where transparency and explainability are crucial. Furthermore, the study explores the classifier's characteristics, including its tendency for overfitting, result consistency, and robustness against label errors. Experimental analyses reveal a low propensity for overfitting, high result consistency, and remarkable resilience to mislabeled data, further solidifying the classifier's practical applicability. The Optimized Count-Based Classifier has been implemented in Python and deployed as a user-friendly package, fostering accessibility and adoption within the machine learning community. By addressing the limitations of traditional approaches and offering a simple yet effective solution, this work contributes to the advancement of count-based classification techniques and their application in real-world scenarios.

关键词： classification algorithms Training Boosting Support vector machines Prediction algorithms Noise measurement Scalability algorithm design and theory Machine learning Supervised learning algorithm design classification algorithm count-based classifier machine learning supervised learning

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algorithm using supervised subspace learning and non-local representation for pose variation recognition

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IET COMPUTER VISION 2020年第7期14卷 528-537页

作者： Liao, Mengmeng Wang, Changzhi Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non-local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra-class differences. The second term is the block-diagonal regularisation term, which promotes the samples to be represented by intra-class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra-class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non-local constraint-based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state-of-the-art algorithms, such as DARG and RRNN.

关键词： sample mapping supervised subspace learning algorithm learned subspace pose variation recognition block-diagonal regularisation term image classification learning (artificial intelligence) feature extraction nonlocal constraint-based extended sparse representation classifier intra-class differences image representation classification algorithm nonlocal representation intra-class samples pose estimation SSLA SSLNR face recognition noise robust term

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Automated identification of sugar beet diseases using smartphones

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PLANT PATHOLOGY 2018年第2期67卷 399-410页

作者： Hallau, L. Neumann, M. Klatt, B. Kleinhenz, B. Klein, T. Kuhn, C. Roehrig, M. Bauckhage, C. Kersting, K. Mahlein, A-K. Steiner, U. Oerke, E-C. Univ Bonn Inst Crop Sci & Resource Conservat INRES Plant Dis & Plant Protect Bonn Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol Bonn Germany Washington Univ Dept Comp Sci & Engn St Louis MO USA Cent Inst Decis Support Syst Crop Protect ZEPP Bad Kreuznach Germany Informat Syst Integrated Plant Prod ISIP Bad Kreuznach Germany Tech Univ Dortmund Comp Sci Dept Dortmund Germany

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB-image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary-class and multi-class classification approaches, i.e. the separation between diseased and non-diseased, and the differentiation among leaf diseases and non-infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision-making in integrated disease control.

关键词： classification algorithm disease identification erosion band signature RGB images sugar beet

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Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins

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JOURNAL OF MOLECULAR RECOGNITION 2006年第3期19卷 209-214页

作者： Söllner, J Intercell AG A-1030 Vienna Austria

Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： machine learning epitope classification algorithm ROC ensemble neural network

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A New algorithm to Classify the Homogeneity of ERS-2 Wave Mode SAR Imagette

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2014年第1期42卷 13-21页

作者： Song, Guiting Panda, Jagabandhu Zhang, Yanhui Chen, Haoliang Krishna, K. Muni Zhejiang Ocean Univ Dept Marine Sci Zhoushan 316000 Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore 639798 Singapore Shandong Agr Univ Coll Resources & Environm Tai An Shandong Peoples R China Shandong Univ Key Lab Agr Environm Jinan 250100 Peoples R China Singapore MIT Alliance Res & Technol Singapore Singapore Andhra Univ Dept Meteorol & Oceanog Visakhapatnam Andhra Pradesh India

A new classification parameter is developed using 1535 ERS-2 wave mode synthetic aperture radar (SAR) test imagettes to better differentiate homogeneous and inhomogeneous imagettes. The comparison between the new parameter (Min) and the previous one (Inhomo) (Schulz-Stellenfleth and Lehner, 2004) was done under varied threshold values of Inhomo. It is concluded that the performance of 'Min' is much better than 'Inhomo' when applying to the 1535 test imagettes. Furthermore, both Min and Inhomo are applied to nearly 1 million imagettes collected for the period from 1 September 1998 to 30 November 2000. The comparisons of the global inhomogeneous distribution between 'Min' and 'Inhomo' reveal that both the areas and percentage of inhomogeneity calculated by 'Min' are larger than that calculated by 'Inhomo'. By analyzing the low wind speed distribution of HOAPS data, we found that low wind speed over the ocean is one of the key reasons for the inhomogeneity of SAR imagettes.

关键词： Synthetic aperture radar imagette Homogeneity classification algorithm

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On the construction of imputation classes in surveys

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INTERNATIONAL STATISTICAL REVIEW 2007年第1期75卷 25-43页

作者： Haziza, David Beaumont, Jean-Francois Univ Montreal Dept Math & Stat Montreal PQ H3C 3J7 Canada STAT Canada Business Survey Methods Div Ottawa ON Canada

This paper explores the problem of the construction of imputation classes using the score method, sometimes called predictive mean stratification or response propensity stratification, depending on the context. This method was studied in Thomsen (1973), Little (1986) and Eltinge & Yansaneh (1997). We use a different framework to evaluate the properties of the resulting imputed estimator of a population mean. In our framework, we condition on the realized sample. This enables us to considerably simplify our theoretical developments in the frequent situation where the boundaries and the number of classes are sample-dependent. We find that the key factor for reducing the non-response bias is to form classes homogeneous with respect to the response probabilities and/or the conditional expectation of the variable of interest. In the latter case, the non-response/imputation variance is also reduced. Finally, we performed a simulation study to fully evaluate various versions of the score method and to compare them with a cross-classification method, which is frequently used in practice. The results showed the superiority of the score method in general.

关键词： classification algorithm cross-classification method imputation model non-response and imputation variance non-response model non-response bias score method

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