Cluster identification using image processing

An Alternative Method to Quantify Solids Phase Separation in a Narrow Rectangular CFB Riser

A varied density-based clustering algorithm

Investigation of the hydrodynamics in the regenerator of fluid catalytic cracking unit integrated by chemical looping combustion

Pulverized coal combustion and entrained flow gasification are two of the main technologies worldwide that use coal as feedstock. In a coal-fired power plant the pulverized fuel is transported pneumatically with air in dilute phase to the boiler whereas in the second case the coal is ientrained into the gasifier in dense phase. Biomass could be an option for the direct replacement of coal, however several past experiences with co-firing have shown several limitations using this feedstock, among which the pneumatic conveying. Torrefaction, followed by densification, is a process that can improve the characteristics of the biomass. However, the behavior of torrefied biomass during pneumatic transport has not been investigated in much detail. The present work will show the pneumatic conveying behavior of coal, raw and torrefied biomass at dilute phase and dense phase. Complementary work on grindability and fluidization will be provided for a better insight. About 25% less gas is necessary to transport torrefied biomass in dilute phase and about 2-3 times more is transported in dense phase when compared with raw biomass. The present work pinpoints the role of particle morphology in the pneumatic transport and how milled torrefied biomass pellets resemble coal particles. There is an attempt to correlate all the results (grindability, fluidization and pneumatic transport) with the defined variable cp (flowability), a simple ratio that has the potential to give a preliminary and easy assessment of the morphological properties of a given material and its expected pneumatic behavior.

Elucidating the dynamics of individual IDPs in a biomolecular condensate using single-molecule FRET and molecular simulation.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard Terra and Aqua satellites provides measurements of several atmospheric parameters.This paper focuses on the cloud fraction data representing the number of cloudy pixels divided by the total number of pixels, and available through 1° x 1° grids spatial resolution with daily or monthly temporal resolution. The aim of the study is to propose a novel method called The Spatial-Temporal Implementation Algorithm (STIA) for analysing satellite daily 1° x 1°grid cloud fraction average values for•Comparing two datasets retrieved by MODIS aboard Aqua and Terra satellites to obtain information on the cloud formation in the afternoon and morning, respectively, thus enhancing the temporal resolution.•Comparing the actual parameter with others retrieved by instruments aboard of different satellites characterized by a better resolution. As an example of STIA application, this study uses the Aerosol Optical Depth (AOD) collected by the Ozone Monitoring Instrument (OMI) on board of Aura satellite for comparison with MODIS instrument to achieve and enhanced spatial resolution of the grid-cell.

A 670nm, 3 mW solid-state laser diode module has been used as light source to monitor quality changes of tomatoes (c.v. Trust) stored at room temperature for 8 days. The scattering images were captured and the total number of pixels in the image was taken as an indicator of fruit ripeness. Non-destructive acoustic and traditional compression tests were used to determine the firmness of tomatoes. The results showed significant differences in image size (number of pixels) for tomatoes at different maturity stages. The image size increased during the storage period. Fruit firmness showed a negative correlation with the image size. The soft tomato corresponds to a larger image size. A second order polynomial equation expressed the relationship between the stiffness factor and the size of scattering image (r2≈ 0.62). The system has the potential to be used to evaluate the ripeness of tomato fruits.

To retrospectively compare computer-aided mammographic density estimation (MDEST) with radiologist estimates of percentage density and Breast Imaging Reporting and Data System (BI-RADS) density classification. Institutional Review Board approval was obtained for this HIPAA-compliant study; patient informed consent requirements were waived. A fully automated MDEST computer program was used to measure breast density on digitized mammograms in 65 women (mean age, 53 years; range, 24-89 years). Pixel gray levels in detected breast borders were analyzed, and dense areas were segmented. Percentage density was calculated by dividing the number of dense pixels by the total number of pixels within the borders. Seven breast radiologists (five trained with MDEST, two not trained) prospectively assigned qualitative BI-RADS density categories and visually estimated percentage density on 260 mammograms. Qualitative BI-RADS assessments were compared with new quantitative BI-RADS standards. The reference standard density for this study was established by allowing the five trained radiologists to manipulate the MDEST gray-level thresholds, which segmented mammograms into dense and nondense areas. Statistical tests performed include Pearson correlation coefficients, Bland-Altman agreement method, kappa statistics, and unpaired t tests. There was a close correlation between the reference standard and radiologist-estimated density (R = 0.90-0.95) and MDEST density (R = 0.89). Untrained radiologists overestimated percentage density by an average of 37%, versus 6% for trained radiologists (P < .001). MDEST showed better agreement with the reference standard (average overestimate, 1%; range, -15% to +18%). MDEST correlated better with percentage density than with qualitative BI-RADS categories. There were large overlaps and ranges of percentage density in qualitative BI-RADS categories 2-4. Qualitative BI-RADS categories correlated poorly with new quantitative BI-RADS categories, and 16 (6%) of 260 views were erroneously classified by MDEST. MDEST compared favorably with radiologist estimates of percentage density and is more reproducible than radiologist estimates when qualitative BI-RADS density categories are used. Qualitative and quantitative BI-RADS density assessments differed markedly.

