The establishment of the Research Network Lukasiewicz (RNL) is aimed at strengthening the research potential and knowledge transfer from research institutes to enterprises. The article presents the results of the research potential analysis of 38 research institutes that are to form the RNL, based on data on scientific publications in 2013–2016. The number of publications of RNL institutes was similar to the number of publications of TNO and VTT institutes but smaller than that of Fraunhofer institutes. The publications of RNL institutes had lower values of indicators of international collaboration and collaboration with business as well as lower values of citation indices. Co-authors of RNL publications were mainly affiliated with national scientific units, whereas co-authorship with Fraunhofer, TNO and VTT institutes was marginal. The article also outlines the limitations and challenges of the adopted research method and future research orientations in this area.
The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network’s hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.
The cognitive aim of this study is to point to the optimum number of local government units and the optimum boundaries of spatial units in Poland with the assumption of minimizing the cumulated theoretical travel time to all settlement units in the country. The methodological aim, in turn, is to present the use of the ArcGIS location-allocation tool for the purposes of delimitation processes as exemplified by administrative boundaries in Poland. The rationale for the implementation of this study is that number and the boundaries of units of all levels of Poland’s current territorial division are far from optimum in the light of minimization of accumulated theoretical travel time to all settlement units in the country. It may be concluded that it would be justifiable to increase the number of voivodships from the current number of 16 to 18. Besides it would be necessary to introduce modifications in relation to units with regional functions. In contrast, the number of districts and communes should be reduced. A continuation of this research may go in the direction of including analysis of public transport network in the research, creating in this way a multimodal set of network data. This would illustrate, apart from the potential itself resulting from the infrastructure, also the actually existing connections.
The article presents results of the influence of the GMDH (Group Method of Data Handling) neural network input data preparation method on the results of predicting corrections for the Polish timescale UTC(PL). Prediction of corrections was carried out using two methods, time series analysis and regression. As appropriate to these methods, the input data was prepared based on two time series, ts1 and ts2. The implemented research concerned the designation of the prediction errors on certain days of the forecast and the influence of the quantity of data on the prediction error. The obtained results indicate that in the case of the GMDH neural network the best quality of forecasting for UTC(PL) can be obtained using the time-series analysis method. The prediction errors obtained did not exceed the value of ± 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.
To reliably calibrate suitable partial safety factors, useful for the specification of global condition describing structural safety level in considered design case, usually the evaluation of adequate failure probability is necessary. In accidental fire situation, not only probability of the collapse of load-bearing structure, but also another probability related to the people staying in a building at the moment of fire occurence should be assessed. Those values are different one from another in qualitative sense but they are coupled because they are determined by similar factors. The first one is the conditional probability with the condition that fire has already occured, whereas the second is the probability of failure in case of a potential fire, which can take place in the examined building compartment, but its ignition has not yet appeared. An engineering approach to estimate such both probabilities is presented and widely discussed in the article.
Lean manufacturing has been the most deliberated concept ever since its introduction. Many organization across the world implemented lean concept and witnessed dramatic improvements in all contemporary performance parameters. Lean manufacturing has been a sort of mirage for the Indian automotive industry. The present research investigated the key lean barriers to lean implementation through literature survey, confirmatory factor analysis, multiple regression, and analytic network process. The general factors to lean implementation were inadequate lean planning, resource constraints, half-hearted commitment from management, and behavioral issues. The most important factor in the context of lean implementation in Indian automotive industry was inadequate lean planning found with the help of confirmatory factor analysis and multiple regression analysis. Further analysis of these extracted factors through analytic network process suggested the key lean barriers in Indian automotive industry, starting from the most important were absence of proper lean implementation methodology, lack of customer focus, absence of proper lean measurement system, inadequate capital, improper selection of lean tools & practices, leadership issues, resistance to change, and poorly defined roles & responsibilities. Though literature identifying various lean barriers are available. The novelty of current research emerges from the identification and subsequent prioritization of key lean barriers within Indian automotive SMEs environment. The research assists in smooth transition from traditional to lean system by identifying key barriers and developing customized framework of lean implementation for Indian automotive SMEs.
Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.
The research into the use of less costly modifications of road links and networks, and changes in the service of road surroundings aimed at ensuring an improvement of through traffic performance in suburban areas, and on roads passing through built-up areas as small localities, with application of simulation model, is presented in this paper. From among possible designs, the authors investigated and presented the effectiveness of two, i.e. implementation of an additional multifunctional median lane in the road cross-section, and construction of service roads with different locations of intersections (end or middle of the road section). The analysis is focused on the impact of such changes on traffic performance and road safety. The authors analysed travel speed, delay and share of platoon traffic on a uniform sections of the road for different types of road surroundings service. The study presents the results of analyses of road network before and after modification, and the assessment of: •impact of access points density and level of their use on road traffic performance,•impact of driving through road sections in built-up area on building platoon traffic,•impact of change in the cross-section type on traffic performance.