scholarly journals Modelling and Control of a Reconfigurable Robot for Achieving Reconfiguration and Locomotion with Different Shapes

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5362
Author(s):  
S. M. Bhagya P. Samarakoon ◽  
M. A. Viraj J. Muthugala ◽  
Raihan E. Abdulkader ◽  
Soh Wei Si ◽  
Thein T. Tun ◽  
...  
Keyword(s):  
State Of The Art ◽  
Area Coverage ◽  
Reconfigurable Robots ◽  
Proposed Model ◽  
Small Set ◽  
Different Shapes ◽  
Reconfigurable Robot ◽  
High Level ◽  
Inspection Robots ◽  
And Control

Area coverage is a crucial factor for a robot intended for applications such as floor cleaning, disinfection, and inspection. Robots with fixed shapes could not realize an adequate level of area coverage performance. Reconfigurable robots have been introduced to overcome the limitations of fixed-shape robots, such as accessing narrow spaces and cover obstacles. Although state-of-the-art reconfigurable robots used for coverage applications are capable of shape-changing for improving the area coverage, the reconfiguration is limited to a few predefined shapes. It has been proven that the ability of reconfiguration beyond a few shapes can significantly improve the area coverage performance of a reconfigurable robot. In this regard, this paper proposes a novel robot model and a low-level controller that can facilitate the reconfiguration beyond a small set of predefined shapes and locomotion per instructions while firmly maintaining the shape. A prototype of a robot that facilitates the aim mentioned above has been designed and developed. The proposed robot model and controller have been integrated into the prototype, and experiments have been conducted considering various reconfiguration and locomotion scenarios. Experimental results confirm the validity of the proposed model and controller during reconfiguration and locomotion of the robot. Moreover, the applicability of the proposed model and controller for achieving high-level autonomous capabilities has been proven.

A Novel Neural Model with Lateral Interaction for Learning Tasks

2020 ◽  
pp. 1-24
Author(s):  
Dequan Jin ◽  
Ziyan Qin ◽  
Murong Yang ◽  
Penghe Chen
Keyword(s):  
Numerical Experiments ◽  
State Of The Art ◽  
Neural Model ◽  
Parameter Tuning ◽  
Small Sample ◽  
Lateral Interaction ◽  
Learning Tasks ◽  
Proposed Model ◽  
High Level ◽  
Elementary Field

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.

scholarly journals Graph Dilated Network with Rejection Mechanism

2020 ◽  
Vol 10 (7) ◽  
pp. 2421
Author(s):  
Bencheng Yan ◽  
Chaokun Wang ◽  
Gaoyang Guo
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Great Success ◽  
Convolution Kernel ◽  
Graph Structure ◽  
Proposed Model ◽  
Rejection Mechanism ◽  
Laplacian Smoothing ◽  
Graph Neural Networks ◽  
High Level

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.

scholarly journals Webshell Detection Based on Executable Data Characteristics of PHP Code

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zulie Pan ◽  
Yuanchao Chen ◽  
Yu Chen ◽  
Yi Shen ◽  
Xuanzhen Guo
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Remote Access ◽  
Detection Accuracy ◽  
Controlled Experiments ◽  
Data Set ◽  
Detection Model ◽  
Proposed Model ◽  
Text Features ◽  
And Control

A webshell is a malicious backdoor that allows remote access and control to a web server by executing arbitrary commands. The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. The key idea is to combine the executable data characteristics of the PHP code with static text features for webshell classification. To verify the proposed model, we construct a cleaned data set of webshell consisting of 2,917 samples from 17 webshell collection projects and conduct extensive experiments. We have designed three sets of controlled experiments, the results of which show that the accuracy of the three algorithms has reached more than 99.40%, the highest reached 99.66%, the recall rate has been increased by at least 1.8%, the most increased by 6.75%, and the F1 value has increased by 2.02% on average. It not only confirms the efficiency of the grammatical features in webshell detection but also shows that our system significantly outperforms several state-of-the-art rivals in terms of detection accuracy and recall rate.

scholarly journals Adaptive Performance Evaluation for SDN Based on the Statistical and Evolutionary Algorithms

2019 ◽  
pp. 36-46
Keyword(s):  
Visible Light Communication ◽  
Adaptive Performance ◽  
Proposed Model ◽  
Different Types ◽  
High Level ◽  
Python Programming ◽  
And Control ◽  
Adaptive Resource ◽  
Business Requirements

Being able to send different types of data (i.e. text, audio, or video) through the network is the most important aspect of networks. Different networks have different issues and restrictions while sending data. These restrictions are basically the QoS (Quality of Service) metrics and security. The recent Software-Defined Networking (SDN) that aims to separate the control plane from the data plane can be applied where Business requirements are not responsible for the way the network is configured; instead, it is the responsibility of the high-level business policies and objectives. SDN gives preferable techniques for centralized dynamic management and control configurations. In this work, a proposed model has been estimated and discussed to promote QoS requirements in some suggested topologies. Adaptive Resource Management (ARM) and control to send different types of data through different hosts have been investigated. The intended requirements are basically the capacity and delay of traffic metrics sent through different hosts through the network. It produces a mathematical model and implementation for three proposed algorithms to enhance the quality of a sample video sent from source host to destination host by Visible Light Communication (VLC)-media player in three different topologies. These algorithms (statistical, MOGA, and PSO) have been implemented using Mininet emulator, FNSS tool, PULP, and network libraries; with two types of controllers which are Floodlight and OVS under Linux operating system and in python programming language.

