Set Of Solutions Research Articles

«Страна еще не была готова к созданию такого комплекса» (О проведении летно-конструкторских испытаний межконтинентальной баллистической ракеты 8К99)

The article aims to consolidate information regarding flight development tests of the 8K99 missile at the 53rd field research facility of the USSR Ministry of Defense (Archangelsk Region, Mirnyj). Besides official and technical documentation, archive papers of testing military units and test participants’ memories are used. The ocean-spanning missile 8K99 was developed in the Yuzhnoe Design Office (Dnepropetrovsk City) during the first half of the 1960s . It was intended for combat use within the Strategic Missile Forces as part of mobile and fixed missile systems. For the flight development tests of the 8K99 missile, there was built an experimental testing base (including technical and missile launch sites, command, measurement and computer complexes, etc.) and infrastructure facilities (roads, bridges), prepared drop areas for separated missile’s parts along its motion path and training target areas, set up testing military units, and trained engineering and technical personnel. During 1967-69, there were carried out flight development tests of the 8K99 missile as a part of the 15П699 mobile ground missile system. Despite overall positive results, the missile’s tests were discontinued. The 8К99 missile was not accepted for service, but its creation contributed to the development of national missilery. A set of ingenious engineering solutions are being used up to now in developing ocean-spanning missiles and space rockets. During flight development tests, testing military units’ personnel gained experience in operating complicated, dangerous, and sensitive systems

Optimizing the max-min function with a constraint on a two-sided linear system

<abstract><p>The behavior of discrete-event systems, in which the individual components move from event to event rather than varying continuously through time, is often described by systems of linear equations in max-min algebra, in which classical addition and multiplication are replaced by $ \oplus $ and $ {\otimes} $, representing maximum and minimum, respectively. Max-min equations have found a broad area of applications in causal models, which emphasize relationships between input and output variables. Many practical situations can be described using max-min systems of linear equations. We shall deal with a two-sided max-min system of linear equations with unknown column vector $ x $ of the form $ A{\otimes} x{\oplus} c = B{\otimes} x{\oplus} d $, where $ A $, $ B $ are given square matrices, $ c $, $ d $ are column vectors and operations $ \oplus $ and $ {\otimes} $ are extended to matrices and vectors in the same way as in the classical algebra. We give an equivalent condition for its solvability. For a given max-min objective function $ f $, we consider optimization problem of type $ f^\top{\otimes} x\rightarrow \max\text{ or } \min $ constraint to $ A{\otimes} x{\oplus} c = B{\otimes} x{\oplus} d $. We solve the equation in the form $ f(x) = v $ on the set of solutions of the equation $ A{\otimes} x{\oplus} c = B{\otimes} x{\oplus} d $ and extend the problem to the case of an interval function $ {{\boldsymbol{f}}} $ and an interval value $ {{\boldsymbol{v}}} $. We define several types of the reachability of the interval value $ {{\boldsymbol{v}}} $ by the interval function $ {{\boldsymbol{f}}} $ and provide equivalent conditions for them.</p></abstract>

Navigating digital transformation in accounting system: Challenges and opportunities

The primary objective of this study was to assess the challenges associated with the digital transformation process in the accounting systems of companies listed on the Khartoum Stock Exchange, focusing on the perspectives of senior management, financial management, and technology management. These departments were considered integral due to their profound understanding of digital transformation processes, requirements, and existing as well as anticipated challenges. Employing a descriptive-analytical approach, the study utilized a questionnaire to gather data from 315 individuals within the study population. The results revealed a favorable inclination among Khartoum Stock Exchange-listed companies towards digital transformation, manifested through a notable automation of accounting procedures, the cultivation of a digital culture, and the adoption of strategic approaches for digital transformation. However, the study identified several challenges, including insufficient technological infrastructure at the national level, the absence of a clear national strategic plan for digital transformation, and limited financial resources allocated to digital transformation in accounting systems. The study also proposed a set of solutions to address these challenges, categorized into national and corporate levels.

Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

An energy consumption optimization strategy for Wireless sensor networks via multi-objective algorithm

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.

