jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinghui Fan
Source: International Journal of e-Collaboration (article, 2023-01-27)
Field: Materials Science — Polymers and Plastics

Clothing Style Recognition and Design by Using Feature Representation and Collaboration Learning In order to recognize the clothing style, this paper establishes a standard clothing style library. The images of clothing style are provided and annotated by fashion design experts. The clothing style image is represented as a set of line segments that is obtained by detecting the lines and corners consisting of the edge feature points in the image. Then, the authors extract the features of the line segment set and use the extracted features to establish clothing style matching rules to make the system automatically produce the matching and recognizing criteria for the clothing style images. When inputting an image of a person wearing clothes, they first find the position of the person through skin color detection and then locate the clothing. The clothing region is segmented by seed growth algorithm. The features of the segmentation are compared with clothing style matching rules to determine the style. The experimental results show that the recognition rate of clothing style can reach more than 92% for the standard clothing images and more than 91% for real clothing images. Clothing ; Style (visual arts) ; Artificial intelligence ; Computer science ; Matching (statistics) ; Segmentation ; Computer vision ; Set (abstract data type) ; Representation (politics) ; Pattern recognition (psychology) ; Mathematics ; Visual arts ; Art ; Geography Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Chin Kui Fern | Chai Chang Yii | Asfarina Abu Bakar | Ismail Saad | Kenneth Tze Kin Teo | Megat Muhammad Ikhsan Megat Hasnan | Nur Aqilah Mohamad
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

Fault localization on power cables using time delay estimation of partial discharge signals Precise localization of partial discharge (PD) sources on power cables is vital to prevent power line failures that can lead to significant economic losses for electrical suppliers. This study proposes four methods to estimate the time delay of PD signals under electromagnetic interference, including white Gaussian noise (WGN) and discrete sinusoidal interference (DSI), using denoised PD signals with signal-to-noise ratios ranging from 10.6 to -7.02 dB. The maximum peak detection (MPD) and cross-correlation (CC) approaches, as well as two new techniques, interpolation cross-correlation (ICC) and envelope cross-correlation (ECC), are evaluated for their effectiveness in PD source localization. The researchers employ the time difference of arrival (TDoA) algorithm to compute PD location using the double-end PD location algorithm, where the PD location precision serves as an indicator of the accuracy of the time delay estimation methods. The study concludes that CC and ICC are the most suitable methods for estimating the time delay of PD signals in the PD location algorithm, as they exhibit the lowest error rates. These results suggest that CC and ICC can be used effectively for precise PD source localization under electromagnetic interference on power cables. Multilateration ; Interference (communication) ; Computer science ; Algorithm ; Interpolation (computer graphics) ; Power (physics) ; Envelope (radar) ; Cross-correlation ; SIGNAL (programming language) ; Partial discharge ; Noise (video) ; Additive white Gaussian noise ; Transmitter ; Fault (geology) ; White noise ; Telecommunications ; Mathematics ; Acoustics ; Statistics ; Electrical engineering ; Voltage ; Physics ; Engineering ; Artificial intelligence ; Geology High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Power Line Communications and Noise
Authors: Hirokazu Masai | Toshiyuki Mihara | Kenji Kintaka
Source: Journal of the Ceramic Society of Japan (article, 2023-07-31)
Field: Materials Science — Ceramics and Composites