Modular Reconfigurable Robotics

2019 ◽  
Vol 2 (1) ◽  
pp. 63-88 ◽  
Author(s):  
Jungwon Seo ◽  
Jamie Paik ◽  
Mark Yim
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Future Research ◽  
Research Directions ◽  
Current State ◽  
Robot Systems ◽  
Technical Issues ◽  
Future Research Directions ◽  
Reconfigurable Robot ◽  
High Level

This article reviews the current state of the art in the development of modular reconfigurable robot (MRR) systems and suggests promising future research directions. A wide variety of MRR systems have been presented to date, and these robots promise to be versatile, robust, and low cost compared with other conventional robot systems. MRR systems thus have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. We begin by introducing the taxonomy of MRRs based on their hardware architecture. We then examine recent progress in the hardware and the software technologies for MRRs, along with remaining technical issues. We conclude with a discussion of open challenges and future research directions.

scholarly journals Planning to fold multiple objects from a single self-folding sheet

Robotica ◽  
2011 ◽  
Vol 29 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Byoungkwon An ◽  
Nadia Benbernou ◽  
Erik D. Demaine ◽  
Daniela Rus
Keyword(s):  
Formation Control ◽  
Multiple Objects ◽  
Modular Systems ◽  
Planning And Control ◽  
New Family ◽  
Reconfigurable Robots ◽  
Fixed Set ◽  
Single Sheet ◽  
Different Shapes ◽  
And Control

SUMMARYThis paper considers planning and control algorithms that enable a programmable sheet to realize different shapes by autonomous folding. Prior work on self-reconfiguring machines has considered modular systems in which independent units coordinate with their neighbors to realize a desired shape. A key limitation in these prior systems is the typically many operations to make and break connections with neighbors, which lead to brittle performance. We seek to mitigate these difficulties through the unique concept of self-folding origami with a universal fixed set of hinges. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation.We describe the planning algorithms underlying these self-folding sheets, forming a new family of reconfigurable robots that fold themselves into origami by actuating edges to fold by desired angles at desired times. Given a flat sheet, the set of hinges, and a desired folded state for the sheet, the algorithms (1) plan a continuous folding motion into the desired state, (2) discretize this motion into a practicable sequence of phases, (3) overlay these patterns and factor the steps into a minimum set of groups, and (4) automatically plan the location of actuators and threads on the sheet for implementing the shape-formation control.

scholarly journals Multi-View Attention Network for Visual Dialog

2021 ◽  
Vol 11 (7) ◽  
pp. 3009
Author(s):  
Sungjin Park ◽  
Taesun Whang ◽  
Yeochan Yoon ◽  
Heuiseok Lim
Keyword(s):  
State Of The Art ◽  
Relevant Information ◽  
Experimental Results ◽  
Multiple Views ◽  
Single Model ◽  
Attention Network ◽  
Proposed Model ◽  
Previous State ◽  
Multimodal Representations ◽  
High Level

Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image) is required. Specifically, it is necessary for an agent to (1) determine the semantic intent of question and (2) align question-relevant textual and visual contents among heterogeneous modality inputs. In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment). Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms previous state-of-the-art methods under both single model and ensemble settings.

Non-Communicable Diseases and Global Health Politics

2019 ◽  
pp. 626-659
Author(s):  
Roger Magnusson
Keyword(s):  
Respiratory Diseases ◽  
Communicable Diseases ◽  
World Health ◽  
Global Response ◽  
Non Communicable Diseases ◽  
Health Politics ◽  
Chronic Respiratory Diseases ◽  
Health Organization ◽  
High Level ◽  
And Control

Non-communicable diseases (NCDs), including cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for around 70 percent of global deaths each year. This chapter describes how NCDs have become prevalent and critically evaluates global efforts to address NCDs and their risk factors, with a particular focus on the World Health Organization (WHO) and United Nations (UN) system. It explores the factors that have prevented those addressing NCDs from achieving access to resources and a priority commensurate with their impact on people’s lives. The chapter evaluates the global response to NCDs both prior to and since the UN High-Level Meeting on Prevention and Control of Non-communicable Diseases, held in 2011, and considers opportunities for strengthening that response in future.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Boundaryless working hours and recovery in Germany

2021 ◽  
Author(s):  
Laura Vieten ◽  
Anne Marit Wöhrmann ◽  
Alexandra Michel
Keyword(s):  
Working Hours ◽  
Working Time ◽  
Overtime Work ◽  
Psychological Detachment ◽  
Representative Study ◽  
Flexible Working ◽  
Recovery Experiences ◽  
High Level ◽  
And Control ◽  
Extended Work

Abstract Objective Due to recent trends such as globalization and digitalization, more and more employees tend to have flexible working time arrangements, including boundaryless working hours. The aim of this study was to investigate the relationships of various aspects of boundaryless working hours (overtime, Sunday work, and extended work availability) with employees’ state of recovery. Besides, we examined the mediating and moderating role of recovery experiences (psychological detachment, relaxation, mastery, and control) in these relationships. Methods We used data from 8586 employees (48% women; average age of 48 years) who took part in the 2017 BAuA-Working Time Survey, a representative study of the German working population. Regression analyses were conducted to test main effects as well as mediation and moderation. Results Overtime work, Sunday work, and extended work availability were negatively related to state of recovery. Psychological detachment mediated these relationships. Furthermore, we found that relaxation and control mediated the association between extended work availability and state of recovery. However, no relevant moderating effects were found. Conclusions Altogether, our findings indicate that various aspects of boundaryless working hours pose a risk to employees’ state of recovery and that especially psychological detachment is a potential mechanism in these relationships. In addition, the results suggest that a high level of recovery experiences cannot attenuate these negative relationships in leisure time. Therefore, employers and employees alike should try to avoid or minimize boundaryless working hours.

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