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"title": "A Study on the characteristics of the impact produced when the bubble on the crankshaft's main bearing ball collapses",
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"abstract": "The impact produced when the bubble on the main bearing wall of an internal combustion engine is an important factor causing cavitation failure. Based on the average Reynolds equation of lubricating oil and the energy of cavitation collapse, the thermoelastic fluid dynamics (TEHD) model of the main bearing and the bubble collapse Volume of Fluid (VOF) model in the oil film attached to the bearing wall were established, the load and flow field distribution when the bubble collapses were analyzed, and the impact characteristics on the bearing wall when the bubble collapses were explored. The results show that: when the bubble collapses, it begins to sag from the top until it ruptures to form two bubbles; and within a very short period of time when the bubble collapses, the pressure of the top jet on the wall can reach 31MPa and above.",
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"content": "The impact produced when the bubble on the main bearing wall of an internal combustion engine is an important factor causing cavitation failure. Based on the average Reynolds equation of lubricating oil and the energy of cavitation collapse, the thermoelastic fluid dynamics (TEHD) model of the main bearing and the bubble collapse Volume of Fluid (VOF) model in the oil film attached to the bearing wall were established, the load and flow field distribution when the bubble collapses were analyzed, and the impact characteristics on the bearing wall when the bubble collapses were explored. The results show that: when the bubble collapses, it begins to sag from the top until it ruptures to form two bubbles; and within a very short period of time when the bubble collapses, the pressure of the top jet on the wall can reach 31MPa and above.",
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"affiliation": "Beijing Institute of Technology,Beijing,China",
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"title": "Research on Cavitation Resistance by Vortex in Water Hydraulic Valves",
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"abstract": "Cavitation is a major failure in water hydraulic valve causing noise, vibration and destruction of material. Many experiments have found that bubbles are not easily formed and exploded near the vortex flow field. For reducing cavitation of water hydraulic valves, based on an original valve, 3 structures models about forming vortex flow field were designed including jet hole structure, spiral groove structure, and combining jet hole and spiral groove structure. Using a computational fluid dynamics approach, adopting to the Mixture model as multiphase flow model and the Full Cavitation model as cavitation model, the relationship between formation of cavitation bubbles and vortex flow field were numerically analyzed. The effects of vortex flow field and the structure forming it was simulated. Compared with original valves, formation and distribution of cavitation, the law of bubble mass transition, and analysis of different flow fields were discussed in this paper. This investigation shows that vortex flow field and the structure forming vortex flow field can improve cavitation resistance in water hydraulic valve.",
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"content": "Cavitation is a major failure in water hydraulic valve causing noise, vibration and destruction of material. Many experiments have found that bubbles are not easily formed and exploded near the vortex flow field. For reducing cavitation of water hydraulic valves, based on an original valve, 3 structures models about forming vortex flow field were designed including jet hole structure, spiral groove structure, and combining jet hole and spiral groove structure. Using a computational fluid dynamics approach, adopting to the Mixture model as multiphase flow model and the Full Cavitation model as cavitation model, the relationship between formation of cavitation bubbles and vortex flow field were numerically analyzed. The effects of vortex flow field and the structure forming it was simulated. Compared with original valves, formation and distribution of cavitation, the law of bubble mass transition, and analysis of different flow fields were discussed in this paper. This investigation shows that vortex flow field and the structure forming vortex flow field can improve cavitation resistance in water hydraulic valve.",
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"normalizedAbstract": "Cavitation is a major failure in water hydraulic valve causing noise, vibration and destruction of material. Many experiments have found that bubbles are not easily formed and exploded near the vortex flow field. For reducing cavitation of water hydraulic valves, based on an original valve, 3 structures models about forming vortex flow field were designed including jet hole structure, spiral groove structure, and combining jet hole and spiral groove structure. Using a computational fluid dynamics approach, adopting to the Mixture model as multiphase flow model and the Full Cavitation model as cavitation model, the relationship between formation of cavitation bubbles and vortex flow field were numerically analyzed. The effects of vortex flow field and the structure forming it was simulated. Compared with original valves, formation and distribution of cavitation, the law of bubble mass transition, and analysis of different flow fields were discussed in this paper. This investigation shows that vortex flow field and the structure forming vortex flow field can improve cavitation resistance in water hydraulic valve.",
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"affiliation": "College of Mechanical and Electrical Engineering, Harbin Engineering University,Harbin,China",
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"affiliation": "College of Mechanical and Electrical Engineering, Harbin Engineering University,Harbin,China",
