1,227 research outputs found
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many
clustering and classification tasks. The classic ways to measure the errors
between the original and the reconstructed matrix are distance or
Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly
handled when we use these error measures. As a consequence, alternative
measures based on nonlinear kernels, such as correntropy, are proposed.
However, the current correntropy-based NMF only targets on the low-level
features without considering the intrinsic geometrical distribution of data. In
this paper, we propose a new NMF algorithm that preserves local invariance by
adding graph regularization into the process of max-correntropy-based matrix
factorization. Meanwhile, each feature can learn corresponding kernel from the
data. The experiment results of Caltech101 and Caltech256 show the benefits of
such combination against other NMF algorithms for the unsupervised image
clustering
Non-parametric Imitation Learning of Robot Motor Skills
Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots with learning capabilities. In this line, imitation learning emerges as an intuitive way to teach robots different motor skills. This learning approach typically mimics human demonstrations by extracting invariant motion patterns and subsequently applies these patterns to new situations. In this paper, we propose a novel kernel treatment of imitation learning, which endows the robot with imitative and adaptive capabilities. In particular, due to the kernel treatment, the proposed approach is capable of learning human skills associated with high-dimensional inputs. Furthermore, we study a new concept of correlation-adaptive imitation learning, which allows for the adaptation of correlations exhibited in high-dimensional demonstrated skills. Several toy examples and a collaborative task with a real robot are provided to verify the effectiveness of our approach
A survey of preference-based reinforcement learning methods
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function of ten requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert\u27s preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL
Measurement-Adaptive Cellular Random Access Protocols
This work considers a single-cell random access channel (RACH) in cellular
wireless networks. Communications over RACH take place when users try to
connect to a base station during a handover or when establishing a new
connection. Within the framework of Self-Organizing Networks (SONs), the system
should self- adapt to dynamically changing environments (channel fading,
mobility, etc.) without human intervention. For the performance improvement of
the RACH procedure, we aim here at maximizing throughput or alternatively
minimizing the user dropping rate. In the context of SON, we propose protocols
which exploit information from measurements and user reports in order to
estimate current values of the system unknowns and broadcast global
action-related values to all users. The protocols suggest an optimal pair of
user actions (transmission power and back-off probability) found by minimizing
the drift of a certain function. Numerical results illustrate considerable
benefits of the dropping rate, at a very low or even zero cost in power
expenditure and delay, as well as the fast adaptability of the protocols to
environment changes. Although the proposed protocol is designed to minimize
primarily the amount of discarded users per cell, our framework allows for
other variations (power or delay minimization) as well.Comment: 31 pages, 13 figures, 3 tables. Springer Wireless Networks 201
Trust-Based Control of Robotic Manipulators in Collaborative Assembly in Manufacturing
Human-robot interaction (HRI) is vastly addressed in the field of automation and manufacturing. Most of the HRI literature in manufacturing explored physical human-robot interaction (pHRI) and invested in finding means for ensuring safety and optimized effort sharing amongst a team of humans and robots. The recent emergence of safe, lightweight, and human-friendly robots has opened a new realm for human-robot collaboration (HRC) in collaborative manufacturing. For such robots with the new HRI functionalities to interact closely and effectively with a human coworker, new human-centered controllers that integrate both physical and social interaction are demanded. Social human-robot interaction (sHRI) has been demonstrated in robots with affective abilities in education, social services, health care, and entertainment. Nonetheless, sHRI should not be limited only to those areas. In particular, we focus on human trust in robot as a basis of social interaction. Human trust in robot and robot anthropomorphic features have high impacts on sHRI. Trust is one of the key factors in sHRI and a prerequisite for effective HRC. Trust characterizes the reliance and tendency of human in using robots. Factors within a robotic system (e.g. performance, reliability, or attribute), the task, and the surrounding environment can all impact the trust dynamically. Over-reliance or under-reliance might occur due to improper trust, which results in poor team collaboration, and hence higher task load and lower overall task performance. The goal of this dissertation is to develop intelligent control algorithms for the manipulator robots that integrate both physical and social HRI factors in the collaborative manufacturing. First, the evolution of human trust in a collaborative robot model is identified and verified through a series of human-in-the-loop experiments. This model serves as a computational trust model estimating an objective criterion for the evolution of human trust in robot rather than estimating an individual\u27s actual level of trust. Second, an HRI-based framework is developed for controlling the speed of a robot performing pick and place tasks. The impact of the consideration of the different level of interaction in the robot controller on the overall efficiency and HRI criteria such as human perceived workload and trust and robot usability is studied using a series of human-in-the-loop experiments. Third, an HRI-based framework is developed for planning and controlling the robot motion in performing hand-over tasks to the human. Again, series of human-in-the-loop experimental studies are conducted to evaluate the impact of implementation of the frameworks on overall efficiency and HRI criteria such as human workload and trust and robot usability. Finally, another framework is proposed for the cooperative manipulation of a common object by a team of a human and a robot. This framework proposes a trust-based role allocation strategy for adjusting the proactive behavior of the robot performing a cooperative manipulation task in HRC scenarios. For the mentioned frameworks, the results of the experiments show that integrating HRI in the robot controller leads to a lower human workload while it maintains a threshold level of human trust in robot and does not degrade robot usability and efficiency
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