Adaptive Tracking Control of an Uncertain Nonholonomic Robot

In this paper, a new controller is proposed by using backstepping method for the trajectory tracking problem of nonholonomic dynamic mobile robots with nonholonomic constraints under the condition that there is a distance between the mass center and the geometrical center and the distance is unknown. And an adaptive feedback controller is also proposed for the case that some kinematic parameters and dynamic parameters are uncertain. The asymptotical stability of the control system is proved with Lyapunov stability theory. The simulation results show the effectiveness of the proposed controller. The comparison with the previous methods is made to show the effectiveness of the method in this article.

The Research of Event Detection and Characterization Technology of Ticket Gate in the Urban Rapid Rail Transit

Making events recognition more reliable under complex environment is one of the most important challenges for the intelligent recognition system to the ticket gate in the urban rapid rail transit. The motion objects passing through the ticket gate could be described as a series of moving sequences got by sensors that located in the walkway side of the ticket gate. This paper presents a robust method to detect some classes of events of ticket gate in the urban rapid rail transit. Diffused reflectance infrared sensors are used to collect signals. In this paper, the motion objects are here referred to passenger(s) or (and) luggage(s), for which are of frequent occurrences in the ticket gate of the urban railway traffic. Specifically, this paper makes two main contributions: 1) The proposed recognition method could be used to identify several events, including the event of one person passing through the ticket gate, the event of two consecutive passengers passing through the ticket gate without a big gap between them, and the event of a passenger walking through the ticket gate pulling a suitcase;2) The moving time sequence matrix is transformed into a one-dimensional vector as the feature descriptor. Deep learning (DL), back propagation neural network (BP), and support vector machine (SVM) are applied to recognize the events respectively. BP has been proved to have a higher recognition rate compared to other methods. In order to implement the three algorithms, a data set is built which includes 150 samples of all kinds of events from the practical tests. Experiments show the effectiveness of the proposed methods.

串联氧化碳-氢键氧官能化和噁唑环化构建2-酰基苯并噁唑

通过铜催化N-(邻羟基苯基)苯乙酰胺的一锅串联亚甲基碳-氢键氧基化以及噁唑环化,实现了有用的2-酰基苯并噁唑类化合物的合成.除了具有基于一步多键转化的独特步骤经济性,以同样原料选择性可控的2-亚甲基苯并噁唑合成以及在合成苯并噻唑方面的扩展应用也是本工作的重要优势.

PerformanceVis:Visual analytics of student performance data from an introductory chemistry course

We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design.Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of PerformanceVis.We study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance data.PerformanceVis includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and exploration.We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation.Finally,we conclude this work by pointing out future work in this direction of learning analytics research.

Adaptive tracking control for uncertain dynamic nonholonomic mobile robots based on visual servoing

The trajectory tracking control problem of dynamic nonholonomic wheeled mobile robots is considered via visual servoing feedback. A kinematic controller is firstly presented for the kinematic model, and then, an adaptive sliding mode controller is designed for the uncertain dynamic model in the presence of parametric uncertainties associated with the camera system. The proposed controller is robust not only to structured uncertainties such as mass variation but also to unstructured one such as disturbances. The asymptotic convergence of tracking errors to equilibrium point is rigorously proved by the Lyapunov method. Simulation results are provided to illustrate the performance of the control law.

ROBUST EXPONENTIAL STABILIZATION OF UNCERTAIN NONHOLONOMIC CHAINED SYSTEMS BASED ON VISUAL FEEDBACK

The visual serving stabilization for a kind of nonholonomic mobile robots with uncalibrated camera parameters is investigated based on the visual feedback and the state and input transforma- tions. The authors obtain a new uncertain model of the nonholonomic kinematic system in the image plane, which is a chained form with uncalibrated visual parameters, from the camera robotic system. A new time varying feedback controller is proposed for the exponential stabilization of the nonholonomic chained system with unknown parameters by using state-scaling and switching technique. The exponential stability of the closed loop system is rigorously proved. Simulation results demonstrate the effectiveness of the proposed methods.

