24 research outputs found
Analysis of approximate nearest neighbor searching with clustered point sets
We present an empirical analysis of data structures for approximate nearest
neighbor searching. We compare the well-known optimized kd-tree splitting
method against two alternative splitting methods. The first, called the
sliding-midpoint method, which attempts to balance the goals of producing
subdivision cells of bounded aspect ratio, while not producing any empty cells.
The second, called the minimum-ambiguity method is a query-based approach. In
addition to the data points, it is also given a training set of query points
for preprocessing. It employs a simple greedy algorithm to select the splitting
plane that minimizes the average amount of ambiguity in the choice of the
nearest neighbor for the training points. We provide an empirical analysis
comparing these two methods against the optimized kd-tree construction for a
number of synthetically generated data and query sets. We demonstrate that for
clustered data and query sets, these algorithms can provide significant
improvements over the standard kd-tree construction for approximate nearest
neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan
15-16, 199
Spatial Patterns Associating Low Birth Weight with Environmental and Behavioral Factors
Low birth weight (LBW) is a significant public health problem in the world. It was estimated globally by the World Health Organization (WHO) that prevalence of LBW was 15% of all births. In Murung Raya district LBW cases remain high. This paper aimed to identify and discuss the relationship between environmental risk factors with LBW in Murung Raya.A spatial analysis was conducted with 150 women as the total participantswho were recruited through the incidence data in 2013-2014. The questionnaires, medical records, and geographic data were measured by Stata software, ArcGis, SatScan, and Geoda. The study results indicated there was significant correlation between health behavior and environmental variables with the strength of external neighborhood effect across LBW risk factors. More intense clustering of high values (hot spots) was found through the spatial analysis showing that most of the cases were located near the defined buffer zone. This research demonstrates that the spatial pattern analysis provided greater statistical power to detect an effect that was not apparent in the previous epidemiology studies
Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentified people? Our research shows that deep learning is possible using relatively low capacity computing. When applied, this demonstrates promising results in spatio-temporal positioning of subjects, in prediction of movement, and assessment of contextual risk. A private surveillance system is particularly suitable in the care of those who may be considered vulnerable
Spatial Patterns Associating Low Birth Weight with Environmental and Behavioral Factors
Low birth weight (LBW) is a significant public health problem in the world. It was estimated globally by the World Health Organization (WHO) that prevalence of LBW was 15% of all births. In Murung Raya district LBW cases remain high. This paper aimed to identify and discuss the relationship between environmental risk factors with LBW in Murung Raya.A spatial analysis was conducted with 150 women as the total participantswho were recruited through the incidence data in 2013-2014. The questionnaires, medical records, and geographic data were measured by Stata software, ArcGis, SatScan, and Geoda. The study results indicated there was significant correlation between health behavior and environmental variables with the strength of external neighborhood effect across LBW risk factors. More intense clustering of high values (hot spots) was found through the spatial analysis showing that most of the cases were located near the defined buffer zone. This research demonstrates that the spatial pattern analysis provided greater statistical power to detect an effect that was not apparent in the previous epidemiology studies
Spatial Analyses of Low Birth Weight Incidence, Indonesia
The etiology of Low Birth Weight (LBW) in Murung Raya is still unclear. This study aimed to find out the relationship between environmental and health behavior risk factors of LBW in Murung Raya. 150 women were recruited through the incidence data 2013- 2014, and the questionnaires, medical records, and geographic data were measured by McNemar, ANOVA, logistic, IRR, MI, z (Gi), and NNI tests. Bivariate analysis showed significant correlation of LBW with TBA care OR= 10, drinking popa OR= 5, smoking OR= 6.1, and accessibility OR = 2.3, with adjusted OR for TBA care OR= 32.78, ANC OR= 27.52 revealing trend lines with ANOVA F=49, and clustering RR=7, MI >0 (four clusters), z (Gi) >1 (two high clusters), and NNI>1 (two high clusters). The spatial analysis provided greater statistical power to detect an effect that was not apparent in the case-control study. This study suggests that preventions, interventions and treatment for LBW not only be conducted by the current epidemiology approach but also by new modern geographic positioning analysis
Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
A large proportion of the population has become used to sharing private infor- mation on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates depends on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentifiable people? Our research shows that deep learning is possible using relatively low capacity computing. The research demonstrates promising results in recognition of human geospatial activity, in prediction of movement, and assessment of contextual risk when applied to spatio-temporal positioning of human subjects. A private surveillance system is thought particularly suitable in the care of those who may, to some, be considered vulnerable
Robust explicit model predictive control for hybrid linear systems with parameter uncertainties
Explicit model-predictive control (MPC) is a widely used control design
method that employs optimization tools to find control policies offline;
commonly it is posed as a semi-definite program (SDP) or as a mixed-integer SDP
in the case of hybrid systems. However, mixed-integer SDPs are computationally
expensive, motivating alternative formulations, such as zonotope-based MPC
(zonotopes are a special type of symmetric polytopes). In this paper, we
propose a robust explicit MPC method applicable to hybrid systems. More
precisely, we extend existing zonotope-based MPC methods to account for
multiplicative parametric uncertainty. Additionally, we propose a convex
zonotope order reduction method that takes advantage of the iterative structure
of the zonotope propagation problem to promote diagonal blocks in the zonotope
generators and lower the number of decision variables. Finally, we developed a
quasi-time-free policy choice algorithm, allowing the system to start from any
point on the trajectory and avoid chattering associated with discrete switching
of linear control policies based on the current state's membership in
state-space regions. Last but not least, we verify the validity of the proposed
methods on two experimental setups, varying physical parameters between
experiments