Performance prediction of roadheaders using ensemble machine learning techniques

Mechanical excavators are widely used in mining, tunneling and civil engineering projects. There are several types of mechanical excavators, such as a roadheader, tunnel boring machine and impact hammer. This is because these tools can bring productivity to the project quickly, accurately and safely. Among these, roadheaders have some advantages like selective mining, mobility, less over excavation, minimal ground disturbances, elimination of blast vibration, reduced ventilation requirements and initial investment cost. A critical issue in successful roadheader application is the ability to evaluate and predict the machine performance named instantaneous (net) cutting rate. Although there are several prediction methods in the literature, for the prediction of roadheader performance, only a few of them have been developed via artificial neural network techniques. In this study, for this purpose, 333 data sets including uniaxial compressive strength and power on cutting boom, 103 data set including RQD, and 125 data sets including machine weight are accumulated from the literature. This paper focuses on roadheader performance prediction using six different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques. Algorithms are ZeroR, random forest (RF), Gaussian process, linear regression, logistic regression and multi-layer perceptron (MLP). As a result, MLP and RF give better results than the other algorithms also the best solution achieved was bagging technique on RF and principle component analysis (PCA). The best success rate obtained in this study is 90.2% successful prediction, and it is relatively better than contemporary research.

     

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.     

Nanoporous Gold Biointerfaces: Modifying Nanostructure to Control Neural Cell Coverage and Enhance Electrophysiological Recording Performance

Nanostructured neural interface coatings have significantly enhanced recording fidelity in both implantable and in vitro devices. As such, nanoporous gold (np‐Au) has shown promise as a multifunctional neural interface coating due, in part, to its ability to promote nanostructure‐mediated reduction in astrocytic surface coverage while not affecting neuronal coverage. The goal of this study is to provide insight into the mechanisms by which the np‐Au nanostructure drives the differential response of neurons versus astrocytes in an in vitro model. Utilizing microfabricated libraries that display varying feature sizes of np‐Au, it is demonstrated that np‐Au influences neural cell coverage through modulating focal adhesion formation in a feature size‐dependent manner. The results here show that surfaces with small (≈30 nm) features control astrocyte spreading through inhibition of focal adhesion formation, while surfaces with large (≈170 nm and greater) features control astrocyte spreading through other mechanotransduction mechanisms. This cellular response combined with lower electrical impedance of np‐Au electrodes significantly enhances the fidelity and stability of electrophysiological recordings from cortical neuron‐glia co‐cultures relative to smooth gold electrodes. Finally, by leveraging the effect of nanostructure on neuronal versus glial cell attachment, the use of laser‐based nanostructure modulation is demonstrated for selectively patterning neurons with micrometer spatial resolution.     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

A comparison of clustering-based privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.     

Synthesis and metal ions sorption properties of styrene‐based polymers

Atactic polystyrene (PS) was chemically modified with maleic (MAH), succinic (SAH), and phtalic (PhAH) anhydrides. Styrene was copolymerized with acrylic (AA) and methacrylic acids (MA). Amount of carboxyl groups (%) bound to polymers was determined in the range of 6.9–25.9. Different modified polystyrenes (MPS) and styrene copolymers were used in the experiments to study Cu(II) and Zn(II) ions adsorption probability and their comparison. Sorption capacity of the polymers for the metal ions were investigated in aqueous media containing different amounts of these ions (5–40 mg/L) and at different pH values (2.0–6.0). Adsorption behavior of heavy metal ions could be modeled using both the Langmuir and Freundlich isotherms. It was found that the adsorption capacity is highest at pH value of 6, whereas it decreases as the pH value decreases at temperature 25°C ± 1°C for 240 min. The results obtained from the adsorption capacity experiments for Cu(II) and Zn(II) ions were 3.47–5.45 and 5.42–6.85 mg/g, respectively. The affinity order of polymers for both metal ions was observed as follows: SMAC > SAAC > MPS with MAH > MPS with SAH > MPS with PhAH. The maximum adsorption capacities of SMAC were 6.85mg/g for Zn(II) and 5.45 mg/g for Cu(II). © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Recycling studies of marble processing waste: Composites based on commercial epoxy resin

Marble waste was obtained from marble processing plant wastewater with precipitation using different coagulants, such as sepiolite, zeolite, and pumice in dosages of 0.5–8 g/500 mL and mixed in 20 wt % with commercial epoxy resin. The effects of marble, coagulant type and dosage on the physicomechanical and thermal properties were investigated. The incorporation of marble processing waste particles increases the 10% decomposition temperature of pure epoxy by 5–50°C. Surface hardness, tensile strength, percentage elongation, and stress at maximum load of the composites were higher than those of pure resin, too. The composites reinforced with marble processing waste-pumice showed about 10% increase in elastic modulus, whereas the composite reinforced with marble processing waste-sepiolite or zeolite showed about 76.67–143.33% increase in elastic modulus over the pure epoxy matrix. Scanning electron microscopy (SEM) was used for characterization of surface and cross sections of the composites to verify the results. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012