Wyniki wyszukiwania - BazTech

1

Civil aviation flight safety: pilot properties soft computing

Plotnikov Nikolai I.

Prace Naukowe Politechniki Warszawskiej. Transport

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2021

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z. 133

5--14

EN

Commercial competition leads to aviation accidents. It forces airlines to reduce the cost of purchasing, leasing, and maintenance of aircraft. The air carrier saves on professional training of personnel, on an arbitrary increase in the workload standards, on the use of flight crews with minimal and untenable experience in chronic fatigue conditions. Theory and methods of the characteristics of specialists remain uncertain. Statistical data and expertise may be piecewise-defined, inaccurate, and inconsistent. It is necessary to establish indicators and values of acceptable accuracy using fuzzy measures to calculate the dependability of flight crews based on workload and experience. It is proposed soft computing, statistical and expert methods for calculating the properties of a person and social groups in the management of dangerous professions. It makes it possible to calculate the dependability of the pilot properties with an assessment of flight safety risk levels for making management decisions. The results of the work are new standards for the workload of flight crews recommended for civil aviation. Results are obtained in qualitative methods for calculating efficiency, security, and risk states in the management of organizational objects as airlines. Indicators for air transport risk management standards and decision-making tools were obtained. Calculated indicators of pilot dependability values are a model for developing the airline's strategy, for quantitative assessments of flight specialists, standardizing professional activities, and managing training costs.

2

Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks

Krzywanski Jarosław, Sztekler Karol, Bugaj Marcin, Kalawa Wojciech, Grabowska Karolina, Chaja Patryk Robert, Sosnowski Marcin, Nowak Wojciech, Mika Łukasz, Bykuć Sebastian

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e137054

EN

Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.

3

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Lawal A. I., Olajuyi S. I., Kwon S., Aladejare A. E., Edo T. M.

Acta Geophysica

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2021

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Vol. 69, no. 4

1313--1324

EN

Blasting is an intrinsic component of mining cycle of operation. However, it is usually associated with negative environmental efects such as blast-induced ground vibration (BIGV) which require accurate prediction and control. Therefore, in this study, Gaussian process regression (GPR) has been proposed for prediction of BIGV in terms of peak particle velocity (PPV), while grey-wolf optimization (GWO) algorithm has been used to optimize the blast-design parameters for the control of BIGV in Obajana limestone quarry, Nigeria. The blast-design parameters such as burden (B), spacing (S), hole depth (Hd), stemming length (T), and number of holes (nh) were obtained from the quarry. The distance from the blasting point to the measuring point (D) and the charge per delay (W) were measured and determined, respectively. The PPV was also measured for the number of blasting operations witnessed. These seven parameters were used as inputs to the proposed GPR model, while the PPV was the targeted output. The performance of the proposed model was evaluated using some statistical indices. The output of the GPR model was compared with ANN model and three empirical models, and the GPR model proved to be more accurate with the coefcient of determination (R2 ) of approximately 1 and variance accounted for VAF of about 100%, respectively. In addition, the GWO was also developed to select the optimum blasting parameters using the ANN model for the generation of objective function. The output of the GWO revealed that if the number of holes (nh) can be reduced by 45% and W by 8%, the PPV will be reduced by about 94%. Hence, the proposed models are both suitable for prediction of PPV and optimization of blast-design parameters.

4

Skull stripping using traditional and soft-computing approaches for magnetic resonance images : a semi-systematic meta-analysis

Humera Azam, Humera Tariq

Machine Graphics and Vision

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2020

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Vol. 29, No. 1/4

33--53

EN

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.

5

Computational Intelligence for Analysing the Mechanical Properties of AA 2219 - (B4C + h-BN) Hybrid Nano Composites Processed by Ultrasound Assisted Casting

Radha P., Selvakumar N., Harichandran R.

Archives of Metallurgy and Materials

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2019

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Vol. 64, iss. 3

1163--1173

EN

The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4 C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4 C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4 C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro/nano composites with the inclusion of B4 C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.

6

A novelty detection approach to monitoring of epicyclic gearbox health

Dworakowski Z., Dziedziech K., Jabłoński A.

Metrology and Measurement Systems

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2018

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Vol. 25, nr 3

459--473

EN

Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries in which these gearboxes operate in a harsh environment and in variable operational conditions. In this paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health state based on vectors of features calculated from the vibration signal. The authors examine various feature vectors, various sources of data and many different damage scenarios in order to compare novel detection algorithms based on three different principles of operation: a distance in the feature space, a probability distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate for non-deterministic results of training of neural networks, which may lead to different network performance, the ensemble technique is used to combine responses from several networks. The methods are tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes, and acquisition of data at variable speed conditions.

7

Reservoir water level forecasting using group method of data handling

Zaji A. H., Bonakdari H., Gharabaghi B.

