566 research outputs found
Optimal trajectory generation with DMOC versus NTG : application to an underwater glider and a JPL aerobot.
Optimal trajectory generation is an essential part for robotic explorers to execute the total exploration of deep oceans or outer space planets while curiosity of human and technology advancements of society both require robots to search for unknown territories efficiently and safely. As one of state-of-the-art optimal trajectory generation methodologies, Nonlinear Trajectory Generation (NTG) combines with B-spline, nonlinear programming, differential flatness technique to generate optimal trajectories for modelled mechanical systems. While Discrete Mechanics and Optimal Control (DMOC) is a newly proposed optimal control method for mechanical systems, it is based on direct discretization of Lagrange-d\u27Alembert principle. In this dissertation, NTG is utilized to generate trajectories for an underwater glider with a 3D B-spline ocean current model. The optimal trajectories are corresponding well with the Lagrangian Coherent Structures (LCS). Then NTG is utilized to generate 3D opportunistic trajectories for a JPL (Jet Propulsion Laboratory) Aerobot by taking advantage of wind velocity. Since both DMOC and NTG are methods which can generate optimal trajectories for mechanical systems, their differences in theory and application are investigated. In a simple ocean current example and a more complex ocean current model, DMOC with discrete Euler-Lagrange constraints generates local optimal solutions with different initial guesses while NTG is also generating similar solutions with more computation time and comparable energy consumption. DMOC is much easier to implement than NTG because in order to generate good solutions in NTG, its variables need to be correctly defined as B-spline variables with rightly-chosen orders. Finally, the MARIT (Multiple Air Robotics Indoor Testbed) is established with a Vicon 8i motion capture system. Six Mcam 2 cameras connected with a datastation are able to track real-time coordinates of a draganflyer helicopter. This motion capture system establishes a good foundation for future NTG and DMOC algorithms verifications
Comparative Study on the Senior Secondary School Mathematics Curricula Development in Ethiopia and Australia
The main objective of this study is to compare the process of the senior secondary school mathematics curricula development in Ethiopia and Australia. The study was investigated qualitatively with document analysis and semi-structured interview research methods. The documents were collected from Federal Democratic Republic of Ethiopia Ministry of Education website and Australian curriculum website. The documents were analyzed and supported by interviews. The study was conducted based on four themes needs assessment, developing/writing the curriculum, implementation, and monitoring and evaluation. The study revealed both similarities and differences. The considerable differences in the senior secondary school mathematics curriculum development process are (1) emphasis given to international research results and contemporary issues on mathematics education as inputs for curriculum development (2) the underlying principle of content standard organizations (3) trialing the curriculum before implementation initiated, and (4) monitoring and evaluation strategies. Even though substantial differences exist, the similarities are (1) conducting needs assessment and (2) the adoption of the constructivism approach. Depending on the findings of the study, the suggested recommendations were presented under conclusion section. Keywords: Ethiopia, Australia, comparative study, senior secondary school curriculum, curriculum developmen
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Continuous influence maximization (CIM) generalizes the original influence
maximization by incorporating general marketing strategies: a marketing
strategy mix is a vector such that for each
node in a social network, could be activated as a seed of diffusion
with probability , where is a strategy activation
function satisfying DR-submodularity. CIM is the task of selecting a strategy
mix with constraint where is a budget
constraint, such that the total number of activated nodes after the diffusion
process, called influence spread and denoted as , is
maximized. In this paper, we extend CIM to consider budget saving, that is,
each strategy mix has a cost where is a
convex cost function, we want to maximize the balanced sum where is a balance parameter, subject
to the constraint of . We denote this problem as
CIM-BS. The objective function of CIM-BS is neither monotone, nor DR-submodular
or concave, and thus neither the greedy algorithm nor the standard result on
gradient method could be directly applied. Our key innovation is the
combination of the gradient method with reverse influence sampling to design
algorithms that solve CIM-BS: For the general case, we give an algorithm that
achieves -approximation, and for the case
of independent strategy activations, we present an algorithm that achieves
approximation.Comment: To appear in AAAI-20, 43 page
Test-Time Compensated Representation Learning for Extreme Traffic Forecasting
Traffic forecasting is a challenging task due to the complex spatio-temporal
correlations among traffic series. In this paper, we identify an underexplored
problem in multivariate traffic series prediction: extreme events. Road
congestion and rush hours can result in low correlation in vehicle speeds at
various intersections during adjacent time periods. Existing methods generally
predict future series based on recent observations and entirely discard
training data during the testing phase, rendering them unreliable for
forecasting highly nonlinear multivariate time series. To tackle this issue, we
propose a test-time compensated representation learning framework comprising a
spatio-temporal decomposed data bank and a multi-head spatial transformer model
(CompFormer). The former component explicitly separates all training data along
the temporal dimension according to periodicity characteristics, while the
latter component establishes a connection between recent observations and
historical series in the data bank through a spatial attention matrix. This
enables the CompFormer to transfer robust features to overcome anomalous events
while using fewer computational resources. Our modules can be flexibly
integrated with existing forecasting methods through end-to-end training, and
we demonstrate their effectiveness on the METR-LA and PEMS-BAY benchmarks.
Extensive experimental results show that our method is particularly important
in extreme events, and can achieve significant improvements over six strong
baselines, with an overall improvement of up to 28.2%.Comment: 13 pages, 10 figures, 5 table
Normalizing Flow with Variational Latent Representation
Normalizing flow (NF) has gained popularity over traditional maximum
likelihood based methods due to its strong capability to model complex data
distributions. However, the standard approach, which maps the observed data to
a normal distribution, has difficulty in handling data distributions with
multiple relatively isolated modes. To overcome this issue, we propose a new
framework based on variational latent representation to improve the practical
performance of NF. The idea is to replace the standard normal latent variable
with a more general latent representation, jointly learned via Variational
Bayes. For example, by taking the latent representation as a discrete sequence,
our framework can learn a Transformer model that generates the latent sequence
and an NF model that generates continuous data distribution conditioned on the
sequence. The resulting method is significantly more powerful than the standard
normalization flow approach for generating data distributions with multiple
modes. Extensive experiments have shown the advantages of NF with variational
latent representation.Comment: 24 pages, 7 figure
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