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"abstract": "Effective use of digital video can be greatly improved by a combination of two technologies: computer vision for automated video analysis and information visualization for data visualization. The unstructured, spatio-temporal nature of video poses tough challenges in the extraction of semantics using fully automated techniques. In the CueVideo project, we combine these automated technologies together with a user interface designed for rapid filtering and comprehension of video content. Our interface introduces two new techniques for viewing video and builds upon existing techniques to provide synergistic views of the video content. We also report on a preliminary user study that compares the efficacy of these views in providing comprehension of video content.",
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"abstract": "For handwritten pattern recognition, multiple classifier system has been shown to be useful in improving recognition rates. One of the most important issues to optimize a multiple classifier system is to select a group of adequate classifiers, known as ensemble of classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection does not give better performance than static selection. We propose four new dynamic selection schemes which explore the property of the oracle concept. The result suggests that the proposed schemes are apparently better than the static selection using the majority voting rule for combining classifiers.",
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"content": "For handwritten pattern recognition, multiple classifier system has been shown to be useful in improving recognition rates. One of the most important issues to optimize a multiple classifier system is to select a group of adequate classifiers, known as ensemble of classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection does not give better performance than static selection. We propose four new dynamic selection schemes which explore the property of the oracle concept. The result suggests that the proposed schemes are apparently better than the static selection using the majority voting rule for combining classifiers.",
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"fullName": "A.H.-R. Ko",
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"abstract": "Weather forecasting is a problem where an enormous amount of data must be processed. Severe storms cause a significant amount of damages and loss every year in part due to the insufficiency of the current techniques in producing reliable forecasts. We propose an algorithm that analyzes satellite images from the vast historical archives to predict severe storms. Conventional weather forecasting involves solving numerical models based on sensory data. It has been challenging for computers to make forecasts based on the visual patterns from satellite images. In our system we extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. Particularly, the algorithm extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts.",
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"abstract": "This paper presents a weather map prediction method using RGB metaphorical feature extraction for atmospheric pressure patterns. In the field of meteorological science, predicting weather based on the analysis of observational data and the knowledge of weather experts is crucial. Weather experts draw weather maps based on air pressure distribution; hence, we believe that weather maps entail the interpretations of weather experts. In this study, we improved the prediction accuracy by using machine learning to recognize patterns of qualitative expert interpretations that cannot be predicted by analyzing observed data alone. The proposed method can be realized via two steps. The first is developing a module for extracting pressure pattern features from a weather map. Certain features, such as tropical cyclones or atmospheric high/low pressure distributions, are emphasized in weather maps to facilitate better understanding of the weather features. Therefore, we can predict weather features based on the knowledge of weather experts using data that contain their interpretations, particularly weather maps. The developed module extracts the atmospheric pressure features from the current weather map as an RGB metaphorical gradation map. The second step is developing a module to design a predicted weather map using the extracted features. The weather map of the following day is predicted using pix2pix. To the best of our knowledge, our method for extracting features from weather maps is the first to create a predicted weather map automatically.",
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"content": "This paper presents a weather map prediction method using RGB metaphorical feature extraction for atmospheric pressure patterns. In the field of meteorological science, predicting weather based on the analysis of observational data and the knowledge of weather experts is crucial. Weather experts draw weather maps based on air pressure distribution; hence, we believe that weather maps entail the interpretations of weather experts. In this study, we improved the prediction accuracy by using machine learning to recognize patterns of qualitative expert interpretations that cannot be predicted by analyzing observed data alone. The proposed method can be realized via two steps. The first is developing a module for extracting pressure pattern features from a weather map. Certain features, such as tropical cyclones or atmospheric high/low pressure distributions, are emphasized in weather maps to facilitate better understanding of the weather features. Therefore, we can predict weather features based on the knowledge of weather experts using data that contain their interpretations, particularly weather maps. The developed module extracts the atmospheric pressure features from the current weather map as an RGB metaphorical gradation map. The second step is developing a module to design a predicted weather map using the extracted features. The weather map of the following day is predicted using pix2pix. To the best of our knowledge, our method for extracting features from weather maps is the first to create a predicted weather map automatically.",