Data analysis of compositional distribution and glass transition temperature of low-melting phosphate glass using big data Regression analysis was performed on the data of low-melting phosphate glasses extracted from a glass database. The data were categorized based on duplication, and the most commonly used components for each temperature range were extracted. The relationship between metal-oxide-based compositions and cation-based compositions was examined. Even though various compositions were included in the database, it was found that the average and standard deviation values of divalent or tetravalent cations in cation-based compositions were approximately 2/3 of those in oxide-based compositions. Multiple regression analysis suggested that cation-based analysis is more suitable than oxide-based analysis. Predictions using cations are expected to become more important in future structure-driven data analyses of glasses. Oxide ; Materials science ; Glass transition ; Phosphate glass ; Compositional data ; Mineralogy ; Analytical Chemistry (journal) ; Thermodynamics ; Metallurgy ; Chemistry ; Polymer ; Computer science ; Chromatography ; Composite material Glass properties and applications ; Luminescence Properties of Advanced Materials ; Mineralogy and Gemology Studies
Authors: Leixiong Wang | Bo Wang | Hengrui Ma | Jiaxing Zhang | Yifan He | Hongxia Wang | Hanqi Zhang
Source: Processes (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules The partial discharge (PD) analysis method has remained one of the key technologies for cable condition assessment to diagnose cable operation state. In this paper, two power cable status evaluation methods are proposed based on electrical tree growth and data association rules, respectively. First, in the field of molecular thermodynamics, based on the law of energy conservation of electrical tree growth, this paper studies the relationship between PD signals in cable insulation and the length of the electrical tree. By using the length of electrical tree growth to calculate the probability of PD failure of cables, a cable state assessment method based on electrical tree growth is proposed. Second, for power cable status evaluation, the strong concealment characteristics of cables result in a long power supply recovery time and a serious threat to power supply reliability. Therefore, a group of comprehensive cable status parameters are set by analyzing the status parameters based on data association rules. The comprehensive state of power cables is divided into four elements, and the weight coefficients of cable states are calculated based on data association rule methods. Finally, the simulation results verify the effectiveness and accuracy for the two different power cable status evaluation methods. Power cable ; Reliability (semiconductor) ; Reliability engineering ; Tree (set theory) ; Power (physics) ; Electric power ; Computer science ; Association rule learning ; State (computer science) ; Electrical engineering ; Engineering ; Data mining ; Mathematics ; Algorithm ; Materials science High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Electrical Fault Detection and Protection
Authors: Rong Zhao | Cheng Du | Jianyong Zhang | Ruixue Cheng | Zhongqiang Yu | Bin Zhou
Source: Applied Sciences (article, 2023-11-06)
Field: Chemistry — Spectroscopy

Reconstruction Algorithm Optimization Based on Multi-Iteration Adaptive Regularity for Laser Absorption Spectroscopy Tomography Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion flames, a new algorithm named ‘multi-iterative adaptive optimization regularization’ (MIARO) is proposed. This algorithm is a further development of another algorithm known as the ‘modified adaptive algebraic reconstruction technique’ (MAART) with the improvement of the initial value and adaptive regularization parameter selections. In MIARO, the problem of the MAART’s initial value sensitivity is compensated for, and in addition, reconstruction parameters are also introduced into the regularization so that both the quality of reconstruction and the convergence of regularization are guaranteed. In butane burner experiments, an average relative error of 1.82% was achieved with MIARO, compared to 2.44% with MAART, which is a significant reduction of 25.1%. The simulation and experimental results clearly demonstrate that the MIARO algorithm can be used to reconstruct dynamic combustion fields and eliminate boundary artifacts with improved measurement accuracy and robustness. Algebraic Reconstruction Technique ; Regularization (linguistics) ; Algorithm ; Combustion ; Combustor ; Tomography ; Computer science ; Mathematics ; Iterative reconstruction ; Mathematical optimization ; Chemistry ; Physics ; Artificial intelligence ; Optics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Calibration and Measurement Techniques
Authors: Sarah C. Rutan | Kathryn Cash | Dwight R. Stoll
Source: ChemRxiv (preprint, 2023-08-13)
Field: Chemistry — Spectroscopy

