Presence Of Dynamic Environment Research Articles

Empirical estimation of various data stream mining methods

Online learning is done in order to work on dynamic environments in which the concept tends to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of a real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilised light emitting diode (LED) data generator for evaluating the performance of the methods.

Evolutionary-learning framework: improving automatic swarm robotics design

PurposeThe purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.Design/methodology/approachFirst, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.FindingsThe discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.Originality/valueThis paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

Mobile GPS carrier phase tracking using a novel intelligent dual‐loop receiver

AbstractCarrier phase information is necessary for accurate measurements in global positioning system (GPS) applications. This paper presents a novel intelligent GPS carrier tracking loop with variable‐bandwidth characteristics for fast acquisition and better tracking capability in the presence of dynamic environments. Our dual‐loop receiver is composed of a frequency‐locked loop‐assisted phase‐locked loop structure, the fuzzy controllers (FCs), and the ATAN discriminator functions. The soft‐computing FCs provide the time‐varying loop gains to perform accurate and reliable control of the dual‐loop paradigm. Once the phase dynamic errors become large under kinematic conditions, the fuzzy loop gains increase adaptively and achieve rapid acquisition. On the other hand, when the tracking errors approach zero in the steady state, the loop gains decrease and the corresponding dual‐loop receiver returns to a narrowband system. Four types of carrier phase signals, i.e. phase offset, decaying sinusoidal phase jitter, frequency offset, and frequency ramp offset, are considered to emulate realistic mobile circumstances. Simulation results show that our proposed receiver does achieve a superior performance over conventional tracking loops in terms of faster settling time and wider acquisition range while preventing the occurrence of cycle slips. Copyright © 2008 John Wiley & Sons, Ltd.