The problems related to construction production are multi-faceted and complex. This has promoted the search for different methods/approaches for analizing the data which supports the decision-making process in the construction industry. In the article the authors focus their attention on well-known methods and tools, and on some new approaches to solving decision-making problems. The aim of the article is to analyze the methods used to analyse data in a construction company, convey their advantages and disadvantages, and specify the degree of efficiency in the discussed area.
In the paper an approach to decision making in situations with non-point-like characterisation and subjective evaluation of the actions is considered. The decision situation is represented mathematically as fuzzy multiobjective linear programming (fMOLP) model, where we apply the reduced fuzzy matrices instead of fuzzy classical numbers. The fMOLP model with reduced parameters is decomposable into the set of point-like models and the point-like models enable effective construction of an optimisation procedure – fBIP, see Wojewnik (2006ab), extending the bireference procedure by Michalowski and Szapiro (1992). The approach is applied to a fuzzy optimization problem in the area of telecommunication services.
The aim of this study is to present an exemplary cartographic visualization of fi re hydrants data consisting of a set of thematic maps containing various information related to the location of hydrants, buildings and driveways in geographic space, and relationships existing between them. Identification of these relationships requires spatial analysis, and illustrating them requires the use of appropriate cartographic presentation methods. The study was conducted on a part of the city of Poznan using data on hydrants’ location and type collected and provided by the Fire Department. Geometric data, obtained using geoprocessing algorithms, were assigned appropriate symbols, which lets differentiate them qualitatively and quantitatively. An emphasis is placed on the use of adequate visual variables and on the cartographic communication efficiency. The result of the study is a cartographic visualization in the form of series of thematic maps arranged in a logical sequence, and providing information about the secured area. The thematic layers presenting the same area were arranged in different arrangements with maintenance of the reference layers, ensuring the ease of correlation. Such a cartographic visualization provides knowledge about the spatial distribution and diversity of objects and the relationships between them. It may be an important source of knowledge both at the identification stage and at the operational stage when conducting a fire fighting action.
The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.
A significant part of the knowledge used in the production processes is represented with natural language. Yet, the use of that knowledge in computer-assisted decision-making requires the application of appropriate formal and development tools. An interesting possibility is created by the use of an ontology that is understandable both for humans and for the computer. This paper presents a proposal for structuring the information about the foundry processes, based on the definition of ontology adapted to the physical structure of the ongoing technological operations that make up the process of producing castings.
The model is developed for the intellectualized decision-making support system on financing of cyber security means of transport cloud-based computing infrastructures, given the limited financial resources. The model is based on the use of the theory of multistep games tools. The decision, which gives specialists a chance to effectively assess risks in the financing processes of cyber security means, is found. The model differs from the existing approaches in the decision of bilinear multistep quality games with several terminal surfaces. The decision of bilinear multistep quality games with dependent movements is found. On the basis of the decision for a one-step game, founded by application of the domination method and developed for infinite antagonistic games, the conclusion about risks for players is drawn. The results of a simulation experiment within program implementation of the intellectualized decision-making support system in the field of financing of cyber security means of cloudbased computing infrastructures on transport are described. Confirmed during the simulation experiment, the decision assumes accounting a financial component of cyber defense strategy at any ratios of the parameters, describing financing process.
In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.
Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with a single neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.
A complex model of mechanically ventilated ARDS lungs is proposed in the paper. This analogue is based on a combination of four components that describe breathing mechanics: morphology, mechanical properties of surfactant, tissue and chest wall characteristics. Physical-mathematical formulas attained from experimental data have been translated into their electrical equivalents and implemented in MultiSim software. To examine the adequacy of the forward model to the properties and behaviour of mechanically ventilated lungs in patients with ARDS symptoms, several computer simulations have been performed and reported in the paper. Inhomogeneous characteristics observed in the physical properties of ARDS lungs were mapped in a multi-lobe model and the measured outputs were compared with the data from physiological reports. In this way clinicians and scientists can obtain the knowledge on the moment of airway zone reopening/closure expressed as a function of pressure, volume or even time. In the paper, these trends were assessed for inhomogeneous distributions (proper for ARDS) of surfactant properties and airway geometry in consecutive lung lobes. The proposed model enables monitoring of temporal alveolar dynamics in successive lobes as well as those occurring at a higher level of lung structure organization, i.e. in a point P0 which can be used for collection of respiratory data during indirect management of recruitment/de-recruitment processes in ARDS lungs. The complex model and synthetic data generated for various parametrization scenarios make possible prospective studies on designing an indirect mode of alveolar zone management, i.e. with
Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.
Complex structural engineering projects that involve information-gathering and decision-makingprocesses need to be approached with appropriate systems and tools. As transactional databasesare found to be insufficient for this purpose, engineers are adopting multidimensional informationsystems that have been successfully used in other areas of management, especially business.