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The battery era has started to compensate the demand of the energy while the charging issues still exist. Thus, demand of reliable and optimized charging is required to charge cell/battery. In this paper novel optimized technique is proposed, based on gravitational search algorithm (GSA) to charge e-rickshaw battery using single sensor based maximum power point tracking (MPPT) of solar photovoltaic (SPV) module. There are various metaheauristic and heuristic techniques are available like Cauchy and Gaussian sine cosine optimization (CGSCO) intelligent technique, evolutionary algorithms, stochastic algorithms, Swarm optimization technique, ant colony technique, neural algorithms, fuzzy logic algorithms to optimize the charging current of cell/battery. These techniques take more iteration to give the optimal solution. Moreover, GSA is the high level intelligent technique which is used in multi area to optimize the various parameters in engineering fields. It is very ease to find the optimal solution in search space. This approach is novel in the field of e-rickshaw battery charging. Therefore, the mathematical algorithm based on GSA has been developed to optimize the current of charging cell/battery. The performance of GSA optimization technique is verified and compared with the metaheauristic based CGSCO optimization technique. It is observed that GSA is easy to design and reduce the cost of charger. [ABSTRACT FROM AUTHOR]

Temporal difference learning and eligibility traces are two mechanisms for solving reinforcement learning problems. The temporal difference technique bootstraps the state value or state-action value at every step as with dynamic programming, and learns by sampling episodes from experience as in the Monte Carlo approach. Eligibility traces is a mechanism that offers a means for recording the degree of which state is eligible for undergoing learning process. This paper aims to investigate the underlying mechanism of eligibility traces strategies using on-policy and off-policy learning algorithms. In doing so, the performance metrics can be obtained by defining the learning problem in a simulation environment, in conjunction with different learning algorithms. However, measuring learning performance and analysing sensibility are very expensive because such performance metrics can only be obtained by running an experiment with different parameter values. This paper proposes a comparative study for analysing the mechanism of eligibility traces. The objective of this paper is to compare and investigate the influences on performance caused by those different approaches. [ABSTRACT FROM AUTHOR]

Pilot error remains the major cause of aircraft accidents. The lack of Situation Awareness (SA), even amongst experienced pilots, is one of the primary reasons for pilot error. It is important to ensure that pilots are able to maintain a high level of SA before they act as members of a flight crew. A pilot's SA can be assessed by monitoring pilot behavior using observations of pilot eye movement. In this paper we discuss a rule-based approach to assess novice pilots' SA against a baseline. A recent experiment confirmed there is a measurable difference between the eye movement of an experienced pilot and the eye movement of several novice pilots. Initially, an expert pilot's eye movement was recorded using an eye tracker device in order to set the baseline. A Gaze Analyzer was used to derive relevant information from eye movement data. After consulting a Subject Matter Expert (SME), a rule-based system was created to monitor the pilot's behavior. The data was analyzed to identify behavioral patterns in order to associate the pilots' level of SA with their mode of attention distribution (fixation). Novice pilot's eye movement data was compared with an expert pilot's eye movement data using an inference engine. [ABSTRACT FROM AUTHOR]