28,926 research outputs found
A metric to represent the evolution of CAD/analysis models in collaborative design
Computer Aided Design (CAD) and Computer Aided Engineering (CAE) models are often used during product design. Various interactions between the different models must be managed for the designed system to be robust and in accordance with initially defined specifications. Research published to date has for example considered the link between digital mock-up and analysis models. However design/analysis integration must take into consideration the important number of models (digital mock-up and simulation) due to model evolution in time, as well as considering system engineering. To effectively manage modifications made to the system, the dependencies between the different models must be known and the nature of the modification must be characterised to estimate the impact of the modification throughout the dependent models. We propose a technique to describe the nature of a modification which may be used to determine the consequence within other models as well as a way to qualify the modified information. To achieve this, a metric is proposed that allows the qualification and evaluation of data or information, based on the maturity and validity of information and model
ΠΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½Π°Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ° Π°Π½Π°Π»ΠΈΠ·Π° Π½Π°Π²ΡΠΊΠΎΠ² ΠΈ ΡΠΌΠ΅Π½ΠΈΠΉ ΠΊΠΎΠ½ΡΠΈΠ½Π³Π΅Π½ΡΠ° ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² Π²ΡΡΡΠ΅Π³ΠΎ ΡΡΠ΅Π±Π½ΠΎΠ³ΠΎ Π·Π°Π²Π΅Π΄Π΅Π½ΠΈΡ
In the below article, the application of the fuzzy logical conclusion method is considered as decision-maker in the process of analyzing the students skills and abilities based on the requirements of potential employers, in order to reduce the time of the first interview for potential candidates on a vacant position. When analyzing the results of the assessment of the competence of university students, a certain degree of fuzziness arises. In modern practice, fuzzy logic is used in many different assessment methods, including questioning, interviewing, testing, descriptive method, classification method, pairwise comparison, rating method, business games competence models, and the like. Each of the methods has its advantages and disadvantages, but they are effective only as part of a unified personnel management system. As a method for implementing a systematic approach to the assessment of the contingent of students, it is proposed to use fuzzy logic, a mathematical apparatus that allows you to build a model of an object based on fuzzy judgments. The use of fuzzy logic, the mathematical apparatus of which allows you to build a model of the object, based on fuzzy reasoning and rules. The most important condition for creating such a model is to translate the fuzzy, qualitative assessments used by man into the language of mathematics, which will be understood by the computer. The most used are fuzzy inferences using the Mamdani and Sugeno methods. In a fuzzy inference of the Mamdani type, the value of the output variable is given by fuzzy terms, in the conclusion of the Sugeno type, as a linear combination of the input variables. Research in the field of application of fuzzy logic in socio-economic systems suggests that it can be used to assess the competencies of university students.Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° Π½Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΡΠ²ΠΎΠ΄Π° Π΄Π»Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² Π·Π°Π΄Π°ΡΠ°Ρ
Π°Π½Π°Π»ΠΈΠ·Π° Π½Π°Π²ΡΠΊΠΎΠ² ΠΈ ΡΠΌΠ΅Π½ΠΈΠΉ ΠΊΠΎΠ½ΡΠΈΠ½Π³Π΅Π½ΡΠ° ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΈΡΡ
ΠΎΠ΄Ρ ΠΈΠ· ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Π΅ΠΉ, Ρ ΡΠ΅Π»ΡΡ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π½Π° ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΊΠ°ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΎΠ² Π½Π° Π²Π°ΠΊΠ°Π½ΡΠ½ΡΡ Π΄ΠΎΠ»ΠΆΠ½ΠΎΡΡΡ. ΠΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² Π²ΡΠ·ΠΎΠ² Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½Π°Ρ ΡΡΠ΅ΠΏΠ΅Π½Ρ Π½Π΅ΡΠ΅ΡΠΊΠΎΡΡΠΈ. Π ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠ°Ρ Π»ΠΎΠ³ΠΈΠΊΠ° ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄Π°Ρ
ΠΎΡΠ΅Π½ΠΊΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π°Π½ΠΊΠ΅ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅, ΠΈΠ½ΡΠ΅ΡΠ²ΡΡ, ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅, ΠΎΠΏΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄, ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΠΏΠ°ΡΠ½ΠΎΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅, ΡΠ΅ΠΉΡΠΈΠ½Π³ΠΎΠ²ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄, Π΄Π΅Π»ΠΎΠ²ΡΠ΅ ΠΈΠ³ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠΎΠΌΡ ΠΏΠΎΠ΄ΠΎΠ±Π½ΠΎΠ΅. ΠΠ°ΠΆΠ΄ΡΠΉ ΠΈΠ· ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΠΌΠ΅Π΅Ρ ΡΠ²ΠΎΠΈ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ, Π½ΠΎ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½Ρ ΠΎΠ½ΠΈ ΡΠΎΠ»ΡΠΊΠΎ Π² ΡΠΎΡΡΠ°Π²Π΅ Π΅Π΄ΠΈΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΎΠΌ. ΠΠ°ΠΊ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΠΎΡΠ΅Π½ΠΊΠ΅ ΠΊΠΎΠ½ΡΠΈΠ½Π³Π΅Π½ΡΠ° ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π½Π΅ΡΠ΅ΡΠΊΡΡ Π»ΠΎΠ³ΠΈΠΊΡ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΠΏΠΏΠ°ΡΠ°Ρ, ΠΊΠΎΡΠΎΡΡΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΡΡΡΠΎΠΈΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΎΠ±ΡΠ΅ΠΊΡΠ°, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ Π½Π° Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΡΡΠΆΠ΄Π΅Π½ΠΈΡΡ
. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠΈ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΠΏΠΏΠ°ΡΠ°Ρ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΡΡΡΠΎΠΈΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΎΠ±ΡΠ΅ΠΊΡΠ°, ΠΎΡΠ½ΠΎΠ²ΡΠ²Π°ΡΡΡ Π½Π° Π½Π΅ΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΡΡΡΠΆΠ΄Π΅Π½ΠΈΡΡ
ΠΈ ΠΏΡΠ°Π²ΠΈΠ»Π°Ρ
. ΠΠ°ΠΆΠ½Π΅ΠΉΡΠ΅Π΅ ΡΡΠ»ΠΎΠ²ΠΈΠ΅ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ°ΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠΎΠΌ, ΡΡΠΎΠ±Ρ ΠΏΠ΅ΡΠ΅Π²Π΅ΡΡΠΈ Π½Π΅ΡΠ΅ΡΠΊΠΈΠ΅, ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΠ΅ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠΎΠΌ, Π½Π° ΡΠ·ΡΠΊ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ Π±ΡΠ΄Π΅Ρ ΠΏΠΎΠ½ΡΡΠ½Π° Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠ°ΡΠΈΠ½Π΅. ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ Π½Π΅ΡΠ΅ΡΠΊΠΈΠ΅ Π²ΡΠ²ΠΎΠ΄Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΠΠ°ΠΌΠ΄Π°Π½ΠΈ ΠΈ Π‘ΡΠ³Π΅Π½ΠΎ. Π Π½Π΅ΡΠ΅ΡΠΊΠΎΠΌ Π²ΡΠ²ΠΎΠ΄Π΅ ΡΠΈΠΏΠ° ΠΠ°ΠΌΠ΄Π°Π½ΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π²ΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΡΡΡ Π½Π΅ΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠ΅ΡΠΌΠ°ΠΌΠΈ, Π² Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠΈ ΡΠΈΠΏΠ° Π‘ΡΠ³Π΅Π½ΠΎ β ΠΊΠ°ΠΊ Π»ΠΈΠ½Π΅ΠΉΠ½Π°Ρ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΡ Π²Ρ
ΠΎΠ΄Π½ΡΡ
ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠΈ Π² ΡΠΎΡΠΈΠΎΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ Π³ΠΎΠ²ΠΎΡΠΈΡΡ ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΅Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² Π²ΡΠ·ΠΎΠ²
Resilience Assignment Framework using System Dynamics and Fuzzy Logic.