Experimental design and re-parameterization of the Neue-Kuss model for accurate and precise prediction of isocratic retention factors from gradient measurements in reversed phase liquid chromatography The present work describes a re-parameterization of the Neue Kuss (NK) model for describing retention in liquid chromatography, and this re-parameterized model is used fit a large set of isocratic retention measurements with improved convergence properties relative to the original parameterization of the model. Next, an experimental design for retention measurements made using mobile phase gradient elution conditions is proposed for the purpose of obtaining accurate and precise NK parameters. Simulated retention data for mobile phase gradient elution conditions with two different levels of noise, as well as an essentially zero noise level were fit with the re-parameterized model. The results showed that the re-parameterized fits yielded average (absolute value) prediction errors for the parameters at the highest noise level of 7.16 % for S1,ref, 17.5 % for S2,ref and 6.19 % for kref. These errors were significantly smaller than those for the original parameterization of the NK model, where the errors were 23.2 % for S1, 24.9 % for S2 and 159 % for kw. Furthermore, isocratic retention factors predicted using these model parameters were found to have an average magnitude of error of 0.509 % for the re-parameterized model, as opposed to 6750 % for the model with the original parameterization. A further test of this approach was carried out for independent experimental measurements for five solutes and a C18 column. The average magnitude of error of the isocratic retention factors predicted from parameters obtained from fits of gradient data was 1.59 %, provided that the range of organic solvent compositions that the solute sampled in the mobile phase gradient experiments was consistent with the isocratic experiments. These results indicate that the reparameterization of the NK model allows for significant improvements in the fitting process, and that the proposed experimental design allows for NK parameters to be extracted from mobile phase gradients experiment that predict isocratic retention factors with an accuracy on the order of 1%. Parameterized complexity ; Noise (video) ; Range (aeronautics) ; Phase (matter) ; Sensitivity (control systems) ; Convergence (economics) ; Chromatography ; Approximation error ; Biological system ; Mathematics ; Analytical Chemistry (journal) ; Algorithm ; Chemistry ; Materials science ; Computer science ; Electronic engineering ; Artificial intelligence Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Crystallization and Solubility Studies
Authors: E. I. Bardyk | Nickolai P. Bolotnyi
Source: Electrical Engineering & Electromechanics (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Development of fuzzy classifier for technical condition ranking of power transformer The work aim is to develop a fuzzy classifier for technical condition ranking of power transformer under condition of vagueness and ambiguity diagnostic information. Methodology. The fuzzy classifier developing for technical condition ranking of power transformer was based on approach of using fuzzy set theory and optimization methods. The proposed approach for power transformer rank assessment by using a classifier was developed on the basis of Takagi-Sugeno fuzzy inference system. The input indicators choice is justified and their efficiency for classifier is evaluated by expert evaluation method. This makes it possible to formalize expert assessments regarding the development of power transformer defects. Results. The formalization of technical condition assessment of power transformer in knowledge base form, which implemented in expert system prototype for technical condition assessment, was carried out. The complex technical condition assessment for each functional unit of power transformer was determined based on expert evaluations with using the test and measurement parameters results. Originality. The considered approach to formalization of uncertainty regarding technical condition of power transformer allows building a deterministic decision-making scheme for further maintenance strategy, in which the ranking and decommissioning procedures for specific objects are implemented on the basis of objective criteria. Practical value. The proposed fuzzy classifier allows determination with a high probability degree of technical condition assessment of power transformer based on the test and measurement parameters results. Thus, an applied aspect of using the obtained scientific result is the possibility to objectively rank of power transformers park based on the identified possible defects and their development degree. This constitutes the prerequisites for determining the failure probability evaluation of power transformer at nearest observation period and emergency risk assessment in integrated electric power systems under power transformer failures. Transformer ; Reliability engineering ; Fuzzy logic ; Computer science ; Classifier (UML) ; Data mining ; Machine learning ; Artificial intelligence ; Engineering ; Electrical engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Environmental and Industrial Safety
Authors: Guiyuan Lin | Shiwo Deng | Xiaoqun Wang
Source: PLoS ONE (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