Tseng-type algorithms for solving variational inequalities over the solution sets of split variational inclusion problems with an application to a bilevel optimization problem

Tseng-type algorithms for solving variational inequalities over the solution sets of split variational inclusion problems with an application to a bilevel optimization problem

Study on deconvolution of alpha-particle spectra using metaheuristic optimization and multivariate analysis

To find a global optimal solution and obtain further insight into the parameters of the peak shape fitting function, metaheuristic optimization algorithms, and multivariate analysis techniques are employed to study the deconvolution of alpha-particle spectra. An improved peak shape model composed of the Bortels-Collaers function and Lévy distribution function, which aims to handle the high-energy tailing, is proposed. A newly developed metaheuristic optimization method, Bonobo Optimizer (BO) is adopted to seek optimal parameters in the peak shape function. Multivariate analysis (MVA) techniques are used to find hidden information in the shape model. Pearson's correlation tells the mutual variation relationship among parameters, while Multidimensional scaling (MDS) shows similarities of parameters through a 2D plot. Effects of parameters upon the regression accuracy are obtained via the Student t-test. Self-organizing Mapping (SOM) is utilized to mine intrinsic relations among these parameters through visual images. AM243-1 test alpha spectra example is selected to examine the proposed methodology. The improved model is more accurate when handling the high-energy tailing features. Compared with traditional gradient-based optimization algorithms, BO can find global solutions without tedious work in initial solution setting and constraint handling. More information is mined through MVA and further understanding of the peak shape function is obtained.

Douglas–Rachford algorithm for control-constrained minimum-energy control problems

Splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to optimal control is less common. In this paper we utilize the Douglas–Rachford (DR) algorithm to solve control-constrained minimum-energy optimal control problems. Instead of the traditional approach where one discretizes the problem and solves it using large-scale finite-dimensional numerical optimization techniques we split the problem in two subproblems and use the DR algorithm to find an optimal point in the intersection of the solution sets of these two subproblems hence giving a solution to the original problem. We derive general expressions for the projections and propose a numerical approach. We obtain analytic closed-form expressions for the projectors of pure, under-, critically- and over-damped harmonic oscillators. We illustrate the working of our approach to solving not only these example problems but also a challenging machine tool manipulator problem. Through numerical case studies, we explore and propose desirable ranges of values of an algorithmic parameter which yield smaller number of iterations.

Solutions to a discrete resonance problem with eigenparameter-dependent boundary conditions

<abstract><p>In this paper, we considered the existence of solutions to a discrete second-order resonance problem with eigenparameter-dependent boundary conditions. We first transformed the resonance problem into its corresponding equivalent system using the Lyapunov-Schmidt method. In addition, using Schauder's fixed-point theorem and the connectivity theories of the solution set of compact vector fields, we obtained the existence and multiplicity of solutions to the second-order discrete resonance problem with eigenparameter-dependent boundary conditions.</p></abstract>

Security Challenges and Solutions in AI-Enhanced Cloud Platforms: A Comprehensive Study.

The integration of Artificial Intelligence (AI) into cloud computing platforms has revolutionized the landscape of modern computing, offering unprecedented capabilities and efficiencies. However, this union introduces a myriad of security challenges that demand meticulous examination and robust solutions. This comprehensive study delves into the security intricacies arising from the amalgamation of AI and cloud platforms, aiming to identify, analyze, and propose effective strategies to address these challenges. The research explores the multifaceted dimensions of security challenges in AI-enhanced cloud platforms, with a focus on data privacy, confidentiality, and the vulnerabilities introduced by adversarial attacks on AI models.[1] Cloud infrastructure susceptibility to traditional security threats and the imperative need for explainability and trustworthiness in AI decision-making processes are also scrutinized. To counter these challenges, the paper proposes a set of mitigation strategies and solutions. Robust encryption mechanisms and fine-grained access controls are advocated to protect sensitive data, while adversarial defense mechanisms, including robust model training and anomaly detection, fortify AI models against evolving threats. Best practices for cloud security, such as multi-factor authentication and regular security audits, are recommended to address infrastructure vulnerabilities. Furthermore, the incorporation of Explainable AI (XAI) techniques, including model interpretability algorithms and visualizations, is proposed to enhance transparency and trust in AI decision-making processes. The paper provides a comprehensive understanding of the security challenges in AI-enhanced cloud platforms and offers practical solutions to fortify the security posture of these environments. The proposed strategies aim to foster trust, transparency, and resilience, ensuring the continued advancement of AI technologies in cloud computing while safeguarding sensitive information. This research calls for ongoing collaboration and exploration to further refine and adapt security measures in the dynamic landscape of AI-enhanced cloud platforms.

Механизмы формирования и развития новых технологических укладов в экономике региона (на примере РТ)

Abstract: The study of current transformations in the global economic space and in the Russian Federation, including in the field of adaptation of technological structures, is a fundamentally significant process. New technological structures set the vector for further economic progress of countries. Periods of recessions, military events, changes in political course, innovations in the field of culture - all this is a consequence of periods of technological renewal. The latest technological paradigm forms an ecosystem that integrates a set of revolutionary technologies, platform solutions and complex innovations, creating a vast system in which each individual innovation mutually reinforces the development and use of other technologies. Current transformations in the regions of Russia, emphasized by increased pressure from international sanctions and the deterioration of foreign trade relations, necessitate the formation of strategic directions for adaptation to changed geo-economic realities. The task of national and regional technological development comes to the fore. The object of the study is economic relations that arise in the process of formation and development of new technological structures. The purpose of the study is to characterize the mechanisms for the formation and development of new technological structures in the Republic of Tatarstan. Research methods - abstract-logical, monographic, economic-statistical, sociological, etc. Scientific novelty lies in the formation of an integrated theoretically based approach, analysis of the processes of innovative transformation of the socio-economic system and the use of the theory of development and change of technological structures in the Republic of Tatarstan.