The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence lowering its accuracy. To alleviate these specific problems and increase the clustering accuracy, we propose two methods that use the statistical data from a given dataset's k-nearest neighbor density distribution in order to determine the optimal ɛ values. Our approach removes the burden on the users, and automatically detects the clusters of a given dataset. Furthermore, a method to identify the accurate border objects of separate clusters is proposed and implemented to solve the unpredictability of the original algorithm. Finally, in our experiments, we show that our efficient re-implementation of the original algorithm to automatically cluster datasets and improve the clustering quality of adjoining cluster members provides increase in clustering accuracy and faster running times when compared to earlier approaches.

Effects of internals and distributors on the distribution and growth of bubbles in the conventional gas–solid fluidized bed

Research on Pest Image Processing Method Based on Android Thermal Infrared Lens

The intent of this study was to develop a technique for weight and ripeness estimation of oil palm (Elaeis guieensis Jacq. var. tenera) bunches from hyperspectral and RGB color images. In the experiments, color and hyperspectral images of the bunch were acquired from four different angles, each differing by 90 degrees. Acquired RGB color images were converted to HSI and L*a*b color space. Gray-scale thresholds were used to identify the area of the bunch and the area of space between the fruits. The total number of pixels in the bunch and the space were counted, respectively. In the hyperspectral images, the total number of pixels in the bunch was also counted from an image composed of three wavelengths (560 nm, 680 nm, and 740 nm), while the total number of pixels of space between fruits was obtained at a wavelength of 910 nm. From these sets of data, weight-estimation equations were determined by linear regression (LR) or multiple linear regression (MLR). As a result, the coefficient of determination (R2) of actual weight and estimated weight were at a level of 0.989 and 0.992 for color and hyperspectral images, respectively. Estimation of oil palm bunch ripeness was also tested. Bunches belonging to 4 classes of ripeness (overripe, ripe, underripe, and unripe) were used for this study. Since ripeness estimation from overall data from a bunch was quite difficult, we focused on the difference in colors or reflectivity of the portion concealed and not-concealed with fronds. Euclidean distances between the test sample and the standard 4 classes of ripeness were calculated, and the test sample was classified into the ripeness class that had the shortest distance from the sample. In the classification based on color image, average RGB values of concealed and not-concealed areas were used, while in hyperspectral images the average intensity values of fruits pixels from the concealed area were used. The results of validation experiments with the developed estimation methods indicated acceptable estimation accuracy, and a possibility for practical use to estimate the ripeness of oil palm bunches.

In this paper, we propose a novel framework to evaluate the depth resolution of axially distributed stereo sensing (ADSS) under fixed resource constraints. The proposed framework can evaluate the performance of ADSS systems based on various sensing parameters such as the number of cameras, the number of total pixels, pixel size and so on. The Monte Carlo simulations for the proposed framework are performed and the evaluation results are presented.

There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the community detection in network data. The algorithm is paired with the benchmark model Graph sample and aggregate (GraphSAGE) to show the adaptability of ILDPC for community detection. The plotted decision diagram shows that the ILDPC algorithm is more discriminative in selecting density peak points compared to the original algorithm. Finally, the performance of K-means and other clustering algorithms on this benchmark model is compared, and the algorithm is proved to be more suitable for community detection in sparse networks with the benchmark model on the evaluation criterion F1-score. The sensitivity of the parameters of the ILDPC algorithm to the low-dimensional vector set output by the benchmark model GraphSAGE is also analyzed.

The clustering of spatio‐temporal events has become one of the most important research branches of spatio‐temporal data mining. However, the discovery of clusters of spatio‐temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio‐temporal window for estimating the density around each event, and the density threshold for evaluating the significance of clusters. To make the clustering of spatio‐temporal events objective, in this study these two parameters were adaptively generated from statistical information about the dataset. More precisely, the density threshold was statistically modeled as an adjusted significance level controlled by the cardinality and support domain of the dataset, and the appropriate sizes of spatio‐temporal windows for clustering were determined by the spatio‐temporal classification entropy and stability analysis. Experiments on both simulated and earthquake datasets were conducted, and the results show that the proposed method can identify clusters of different shapes and densities.