scholarly journals Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

2021 ◽  
Vol 14 ◽  
Author(s):  
Andrea Cherubini ◽  
David Navarro-Alarcon
Keyword(s):  
Integration Method ◽  
Control Level ◽  
Direct Interaction ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Heterogeneous Sensors ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
Future Research Directions

The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions.

scholarly journals Topological phases in ring resonators: recent progress and future prospects

Nanophotonics ◽  
2020 ◽  
Vol 9 (15) ◽  
pp. 4473-4487 ◽  
Author(s):  
Daniel Leykam ◽  
Luqi Yuan
Keyword(s):  
Ring Resonator ◽  
Ring Resonators ◽  
Future Research ◽  
Device Performance ◽  
Open Problems ◽  
Research Directions ◽  
Optical Isolation ◽  
Potential Applications ◽  
Future Research Directions ◽  
Light Generation

AbstractTopological photonics has emerged as a novel paradigm for the design of electromagnetic systems from microwaves to nanophotonics. Studies to date have largely focused on the demonstration of fundamental concepts, such as nonreciprocity and waveguiding protected against fabrication disorder. Moving forward, there is a pressing need to identify applications where topological designs can lead to useful improvements in device performance. Here, we review applications of topological photonics to ring resonator–based systems, including one- and two-dimensional resonator arrays, and dynamically modulated resonators. We evaluate potential applications such as quantum light generation, disorder-robust delay lines, and optical isolation, as well as future research directions and open problems that need to be addressed.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Electrochemical polymerization for two-dimensional conjugated polymers

2018 ◽  
Vol 6 (40) ◽  
pp. 10672-10686 ◽  
Author(s):  
Qing Zhang ◽  
Huanli Dong ◽  
Wenping Hu
Keyword(s):  
Conjugated Polymers ◽  
Electrochemical Polymerization ◽  
Research Progress ◽  
Future Research ◽  
Special Focus ◽  
Two Dimensional ◽  
Research Directions ◽  
Potential Applications ◽  
Future Research Directions ◽  
Recent Research Progress

This article places special focus on the recent research progress of the EP method in synthesizing CPs. In particular, their potential applications as 2D CPs are summarized, with a basic introduction of the EP method, its use in synthesizing CPs as well as the promising applications of the obtained CPs in different fields. Discussions of current challenges in this field and future research directions are also given.

scholarly journals Challenges and Opportunities in Robotic Food Handling: A Review

2022 ◽  
Vol 8 ◽  
Author(s):  
Zhongkui Wang ◽  
Shinichi Hirai ◽  
Sadao Kawamura
Keyword(s):  
Low Cost ◽  
Food Products ◽  
Future Research ◽  
Food Handling ◽  
Research Directions ◽  
Fundamental Information ◽  
Food Recognition ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
End Effectors

Despite developments in robotics and automation technologies, several challenges need to be addressed to fulfill the high demand for automating various manufacturing processes in the food industry. In our opinion, these challenges can be classified as: the development of robotic end-effectors to cope with large variations of food products with high practicality and low cost, recognition of food products and materials in 3D scenario, better understanding of fundamental information of food products including food categorization and physical properties from the viewpoint of robotic handling. In this review, we first introduce the challenges in robotic food handling and then highlight the advances in robotic end-effectors, food recognition, and fundamental information of food products related to robotic food handling. Finally, future research directions and opportunities are discussed based on an analysis of the challenges and state-of-the-art developments.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Person–Environment Fit and Work Satisfaction: Exploring the Conditional Effects of Age

2019 ◽  
Author(s):  
Rachel S Rauvola ◽  
Cort W Rudolph ◽  
Lena K Ebbert ◽  
Hannes Zacher
Keyword(s):  
Work Satisfaction ◽  
Work Outcomes ◽  
Future Research ◽  
Older Individuals ◽  
Research Directions ◽  
Socioemotional Selectivity ◽  
Conditional Effects ◽  
Person Environment Fit ◽  
Potential Applications ◽  
Future Research Directions

Abstract Person–environment (PE) fit, a broad constellation of constructs related to an individual’s congruence with their work environment, is of great interest to research and practice given its implications for positive work outcomes and sustainable employment. Informed by a life-span perspective, particularly socioemotional selectivity theory, the present studies investigated potential age-conditional effects of PE fit types (person–job [PJ], person–group [PG], and person–organization [PO] fit) on work satisfaction. In two studies, a policy-capturing approach was used in which participants read a series of work scenario vignettes and then rated their hypothetical work satisfaction in these scenarios. In Study 1, these cues varied by fit type and levels of fit (i.e., low, medium, high), while in Study 2, they varied by fit type and level in addition to goal type (i.e., socioemotional, instrumental). It was expected that PJ fit would be more important for work satisfaction of relatively younger participants and PO fit would be more important for relatively older participants; potential age-conditional PG effects were explored as well. Findings provided support for the assumption that PO fit is more important for older individuals’ work satisfaction, while PJ and PG fit manifested mixed results; moreover, we did not find significant effects of goal type as anticipated in Study 2. These results are interpreted in light of existing theory, and future research directions and potential applications are discussed.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

A Survey of Multimedia Recommender Systems: Challenges and Opportunities

2017 ◽  
Vol 11 (03) ◽  
pp. 411-428 ◽  
Author(s):  
Mouzhi Ge ◽  
Fabio Persia
Keyword(s):  
Multimedia Information ◽  
Recommender Systems ◽  
Recommender System ◽  
Future Research ◽  
Research Directions ◽  
Research Challenges ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
Research Challenge

Multimedia information has been extensively growing from a variety of sources such as cameras or video recorders. In order to select the useful multimedia objects, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia objects, it is challenging to provide effective multimedia recommendations. In this paper, we therefore conduct a survey in both the multimedia information system and recommender system communities. We further focus on the works that span the two communities, especially the research on multimedia recommender systems. Based on our review, we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights of how to perform the follow-up research.

Sign in / Sign up

Export Citation Format

Share Document