280 research outputs found
Trajectory Servoing: Image-Based Trajectory Tracking without Absolute Positioning
The thesis describes an image based visual servoing (IBVS) system for a non-holonomic robot to achieve good trajectory following without real-time robot pose information
and without a known visual map of the environment. We call it trajectory servoing. The critical component is a feature based, indirect SLAM method to provide a pool of available features with estimated depth and covariance, so that they may be propagated forward in time to generate image feature trajectories with uncertainty information for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than SLAM pose-based feedback and further improved by a weighted least square controller using covariance from the underlying SLAM system.M.S
RESEARCH ON THE INFLUENCE OF THE NEW GENERATION STAFF’S WORK STRESS ON JOB BURNOUT-THE REGULATING EFFECT OF NERVOUSNESS AND ACCOUNTABILITY
RESEARCH ON THE INFLUENCE OF THE NEW GENERATION STAFF’S WORK STRESS ON JOB BURNOUT-THE REGULATING EFFECT OF NERVOUSNESS AND ACCOUNTABILITY
A topological approach for protein classification
Protein function and dynamics are closely related to its sequence and
structure. However prediction of protein function and dynamics from its
sequence and structure is still a fundamental challenge in molecular biology.
Protein classification, which is typically done through measuring the
similarity be- tween proteins based on protein sequence or physical
information, serves as a crucial step toward the understanding of protein
function and dynamics. Persistent homology is a new branch of algebraic
topology that has found its success in the topological data analysis in a
variety of disciplines, including molecular biology. The present work explores
the potential of using persistent homology as an indepen- dent tool for protein
classification. To this end, we propose a molecular topological fingerprint
based support vector machine (MTF-SVM) classifier. Specifically, we construct
machine learning feature vectors solely from protein topological fingerprints,
which are topological invariants generated during the filtration process. To
validate the present MTF-SVM approach, we consider four types of problems.
First, we study protein-drug binding by using the M2 channel protein of
influenza A virus. We achieve 96% accuracy in discriminating drug bound and
unbound M2 channels. Additionally, we examine the use of MTF-SVM for the
classification of hemoglobin molecules in their relaxed and taut forms and
obtain about 80% accuracy. The identification of all alpha, all beta, and
alpha-beta protein domains is carried out in our next study using 900 proteins.
We have found a 85% success in this identifica- tion. Finally, we apply the
present technique to 55 classification tasks of protein superfamilies over 1357
samples. An average accuracy of 82% is attained. The present study establishes
computational topology as an independent and effective alternative for protein
classification
Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM
This paper describes an image based visual servoing (IBVS) system for a
nonholonomic robot to achieve good trajectory following without real-time robot
pose information and without a known visual map of the environment. We call it
trajectory servoing. The critical component is a feature-based, indirect SLAM
method to provide a pool of available features with estimated depth, so that
they may be propagated forward in time to generate image feature trajectories
for visual servoing. Short and long distance experiments show the benefits of
trajectory servoing for navigating unknown areas without absolute positioning.
Trajectory servoing is shown to be more accurate than pose-based feedback when
both rely on the same underlying SLAM system
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Data-free meta-learning (DFML) aims to enable efficient learning of new tasks
by meta-learning from a collection of pre-trained models without access to the
training data. Existing DFML work can only meta-learn from (i) white-box and
(ii) small-scale pre-trained models (iii) with the same architecture,
neglecting the more practical setting where the users only have inference
access to the APIs with arbitrary model architectures and model scale inside.
To solve this issue, we propose a Bi-level Data-free Meta Knowledge
Distillation (BiDf-MKD) framework to transfer more general meta knowledge from
a collection of black-box APIs to one single meta model. Specifically, by just
querying APIs, we inverse each API to recover its training data via a
zero-order gradient estimator and then perform meta-learning via a novel
bi-level meta knowledge distillation structure, in which we design a boundary
query set recovery technique to recover a more informative query set near the
decision boundary. In addition, to encourage better generalization within the
setting of limited API budgets, we propose task memory replay to diversify the
underlying task distribution by covering more interpolated tasks. Extensive
experiments in various real-world scenarios show the superior performance of
our BiDf-MKD framework
Preparation of graphene film reinforced CoCrFeNiMn high-entropy alloy matrix composites with strength-plasticity synergy via flake powder metallurgy method
Inspired by the design principle of pearl structure, a bottom-up flake powder self-assembly arrangement strategy, flake powder metallurgy, is used to prepare graphene films (GFs) reinforced CoCrFeNiMn high-entropy alloy (HEA) matrix composites with a pearl laminated structure. Flaky HEA powder was prepared by ball milling method and homogeneously mixed with Ni plated GFs. Vacuum hot-press sintering (VHPS) technique was carried out to solidify the mixed powders to obtain composites with uniform distribution of GFs(Ni) and flaky HEA. The results show that the bottom-up preparation strategy can effectively fabricate bionic laminated HEA matrix composites, and the composites have a distinct pearly laminated structure. The tensile strength of the composites with 5 vol% GFs(Ni) content reached 834.04 MPa, and the elongation reached 26.58 %. The compressive strength in parallel and perpendicular laminar directions reached 2069.66 MPa and 2418.45 MPa at 50 % strain, respectively. The laminated GFs(Ni)/HEA matrix composites possessed excellent strength and maintained good plasticity. In this study, the strengthening and toughening mechanism of the laminated GFs(Ni)/HEA matrix composites is discussed in detail, and the results show that the laminated structure and GFs(Ni) are favorable for the hardening and strengthening of the HEA matrix
Learning Models of Adversarial Agent Behavior under Partial Observability
The need for opponent modeling and tracking arises in several real-world
scenarios, such as professional sports, video game design, and drug-trafficking
interdiction. In this work, we present Graph based Adversarial Modeling with
Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent
agent. GrAMMI is a novel graph neural network (GNN) based approach that uses
mutual information maximization as an auxiliary objective to predict the
current and future states of an adversarial opponent with partial
observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion
domains inspired by real-world scenarios, where a team of heterogeneous agents
is tasked with tracking and interdicting a single adversarial agent, and the
adversarial agent must evade detection while achieving its own objectives. With
the mutual information formulation, GrAMMI outperforms all baselines in both
domains and achieves 31.68% higher log-likelihood on average for future
adversarial state predictions across both domains.Comment: 8 pages, 3 figures, 2 table
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search
agents must collaborate to track an adversarial, evasive agent. The
heterogeneous search team may only have access to a limited number of past
adversary trajectories within a large search space. This problem is challenging
for both model-based searching and reinforcement learning (RL) methods since
the adversary exhibits reactionary and deceptive evasive behaviors in a large
space leading to sparse detections for the search agents. To address this
challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages
the estimated adversary location from our learnable filtering model. We show
that our MARL architecture can outperform all baselines and achieves a 46%
increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent
Systems (MRS) 202
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