An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.

Multi-objective optimization of the dynamic performance of a suspended monorail vehicle based on an effective surrogate model

This work presents the multi-objective optimization (MOO) of the dynamic performance of a suspended monorail vehicle (SMV) moving on a curved bridge, based on an effective surrogate model. First, the vehicle-bridge dynamic interaction features are analysed through a dynamic model. Then, an MOO method is developed based on an effective surrogate model. In this method, the accuracy and stability of the radial basis function are enhanced by proposing an innovative improvement strategy and adopting the particle swarm optimization algorithm. The proposed method is validated by a few numerical tests. Based on this verification, the MOO model between key parameters and several optimization objectives is formulated, and the optimization solution set of the vehicle key parameters is obtained using the non-dominated sorting genetic algorithm II. Finally, the optimization effects are revealed by comparing the dynamic performance of the vehicle obtained from the original values and the optimal solution set.

Optical solitons for the dispersive concatenation model with spatio-temporal dispersion having multiplicative white noise

This investigation of the current work presents the dispersive concatenation model that integrates the spatio-temporal dispersion and the effect of multiplicative white noise in the Itô sense. The study aims to secure stochastic optical soliton solutions within this framework. To achieve this purpose, we implemented the enhanced Kudryashov technique and the new projective Riccati equations strategy. Initially, the traveling wave transform hypothesis was applied to convert the stochastic model into its nonlinear ordinary differential equation form, encompassing real and imaginary components with emerging the related parametric restrictions. Subsequently, we introduced the enhanced Kudryashov and the new projective Riccati equations techniques successfully employed in analyzing the generated nonlinear ordinary differential equation. By utilizing the appropriate solution sets, which encompass variables with unknown values and necessary transformations, we have derived many stochastic optical soliton-type solutions for the model. Detailed discussions for the dynamics of some selected soliton solutions are presented with the supporting of the 3D and 2D graphs that illustrate the white noise effect on these solitons.

Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit

In this work, the prediction and optimisation of copper flotation has been conducted in the rougher flotation circuit. The copper-recovery prediction involved the application of support vector machine (SVM), Gaussian process regression (GPR), multi-layer perceptron artificial neural network (ANN), linear regression (LR), and random forest (RF) algorithms on 15 rougher flotation variables at the BHP Olympic Dam. The predictive models’ performance was assessed using linear correlation (r), root mean square error (RMSE), mean absolute percentage error (MAPE), and variance accounted for (VAF). A simulated annealing (SA) optimisation algorithm, particle swarm optimisation (PSO) algorithm, surrogate optimisation (SO) algorithm, and genetic algorithm (GA) were investigated, using the GPR predictive function, to determine the optimal operating condition for maximising copper recovery. The predictive function of the best-performing model was extracted and used in optimising the flotation circuit. The results showed that the GPR model developed with the matern 3/2 kernel function makes the most precise copper-recovery prediction as compared to the other investigated predictive models, obtaining r values > 0.96, RMSE values < 0.42, MAPE values < 0.25%, and VAF values > 94%. A hypothetical optimisation solution assessment showed that SA provides the best set of solutions for the maximisation of rougher copper recovery, obtaining a throughput of 638.02 t/h and a total net gain percentage of 14%–15.5% over the other optimisation algorithms with a maximum copper recovery of 94.76%. The operational benefits of implementing these algorithms have been highlighted.

Optimization design of hybrid energy storage capacity configuration for electric ship

Electric propulsion has gained significant popularity in the marine vessel industry due to its numerous advantages. One of the key challenges faced in this domain is effectively managing the fluctuation of power grid caused by sudden load changes. While energy storage systems offer a viable solution, striking the right balance between cost and benefit remains a complex task. To address this issue, establish an optimization model and constraint conditions for capacity configuration of hybrid energy storage systems, and propose a decision-making method based on NSGA-II algorithm and cost-effectiveness method. To address the issue of NSGA-II algorithm being prone to local optima and premature convergence, chaotic thinking and adaptive crossover operators are introduced to improve the NSGA-II algorithm; In order to obtain the optimal configuration scheme from the obtained Pareto optimal solution set, a decision method based on cost-effectiveness was adopted to further analyze the bias between the Pareto optimal solution set and each objective. By comparing the decision results with the ideal solution (TOPSIS), the optimal capacity configuration scheme was obtained.