An efficient quasi-Monte Carlo method with forced fixed detection for photon scatter simulation in CT Detected scattered photons can cause cupping and streak artifacts, significantly degrading the quality of CT images. For fast and accurate estimation of scatter intensities resulting from photon interactions with a phantom, we first transform the path probability of photons interacting with the phantom into a high-dimensional integral. Secondly, we develope a new efficient algorithm called gQMCFFD, which combines graphics processing unit(GPU)-based quasi-Monte Carlo (QMC) with forced fixed detection to approximate this integral. QMC uses low discrepancy sequences for simulation and is deterministic versions of Monte Carlo. Numerical experiments show that the results are in excellent agreement and the efficiency improvement factors are 4 ∼ 46 times in all simulations by gQMCFFD with comparison to GPU-based Monte Carlo methods. And by combining gQMCFFD with sparse matrix method, the simulation time is reduced to 2 seconds in a single projection angle and the relative difference is 3.53%. Monte Carlo method ; Imaging phantom ; Photon ; Graphics processing unit ; Physics ; Monte Carlo method for photon transport ; Projection (relational algebra) ; Algorithm ; Computer science ; Computational physics ; Hybrid Monte Carlo ; Optics ; Statistical physics ; Markov chain Monte Carlo ; Mathematics ; Statistics ; Parallel computing Radiation Shielding Materials Analysis ; Medical Imaging Techniques and Applications ; Advanced X-ray and CT Imaging
Authors: Dongyun Dai | Quanchang Liao | Zhongqing Sang | Yimin You | Rui Qiao | Huisheng Yuan
Source: Applied Sciences (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective. Switchgear ; Mel-frequency cepstrum ; Computer science ; Speech recognition ; Pattern recognition (psychology) ; Cepstrum ; Support vector machine ; Process (computing) ; Waveform ; Artificial intelligence ; Feature extraction ; Engineering ; Telecommunications High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Power Transformer Diagnostics and Insulation
Authors: Andrzej Wójtowicz | Jan Piekarczyk | Marek Wójtowicz | Jarosław Jasiewicz | Sławomir Królewicz | Elżbieta Starzycka-Korbas
Source: Agriculture (article, 2023-11-30)
Field: Chemistry — Analytical Chemistry

Classification of Plenodomus lingam and Plenodomus biglobosus in Co-Occurring Samples Using Reflectance Spectroscopy Under natural conditions, mixed infections are often observed when two or more species of plant pathogens are present on one host. Thus, the detection and characterization of co-occurring pest species is a challenge of great importance. In this study, we focused on the development of a spectral unmixing method for the discrimination of two fungi species, Plenodomus lingam and Plenodomus biglobosus, the pathogens of oilseed rape. Over 24 days, spectral reflectance measurements from Petri dishes inoculated with fungi were conducted. Four experimental combinations were used: the first two were pure fungal samples, while the other two were co-occurring fungal samples. The results of the study show the possibility of distinguishing, based on spectral characteristics, between P. lingam and P. biglobosus not only in pure but also in co-occurring samples. We observed the changes in the reflectance of electromagnetic radiation from the tested fungi over time and a strong correlation between the reflectance and changes in the areas of the mycelia on the Petri dishes. Moreover, the wavelengths most useful for spectral classification of the tested fungal mycelia were selected. Finally, a spectral unmixing model was proposed, which enables the estimation of the areas of two pathogens in co-occurring samples based on the spectral characteristics of the entire plate with an error smaller than 0.2. To our knowledge, the present study is the first report examining the use of reflectance spectroscopy methods for classifying pathogens on the same Petri dish. The study results indicate the feasibility of reflectance spectroscopy as a nondestructive sampling method for plant pathogen detection and classification. Petri dish ; Reflectivity ; Mycelium ; Diffuse reflectance infrared fourier transform ; Spectroscopy ; Biology ; Near infrared reflectance spectroscopy ; Botany ; Remote sensing ; Biological system ; Environmental science ; Near-infrared spectroscopy ; Optics ; Microbiology ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Kyle M. Paaren | Pavel Medvedev | Robert D. Mariani
Source: Energies (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

Optimization of Conductive Fins to Minimize UO2 Fuel Temperature and Radial Temperature Gradient To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor. Temperature gradient ; Materials science ; Volume (thermodynamics) ; Thermal conductivity ; Work (physics) ; Electrical conductor ; Fin ; Nuclear engineering ; Parametric statistics ; Thermal ; Thermal conduction ; Mechanics ; Composite material ; Mechanical engineering ; Thermodynamics ; Engineering ; Physics ; Mathematics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics