The multi-port charging station for EV application is being widely used nowadays.The renewable energy-based charge generation schemes are employed with various controlling and optimization algorithms.The conventional method is applied with a PI controller based inverter circuit for accessing the PV grid-connected system interconnected with a charging station.Here the gating pulse is generated through Sinusoidal Pulse Width Modulation (SPWM) and it is controlled with a PI controller.The main objective of the proposed method is to increase the percentage of the State of Charge (SoC) and improve the grid utility circuit so that it could utilize a large load.In the proposed work, the optimized circuit of the PV grid system is designed by using Particle Swarm Optimization and the controller of the inverter circuit utilizes the Fuzzy based PI logic, then it generates the gate signal by Pulse Width Modulation Generator.The system is designed based on the utility grid and the EV capacity and the inverter block plays a vital part in this circuit.The controlling technique of the inverter improves the results by activating the charge depending on the load demand.The proposed PSO technique is improved through frequency scaling activation and the maximum power point tracking approach.The experimental results show the state of the utility charge by inverter, State of Charge, DC link voltage, PV tracking range and EV charging current.Thus, it can be concluded that the proposed work has achieved better results than other existing works by the design of the above-mentioned optimized PV model.The overall design model is performed with the help of the 2018a version of MATLAB/Simulink.

This paper focuses on extracting emotions from works published by Romanian canonical authors.The information about canonical authors represents a part of the data provided by the INTELLIT platform (Gavrilă, Băjenaru & Dobre, 2019a).The INTELLIT platform aims to be the largest digital archive in Romania containing data regarding our national literary heritage.

This paper focuses on an optimal production planning algorithm solution for an integrated flexible manufacturing system (IFSM) assisted by robotic systems. The assembly system is composed of 6 individual interconnected workstations, of which two cells equipped with six degree of freedom industrial robotic manipulators (IRM). Parallel with the interconnected workstations a SCARA robotic transportation system (SRTS) permits the movement of products forward and backward between stations. The entire system permits the production of two product types on two different production processes. One production process takes place completely in an assembly cell equipped with an ABB IRM and can produce only one product type. The other production process takes place on the interconnected stations, of which one is equipped with a Fanuc IRM and it can assembly both types of products being assisted by the SRTS in the production. An IFSM interconnected server is dedicated to the centralized control of the two parallel manufacturing processes, on-line interconnected station manufacturing and flexible cell manufacturing. As a result of introducing an algorithm aimed at minimizing the total production time, an optimized production planning is obtained on the two parallel flows, based on the request taken from the customer.

Different frameworks can be chosen to solve multicriteria decision making (MCDM) problems emerging in business, economy, health care, engineering and other areas. Uncertainty, vagueness and non-rigid boundaries of the initial information are frequently noticed when dealing with the practicalities of the MCDM tasks. Single-valued neutrosophic sets are considered as the effective tool to express uncertainty of the information, however in some cases it lacks the desirable generality and flexibility. The m-generalized q-neutrosophic sets (mGqNNs) were recently proposed to deal with this situation. The novel MCDM methodology MULTIMOORA-mGqNN is presented in this paper. An illustrative example that analyses the effectiveness of different cable root management decisions for the application in chemical industry is also provided to demonstrate the practicalities of the MULTIMOORA-mGqNN.

This paper proposes an approach to the tuning of model-free controllers for the midcarpal joint angles, which are important finger angles that ensure the desired finger dynamics in prosthetic hand myoelectric-based control systems. The process in these control systems is characterized by fuzzy models that operate with myoelectric signals obtained from eight myoelectric sensors and past inputs and/or outputs, where the controlled outputs are five finger angles. Since the fuzzy models exhibit very good performance as shown in authors' recent papers that produced evolving fuzzy models, they are used to simulate the process behaviour. The Multi Input-Multi Output (MIMO) control system structure consists of five separate Single Input-Single Output control loops with the most simple model-free controllers represented by intelligent Proportional (iP) controllers, separately designed and tuned for each finger. Digital simulation results are included to suggestively illustrate the very good performance of the control systems with iP controllers. The MIMO control system performance is compared with that of the same system but with Proportional-Integral controllers, which are optimally tuned in a model-based manner by a metaheuristic Grey Wolf Optimizer (GWO) algorithm. The fair comparison is ensured by the optimal tuning of the free parameters of iP controllers in a model-based manner using GWO.

The paper describes the micro-world-based dialog manager which was developed in the ROBIN project. The manager was designed to be loaded into the Pepper robot, used in real-world scenarios and interface with real-time automatic speech recognition and synthesis for Romanian language. A strict requirement for the development of the dialog manager was that it had to be configurable, with minimum user intervention, to a wide range of dialogue situations, such as assisting the elderly in day to day routine tasks or providing orientation in a building for new visitors. Thus, the dialog manager was programmed to configure itself from micro-world specification files containing definitions of the concepts one can speak about and definitions of the first-order predicates that are true in the micro-world.

The aim of this paper is to recognize human emotions from physiological electroencephalography (EEG) signal.Indeed, this paper demonstrates that neural networks in conjunction with recursive least squares can be used sprucely for the identification of emotional states.In this regards, this report uses online sequential extreme learning machine to conceive the present emotion recognition system.To validate the efficiency of this machine learning, the performance of various popular feature extraction methods with the Database for Emotion Analysis using Physiological Signals DEAP dataset, and two newly developed EEG datasets are systematically evaluated for this study.The first emotional experience was to conceive an emotional dataset containing the samples EEG of six selected emotion states (neutrality, amusement, surprise, compassion, attraction, and disgust), and the second one was to predict the difficulties encountered during playing a hard game.This has the goal to detect the concentration and anger emotional states.The purpose of this paper is to improve the accuracy of emotion recognition based on different emotional states.The features are extracted from the delta, theta, alpha and beta bands.Based on the analysis of the identification system, the most significant features are extracted for each emotional state.The selected ones are then utilized in the emotion classification system.The present recognition system has 80% accuracy when using the DEAP database, 74.07%accuracy when using the first database, and obtains 61.11% accuracy when predicting difficulties in video games.

The trading risk management implies analysing several types of risks: capital, market, liquidity, insolvency, business, credit, operational or financial risk. Trading models and techniques must be seen as tools that can provide an informed manager with useful insights, so they are indispensable in an increasingly integrated and sophisticated market.

This study examines a computational technique used to design a novel feedback control law based on an expansion strategy of the Attraction Domain (DA) for a class of nonlinear switched systems.It is supposed that the state space is distributed into numerous regions without any intersections and modelled by polynomial inequalities.The main concept involves the maximization of the DA for local subsystems surrounding particular operational points.It was demonstrated that the DA can be ascertained by joining a Genetic Algorithmic method (GA) as an enhanced optimisation approach with the LMI method for a specified Lyapunov function.The feedback controller can then be constructed in order to ensure a global stability by using the Multiple Lyapunov function sets via switching signals.The effectiveness of this evolved strategy is eventually confirmed via a simulation examination by means of the benchmark Van der Pol oscillator.