The spatial model of the classroom and its immediate surroundings: A variety of learning spaces

This paper looks into the spatial dispositions of the classroom and its immediate surroundings in elementary schools, with the goal of defining a broad learning space, in accordance with modern intentions in pedagogy. The starting assumption is that the learning space may offer versatility and a variety of options in the educational process. In the development of the spatial model two key contributing factors have been taken into account: the implications of the modern educational process and potential spatial characteristics. Various levels of spatial interrelationship are considered between the classroom and the adjacent classroom, the break-out space, communication area, social activity zones, and the outdoor classroom. Accordingly, by using the modelling method, a conceptual spatial model of the classroom and its immediate surroundings is defined such that it can receive specific applications in the design of elementary schools

The spatial model of the classroom and its immediate surroundings: A variety of learning spaces

This paper looks into the spatial dispositions of the classroom and its immediate surroundings in elementary schools, with the goal of defining a broad learning space, in accordance with modern intentions in pedagogy. The starting assumption is that the learning space may offer versatility and a variety of options in the educational process. In the development of the spatial model two key contributing factors have been taken into account: the implications of the modern educational process and potential spatial characteristics. Various levels of spatial interrelationship are considered between the classroom and the adjacent classroom, the break-out space, communication area, social activity zones, and the outdoor classroom. Accordingly, by using the modelling method, a conceptual spatial model of the classroom and its immediate surroundings is defined such that it can receive specific applications in the design of elementary schools

FOREWORD

Foreword to the issue dedicated to the SAUM 2016 conference

AUTOMATIC GENERATION OF THE PLC PROGRAMS FOR THE SEQUENTIAL CONTROL OF PNEUMATIC ACTUATORS

Nowadays, programmable logic controllers (PLC) are widely used in many automated systems, especially for the control of various actuators. The most common PLC programming is performed by either using a ladder diagram or a structured text. The paper presents the automatic generation of PLC programs for the purpose of sequentially controlling pneumatic actuators. In this paper, the pneumatic actuators are supplied and controlled by 5/2-way as well as 5/3-way bistable pneumatic valves with electric activation. The valve type depends on the number of positions in which the actuator should come, and the position sensors are used for detecting its movement. The characteristic encoding of the movement of actuators, position sensors and control commands is performed. The advantages of the automatic generation of the PLC commands and the entire program described in this paper are illustrated in a real example

Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms

Since the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolationThis is the peer-reviewed version of the article: Petković, D., Nikolić, V., Mitić, V.V., Kocić, L., 2017. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. Flow Measurement and Instrumentation 54, 172–176. [https://doi.org/10.1016/j.flowmeasinst.2017.01.007

ARTIFICIAL NEURAL NETWORK APPLICATION FOR THE TEMPORAL PROPERTIES OF ACOUSTIC PERCEPTION

Though acoustic perception is well established in literature, it seems to be insufficiently implemented in practice. Experimental results are excellent but a lot of issues arise when it comes to the application in real conditions. Using artificial neural networks makes acoustic signal processing very comfortable from the mathematical point of view. However, a great job has to be done in order to make it possible. The procedure includes data acquisition, filtering, feature extraction and selection. These techniques require much more resources than mere artificial neural networks and this represents a limiting factor for the implementation. The paper investigates the complete procedure of acoustic perception, in terms of time, in order to identify limitations

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

FUZZY CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT FOR MOVING TARGET TRACKING

Tracking of moving objects, including humans has important role in mobile robotics. In this paper, the hierarchical control structure for target/human tracking consisted of high and low level control was presented. The low level subsystem deals with the control of the linear and angular velocities using multivariable PD controller whose parameters are obtained by Particle swarm optimization. The position control of the mobile robot represents the high level control, where we use two fuzzy logic Mamdani controllers for distance and angle control. In order to test the effectiveness of the proposed control scheme a simulation was performed. Two cases, when the mobile robot pursues a target moving along a circular path and when the mobile robot pursues a target moving along a rectangle path, were simulated

INPUT VECTOR IMPACT ON SHORT-TERM HEAT LOAD PREDICTION OF SMALL DISTRICT HEATING SYSTEM

Short-term load prediction is very important for advanced decision making in district heating systems. The idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. The common thing for each way of prediction is usage of historical data for certain last period which makes possible development of many methodologies for adequate prediction and control. In this paper, application of feedforward artificial neural network for short-term load prediction for period of 1, 3 and 7 days, of one small district heating system, is presented. Three different input vectors are implemented and their impact on quality of prediction discussed. The simulation results are compared and detailed analysis is done where operation in transient regime is of special importance. Satisfied prediction average error is obtained

Vision-Based Inspection of Tyre Tread Depth

In this paper, an approach for visual, non-contact automatic inspection of tyre tread depth based on existing image processing techniques is presented. Histograms of oriented gradient are used for feature extraction from images. In order to analyse which set of features gives the best classification results, a linear support-vector machine classifier was trained and tested using different numbers of pixels and numbers of cells per block. The obtained processing and experimental results are presented in this paper