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Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

International Journal of Coal Science & Technology 2023年第5期10卷 72-86页

作者： Sureka Thiruchittampalam Sarvesh Kumar Singh Bikram Pratap Banerjee Nancy F.Glenn Simit Raval School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Agriculture Victoria Grains Innovation Park110 Natimuk RoadHorshamVIC 3400Australia Department of Geosciences Boise State UniversityBoiseIDUSA

The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple *** temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over ***,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation *** this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote *** textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed *** raw images were converted to an orthomosaic using structure from motion aided ***,structural descriptors were computed per pixel to enhance feature modalities of the spoil ***,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human *** characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

关键词： Lithology Fabric structure Consistency/relative density Dimensionality reduction supervised learning algorithms

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Decent direction methods on the feasible region recognized by supervised learning metamodels to solve unstructured problems

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2018年第6期39卷 1245-1262页

作者： Abpeykar, Shadi Ghatee, Mehdi Amirkabir Univ Technol Dept Comp Sci 424 Hafez Ave Tehran 158754413 Iran

This paper treats with an unstructured optimization problem with three kinds of data: some feasible solutions, some infeasible solutions and an objective function. Then a decision support system (DSS) is developed to recognize the feasible region by using radial basis function (RBF) neural network, feed forward neural network, support vector machine (SVM) and decision tree algorithms. To optimize the problem, a penalty function is defined as a weighted combination of the objective function and the output of the feasible region recognition part. This function can be optimized using some gradient decent direction and quasi-Newton methods. The accuracy of feasible and infeasible solutions recognition of five types of optimization models are greater than 65% and in some cases 100%. In addition, the relative error of optimization part is 0.05%. Furthermore, toll pricing for Sadr Bridge is modeled by the aid of simulation results in AIMSUN where the accuracy of the proposed DSS is greater than 99%.

关键词： Nonlinear Programming supervised learning algorithms Decent Direction Methods Decision Support System Toll Pricing Metamodel

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Improved cross entropy loss for noisy labels in vision leaf disease classification

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IET IMAGE PROCESSING 2022年第6期16卷 1511-1519页

作者： Chen, Yipeng Xu, Ke Zhou, Peng Ban, Xiaojuan He, Di Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Xueyuan Rd 30 Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing Peoples R China

The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains where the annotation cost is high. Such problems are extremely serious in the domain of agricultural imaging in leaf disease classification. Due to the limitation of acquisition methods, noisy examples are often not only the normal mislabelled ones, but also the samples which contain more than one instance. To cope with the combination of the two noises, label smoothing is blended in the point of increasing entropy for uncertain labels, with Taylor cross entropy loss, which is proved to be efficient to solve the problem of artificial noisy labels on public datasets. And the proposed method is called smooth-Taylor cross entropy loss, which can deal with the real-world noises in vision leaf disease dataset. Extensive experimental results on cassava leaf disease dataset demonstrate that our proposed approach significantly outperforms the state-of-the-art counterparts.

关键词： entropy artificial noisy labels supervised learning vision leaf disease classification label smoothing improved cross entropy loss vision leaf disease dataset uncertain labels supervised learning algorithms pattern classification learning (artificial intelligence) multiple annotators cassava leaf disease dataset annotation cost subjective noisy estimates typical label collection process noisy examples agriculture predictive performance varying skill-levels plant diseases smooth-Taylor cross entropy loss ground truth image classification diseases

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Support vector machine: A tool for mapping mineral prospectivity

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COMPUTERS & GEOSCIENCES 2011年第12期37卷 1967-1975页

作者： Zuo, Renguang Carranza, Emmanuel John M. China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China Univ Twente Dept Earth Syst Anal Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands

In this contribution, we describe an application of support vector machine (SVM), a supervised learning algorithm, to mineral prospectivity mapping. The free R package e1071 is used to construct a SVM with sigmoid kernel function to map prospectivity for Au deposits in western Meguma Terrain of Nova Scotia (Canada). The SVM classification accuracies of 'deposit' are 100%, and the SVM classification accuracies of the 'non-deposit' are greater than 85%. The SVM classifications of mineral prospectivity have 5-9% lower total errors, 13-14% higher false-positive errors and 25-30% lower false-negative errors compared to those of the WofE prediction. The prospective target areas predicted by both SVM and WofE reflect, nonetheless, controls of Au deposit occurrence in the study area by NE-SW trending anticlines and contact zones between Goldenville and Halifax Formations. The results of the study indicate the usefulness of SVM as a tool for predictive mapping of mineral prospectivity. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： supervised learning algorithms Kernel functions Weights-of-evidence Turbidite-hosted Au Meguma Terrain

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KISS: Stochastic Packet Inspection Classifier for UDP Traffic

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IEEE-ACM TRANSACTIONS ON NETWORKING 2010年第5期18卷 1505-1515页

作者： Finamore, Alessandro Mellia, Marco Meo, Michela Rossi, Dario Politecn Torino I-10129 Turin Italy ENST Telecom ParisTech F-75634 Paris France

This paper proposes KISS, a novel Internet classification engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming applications, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square (chi(2))-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Support Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asymmetry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server protocols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are almost perfect when dealing with new P2P streaming applications.