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"abstract": "The nano-silica was employed to prepare HIPS/silica nanocomposites through melt blending method. The microstructure of HIPS/silica nanocomposites was observed by transmission electron microscopy technique. The flammability of the nanocomposites was evaluated by the cone calorimeter. Digital camera and scanning electron microscopy were used to examine the macro-structure, submicro-structure and micro-structure of fire residues. The results show that the addition of nano-silica significantly reduces the heat release rates (HRR) and mass loss rates (MLR) of the HIPS. Compared with the pure HIPS, the peak heat release rates (HRR) of HIPS/silica nanocomposites containing 5%, 10% and 20% nano-silica by mass are 27.52%, 46.98% and 64.76% down respectively. The addition of nano-silica also forms the fire residues with featured structures which has a vital impact on flammability reduction of the composites. The fine fire residues of HIPS/silica nanocomposites are composed as cellular structure in sub-micro scale and three-dimensional network structures in micro scale.",
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{
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"content": "The nano-silica was employed to prepare HIPS/silica nanocomposites through melt blending method. The microstructure of HIPS/silica nanocomposites was observed by transmission electron microscopy technique. The flammability of the nanocomposites was evaluated by the cone calorimeter. Digital camera and scanning electron microscopy were used to examine the macro-structure, submicro-structure and micro-structure of fire residues. The results show that the addition of nano-silica significantly reduces the heat release rates (HRR) and mass loss rates (MLR) of the HIPS. Compared with the pure HIPS, the peak heat release rates (HRR) of HIPS/silica nanocomposites containing 5%, 10% and 20% nano-silica by mass are 27.52%, 46.98% and 64.76% down respectively. The addition of nano-silica also forms the fire residues with featured structures which has a vital impact on flammability reduction of the composites. The fine fire residues of HIPS/silica nanocomposites are composed as cellular structure in sub-micro scale and three-dimensional network structures in micro scale.",
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"normalizedAbstract": "The nano-silica was employed to prepare HIPS/silica nanocomposites through melt blending method. The microstructure of HIPS/silica nanocomposites was observed by transmission electron microscopy technique. The flammability of the nanocomposites was evaluated by the cone calorimeter. Digital camera and scanning electron microscopy were used to examine the macro-structure, submicro-structure and micro-structure of fire residues. The results show that the addition of nano-silica significantly reduces the heat release rates (HRR) and mass loss rates (MLR) of the HIPS. Compared with the pure HIPS, the peak heat release rates (HRR) of HIPS/silica nanocomposites containing 5%, 10% and 20% nano-silica by mass are 27.52%, 46.98% and 64.76% down respectively. The addition of nano-silica also forms the fire residues with featured structures which has a vital impact on flammability reduction of the composites. The fine fire residues of HIPS/silica nanocomposites are composed as cellular structure in sub-micro scale and three-dimensional network structures in micro scale.",
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"proceeding": {
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"title": "2016 32nd Southern Biomedical Engineering Conference (SBEC)",
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"title": "Cellulose Nanofibers as a Nucleating Template for Interfacial Crystallization of Poly (Ethylene)-b-Poly (Ethylene Glycol)",
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"abstract": "We investigated cellulose nanofibers as a unique template to provide nucleating surface for interfacial crystallization of a block copolymer. Cellulose microfibers were ultrasonically dispersed to generate cellulose nanofibers in the size range of ~ 20-30 nm in diameter, while the block copolymer was crystallized using a solution crystallization approach. The polymer was crystallized on the surface of the cellulose nanofibers as periodically arranged lamellar crystals. The crystal morphology was investigated using scanning electron microscopy (SEM), while the crystallization behavior was studied using differential scanning calorimetry (DSC), and polarized light microscopy (PLM). Fourier transform infra-red spectroscopy (FTIR) was used to study the chemical structure of cellulose-copolymer hybrid system. The crystallinity and the morphology of the polymer varied according to the composition and the concentration of the polymer, respectively. Biological activity of the hybrid system was studied using simulated body fluid (SBF), and the results obtained using osteoblasts cells suggests a novel strategy and potential biomaterial for tissue engineering applications.",
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"content": "We investigated cellulose nanofibers as a unique template to provide nucleating surface for interfacial crystallization of a block copolymer. Cellulose microfibers were ultrasonically dispersed to generate cellulose nanofibers in the size range of ~ 20-30 nm in diameter, while the block copolymer was crystallized using a solution crystallization approach. The polymer was crystallized on the surface of the cellulose nanofibers as periodically arranged lamellar crystals. The crystal morphology was investigated using scanning electron microscopy (SEM), while the crystallization behavior was studied using differential scanning calorimetry (DSC), and polarized light microscopy (PLM). Fourier transform infra-red spectroscopy (FTIR) was used to study the chemical structure of cellulose-copolymer hybrid system. The crystallinity and the morphology of the polymer varied according to the composition and the concentration of the polymer, respectively. Biological activity of the hybrid system was studied using simulated body fluid (SBF), and the results obtained using osteoblasts cells suggests a novel strategy and potential biomaterial for tissue engineering applications.",
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"fno": "07458998",
"keywords": [
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"affiliation": null,
"fullName": "Dilip Depan",
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"affiliation": null,
"fullName": "Morganna Ochoa",
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"affiliation": null,
"fullName": "Nina Collazos",
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{
"proceeding": {
"id": "18jXU1FV7vW",
"title": "2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC)",
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"doi": "10.1109/MLHPC.2018.8638633",
