- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE ISAF-FMA-AMF-AMEC-PFM Joint Conference (IFAAP) - Hiroshima (2018.5.27-2018.6.1)] 2018 IEEE ISAF-FMA-AMF-AMEC-PFM Joint Conference (IFAAP) - Data-Mining Driven Design for Novel Perovskite-type Piezoceramics
摘要： Materials Genome Initiative is envisioning the discovery, development, manufacturing and deployment of advanced materials twice as fast and at a fraction of cost. High throughput computation and experimentation will generate big data, which underscores the emergence of the fourth paradigm---data science. In contrast to machine-learning needing big-data, data-mining assisted by domain knowledge and expertise works well with a limited number of data. In this presentation, data-mining based on material genome approach were performed in field of perovskite-type oxides. New ferroelectric ceramics based on BiFeO3 for high temperature piezoelectric applications are realized with piezoresponse of 1.5~4.0 times the present commercial non-perovskite counterpart. Our essay demonstrates data-mining driven searching sure able to reduce time-to-insight and human effort on synthesization, accelerating new materials discovery and deployment.
关键词： piezoceramics,perovskite-type oxides,material genome approach,high Curie temperature,data-mining
更新于2025-09-23 15:23:52
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Ultrafast data mining of molecular assemblies in multiplexed high-density super-resolution images
摘要： Multicolor single-molecule localization super-resolution microscopy has enabled visualization of ultrafine spatial organizations of molecular assemblies within cells. Despite many efforts, current approaches for distinguishing and quantifying such organizations remain limited, especially when these are contained within densely distributed super-resolution data. In theory, higher-order correlation such as the Triple-Correlation function is capable of obtaining the spatial configuration of individual molecular assemblies masked within seemingly discorded dense distributions. However, due to their enormous computational cost such analyses are impractical, even for high-end computers. Here, we developed a fast algorithm for Triple-Correlation analyses of high-content multiplexed super-resolution data. This algorithm computes the probability density of all geometric configurations formed by every triple-wise single-molecule localization from three different channels, circumventing impractical 4D Fourier Transforms of the entire megapixel image. This algorithm achieves 102-folds enhancement in computational speed, allowing for high-throughput Triple-Correlation analyses and robust quantification of molecular complexes in multiplexed super-resolution microscopy.
关键词： molecular assemblies,computational algorithm,multiplexed high-density super-resolution images,Triple-Correlation function,Ultrafast data mining
更新于2025-09-23 15:23:52
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - FACE - Face At Classroom Environment: Dataset and Exploration
摘要： The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.
关键词： image dataset,face recognition,face detection,computer vision,data collection,educational data mining,automated attendance system
更新于2025-09-23 15:22:29
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[IEEE 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) - Mexico City (2018.9.5-2018.9.7)] 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) - Data Mining for the Analysis of Eye Tracking Records
摘要： It is proposed the implementation of a methodology for the analysis and classification of large volumes of records. It is studied and evaluated the application of DM as a tool to analysis qualitatively and quantitatively the register obtained by an eye movement tracking device, eye-tracking, when bring under to people with different levels of orthographic knowledge (OK: High, Medium and Low), in the face of two tasks; (i) detection of spelling error and (ii) in the detection of a simple character, in the brief exposure (1500 milliseconds) of words without and with misspelling. It used some analytical procedure series of DM such as: the search for response patterns; the creation of secondary variables; the use of classification of trees and grouping the data (k-means). New models were created as of the distance between the position of the spelling error and the position of the gaze of the participants. Differences in the visual attention were found between the participants; in the same way, it was observed that the misspelling influences the performance of the task (ii), diverting visual attention to spelling error, in the participants with High OK. It is concluded that the DM helps to find the particularities of eye movements from large volumes of data that generates eye-tracking, which cannot be analysed with simple procedures.