关键词： supervised learning algorithms traffic classification

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Predicting tensile strength of reinforced concrete composited with geopolymer using several machine learning algorithms

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STEEL AND COMPOSITE STRUCTURES 2024年第3期52卷 293-312页

作者： Albaijan, Ibrahim Samadi, Hanan Mahmoodzadeh, Arsalan Fakhri, Danial Hosseinzadeh, Mehdi Ghazouani, Nejib Elhadi, Khaled Mohamed Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Mech Engn Dept Al Kharj 16273 Saudi Arabia Univ Halabja Civil Engn Dept IRO Halabja 46018 Iraq Univ Quebec Chicoutimi Dept Appl Sci Chicoutimi PQ G7H 2B1 Canada Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Med & Pharm Da Nang Vietnam Northern Border Univ Coll Engn Dept Civil Engn Ar Ar 73222 Saudi Arabia King Khalid Univ Coll Engn Civil Engn Dept Abha Saudi Arabia King Khalid Univ Ctr Engn & Technol Innovat Abha 61421 Saudi Arabia

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190 degrees F for 28 days has superior tensile strength.

关键词： geopolymer concrete reinforced concrete supervised learning algorithms tensile strength

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DETERMINATION OF ROAD DIRECTIONS USING FEEDBACK NEURAL NETS

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SIGNAL PROCESSING 1993年第1-2期32卷 147-160页

作者： DELBIMBO, A LANDI, L SANTINI, S Dipartmento di Sistemi e Informatica Facoltà di Ingegneria Università degli Studi di Firenze Firenze Italy

Autonomous vehicles may be driven by image data of real-world scenes collected through a TV camera. Detecting the clues for safe navigation requires, among other things, the estimation of the path to be followed by the vehicle, which has proven to be a formidable task in outdoor scenes. In this paper, an innovative system for road direction detection is proposed which is composed of three specialized blocks performing edge extraction, image-segments detection and road estimation. The road direction estimation block is implemented as a feedback neural network and is not fed directly with image data but with higher-level image features which are extracted through the preprocessing stages. The use of feedback, while reducing the complexity of the network, improves the estimation robustness and the noise immunity. A novel algorithm is defined and employed for the training step and experimental results in outdoor scenes are reported.

关键词： AUTONOMOUS VEHICLES ROAD DIRECTION DETECTION COMPUTER VISION FEEDBACK NEURAL NETWORKS supervised learning algorithms

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Fast converging minimum probability of error neural network receivers for DS-CDMA communications

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2004年第2期15卷 445-454页

作者： Matyjas, JD Psaromiligkos, IN Batalama, SN Medley, MJ SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA McGill Univ Dept Elect & Comp Engn Montreal PQ H3H 2N4 Canada USAF Res Lab IFGC Rome NY 13441 USA

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multipie-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

关键词： accelerated convergence approximation theory direct-sequence code-division-multiple-access (DS-CDMA) systems importance sampling (IS) minimum bit-error rate (BER) minimum probability of error multilayer perceptron neural networks (NNs) stochastic approximation supervised learning algorithms

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Urban Region Function Mining Service Based on Social Media Text Analysis

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2021年第4期31卷 563-586页

作者： Sun, Yanchun Yin, Hang Wen, Jiu Sun, Zhiyu Peking Univ Dept Comp Sci & Technol Beijing 100871 Peoples R China Minist Educ Key Lab High Confidence Software Technol Beijing 100871 Peoples R China Peking Univ Informat Technol Inst Tianjin Binhai Tianjin 300450 Peoples R China

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions' functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region's main functions, we design a service which extracts human activities using Sina Weibo (***;the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region's main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions' function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

关键词： Urban function mining service geographic semantic analysis Location-Based Social Network supervised learning algorithms

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Prediction of ground reaction force and joint moments based on optical motion capture data during gait

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MEDICAL ENGINEERING & PHYSICS 2020年 86卷 29-34页

作者： Mundt, Marion Koeppe, Arnd David, Sina Bamer, Franz Potthast, Wolfgang Markert, Bernd Rhein Westfal TH Aachen Inst Gen Mech Aachen Germany German Sport Univ Cologne Inst Biomech & Orthopaed Cologne Germany

The standard cameraand force plate-based set-up for motion analysis suffers from the disadvantage of being limited to laboratory settings. Since adaptive algorithms are able to learn the connection between known inputs and outputs and generalise this knowledge to unknown data, these algorithms can be used to leverage motion analysis outside the laboratory. In most biomechanical applications, feedforward neural networks are used, although these networks can only work on time normalised data, while recurrent neural networks can be used for real time applications. Therefore, this study compares the performance of these two kinds of neural networks on the prediction of ground reaction force and joint moments of the lower limbs during gait based on joint angles determined by optical motion capture as input data. The accuracy of both networks when generalising to new data was assessed using the normalised root mean-squared error, the root-mean-squared error and the correlation coefficient as evaluation metrics. Both neural networks demonstrated a high performance and good capabilities to generalise to new data. The mean prediction accuracy over all parameters applying a feedforward network was higher (r = 0.963) than using a recurrent long short-term memory network (r = 0.935). (c) 2020 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Artificial neural networks LSTM supervised learning algorithms Force plates Kinetics

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