scholarly journals Bayesian Active Edge Evaluation on Expensive Graphs

2018 ◽  
Author(s):  
Sanjiban Choudhury ◽  
Siddhartha Srinivasa ◽  
Sebastian Scherer
Keyword(s):  
Mobile Robots ◽  
Possible Worlds ◽  
State Of The Art ◽  
Complex Geometries ◽  
Time Motion ◽  
Decision Region ◽  
The World ◽  
Autonomous Helicopters ◽  
Explicit Enumeration ◽  
Planning Problems

We consider the problem of real-time motion planning that requires evaluating a minimal number of edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots sensing the world online like UAVs. Until now, this challenge has been addressed via laziness, i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters. 

scholarly journals Data-driven planning via imitation learning

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1632-1672 ◽  
Author(s):  
Sanjiban Choudhury ◽  
Mohak Bhardwaj ◽  
Sankalp Arora ◽  
Ashish Kapoor ◽  
Gireeja Ranade ◽  
...  
Keyword(s):  
Partial Information ◽  
State Of The Art ◽  
Imitation Learning ◽  
Data Driven ◽  
Sequential Decision ◽  
Efficient Manner ◽  
World State ◽  
Performance Guarantees ◽  
The World ◽  
Planning Problems

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to [Formula: see text] more reward than state-of-the art information-gathering heuristics and a [Formula: see text] speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

scholarly journals D-Penicillamine: The State of the Art in Humans and in Dogs from a Pharmacological and Regulatory Perspective

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Michela Pugliese ◽  
Vito Biondi ◽  
Enrico Gugliandolo ◽  
Patrizia Licata ◽  
Alessio Filippo Peritore ◽  
...  
Keyword(s):  
Veterinary Medicine ◽  
State Of The Art ◽  
European Medicines Agency ◽  
Therapeutic Goals ◽  
First Choice ◽  
Regulatory Perspective ◽  
The World ◽  
United States Food ◽  
States Food ◽  
Food And Drugs Administration

Chelant agents are the mainstay of treatment in copper-associated hepatitis in humans, where D-penicillamine is the chelant agent of first choice. In veterinary medicine, the use of D-penicillamine has increased with the recent recognition of copper-associated hepatopathies that occur in several breeds of dogs. Although the different regulatory authorities in the world (United States Food and Drugs Administration—U.S. FDA, European Medicines Agency—EMEA, etc.) do not approve D-penicillamine for use in dogs, it has been used to treat copper-associated hepatitis in dogs since the 1970s, and is prescribed legally by veterinarians as an extra-label drug to treat this disease and alleviate suffering. The present study aims to: (a) address the pharmacological features; (b) outline the clinical scenario underlying the increased interest in D-penicillamine by overviewing the evolution of its main therapeutic goals in humans and dogs; and finally, (c) provide a discussion on its use and prescription in veterinary medicine from a regulatory perspective.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

scholarly journals Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

2019 ◽  
Vol 9 (12) ◽  
pp. 2535
Author(s):  
Di Fan ◽  
Hyunwoo Kim ◽  
Jummo Kim ◽  
Yunhui Liu ◽  
Qiang Huang
Keyword(s):  
Human Computer Interaction ◽  
Video Surveillance ◽  
State Of The Art ◽  
Learning Task ◽  
Face Verification ◽  
Task Learning ◽  
The World ◽  
In The Wild ◽  
And Gender ◽  
Learning Architectures

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

Rose Fitzgerald Kennedy bridge over the River Barrow. Design and Construction of a long span extrados bridge

2021 ◽  
Author(s):  
Marcos Sanchez Sanchez ◽  
John Iliff
Keyword(s):  
State Of The Art ◽  
Single Plane ◽  
Robust Approach ◽  
Analysis Techniques ◽  
Long Span ◽  
The World ◽  
Art Analysis ◽  
Concrete Deck ◽  
Design And Construction

<p>This paper describes the key elements from early planning to completion of a new bridge over the River Barrow which is part of the New Ross bypass in the south of Ireland. The structure has a total length of 887m, with a span arrangement of 36-45-95-230-230-95-70-50-36m. The two central twin spans are the longest of its kind in the world (extrados with a full concrete deck). The bridge carries a dual carriageway with a cable arrangement consisting of a single plane of cables located in the central axis of the deck. The design and construction focused in providing a structure with long term durability, resilience, and a robust approach to design scenarios using the Eurocodes and state of the art analysis techniques, including extreme events such as fire and ship impact<i>.</i></p>

Sir Charles Parsons and Electrical Power Generation—a Turbine Designer's Perspective

1994 ◽  
Vol 208 (3) ◽  
pp. 159-176 ◽  
Author(s):  
J R Bolter
Keyword(s):  
Power Generation ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Electrical Power Generation ◽  
Analytical Methods ◽  
State Of The Art ◽  
Electrical Power ◽  
Turbine Generator ◽  
Current State ◽  
The World

Sir Charles Parsons died some three years after the author was born. In this paper the author looks back at the pioneering work of Parsons in the field of power generation. It shows how he was able to increase output of the steam turbine generator from 7.5 kW in 1884 to 50000 kW in 1930 while increasing efficiency from 1.6 to 36 per cent, and relates these achievements to the current state of the art. Blading design, rotor construction and other aspects of turbine engineering are considered. The conclusion is that Parsons and his associates charted the course which manufacturers and utilities throughout the world have continued to follow, although increasingly sophisticated design and analytical methods have succeeded the intuitive approach of Parsons. His constant search for improved efficiency was and is highly relevant to today's concern for the environment. Finally, although it did not become a practical proposition in his lifetime, the paper reviews Parsons' vision of, and continuing interest in, the gas turbine, first mentioned in his 1884 patents.

scholarly journals Turbulence and Flow–Sediment Interactions in Open-Channel Flows

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3169
Author(s):  
Roberto Gaudio
Keyword(s):  
Open Channel ◽  
Field Experiments ◽  
State Of The Art ◽  
Channel Flows ◽  
Special Issue ◽  
Panoramic View ◽  
Open Channel Flows ◽  
River Hydraulics ◽  
The World ◽  
Computing Skills

The main focus of this Special Issue of Water is the state-of-the-art and recent research on turbulence and flow–sediment interactions in open-channel flows. Our knowledge of river hydraulics is becoming deeper and deeper, thanks to both laboratory/field experiments related to the characteristics of turbulence and their link to the erosion, transport, deposition, and local scouring phenomena. Collaboration among engineers, physicists, and other experts is increasing and furnishing new inter/multidisciplinary perspectives to the research in river hydraulics and fluid mechanics. At the same time, the development of both sophisticated laboratory instrumentation and computing skills is giving rise to excellent experimental–numerical comparative studies. Thus, this Special Issue, with ten papers by researchers from many institutions around the world, aims at offering a modern panoramic view on all the above aspects to the vast audience of river researchers.

scholarly journals Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6450
Author(s):  
Taimur Hassan ◽  
Muhammad Shafay ◽  
Samet Akçay ◽  
Salman Khan ◽  
Mohammed Bennamoun ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Aviation Security ◽  
Dual Energy ◽  
Average Precision ◽  
X Ray ◽  
Shot Classification ◽  
Object Proposals ◽  
The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

scholarly journals Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1539
Author(s):  
Kai Kwong Hon ◽  
Pak Wai Chan
Keyword(s):  
Detection Efficiency ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Doppler Lidar ◽  
Hit Rate ◽  
Glide Path ◽  
The World ◽  
Alerting System ◽  
The Difference ◽  
Hong Kong International Airport

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

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