Trusted Control Systems

Nowadays, the problem of ensuring security of systems with a critical mission has become particularly relevant. An increased opportunity for unauthorized exposure on such systems via hardware, software and communication networks is the main reason to discuss this problem. It is confirmed by a plenty of accidents when equipment is out of order by means of malicious embedded elements and viruses. Currently, in the Russian Federation the majority of control systems are based on foreign hardware and software platforms, including strategic enterprises and objects with a critical mission. Herewith, the proportion of foreign microelectronic components in such systems is more than 85 %. The article is devoted to the development of scientific basis and techniques of the assurance assessment to control systems of objects with a critical mission. It was shown, that assurance assessment to a control system is a broader index than its reliability and fault tolerance. Such index must integrate various evidences and approvals, which can be objective, based on physical and mathematical assurance assessment methods, as well as they can be subjective, based on the experts experience. A method of assurance assessment to a control system of objects with a critical mission, based on Shortliffe’s scheme, was proposed in this paper. The Shortliffe’s scheme is used in the theories of fuzzy logic for assurance assessment to a hypothesis on the basis of various evidences and statements. An important advantage of a Shortliffe’s scheme is the set of evidences, which can be broadened and augmented (for instance, on the basis of obtained experience). It allows us to clarify a certainty factor. The assessment methods of truth degree of terminal statements of various types, including those, which require the combination of objective and subjective methods of their truth degree assessment, are proposed. The proposed assurance assessment method for national development and creation standards of control systems of objects with a critical mission allows to significantly increase their functional security.

Big Data Validity Evaluation Based on MMTD

Big data has been studied extensively in recent years. With the increase in data size, data quality becomes a priority. Evaluation of data quality is important for data management, which influences data analysis and decision making. Data validity is an important aspect of data quality evaluation. Based on 3V properties of big data, dimensions that have a major influence on data validity in a big data environment are analyzed. Each data validity dimension is analyzed qualitatively using medium logic. The measuring of medium truth degree is used to propose models to measure single and multiple dimensions of big data validity. The validity evaluation method based on medium logic is more reasonable and scientific than general methods.

An Extension of Fuzzy Linguistic Summarization Considering Probabilistic Uncertainty

Linguistic summarization provides to express large volumes of quantitative data in easy understandable natural language based forms. While various methods have been recommended for linguistic summarization, there is not any approach for linguistic summarization where possibilistic and probabilistic uncertainties exist together in data. In this study, we establish a tie between Z-number concept and type-I and type-II quantified sentences in order to calculate the truth degree of a linguistic summary covering possibilistic and probabilistic information. The proposed approach employs copulas to obtain a joint probability distribution of the variables included in type-II quantified sentence.

Fuzzy logic, deductive rules of inference and linguistic reasoning on knowledge base

In this paper we introduce a notion of knowledge base consisting of statements with truth degree in which every statement may have several truth degrees. A set of rules of inference handling this kind of statements, a deductive reasoning method based on these rules will be considered. The consistency of the knowledge base will be also investigated.

An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.