jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kemal Özkan | Erol Seke | Şahin Işık
Source: Pamukkale University Journal of Engineering Sciences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Wheat kernels classification using visible-near infrared camera based on deep learning This paper presents a smart machine learning system for classification of hyperspectral wheat data based on deep learning methodology. For this purpose, the performances of AlexNet and VGG16 models were investigated for the classification of hyperspectral wheat samples. In this study, the Support Vector Machine (SVM) and Softmax classifiers were carried out to predict labels of wheat kernels. In order to evaluate the system performance, a new hyperspectral wheat test dataset was constructed using Visible-Near Infrared images (VNIR) including 50 wheat species with 220 images per specimen, as 11000 samples in total. With experiments applied on newly created test dataset, overall approximated accuracy rates of 96.00% and 99.00% determined by linear SVM classifier, in case of fully connected layer (FC6 and FC7) features for AlexNet and VGG16, respectively. From the Softmax predictions, the 92% and 70% of samples were correctly discriminated based on trained VGG16 and AlexNet models, respectively. The obtained superior results show that using a deep Convolutional Neural Networks (CNN) architecture is more efficient by the means of accurate discrimination of wheat species. The proposed deep learning based categorization system promises high accuracy results for the quality analysis, classification and disease detection in food. Infrared ; Artificial intelligence ; Computer science ; Computer vision ; Remote sensing ; Pattern recognition (psychology) ; Geology ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: Yuliana Zamora | Logan Ward | Ganesh Sivaraman | Ian Foster | Henry Hoffmann
Source:  (article, 2021-06-03)
Field: Materials Science — Materials Chemistry

Proxima Atomistic-scale simulations are prominent scientific applications that require the repetitive execution of a computationally expensive routine to calculate a system's potential energy. Prior work shows that these expensive routines can be replaced with a machine-learned surrogate approximation to accelerate the simulation at the expense of the overall accuracy. The exact balance of speed and accuracy depends on the specific configuration of the surrogate-modeling workflow and the science itself, and prior work leaves it up to the scientist to find a configuration that delivers the required accuracy for their science problem. Unfortunately, due to the underlying system dynamics, it is rare that a single surrogate configuration presents an optimal accuracy/latency trade-off for the entire simulation. In practice, scientists must choose conservative configurations so that accuracy is always acceptable, forgoing possible acceleration. As an alternative, we propose Proxima, a systematic and automated method for dynamically tuning a surrogate-modeling configuration in response to real-time feedback from the ongoing simulation. Proxima estimates the uncertainty of applying a surrogate approximation in each step of an iterative simulation. Using this information, the specific surrogate configuration can be adjusted dynamically to ensure maximum speedup while sustaining a required accuracy metric. We evaluate Proxima using a Monte Carlo sampling application and find that Proxima respects a wide range of user-defined accuracy goals while achieving speedups of 1.02--5.5X relative to a standard Computer science ; Speedup ; Surrogate model ; Workflow ; Monte Carlo method ; Metric (unit) ; Acceleration ; Simulation ; Machine learning ; Parallel computing ; Statistics ; Mathematics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Software Engineering Research
Authors: Cuiping Yang | Cong Xu | Jilai Su | Wei He | Zhenhua Gao
Source: BioResources (article, 2021-02-09)
Field: Chemistry — Analytical Chemistry

A new model based on principal component regression-random forest for analyzing and predicting the physical and mechanical properties of particleboard The physical and mechanical properties are key indexes for determining the quality of particleboards. For this reason, a study on evaluating the physical and mechanical properties of particleboard via a new method has considerable value. Thus, a method based on principal component regression (PCR) analysis and random forest (RF) is proposed in this paper. First, the problems requiring resolution are described after analyzing the production process parameters as well as the physical and mechanical properties of particleboard. Then, an analysis and prediction models based on the PCR and RF method is established. On the basis of the PCR method, the key process parameters that affect various physical and mechanical properties are determined. Based on the RF method, the analysis and prediction model are built from the previously determined process parameters of the physical and mechanical properties. Finally, through experimental analysis, the effectiveness of the analysis and prediction models based on the PCR and RF method are verified. This work was able to determine the relationship between the process parameters and the physical and mechanical properties, which can help improve practical industrial manufacturing effectivity. Principal component analysis ; Process (computing) ; Random forest ; Principal component regression ; Regression analysis ; Component (thermodynamics) ; Computer science ; Work (physics) ; Materials science ; Process engineering ; Biological system ; Mechanical engineering ; Machine learning ; Engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Syed Imran Ali | Shaine Mohammadali Lalji | Javed Haneef | Clifford Louis | Abdus Saboor | Nimra Yousaf
Source: Journal of Petroleum Exploration and Production Technology (article, 2021-08-07)
Field: Chemistry — Analytical Chemistry

