Hydra: A Parallel Adaptive Grid Code

We describe the first parallel implementation of an adaptive particle-particle, particle-mesh code with smoothed particle hydrodynamics. Parallelisation of the serial code, ``Hydra'', is achieved by using CRAFT, a Cray proprietary language which allows rapid implementation of a serial code on a parallel machine by allowing global addressing of distributed memory. The collisionless variant of the code has already completed several 16.8 million particle cosmological simulations on a 128 processor Cray T3D whilst the full hydrodynamic code has completed several 4.2 million particle combined gas and dark matter runs. The efficiency of the code now allows parameter-space explorations to be performed routinely using $64^3$ particles of each species. A complete run including gas cooling, from high redshift to the present epoch requires approximately 10 hours on 64 processors. In this paper we present implementation details and results of the performance and scalability of the CRAFT version of Hydra under varying degrees of particle clustering.Comment: 23 pages, LaTex plus encapsulated figure

Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

On adaptive filter structure and performance

SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hours before strong solar flare events

Individualized Wrist Motion Models for Detecting Eating Episodes Using Deep Learning

This thesis considers the problem of detecting eating episodes such as meals and snacks, by tracking wrist motion using smartwatch device. Previous work by our group has trained a wrist motion classifier using a large data set collected from 351 people to learn general eating behaviors. We call this a group model. This thesis investigates training the classifier with the same model architecture on new data collected by 8 people, and training the individualized classifier separately for each person. We call these individual models. The main goal in this work is to determine if individual models provide higher accuracy in detecting eating episodes, with fewer false positives, compared to the group model. By comparing their performance, we can also know if the improvement from individual models varies for each individual. In data collection, two data sets were used. One is the individual data set, which was collected from 8 participants and each participant has at least 10 days of wrist motion 6-axis timeseries data. There are 115 days, 1,064.5 hours and 246 meals collected in total in this data set. The second one is the group data set, called Clemson All-day Data set (CAD), collected in previous work. This group data set collected from 351 participants contains 354 days, 1,133 meals, 250 eating hours and 4,680 hours in total. Two data sets were first processed using smoothing and normalization techniques and then cut along time by a sliding window to generate training and testing samples for training models. In model training and evaluation, all models used the same convolution neural network architecture. Only one group model was trained on CAD group data set and this group model was used to compare with all other individual models. We used 5-fold cross validation to train and evaluate 5 individual models per individual. In model evaluation, we selected weighted accuracy (WAcc) as time metric to measure the models’ ability of classifying each window sample as eating or non-eating. We also selected true positive rate (TPR) and ratio of false positive over true positive (FP/TP) as episode metrics to measure model’s ability of detecting each meal episode. TPR measures how many true eating episode are detected correctly and FP/TP measures the ratio of wrong detection amount over true detection amount. Hence when TPR is larger and FP/TP is smaller, model performs better. WAcc, TPR and FP/TP were measured by cross validation. When measuring the time metric, we found that over 8 participants, the average WAcc on all individual models is 0.819 and the average WAcc on the group model is 0.780. On average, the individual models outperform the group model. Moreover, the improvement of individual models over the group model can vary per individual. For example, in one individual data set, individual models with WAcc of 0.897 have obvious improvement compared to the group model with WAcc of 0.774. In another individual data set, WAcc of 0.958 from individual models is very close to WAcc of 0.956 from the group model. In the measurement of episode metrics, we found that before tuning hyper-parameters Ts and Te, compared to the group model, individual models have the average improvement of 8.6% on TPR, but -14.4% on FP/TP. After tuning Ts and Te, individual models have the average improvement of 10.1% on TPR and 33.2 % on FP/TP. Tuning Ts and Te can improve the individual models’ episode metrics