Multi-objective price based flexible reserve scheduling of virtual power plant

In this research, multi-objective optimization of VPP is performed by considering multiple renewables and co-generation sources and solved by using the recently developed multi-objective Harris Hawk’s optimization (MOHHO) algorithm. Renewable sources comprise solar Photovoltaic (PV), wind power, fuel cells along with energy storage systems (ESS), electric vehicles (EV), along with CHP units are associated to form a VPP system. The penalty factor approach is implemented along with the weighting factor to develop a multi-objective framework that can simultaneously capitalize on the net profit and minimalize the emission while taking proper care of the related constraints. In this research, EVs are considered as a flexible reserve option along with a dedicated ESS applicable as a spinning reserve. Also, a dynamic pricing-based strategy is also presented and the optimization of VPP is accomplished by implementing a multi-objective-based day-ahead scheduling (MODAS) to see the behaviour of the anticipated system with a suitable method of constraint handling. Four diverse illustrations are considered in three different case study which is performed for hourly-based scheduling and the results are compared with the proposed technique. Statistical analysis is performed and the quality solution sets are attained by the HHO algorithm after performing 100 independent trials and the same is correlated with the available studies that indicate the efficacy and appropriateness of the selected approach.

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator.

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) was improved by combining numerical simulations and experiments. A finite element model of the MPSA was developed; its maximum and mean relative absolute errors of electromagnetic force were 4.3% and 2.3%, respectively, under typical work conditions. Seven design parameters including the cone angle, cone length, depth of the inner hole of the coil skeleton, cone width of the armature, inner cone diameter, and initial position of the moving-iron core were selected for developing the model, and the coefficient of the variation in electromagnetic force, nominal acceleration, 95% of the maximum stable output electromagnetic force, and corresponding response time were used as the performance indicators. The constraint relation between each performance indicator and the influence of each design parameter on the performance indicators were revealed using the uniform Latin hypercube experiment design, correlation analysis, and the main effect analysis method. A multi-objective optimization mathematical model of the MPSA was developed by combining traditional surrogate and machine learning models. The Pareto solution set was obtained using the nondominated sorting genetic algorithm II (NSGA-II), and three decision schemes with different attitudes were determined using the Hurwicz multi-criteria decision-making method. The results showed that a strong contradiction exists among the 95% of the maximum stable output electromagnetic force and its corresponding response time and the coefficient of the variation in electromagnetic force. The cone angle considerably influenced the performance indicators. Compared with the initial design, the coefficient of the variation in electromagnetic force was reduced by 54.08% for the positive decision, the corresponding response time was shortened by 15.65% for the critical decision, and the corresponding acceleration was enhanced by 10.32% for the passive decision. Thus, the overall performance of the long-stroke MPSA effectively improved.

A fundamental sequences method with time-reduction for one-dimensional lateral Cauchy problems

A fundamental sequences method is derived for the numerical solution of an ill-posed one-dimensional lateral Cauchy problem for a hyperbolic damped wave equation, including as a special case the parabolic heat equation. Either the Laguerre transform or the Houbolt finite difference scheme is applied to reduce the time-dependent lateral Cauchy problem to a sequence of second-order ordinary differential equations (ODEs) with function values and derivatives specified at the right endpoint of a finite space interval. A set of fundamental solutions is constructed, termed a fundamental sequence, to the differential equations. The solution of the obtained ODEs is approximated by a linear combination of elements in the fundamental sequence. Source points are placed outside of the solution interval in space, and by collocating at the endpoints of this interval a sequence of linear equations is obtained for finding the unknown coeficients. Tikhonov regularization is used to render a stable solution to the obtained systems of linear equations. Numerical results both for the parabolic and hyperbolic case confirm the efficiency of the proposed method including noisy data. The presented results complement the higher-dimensional case initiated in our previous researches.

Practical Applications of a Set-Based Camera Deployment Methodology.

This work establishes a complete methodology for solving continuous sets of camera deployment solutions for automated machine vision inspection systems in industrial manufacturing facilities. The methods presented herein generate constraints that realistically model cameras and their associated intrinsic parameters and use set-based solving methods to evaluate these constraints over a 3D mesh model of a real part. This results in a complete and certifiable set of all valid camera poses describing all possible inspection poses for a given camera/part pair, as well as how much of the part's surface is inspectable from any pose in the set. These methods are tested and validated experimentally using real cameras and precise 3D tracking equipment and are shown to accurately align with real imaging results according to the hardware they are modelling for a given inspection deployment. In addition, their ability to generate full inspection solution sets is demonstrated on several realistic geometries using realistic factory settings, and they are shown to generate tangible, deployable inspection solutions, which can be readily integrated into real factory settings.