Asphaltene precipitation modeling in dead crude oils using scaling equations and non-scaling models: comparative study Abstract This research study aims to conduct a comparative performance analysis of different scaling equations and non-scaling models used for modeling asphaltene precipitation. The experimental data used to carry out this study are taken from the published literature. Five scaling equations which include Rassamadana et al., Rassamdana and Sahimi, Hu and Gou, Ashoori et al., and log–log scaling equations were used and applied in two ways, i.e., on full dataset and partial datasets. Partial datasets are developed by splitting the full dataset in terms of Dilution ratio ( R ) between oil and precipitant. It was found that all scaling equations predict asphaltene weight percentage with reasonable accuracy (except Ashoori et al. scaling equation for full dataset) and their performance is further enhanced when applied on partial datasets. For the prediction of Critical dilution ratio ( R c ) for different precipitants to detect asphaltene precipitation onset point, all scaling equations (except Ashoori et scaling equation when applied on partial datasets) are either unable to predict or produce results with significant error. Finally, results of scaling equations are compared with non-scaling model predictions which include PC-Saft, Flory–Huggins, and solid models. It was found that all scaling equations (except Ashoori et al. scaling equation for full dataset) either yield almost the same or improved results for asphaltene weight percentage when compared to best case (PC-Saft). However, for the prediction of R c , Ashoori et al. scaling equation predicts more accurate results as compared to other non-scaling models. Scaling ; Asphaltene ; Thermodynamics ; Dilution ; Precipitation ; Mathematics ; Statistical physics ; Chemistry ; Physics ; Organic chemistry ; Meteorology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Peng Zhao | Zhenyu Li | Cheng-Kun Wang
Source: Journal of Spectroscopy (article, 2021-07-22)
Field: Chemistry — Organic Chemistry

Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity. Pattern recognition (psychology) ; Principal component analysis ; Artificial intelligence ; Fuzzy logic ; Classifier (UML) ; Fuzzy rule ; Computer science ; Near-infrared spectroscopy ; Fuzzy set ; Mathematics ; Optics ; Physics Wood and Agarwood Research ; Identification and Quantification in Food ; Industrial Vision Systems and Defect Detection
Authors: Elke De Zitter | Siewert Hugelier | Sam Duwé | Wim Vandenberg | Alison G. Tebo | Luc Van Meervelt | Peter Dedecker
Source: Angewandte Chemie International Edition (article, 2021-02-06)
Field: Materials Science — Materials Chemistry

