Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.
@cite_12 , data from the charge-discharge simulation of lithium-ions battery energy storage are used for classification purposes with recurrent NNs and PNNs by means of a theoretical framework based on signal theory.
@cite_12
1994640288 2007672025 2038012452 2141842132
In this paper is reported a critical review, experiences and results about state of charge (SOC) and voltage prediction of Lithium-ions batteries obtained by recurrent neural network (RNN) and pipelined recurrent neural network (PRNN) based simulation. These soft computing technologies will be here presented, utilized and implemented to obtain the typical charge characteristics and the charge discharge simulation procedure of a commercial solid-polymer technology based cell. Simulations are compared with experimental data manufacturers. The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction. Abstract Lithium-ion batteries are used as the main power source in many electronic and electrical devices. In particular, with the growth in battery-powered electric vehicle development, the lithium-ion battery plays a critical role in the reliability of vehicle systems. In order to provide timely maintenance and replacement of battery systems, it is necessary to develop a reliable and accurate battery health diagnostic that takes a prognostic approach. Therefore, this paper focuses on two main methods to determine a battery's health: (1) Battery State-of-Health (SOH) monitoring and (2) Remaining Useful Life (RUL) prediction. Both of these are calculated by using a filter algorithm known as the Support Vector Regression-Particle Filter (SVR-PF). Models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. Moreover, the RUL prediction model is proposed, which is able to provide the RUL value and update the RUL probability distribution to the End-of-Life cycle. Results for both methods are presented, showing that the proposed SOH monitoring and RUL prediction methods have good performance and that the SVR-PF has better monitoring and prediction capability than the standard particle filter (PF). In this work we present a data-driven approach to the rational design of battery materials based on both resource and performance considerations. A large database of Li-ion battery material has been created by abstracting information from over 200 publications. The database consists of over 16 000 data points from various classes of materials. In addition to reference information, key parameters and variables determining the performance of batteries were collected. This work also includes resource considerations such as crustal abundance and the Herfindahl–Hirschman index, a commonly used measure of market concentration. The data is organized into a free web-based resource where battery researchers can employ a unique visualization method to plot database parameters against one another. This contribution is concerned with cathode and anode electrode materials. Cathode materials are mostly based on an intercalation mechanism, while anode materials are primarily based on conversion and alloying. Results indicate that cathode materials follow a common trend consistent with their crystal structure. On the other hand anode materials display similar behavior, based on elemental composition. Of particular interest is that high energy cathodes are scarcer than high power materials and high performance anode materials are less available. More sustainable materials for both electrodes based on alternative compositions are identified.