A monitoring network system was designed and developed to be deployed in two Romanian cities during the ROkidAIR project for measuring in real time the PM2.5 concentrations. The system comprises 8 stations that were built following the successful design and testing of a PM2.5 optical instrument prototype. The System�s Web services, the data acquisition from the monitoring stations, and the data sending to a GIS-integrated geoportal that includes a decision support system, were firstly evaluated for testing their effectiveness and for eventually defining necessary corrections. After describing the station�s structure and the Web services� main functions, the paper presents data about the most important measured operating parameters of the stations, results from data processing, and conclusions for further developments. Preliminary information collected from a reference gravimetric sampler installed for data inter-comparison with the developed stations is also presented.
The amount of computing power assigned to the intelligent components of a production system must be justified by the benefits brought following the decisions these components are taking. The paper proposes a state machines and messages exchange based model for developing simulators used to estimate the system’s behavior and performance in different configurations. A simple system was modeled and simulations were performed for different numbers of electric transporters in the system, different charging times and for different degrees of independence concerning the decisions taken by the manufactured products. It was possible to determine the most effective number of transporters, a value which is increasing, as expected, with the charging time. A second conclusion was that allowing the manufactured products to decide themselves concerning the transporter to use at a certain moment does not necessarily increase the overall system’s performance.
The paper is describing the work performed for choosing and designing a path planning and obstacle avoidance solution for a car-like vehicle moving in an indoor environment. The proposed algorithm first consists in providing the vehicle with a set of passing points generated by a Dijkstra path planning algorithm. The vehicle is using the points for establishing its path and is avoiding the obstacles using scanned data from a distance sensor. In a simulation environment, initial tests were performed for estimating correct values of the vehicle’s forward and angular velocities and for estimating the effect of the scanning speed on the vehicle’s behaviour. Obstacle avoidance tests were performed for identifying specific situations possible to appear due to high velocities or to low scanning speed. Even not always choosing a smooth avoidance path, the algorithm proved to find a way for avoiding the obstacle in a clear and fast enough manner.
When an autonomous industrial vehicle moves to a certain point to take over or leave an order, intermediate commands may appear on its route that can be taken together with the first order. Orders will be allocated through an algorithm that analyzes delivery times and priorities. This paper presents results of researches regarding the development and use of a simulator to determine the efficiency of the algorithm for allocating transport orders in an intelligent manufacturing line for polymeric products. It is presented for comparison the variant in which the vehicle deals only with the first order and after its finish can take a new order. The paper comes from the development of previously made articles in which an algorithm is used to allocate the transport loads of autonomous industrial vehicles and a neural network that analyzes the capacity of batteries, thus estimating the distances that the vehicle can travel with certain percentages of battery.
Recycling is a key process in any sustainable development strategy. This paper proposes solutions for the increasing waste collection rates by developing an educational model for developing innovative waste management solutions. The focus in this paper will be on making the correlation between experimental studies on compressive properties of recycling waste and designing a smart waste management compactor. Based on previous achievements on developing an innovative compactor system with selective waste collection, actual experimental trials will be analysed for generating compression patterns for different types of common waste containers which will be used in the conceptual design process of a compactor, impacting concept definition of all 3 subsystems: mechanical, electrical and software. A dedicated software module for compression parameters will be developed for importing experimental data trials and based on these to process and identify relevant compression parameters defining compression pattern for different common waste containers. These parameters will be used to assist the wok mode state machines for compacting wastes. This will improve compactor performance by optimization of compactor usage smart adaptability.
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