The author’s aim was to present actual conditions of rural primary schools functioning and the spatial differentiation of their network reorganization with particular emphasis on the consequences of those schools liquidation change their a governing body other from the local government units (LGU) to local community organizators. The study was focused on rural areas of the Małopolskie Voivodship over 2000–2016 period. In the paper were presented the number of pupils and schools (open and closed) and the school governing bodies structure too. Those data, obtained by the author from the Local Data Banks and the Board of Education in Cracow and were presented for each statistical locality. A population and settlement concentration in many rural areas made costs of schools maintenance higher and higher. Thus school governing bodies faced a difficult decision – either to reorganize the actual school network or to spend more on education from the municipal budget. Most complicated structures is observed in the rural areas showing depopulation and dispersed settlement, the zones of traditional agricultural. In all rural areas of the Małopolskie Voivodship, the number of pupils in primary schools during the analysed period decreased nearly by 30%. Thus 118 small rural schools were closed i.e. in the county Miechów, of 43 schools remained only 21. The number of closed schools would be much higher without a activity of the local communities, which began to take over their schools from the LGU. Within rural areas the Małopolskie Voivodship in 2016, 123 schools were run by local organization i.e. over 11,5% of all the rural primary schools.
Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.
The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.
This work aims to comprehensively describe the current state of the concept of green infrastructure. It is thus meant to fill in a gap in Polish literature as no comprehensive works concerning green infrastructure have been published in our country even though we have witnessed several such works in other places in the world. The book is mostly addressed to urban planners, spatial planners and landscape architects and it focuses on issues related to developing strategies or green nalyzingture network designs. It is difficult to establish when (and by whom) the term “green infrastructure” was actually coined. The performed literature search indicates that various authors attribute its beginnings to different publications. There is, however, much more consensus regarding the origins of the idea of green infrastructure. Among the concepts regarded as the bases for the notion of green infrastructure we can discern two principal ones: the concept of ecological networks and the concept of greenways (in the US). In Poland, such concepts included the Ecological System of Protected Areas (in Polish: Ekologiczny System Obszarów Chronionych) and System of Open Spaces (in Polish: System terenów otwartych). There is some disagreement regarding the origins of green infrastructure in cities. Analysis of defi nitions of green infrastructure seen in both scientific publications as well as guides and formal documents leads to a single conclusion – we should accept the diversity of interpretations and approaches. A similar diversity in approaches can also be found when looking at the presented typologies. By analyzing the rationale behind the typologies, we can discern three major criteria used by the authors: land cover, land use and ecological value, which is usually associated with formal protection of specifi c areas. The principles of green infrastructure development can be divided into planning-related (multi-functionality, connectivity, multi-scale approach, multi-object approach, cost-effective approach) and governance-related (strategic approach, integration, social inclusion, transdisciplinarity, stakeholder inclusion). Green infrastructure provides people with a multitude of more or less measurable benefits. For the last several years they have been identified and quantified using a concept of ecosystem services. These services are always provided in certain confi gurations, which means that it is only possible to obtain the benefits if the services generating those benefi ts are not contradictory to each other. For several years now, the European Commission has been conducting research on the scope, possibilities and methods of implementing the concept of green infrastructure in the member states. However, the EU’s offi cial position on this subject was declared only in 2013 via Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions – Green Infrastructure (GI) — Enhancing Europe’s Natural Capital. In both EU member states and the United States, non-governmental organizations are the main advocates of the concept of green infrastructure. They have been recently joined by governmental and self-government agencies. The case studies of already developed strategies and designs of the concept of green infrastructure presented in this book illustrate a great diversity of approaches. It is particularly noticeable in the way of identifying specific components and principles of planning and implementation of green infrastructure networks. These differences come mainly from the varying scale of development, adopted interpretations of the notion of green infrastructure as well as specifi c natural, social and sometimes economic conditions in particular areas. Based on the knowledge and experience gathered from the analysis of those cases, we can point out the following problems that Polish planners need to face in order to develop and implement green infrastructure for Polish rural communes, cities and regions: • good selection of the formula and defi nition of green infrastructure that is appropriate for the scale, specifi c conditions of the area, needs of the inhabitants and ambitions of the authorities; • good identification of areas with potential for green infrastructure development that is appropriate for the scale and problems of a specific area (city, village, region) • identification of the scope and degree of confl ict between ecosystem services provided by individual components of green infrastructure; • development of a spatial concept that includes the problem of the inherent conflict between the expected benefits (especially regulation and maintenance versus cultural) coming from individual components of green infrastructure; • proposal of appropriate instruments for implementing the concept and resolving the problem of its coexistence with other concepts of shaping the ecological structure of cities, rural communes and regions in Poland. Summing up, the concept of green infrastructure can be viewed as the ultimate synthesis of all former ideas dealing with the development of ecological structure of cities, open landscapes and regions. In most European countries, apart from Great Britain, the concept of green infrastructure is currently in its implementation phase. Therefore, its true – not paper – history is about to begin and it will probably look diff erent in every country. It will be aff ected by various traditions of development planning, the already developed concepts, degree of involvement of the authorities and – probably above all – the will of those that expect quantifiable benefits from green infrastructure.
This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.
The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time-consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.
The paper analyzes the monthly day equivalent levels, Lday (06–22 h) and night equivalent levels, Lnight (22–06 h) values observed in year 2015 and 2016 for the 70 locations whereby continuous noise monitoring is conducted under the National Ambient Noise Monitoring Network (NANMN). The study exclusively analyzes the ambient noise data acquired for 25 locations in commercial zone, 12 in industrial, 16 in residential and 17 in silence zones. The analysis of (Lday–Lnight) for 70 locations under observations reveals that 10 dB night time adjustment in day-night average sound level descriptor is not appropriate in such a scenario and as such it is recommended to use day-night average sound level and day-eveningnight average sound level descriptors without any 10 dB night time adjustment or 5 dB evening time adjustments. The analysis and conclusions of the present study shall be very useful for developing single value noise descriptor correlating the noise annoyance and health effects in Indian perspectives.