关键词： k-means,Data Mining,Orthographic Knowledge,Eye Tracking,Eye Movements
更新于2025-09-23 15:22:29
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Using Data Mining to Search for Perovskite Materials with Higher Specific Surface Area
摘要： The specific surface area (SSA) of ABO3-type perovskite is one of important properties associating with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (ranged 1-60m2g-1) of perovskite with its features including chemical compositions and technical parameters. The genetic algorithm (GA)-support vector regression (SVR) method was used to screen the main features for modeling. The correlation coefficient (R) between predicted SSA and experimental SSA reached as high as 0.986 for training set and 0.935 for leave-one-out cross validation (LOOCV), respectively. The ABO3-type perovskite with higher SSA can be screened out by using OCPMDM (online computation platform for materials data mining) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite，which is accessible at the web address：http://118.25.4.79/material_api/csk856q0fulhhhwv
关键词： Visual screening,Online service,Data mining,Specific surface area,Perovskite
更新于2025-09-23 15:21:21
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[Lecture Notes in Computer Science] Intelligence Science and Big Data Engineering Volume 11266 (8th International Conference, IScIDE 2018, Lanzhou, China, August 18–19, 2018, Revised Selected Papers) || An Improved Spectral Clustering Algorithm Based on Dynamic Tissue-Like Membrane System
摘要： With vast amount of data generated, it is becoming a main aspect to mine useful information from such data. Clustering research is an important task of data mining. Traditional clustering algorithms such as K-means algorithm are too old to propose high-dimensional data, so an ef?cient clustering algorithm, spectral clustering is generated. In recent years, more and more scholars has been ?rmly committing to studying spectral clustering algorithm for its solid theoretical foundation and excellent clustering results. In this paper we propose an improved spectral clustering algorithm based on Dynamic Tissue-like P System abbreviated as ISC-DTP. ISC-DTP algorithm takes use of the advantages of maximal parallelism in tissue-like membrane system. Experiment is conducted on an arti?cial data set and four UCI data sets. And we compare the ISC-DTP algorithm with original spectral clustering algorithm and K-means algorithm. The experiments demonstrate the effectiveness and robustness of the proposed algorithm.
关键词： Spectral clustering algorithm,Tissue-like membrane system,Data mining,ISC-DTP algorithm
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Sonderborg, Denmark (2019.4.23-2019.4.25)] 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Modelling properties of solar cells irradiated from different lighting sources
摘要： The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The aims of the present study were to assess the association between the HW phenotype and type 2 diabetes in Korean adults and to evaluate the predictive power of various phenotypes consisting of combinations of individual anthropometric measurements and TG levels. Between November 2006 and August 2013, 11 937 subjects participated in this retrospective cross-sectional study. We measured fasting plasma glucose and TG levels and performed anthropometric measurements. We employed binary logistic regression (LR) to examine statistically significant differences between normal subjects and those with type 2 diabetes using HW and individual anthropometric measurements. For more reliable prediction results, two machine learning algorithms, naive Bayes (NB) and LR, were used to evaluate the predictive power of various phenotypes. All prediction experiments were performed using a tenfold cross validation method. Among all of the variables, the presence of HW was most strongly associated with type 2 diabetes (p < 0.001, adjusted odds ratio (OR) = 2.07 [95% CI, 1.72–2.49] in men; p < 0.001, adjusted OR = 2.09 [1.79–2.45] in women). When comparing waist circumference (WC) and TG levels as components of the HW phenotype, the association between WC and type 2 diabetes was greater than the association between TG and type 2 diabetes. The phenotypes tended to have higher predictive power in women than in men. Among the phenotypes, the best predictors of type 2 diabetes were waist-to-hip ratio + TG in men (AUC by NB = 0.653, AUC by LR = 0.661) and rib-to-hip ratio + TG in women (AUC by NB = 0.73, AUC by LR = 0.735). Although the presence of HW demonstrated the strongest association with type 2 diabetes, the predictive power of the combined measurements of the actual WC and TG values may not be the best manner of predicting type 2 diabetes. Our findings may provide clinical information concerning the development of clinical decision support systems for the initial screening of type 2 diabetes.
