Novosibirsk State Technical University (Abbreviation: NSTU), until 1992 the Novosibirsk Electrotechnical Institute (NETI), is one of the major research and educational centers of Russia as well as one of the top technical universities located in Novosibirsk, Russia.
The theory of fuzzy/blurry models is an alternative approach to the formalization of uncertainty and incompleteness of knowledge with respect to formalization by means of fuzzy sets proposed by Lotfi Zadeh. The fundamental distinctive feature of the mathematical foundations of this approach is that initially one deals with a set of precedents in which certain events occur, rather than with the numerical estimates of the probability that these events occur in a given object field. Within the approach proposed, all available information is first completely processed, and only the final result is fuzzified and converted into numbers in the interval [0, 1]. This approach to information processing is also used in quantum computing. One of the main differences of quantum computing from the classical one is that before starting calculations we know only the probabilistic (fuzzy) values of qubit states. Thus, all quantum computations are performed under conditions of incomplete knowledge, and the measurement (i.e., digitization) of the results is performed only at the final stage. The paper presents a semantic interpretation of finite fuzzy models as a generalization of quantum systems. The concepts of a separable system and a totally entangled system are considered and generalized to the fuzzy case. Existence and uniqueness theorems of such systems are proved.
In this paper, fiber-optic sensors of temperature based on planar AWG multiplexers/demultiplexers are discussed. The design of the distributed temperature and temperature drops sensor is described and its transformative characteristics are obtained.
The development of high-viscosity oil fields requires technologies that provide not only the thermal mobilization of oil, but also an economically justified level of production with a high energy intensity of the process. One of the most effective technologies of this type is steam-assisted gravity oil drainage (SAGD), but its practical effectiveness is determined by the combined influence of reservoir geology, heat-transfer parameters, and market conditions. The paper proposes a reduced physics-guided model for the rapid technical and economic screening of SAGD in high-viscosity oil fields. The methodological contribution lies in linking geological screening, steam energy input, useful heat delivered to the reservoir, production response, and operating profit within one interpretable analytical chain suitable for pre-feasibility assessment. The study is based on an extended-scenario thermoeconomic analysis of representative heavy-oil development conditions. It is shown that, in a favorable mode, at a depth of about 400 m, oil viscosity of 15,000 cP, steam consumption of 500 t/day and heat-transfer coefficient of 0.7, the estimated production reaches 513–520 t/day, and the net profit is 20,000–22,000 USD/day. In an unfavorable mode, with a depth of about 1000 m, a viscosity of 20,000 cP, a heat-transfer coefficient of 0.4, and a high steam cost, production decreases to 210–230 t/day, and the economic result becomes negative. It has been established that the cost of steam, heat transfer, and the price of oil have a decisive impact on profitability.
The assessment of reliability in non-repairable subsystems of mining electronic equipment represents a computationally challenging problem, particularly for complex and highly connected structures. This study presents a systematic comparative analysis of several deterministic approaches for reliability estimation, focusing on their computational efficiency, accuracy, and applicability. The investigated methods include classical boundary techniques (minimal paths and cuts), analytical decomposition based on the Bayes theorem, the logic–probabilistic method (LPM) employing triangle–star transformations, and the algorithmic Structure Convolution Method (SCM), which is based on matrix reduction of the system’s connectivity graph. The reliability problem is formally represented using graph theory, where each element is modeled as a binary variable with independent failures, which is a standard and practically justified assumption for power electronic subsystems operating without common-cause coupling. Numerical experiments were carried out on canonical benchmark topologies—bridge, tree, grid, and random connected graphs—representing different levels of structural complexity. The results demonstrate that the SCM achieves exact reliability values with up to six orders of magnitude acceleration compared to the LPM for systems containing more than 20 elements, while maintaining polynomial computational complexity. Qualitatively, the compared approaches differ in the nature of the output and practical applicability: boundary methods provide fast interval estimates suitable for preliminary screening, whereas decomposition may exhibit a systematic bias for highly connected (non-series–parallel) topologies. In contrast, the SCM consistently preserves exactness while remaining computationally tractable for medium and large sparse-to-moderately dense graphs, making it preferable for repeated recalculations in design and optimization workflows. The methods were implemented in Python 3.7 using NumPy and NetworkX, ensuring transparency and reproducibility. The findings confirm that the SCM is an efficient, scalable, and mathematically rigorous tool for reliability assessment and structural optimization of large-scale non-repairable systems. The presented methodology provides practical guidelines for selecting appropriate reliability evaluation techniques based on system complexity and computational resource constraints.
The rapid development of digital manufacturing and robotic systems places increased demands on the accuracy and reliability of industrial manipulators. Traditional time-based reliability metrics do not reflect the robot’s ability to consistently achieve the desired position and orientation within process tolerances or the probability of the end-effector falling into a given area of permissible poses. The proposed framework integrates a deterministic kinematic model, a stochastic representation of Denavit–Hartenberg parameters and control variables, analytical methods for estimating probabilities, and numerical modeling using the Monte Carlo method. The methodology has been tested on the widely used industrial robot FANUC LR Mate 200iD/7L. The results demonstrate a significant dependence of geometric reliability on the kinematic configuration of the manipulator, with maximum reliability in compact poses and a significant reduction in elongated configurations near singularities. Comprehensive validation was carried out, including numerical experiments on a planar prototype, high-precision physical measurements on a real robot and analysis of operational data, which confirmed the adequacy of the proposed model. The developed approach provides a powerful tool for designing, optimizing and predicting the reliability of robotic cells in high-precision automation environments.