A unified framework for exploring time-varying volumetric data based on block correspondence

Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific visualization.Although great advances have been made over the years,existing solutions typically focus on only one or two aspects of data analysis and visualization.A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still missing.Towards this goal,we present a novel approach for time-varying data visualization that encompasses keyframe identification,feature extraction and tracking under a single,unified framework.At the heart of our approach lies in the GPU-accelerated BlockMatch method,a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D voxels.Based on the results of dense correspondence,we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity measure.Furthermore,in conjunction with the graph cut algorithm,this framework enables us to perform fine-grained feature extraction and tracking.We tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.

A Study of Animated Transition in Similarity-Based Tiled Image Layout

For many information visualization applications, showing the transition when interacting with the data is critically important as it can help users better perceive the changes and understand the underlying data. In this paper, we investigate the effectiveness of animated transition in a tiled image layout where the spiral arrangement of the images is based on their similarity. Three aspects of animated transition are considered, including animation steps, animation actions, and flying paths. Exploring and weighting the advantages and disadvantages of different methods for each aspect and in conjunction with the characteristics of the spiral image layout, we present an integrated solution, called AniMap, for animating the transition from an old layout to a new layout when a different image is selected as the query image. We show the effectiveness of our animated transition solution by demonstrating experimental results and conducting a comparative user study.

OnionGraph:Hierarchical topology+attribute multivariate network visualization

Hierarchical abstraction is a scalable strategy to deal with large networks.Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology,each of which has its own advantage.Very few previous system has the capability to enjoy the best of both worlds.This paper presents OnionGraph,an integrated framework for the exploratory visual analysis of heterogeneous multivariate networks.OnionGraph allows nodes to be aggregated based on either node attributes,topology,or a hierarchical combination of both.These aggregations can be split,merged and filtered under the focus+context interaction model,or automatically traversed by the information-theoretic navigation method.Node aggregations that contain subsets of nodes are displayed by the onion metaphor,indicating the level and details of the abstraction.We have evaluated the OnionGraph tool in three real-world cases.Performance experiments demonstrate that on a commodity desktop,our method can scale to million-node networks while preserving the interactivity for analysis.

AntVis:A web-based visual analytics tool for exploring ant movement data

We present AntVis,a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches.Our goal is to enable domain experts to visually explore massive ant movement data and gain valuable insights via effective visualization,filtering,and comparison.This is achieved through a deep learning framework for automatic detection,segmentation,and labeling of ants,ant movement clustering based on their trace similarity,and the design and development of five coordinated views(the movement,similarity,timeline,statistical,and attribute views)for user interaction and exploration.We demonstrate the effectiveness of AntVis with several case studies developed in close collaboration with domain experts.Finally,we report the expert evaluation conducted by an entomologist and point out future directions of this study.

VCNet:A generative model for volume completion

We present VCNet,a new deep learning approach for volume completion by synthesizing missing subvolumes.Our solution leverages a generative adversarial network(GAN)that learns to complete volumes using the adversarial and volumetric losses.The core design of VCNet features a dilated residual block and long-term connection.During training,VCNet first randomly masks basic subvolumes(e.g.,cuboids,slices)from complete volumes and learns to recover them.Moreover,we design a two-stage algorithm for stabilizing and accelerating network optimization.Once trained,VCNet takes an incomplete volume as input and automatically identifies and fills in the missing subvolumes with high quality.We quantitatively and qualitatively test VCNet with volumetric data sets of various characteristics to demonstrate its effectiveness.We also compare VCNet against a diffusion-based solution and two GAN-based solutions.

Visualization Laboratory at University of Notre Dame

This article introduces the Visualization Laboratory at the Department of Computer Science&Engineering,the University of Notre Dame,including the lab’s overview,current research directions,facilities,and international collaborations.