Acta Geophysica

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2018

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Vol. 66, no. 4

717--730

EN

Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models’ performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models’ applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L-1) and (L, L-1, L-12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L-7) and (L, L-7, L-14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www. typingclub.com/st. Accordingly, (L, L-1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.

8

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Zarychta P., Badura P., Pietka E.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2017

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Vol. 65, nr 1

63--70

EN

A study on computer aided diagnosis of posterior cruciate ligaments is presented in this paper. The diagnosis relies on T1-weighted magnetic resonance imaging. During the image analysis stage, the ligament region is automatically detected, localized, and extracted using fuzzy segmentation methods. Eight geometric features are defined for the ligament object. With a clinical reference database containing 107 cases of both healthy and pathological cases, a Fisher linear discriminant is used to select 4 most distinctive features. At the classification stage we employ five different soft computing classifiers to evaluate the feature vector suitability for the computerized ligament diagnosis. Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems. The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the computer aided ligaments diagnosis.

9

Development of a fuzzy-driven system for ovarian tumor diagnosis

Żywica P., Dyczkowski K., Wójtowicz A., Stachowiak A., Szubert S., Moszyński R.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

632--643

EN

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data, which is a unique feature of the developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Three diagnostic modules are described, along with examples. The first module is based on aggregation of existing prognostic models for ovarian tumor. The second presents the novel concept of an Interval-Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty. The third approach draws from cardinality theory of fuzzy sets and IVFSs and leads to a bipolar result that supports or rejects certain diagnoses.

10

OvaExpert: an intelligent medical diagnosis support system for ovarian tumor

Żywica P., Apolinarski S., Kubiński B.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

183--190

EN

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data which is an unique feature of developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Two diagnostic modules are described in the paper along with examples. First module is based on aggregation of existing prognostic models for ovarian tumor. Second, on novel concept of Interval– Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty.

11

Approximation of phenol concentration using novel hybrid computational intelligence methods

Pławiak P., Tadeusiewicz R.

International Journal of Applied Mathematics and Computer Science

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2014

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Vol. 24, no. 1

165--181

EN

This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

12

Modelowanie neuronowe procesów technicznych z zastosowaniem inżynierii chaosu

Przystałka P.

Modelowanie Inżynierskie

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2013

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T. 17, nr 48

112--123

PL

Artykuł dotyczy problemu modelowania neuronowego z zastosowaniem inżynierii chaosu. Główna część pracy poświęcona jest lokalnie rekurencyjnej globalnie jednokierunkowej sieci neuronowej zbudowanej z jednostek przetwarzających, dla których możliwe jest uzyskanie zachowania chaotycznego. Inżynieria chaosu wykorzystana jest w algorytmie ewolucyjnym w celu poprawy efektywności procesu uczącego. Problem wyboru wejść istotnych modelu rozwiązano, modyfikując metodę Z. Hellwiga. Izolinie kryterialne oraz wybrane metody wrażliwościowe zastosowano do poszukiwania optymalnej struktury sieci. W celu przedstawienia zalet i ograniczeń proponowanego podejścia przedstawiono wyniki modelowania neuronowego z wykorzystaniem danych zgromadzonych na obiekcie rzeczywistym.

EN

The paper deals with the problem of neural modeling with the use of chaos engineering. The main part of the paper is focused on a locally recurrent neural network that is composed of complex dynamic neural units for which chaotic behaviour can be obtained. Chaos engineering is incorporated into the evolutionary algorithm in order to improve the efficiency of the tuning procedure. The problem of relevant inputs selection is solved by means of the method of extended Hellwig's coefficient of integral capacity of information. Criteria isolines and some sensitive methods are used to find the suitable architecture of a network. The merits and limitations of the proposed approach is illustrated using real-world data.

13

An Insight Into The Z-number Approach To CWW

Pal S.K., Banerjee R., Dutta S., Sarma S. S.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

197--229

EN

The Z-number is a new fuzzy-theoretic concept, proposed by Zadeh in 2011. It extends the basic philosophy of Computing With Words (CWW) to include the perception of uncertainty of the information conveyed by a natural language statement. The Z-number thus, serves as a model of linguistic summarization of natural language statements, a technique to merge human-affective perspectives with CWW, and consequently can be envisaged to play a radical role in the domain of CWW-based system design and Natural Language Processing (NLP). This article presents a comprehensive investigation of the Z-number approach to CWW. We present here: a) an outline of our understanding of the generic architecture, algorithm and challenges underlying CWW in general; b) a detailed study of the Z-number methodology - where we propose an algorithm for CWW using Z-numbers, define a Z-number based operator for the evaluation of the level of requirement satisfaction, and describe simulation experiments of CWW utilizing Z-numbers; and c) analyse the strengths and the challenges of the Z-numbers, and suggest possible solution strategies. We believe that this article would inspire research on the need for inclusion of human-behavioural aspects into CWW, as well as the integration of CWW and NLP.