Structure–Function Dataset Reveals Environment Effects within a Fluorescent Protein Model System** Anisotropic environments can drastically alter the spectroscopy and photochemistry of molecules, leading to complex structure-function relationships. We examined this using fluorescent proteins as easy-to-modify model systems. Starting from a single scaffold, we have developed a range of 27 photochromic fluorescent proteins that cover a broad range of spectroscopic properties, including the determination of 43 crystal structures. Correlation and principal component analysis confirmed the complex relationship between structure and spectroscopy, but also allowed us to identify consistent trends and to relate these to the spatial organization. We find that changes in spectroscopic properties can come about through multiple underlying mechanisms, of which polarity, hydrogen bonding and presence of water molecules are key modulators. We anticipate that our findings and rich structure/spectroscopy dataset can open opportunities for the development and evaluation of new and existing protein engineering methods. Spectroscopy ; Fluorescence spectroscopy ; Fluorescence ; Biological system ; Principal component analysis ; Function (biology) ; Chemical physics ; Molecule ; Photochromism ; Hydrogen bond ; Molecular machine ; Polarity (international relations) ; Biophysics ; Chemistry ; Materials science ; Nanotechnology ; Computer science ; Biology ; Physics ; Optics ; Evolutionary biology ; Biochemistry ; Artificial intelligence Photochromic and Fluorescence Chemistry ; Advanced Fluorescence Microscopy Techniques ; Photoreceptor and optogenetics research
Authors: Sebastian Sławski | Marek Kciuk | W. Klein
Source: Sensors (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Assessment of SMA Electrical Resistance Change during Cyclic Stretching with Small Elongation In this article, changes in NiTi alloy (Flexinol) electrical resistance during cyclic stretching with small elongation were investigated. A dedicated test stand consisting of motorized vertical test stand, force gauge, and electric resistance measuring device with an accuracy of 0.006 Ω was developed. A dedicated control algorithm was developed using LabVIEW software. Changes in electrical resistance were investigated for the 0.1 mm Flexinol wire with length of 120 mm. Testing was performed in the elongation range between 0.25% and 1.5% in martensite phase. Tested samples were subjected to 30 stretching cycles with a movement speed of 10 mm/min. Obtained results show that the cyclic stretching of Flexinol wire reduces its electrical resistance with each stretching cycle. Moreover, it was noted that changes in Flexinol electrical resistance during cycling stretching depend on the assumed elongation and number of the already performed stretching cycles. The observed electrical resistance change decreases with each stretching cycle. Thus, the observed changes are greater during the first stretching cycles. For elongations exceeding 1%, the Flexinol electrical resistance in the first stretching cycle increases. In each subsequent cycle, electrical resistance decreases, as in the case of the smallest value of assumed elongation. In almost all tested cases (except in the case with 1.5% of assumed elongation), Flexinol electrical resistance after 30 stretching cycles was smaller than before the test. Elongation ; Electrical resistance and conductance ; Materials science ; Nickel titanium ; Electrical resistivity and conductivity ; Martensite ; Shape-memory alloy ; Composite material ; Ultimate tensile strength ; Electrical engineering ; Microstructure Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Force Microscopy Techniques and Applications
Authors: Manasi Doshi | Eric P. Fahrenthold
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Explosive molecule sensing at lattice defect sites in metallic carbon nanotubes Adsorption of a TNT molecule at a divacancy defect on a metallic single walled carbon nanotube. Carbon atoms corresponding to the defect are highlighted in green. Carbon nanotube ; Materials science ; Molecule ; Explosive material ; Metal ; Carbon fibers ; Nanotechnology ; Carbon nanobud ; Lattice (music) ; Adsorption ; Fullerene ; Chemical physics ; Nanotube ; Optical properties of carbon nanotubes ; Chemistry ; Physical chemistry ; Composite material ; Metallurgy ; Organic chemistry ; Composite number ; Physics Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Graphene research and applications
Authors: Ernest Mbamalu Ezeh
Source: Bulletin of the National Research Centre/Bulletin of the National Research Center (article, 2021-11-18)
Field: Materials Science — Polymers and Plastics

Cone calorimetric fire properties and thermal analysis of polyester-banana peduncle fibre composite fused with bio-fire retardant additive Abstract Background Lately, thermoplastic and thermoset polymers are integrated with natural fillers to harvest composites. Due to an excellent property profile, these composites find wide applications in engineering fields. This research work aimed to investigate the parameters that influence the thermal degradation of Polyester-Banana peduncle fibre-reinforced composite incorporated with cow horn ash particle (CHAp) as a fire retardant additive and optimize the total heat produced for the smaller the better. The major instruments used in this study were cone calorimeter and the thermogravimetric analyser. Result The parameters considered were time, reinforcement type, and weight per cent of reinforcement material. The composites were made by varying the ratios of CHAp and BPF in the polyester matrix from 0:0, 2.5:2.5, 5:5 and 7.5:7.5 and 10:10 weight per cent. The flammability properties of the developed composites were examined, using a cone calorimeter. The controlling parameters were analysed using the Taguchi robust design method. The ANOVA showed that time had the greatest influence on the total heat release rate (81.72%). The weight per cent reinforcement type (10.37%) and reinforcement type (5.28%) had smaller influences on the heat release rate. The S/N ratio obtained, using optimal testing shows that time had the greatest influence on the total heat release rate, followed by weight per cent of reinforcement type, while reinforcement type had the least influence. The corresponding multiple regression models for total heat released revealed that the total heat release rate increased with an increase in time and reinforcement type and decreased with an increase in weight per cent of the fire retardant additive material. Conclusion This work indicated that the parameter design of the Taguchi method provided an efficient methodology for the analyses of the effects of thermal degradation parameters of composites. The controlling parameters of time, the weight of reinforcement material and the type of reinforcement material had significant contributions to the value of heat production during composites thermal decomposition. Time had the greatest contribution, followed by the weight of reinforcement type, and type of reinforcement material. Cone calorimeter ; Materials science ; Composite material ; Fire retardant ; Thermogravimetric analysis ; Reinforcement ; Thermosetting polymer ; Flammability ; Calorimeter (particle physics) ; Composite number ; Waste management ; Pyrolysis ; Chemical engineering Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Ronald Ssebadduka | Nam Nguyen Hai Le | Ronald Nguele | Olalekan Alade | Yuichi Sugai
Source: Energies (article, 2021-12-17)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32°) and bitumen (API 7.39°). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values. Asphalt ; Artificial neural network ; Empirical modelling ; Viscosity ; Rheology ; Oil viscosity ; Light crude oil ; Petroleum engineering ; Materials science ; Thermodynamics ; Chemistry ; Geology ; Computer science ; Artificial intelligence ; Simulation ; Organic chemistry ; Composite material ; Physics Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Vasumathi M.T. | M. Kamarasan
Source: Indian Journal of Computer Science and Engineering (article, 2021-04-20)
Field: Chemistry — Analytical Chemistry