This paper is concerned with the development of a conceptual framework that measures the resilience of the transport network under climate change related events. However, the conceptual framework could be adapted and quantified to suit each disruptionβs unique impacts. The proposed resilience framework evaluates the changes in transport network performance in multi-stage processes; pre, during and after the disruption. The framework will be of use to decision makers in understanding the dynamic nature of resilience under various events. Furthermore, it could be used as an evaluation tool to gauge transport network performance and highlight weaknesses in the network.
In this paper, the system dynamics approach and fuzzy logic theory are integrated and employed to study three characteristics of network resilience. The proposed methodology has been selected to overcome two dominant problems in transport modelling, namely complexity and uncertainty. The system dynamics approach is intended to overcome the double counting effect of extreme events on various resilience characteristics because of its ability to model the feedback process and time delay. On the other hand, fuzzy logic is used to model the relationships among different variables that are difficult to express in numerical form such as redundancy and mobility
Constructing Fuzzy for Socio Economic Urban Growth Dynamic In Surabaya Based on GIS
Urban modeling is an important tool for efficient policy designing in a big city. Surabaya, a big city are now recognized as complex systems through which nonlinear and dynamic processes occur. The paper present a methodological framework for urban modeling from socio economic point of view, which suggested framework incorporates a set of fuzzy systems. In this case, the variable consist of manufacture, hospital, school and shopping centre. Combining with spatial analysis in GIS, the result is a dynamic model was shown to be capable of replicating the trends and characteristics of an urban environment, in this case the city of Surabaya
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Applying concepts of fuzzy cognitive mapping to model IT/IS investment evaluation factors
The justification process is a major concern for many organisations that are considering the adoption of Information Technology (IT) and Information Systems (IS), and is a barrier to its implementation. As a result, the competitive advantage of many companies is being put at risk because of management's inability to evaluate the holistic implication of adopting new technology, both in terms of on the benefit and cost portfolios. This paper identifies a number of well-known project appraisal techniques used in IT/IS investment justification. Furthermore, the concept of multivalent, or fuzzy logic, is used to demonstrate how inter-relationships can be modeled between key dimensions identified in the proposed conceptual evaluation model. This is highlighted using fuzzy cognitive mapping (FCM) as a technique to model each IT/IS evaluation factor (integrating strategic, tactical, operational and investment considerations). The use of an FCM is then shown to be as a complementary tool which can serve to highlight interdependencies between contributory justification factors
A robust fuzzy possibilistic AHP approach for partner selection in international strategic alliance
The international strategic alliance is an inevitable solution for making competitive advantage and reducing the risk in todayβs business environment. Partner selection is an important part in success of partnerships, and meanwhile it is a complicated decision because of various dimensions of the problem and inherent conflicts of stockholders. The purpose of this paper is to provide a practical approach to the problem of partner selection in international strategic alliances, which fulfills the gap between theories of inter-organizational relationships and quantitative models. Thus, a novel Robust Fuzzy Possibilistic AHP approach is proposed for combining the benefits of two complementary theories of inter-organizational relationships named, (1) Resource-based view, and (2) Transaction-cost theory and considering Fit theory as the perquisite of alliance success. The Robust Fuzzy Possibilistic AHP approach is a noveldevelopment of Interval-AHP technique employing robust formulation; aimed at handling the ambiguity of the problem and let the use of intervals as pairwise judgments. The proposed approach was compared with existing approaches, and the results show that it provides the best quality solutions in terms of minimum error degree. Moreover, the framework implemented in a case study and its applicability were discussed
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