14

Application of neural networks for social capital analysis

Siderska J.

Czasopismo Techniczne. Automatyka

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2013

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R. 110, z. 2-AC

57--66

EN

The paper investigates the possibility of using soft computing for estimating the value of social capital. Our approach is applied to the case of Red Hat Inc. – the world’s leading provider of open source solutions. The objective of the research was to develop an artificial neural network for forecasting the value of social capital. These studies also allow us to identify variables significantly affecting the value of social capital. Computer simulations and assessments were done using software package STATISTICA Automated Neural Networks. The paper concludes with discussion and proposals for further research.

PL

Głównym celem artykułu jest analiza możliwości zastosowania obliczeń inteligentnych do modelowania kapitału społecznego firmy Red Hat Inc. – światowego lidera rozwiązań open source dla biznesu. Zasadniczym celem badań jest zaproponowanie struktury sztucznej sieci neuronowej do analizy wartości kapitału społecznego. Zidentyfikowano zmienne istotnie wpływające na wartość tego kapitału. Wszystkie symulacje komputerowe oraz oszacowania przeprowadzono w pakiecie statystycznym STATISTICA Automatyczne Sieci Neuronowe. W artykule przedyskutowano wyniki testów otrzymanych z zastosowaniem zaprojektowanego modelu oraz zaproponowano tematykę dalszych badań.

15

Federated Database Systems in Supply Chain Management

Paszkowski J., Marchlewska A., Przybyszewski K.

Journal of Applied Computer Science Methods

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2012

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Vol. 4 No. 2

55--66

EN

The article shows the possible ways of use of federated database technologies in data management in the logistic chains of companies. The architectures and components of software for creating the federated systems and some suggestions for the architecture of data management information systems for companies are presented in the text. The mentioned solution stands a proposal to resolve the difficult problem of cooperation between the computer information systems in companies, which refers to making the data of company available to the external users and managing data in the logistic chain. The authors included an overall review of commercial software, which can be used to build the software of data managing in the chains of cooperating companies.

16

Structural design engineering of woven fabric by soft computing: part I - plain weave

Mishra R., Kremenakova D., Behera B K, Militky J.

Autex Research Journal

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2011

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Vol. 11, no. 2

37--41

EN

An attempt has been made to optimise the engineering attributes of a plain weave fabric according to certain requirements. A simplified algorithm was used to solve fabric geometrical model equations, and relationships were obtained between useful fabric parameters such as thread spacing and crimp, fabric cover and crimp, warp and weft cover. Such relationships help in guiding the direction for moderating fabric parameters. The full potential of the Peirce fabric geometrical model for plain weave has been exploited by soft computing. The interrelationships between different fabric parameters for jammed structures, non-jammed structures and special cases in which the cross-threads are straight were obtained using a suitable algorithm. It is hoped that the fabric designer will benefit from the flexibility in choosing fabric parameters for achieving any end use with the desired fabric properties.

17

The deterministic fuzzy linear approach in planning the production of mine system with several open pits

Vujic S., Miljanovic I., Kuzmanovic M., Bartulovic Z., Gajic G., Lazic P.

Archives of Mining Sciences

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2011

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Vol. 56, no 3

489-497

EN

The paper deals with the problem of mining production planning by means of deterministic and fuzzy linear programming (LP). After the introduction, short inspection of the general settings in deterministic and fuzzy LP model is presented. An application and a comparative analysis of results obtained by both LP models was demonstrated on the example of the Mining Basin "Kolubara", with four working open pit mines (OPM). Along with the assessment that the LP is an efficient mathematical modelling tool in mining planning, and after comparing advantages and deficiencies of the deterministic and fuzzy LP, the conclusion states that it is necessary to involve both of LP model approach in searching for the optimal production plan. The final selection of solution lies with the decision maker.

PL

Wieloelementowe systemy górnicze charakteryzują się następującymi cechami: niepewność, subiektywność, niedokładność, polisemia, niestabilność, nieokreśloność oraz brak danych. Z punktu widzenia nauki współczesnej, układy te należą do kategorii układów rozmytych. Mając to na uwadze, proponuje się wykorzystanie zbiorów rozmytych jako metodologii sprawdzającej się w przypadku niedokładności, nieokreśloności oraz złożoności problematyki produkcji górniczej. Modelowanie rozmyte, będące tematem obecnej pracy, zastosowane zostało jako implementacja matematyczna. Celem pracy jest przedstawienie zagadnienia optymalizacji produkcji w praktyce, na przykładzie kopalni odkrywkowej boksytu i przy wykorzystaniu metod programowania liniowego, jako kryterium przyjęto wielkość dochodu. Zmiennymi w funkcji kryterium są zmienne językowe, obydwie zmienne oraz ograniczenia przedstawiono za pomocą rozmytych liczb trójkątnych. Określenie "wystarczający dochód" został przedstawiony za pomocą liczb rozmytych trójkątnych, zamiast stosowania warunku "maksymalizacji dochodu". Przeprowadzono analizę wrażliwości otrzymanych rozwiązań.