关键词： type 2 diabetes,hypertriglyceridemic waist (HW) phenotype,Anthropometric measurements,triglycerides (TG),predictor,machine learning,data mining
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Determination of Maximum Power Point with a Module to Module Monitoring System, M3S
摘要： We developed and successfully applied data-driven models that heavily rely on readily available remote sensing datasets to investigate probabilities of algal bloom occurrences in Kuwait Bay. An artificial neural network (ANN) model, a multivariate regression (MR) model, and a spatiotemporal hybrid model were constructed, optimized, and validated. Temporal and spatial submodels were coupled in a hybrid modeling framework to improve on the predictive powers of conventional ANN and MR generic models. Sixteen variables (sea surface temperature [SST], chlorophyll a OC3M, chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel-Maritorena (GSM), precipitation, CDOM, turbidity index, PAR, euphotic depth, Secchi depth, wind direction, wind speed, bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture) were used as inputs for the spatial submodel; all of these, with the exception of bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture were used for the temporal submodel as well. Findings include: 1) the ANN model performance exceeded that of the MR model and 2) the hybrid models improved the model performance significantly; 3) the temporal variables most indicative of the timing of bloom propagation are sea surface temperature, Secchi disk depth, wind direction, chlorophyll a (OC3M), and wind speed; and 4) the spatial variables most indicative of algal bloom distribution are the ocean chlorophyll from OC3M, GSM, and the GIOP products; distance to shore; and SST. The adopted methodologies are reliable, cost-effective and could be used to forecast algal bloom occurrences in data-scarce regions.
关键词： remote sensing,Coupled spatiotemporal algal bloom model,data mining,Kuwait bay,neural networks
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network
摘要： Data-driven modeling is one of the research hotspots of photovoltaic (PV) power prediction. However, for some newly built PV power plants, there are not enough historical data to train an accurate model. Therefore, constructing a forecasting model for the PV plants lacking historical data is an urgent problem to be solved. In this paper, we propose a method to transfer the knowledge obtained from historical solar irradiance data to the output prediction. Firstly, the based on hyperparameters of the long short-term memory neural network (LSTM) are optimized and the weights in the neurons are pre-trained, then fine-tuning the deep transfer model with PV output data. In this way, knowledge can be transferred to PV output data. The from solar experimental results show that the proposed method can significantly reduce the prediction error.
关键词： Long short-term memory,Transfer learning,Photovoltaic power forecasting,Hyperparameter optimization,Data mining
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Measurement Uncertainty as a Test Model Assessment Tool
摘要： We developed and successfully applied data-driven models that heavily rely on readily available remote sensing datasets to investigate probabilities of algal bloom occurrences in Kuwait Bay. An artificial neural network (ANN) model, a multivariate regression (MR) model, and a spatiotemporal hybrid model were constructed, optimized, and validated. Temporal and spatial submodels were coupled in a hybrid modeling framework to improve on the predictive powers of conventional ANN and MR generic models. Sixteen variables (sea surface temperature [SST], chlorophyll a OC3M, chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel-Maritorena (GSM), precipitation, CDOM, turbidity index, PAR, euphotic depth, Secchi depth, wind direction, wind speed, bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture) were used as inputs for the spatial submodel; all of these, with the exception of bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture were used for the temporal submodel as well. Findings include: 1) the ANN model performance exceeded that of the MR model and 2) the hybrid models improved the model performance significantly; 3) the temporal variables most indicative of the timing of bloom propagation are sea surface temperature, Secchi disk depth, wind direction, chlorophyll a (OC3M), and wind speed; and 4) the spatial variables most indicative of algal bloom distribution are the ocean chlorophyll from OC3M, GSM, and the GIOP products; distance to shore; and SST. The adopted methodologies are reliable, cost-effective and could be used to forecast algal bloom occurrences in data-scarce regions.
关键词： remote sensing,Coupled spatiotemporal algal bloom model,data mining,Kuwait bay,neural networks
更新于2025-09-23 15:21:01