AN LSTM BASED CNN MODEL FOR POMEGRANATE FRUIT CLASSIFICATION WITH WEIGHT OPTIMIZATION USING DRAGONFLY TECHNIQUE Pomegranate is a widely grown plant in India. This highly beneficial fruit is infected by multiple pests and diseases which cause great economical losses. Different forms of pathogen diseases on leaf, stem and the fruits are present. Some of the diseases that affect pomegranate fruits are anthracnose, cercospora, heart rot and bacterial blight. There is a need for disease control strategies to incorporate timely action on the developed diseases. Thus, there is a need for intelligent and self-learning recognition systems to detect these diseases on time. This study is aiming to classify pomegranate fruits into two classes normal and abnormal using CNN LSTM technique. This research work uses a hybrid CNN-LSTM technique to detect four types of diseases present in the pomegranate fruits and classify them into four classes. The results obtained using CNN LSTM are then optimized using dragonfly algorithm. The features like colour, texture and shape of the fruits are collected and fed into the hybrid CNN-LSTM. The dataset for the classifier is given as an excel file which is initially pre-processed using map reduce technique and dimensionality reduction carried using Principal Component Analysis and Discriminant analysis. The CNN LSTM classifier identifies the 4 types of diseases and normal fruit. The classification is further optimized using dragonfly algorithm. The optimized weight and cost function has further explored to support the multi-class disease detection process. Experimental results have shown an accuracy of 92% in classification using CNN-LSTM technique and optimization using dragonfly techniques shows an improved classification accuracy of 97.1%. Dragonfly ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Qiang Li | Qinyuan Huang | Ying Zhou | Tian Yang | Maoxia Ran | Xin Liu
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Metals and Alloys

Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types This paper focuses on developing the acoustic-based detection of arc magnet internal defects under different noises and object types. The major challenge in such a detection case is that the traditional approaches mainly rely on expert knowledge and experiential features, which results in a poor generalization performance of the algorithm for new types of detection objects and is susceptible to noise interference. This work presents a novel detection framework based on an end-to-end one-dimensional (1D) convolutional long short-term memory (LSTM) model, where both the spatial and temporal features of the measured acoustic signals are extracted and then jointed for determining the internal defects of arc magnets. In addition, the LSTM layers are employed behind the 1D convolutional neural network, which makes the number of time steps in the LSTM layers for the temporal feature extraction is much smaller than the length of the input segments, thus the computational complexity of the LSTM layers can be highly reduced. Experimental results show that our method is superior to existed methods in the detection accuracy for the internal defects of arc magnets, and the diagnosis time per a single arc magnet is controlled at the millisecond, making it appropriate for real-time applications. Furthermore, the robustness of the proposed framework is validated through experiments on different signal-to-ratios and multiple object types of arc magnets. Computer science ; Convolutional neural network ; Robustness (evolution) ; Object detection ; Pattern recognition (psychology) ; Artificial intelligence ; Feature extraction ; Benchmark (surveying) ; Magnet ; Noise (video) ; Physics ; Image (mathematics) Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Structural Integrity and Reliability Analysis