18

ANFIS and neural network techniques for non-parametric modelling of a twin rotor system

Toha S. F., Tokhi M. O.

Systems Science

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2010

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Vol. 36, no 4

13-31

EN

Interest in system identification especially for nonlinear systems has significantly increased in the past few decades. Soft-computing methods which concern computation in an imprecise environment have gained significant attention amid widening studies of explicit mathematical modelling. In this research, three different soft computing techniques that are multi-layered perceptron neural network using Levenberg-Marquardt (LM), Elman recurrent neural network and adaptive neuro-fuzzy inference system (ANFIS) network are deployed and used for modelling a twin rotor multi-input multi-output system (TRMS). The system is perceived as a challenging engineering problem due to its high nonlinearity, cross coupling between horizontal and vertical axes and inaccessibility of some of its states and outputs for measurements. Accurate modelling of the system is thus required so as to achieve satisfactory control objectives. It is demonstrated experimentally that soft computing methods can be effectively used for modelling the system with highly accurate results. The accuracy of the modelling results is demonstrated through validation tests including training and test validation and correlation tests.

19

A crisis management approach to mission survivability in computational multi-agent systems

Byrski A., Kisiel-Dorohinicki M., Carvalho M.

Computer Science

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2010

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Vol. 11

99-113

EN

In this paper we present a biologically-inspired approach for mission survivability (considered as the capability of fulfilling a task such as computation) that allows the system to be aware of the possible threats or crises that may arise. This approach uses the notion of resources used by living organisms to control their populations. We present the concept of energetic selection in agent-based evolutionary systems as well as the means to manipulate the configuration of the computation according to the crises or user's specific demands.

PL

W artykule prezentujemy biologicznie inspirowany mechanizm wspomagający utrzymanie krytycznych zadań (tzw. mission survivability) który umożliwia wykrywanie oraz przeciwdziałanie wybranym zagrożeniom. Przedstawione podejście wzorowane jest na wykorzystywaniu przez żywe organizmy zasobów do kontroli populacji. Prezentujemy koncepcje selekcji energetycznej mającej zastosowanie w ewolucyjnych systemach wieloagentowych (EMAS) oraz sposoby konfiguracji obliczenia w celu przeciwdziałania sytuacjom kryzysowym, według preferencji użytkownika.

20

Optimal design of sandwich panels with a soft core

Studziński R., Pozorski Z., Garstecki A.

Journal of Theoretical and Applied Mechanics

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2009

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Vol. 47 nr 3

685-699

EN

The main issue taken up in the paper is to find optimal designs of multispan sandwich panels with slightly profiled steel facings and polyurethane foam core (PUR), which would satisfy conflicting demands of the market, i.e. minimal variance in types of panels, maximum range of application and minimum cost. The aim is to find dimensional and material parameters of panels which generate minimum cost and maximum length of span under prescribed loads in ultimate and serviceability limit states. The multi-criterion optimization problem is formulated in such a way, where the length of the span plays two roles, namely a design variable and a component of a vector objective function. An evolutionary algorithm is used. Numerous inequality constraints are introduced in two ways: directly and by external penalty functions.

PL

Wpracy podejmuje się problem optymalizacji wieloprzęsłowych płyt warstwowych z rdzeniem z poliuretanu (PUR) i okładzinami stalowymi lekko profilowanymi. Poszukuje się rozwiązań, które spełnią sprzeczne wymagania rynku, mianowicie: minimalizację typoszeregu płyt, maksymalizację zakresu ich zastosowania oraz minimalizację kosztu produkcji. Celem optymalizacji jest znalezienie parametrów geometrycznych i materiałowych płyt warstwowych, które minimalizują koszt oraz maksymalizują dopuszczalną rozpiętość dla ustalonych obciążeń i przy spełnieniu stanów granicznych nośności i użytkowalności. W wielokryterialnym sformułowaniu problemu optymalizacyjnego rozpiętość pełni dwie funkcje. Jest ona równocześnie zmienną projektową i składową wektora funkcji celu. Jako narzędzie optymalizacji wykorzystano algorytmy genetyczne. Ograniczenia nierównościowe wprowadzono do procedury optymalizacyjnej za pomocą zewnętrznej funkcji kary oraz jawnie.