"title": "Automated Labeling of Electron Microscopy Images Using Deep Learning",
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"abstract": "Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of 512 × 512 pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope - transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning - i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem- to generalize labels and achieve an accuracy of approximately 70% despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.",
"abstracts": [
{
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"content": "Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of 512 × 512 pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope - transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning - i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem- to generalize labels and achieve an accuracy of approximately 70% despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.",
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"normalizedAbstract": "Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of 512 × 512 pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope - transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning - i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem- to generalize labels and achieve an accuracy of approximately 70% despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.",
"fno": "08638633",
"keywords": [
"Convolutional Neural Nets",
"Electron Microscopy",
"Image Classification",
"Learning Artificial Intelligence",
"Meta Data",
"Electron Microscopy Images",
"Deep Learning",
"Image Corpus",
"Convolutional Network Architecture",
"Convolution Layers",
"Automated Labeling",
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"affiliation": "Lawrence Berkeley National Laboratory, Computational Research Division, Berkeley, One Cyclotron Road, California, 94720",
"fullName": "Gunther H. Weber",
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"affiliation": "Molecular Foundry, Lawrence Berkeley National Laboratory, National Center for Electron Microscopy, Berkeley, One Cyclotron Road, California, 94720",
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"affiliation": "Lawrence Berkeley National Laboratory, Computational Research Division, Berkeley, One Cyclotron Road, California, 94720",
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"abstract": "In many computer systems with large data computations, the delay of memory access is one of the major performance bottlenecks. In this paper, we propose an enhanced field remapping scheme for dynamically allocated structures in order to provide better locality than conventional field layouts. Our proposed scheme reduces cache miss rates drastically by aggregating and grouping fields from multiple instances of the same structure, which implies the performance improvement and power reduction. Our methodology will become more important in the design space exploration, especially as the embedded systems for data oriented application become prevalent. Experimental results show that average L1 and L2 data cache misses are reduced by 23% and 17%, respectively. Due to the enhanced localities, our remapping achieves 13% faster execution time on average than original programs. It also reduces power consumption by 18% for data cache",
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"abstract": "With the rapid advances in supercomputing and numerical simulations, the output data of scientific computing is expanding rapidly, bringing tough challenges for data sharing and data archiving. Data compression can mitigate these challenges by reducing the size of the data to be stored or transferred. However, data compression has to achieve a good balance between compression ratios and throughput, before it can be employed in the high-end computing environments. In this paper, we propose and evaluate a versatile compression method for floating-point data. Firstly, it can achieve much better compression ratios than existing general purpose compression methods with promising throughputs. Secondly, it supports asymmetric decompression: losslessly compressed data can be decompressed lossily, thus facilitating data analysis in different precision requirements. Thirdly, it can leverage existing different kinds of general purpose compressors (zlib, lz4, for instance), and provide more flexible trade-offs between compression ratios and throughputs. Evaluations demonstrate that our compressor can achieve comparable compression ratios with the best compressors, while the compression and decompression throughputs can be 10 times higher than them. The single thread compression throughputs can be 135 MB/s, and the decompression throughputs can be 194 MB/s.",
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"normalizedAbstract": "With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.",
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"abstract": "We propose the combination of two recently introduced methods for the interactive visual data mining of large collections of data. Both, Hyperbolic Multi-Dimensional Scaling (HMDS) and Hyperbolic Self-Organizing Maps (HSOM) employ the extraordinary advantages of the hyperbolic plane (H2): (i) the underlying space grows exponentially with its radius around each point - ideal for embedding high-dimensional (or hierarchical) data; (ii) the Poincar? model of the IH2 exhibits a fish-eye perspective with a focus area and a context preserving surrounding; (iii) the mouse binding of focus-transfer allows intuitive interactive navigation. The HMDS approach extends multi-dimensional scaling and generates a spatial embedding of the data representing their dissimilarity structure as faithfully as possible. It is very suitable for interactive browsing of data object collections, but calls for batch precomputation for larger collection sizes. The HSOM is an extension of Kohonen's Self-Organizing Map and generates a partitioning of the data collection assigned to an IH2 tessellating grid. While the algorithm's complexity is linear in the collection size, the data browsing is rigidly bound to the underlying grid. By integrating the two approaches we gain the synergetic effect of adding advantages of both. And the hybrid architecture uses consistently the IH2 visualization and navigation concept. We present the successfully application to a text mining example involving the Reuters-21578 text corpus.",
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