Sequence Of Actions Research Articles

From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation

The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there is a significant correlation between different actions, which provides valuable prior knowledge for the action anticipation task. However, previous methods have not effectively modeled this underlying statistical relationship. To address this issue, we propose a novel end-to-end video modeling architecture that utilizes attention mechanisms, named Anticipation via Recognition and Reasoning (ARR). ARR decomposes the action anticipation task into action recognition and sequence reasoning tasks, and effectively learns the statistical relationship between actions by next action prediction (NAP). In comparison to existing temporal aggregation strategies, ARR is able to extract more effective features from observable videos to make more reasonable predictions. In addition, to address the challenge of relationship modeling that requires extensive training data, we propose an innovative approach for the unsupervised pre-training of the decoder, which leverages the inherent temporal dynamics of video to enhance the reasoning capabilities of the network. Extensive experiments on the Epic-kitchen-100, EGTEA Gaze+, and 50salads datasets demonstrate the efficacy of the proposed methods. The code is available at https://github.com/linuxsino/ARR .

The origin of great ape gestural forms.

Two views claim to account for the origins of great ape gestural forms. On the Leipzig view, gestural forms are ontogenetically ritualised from action sequences between pairs of individuals. On the St Andrews view, gestures are the product of natural selection for shared gestural forms. The Leipzig view predicts within- and between-group differences between gestural forms that arise as a product of learning in ontogeny. The St Andrews view predicts universal gestural forms comprehensible within and between species that arise because gestural forms were a target of natural selection. We reject both accounts and propose an alternative "recruitment view" of the origins of great ape gestures. According to the recruitment view, great ape gestures recruit features of their existing behavioural repertoire for communicative purposes. Their gestures inherit their communicative functions from visual (and sometimes tactile) presentations of familiar and easily recognisable action schemas and states and parts of the body. To the extent that great ape species possess similar bodies, this predicts mutual comprehensibility within and between species - but without supposing that gestural forms were themselves targets of natural selection. Additionally, we locate great ape gestural communication within a pragmatic framework that is continuous with human communication, and make testable predications for adjudicating between the three alternative views. We propose that the recruitment view best explains existing data, and does so within a mechanistic framework that emphasises continuity between human and non-human great ape communication.

Decision-making in action: How international-level professional football players gain an advantage

Understanding how both visual and contextual in-game information influences player’s attempts to gain an advantage over their opponent is key to understanding skilled decision-making in fast-ball sports. In the present study, we conducted semi-structured interviews with 15 male professional football players to explore their reported behaviours and perspectives on their in-game decision-making and the ways in which they adapt to gain an advantage over their opponent. Professional players who have competed internationally at either Under 17, Under 19, Under 21, or senior level took part in semi-structured interviews. The data were analysed using reflexive thematic analysis which generated four higher-order themes about players reported behaviours and perspectives on their decision-making and ways of gaining an advantage: (i) being “unpredictable”; (ii) option generation and invitation, comprising two sub-themes: (iia) generating and realising options in action; and (iib) act on what invites you in the moment; (iii) anticipation and awareness; and (iv) dictating and controlling the game. A key finding was that players attempts to gain an advantage were largely grounded in the aim of ‘being unpredictable’ through the use of deception and disguise, as well as deliberate manipulation of action sequences in order to make their actions ‘hard to read’. A further important finding was that players do not always seek a universal optimal decision, as their individual action capabilities influence their available options. These findings have important implications for the assessment of decision-making performance in future empirical research.

Task and motion planning using mixed integer linear programming for solving fetch-and-carry tasks by a mobile manipulator

This manuscript describes a TAsk and Motion Planning (TAMP) method for mobile manipulators. We focus on fetch-and-carry tasks, and we aim to simultaneously generate both a sequence of actions and a motion sequence for each action. The number of factors to be considered in solving such a problem, and the interactions among them are complex due to the multifaceted characteristics of mobile manipulation. As a result, straightforwardly performing simultaneous TAMP may require a significant amount of processing time. Therefore, we reduce the complexity of the problem by formulating fetch-and-carry planning for a Mixed Integer Linear Programming (MILP) problem. The proposed method can obtain the sequence of actions and the movement of the mobile manipulator in less than one second in many cases. The effectiveness of the proposed method is verified in environments in which delivery objects and obstacles are placed in various patterns, using a robot with an omnidirectional mobile platform and a serial link manipulator.

Distributionally robust sequential load restoration of distribution system considering random contingencies

AbstractNatural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

DRL-based progressive recovery for quantum-key-distribution networks

With progressive network recovery, operators restore network connectivity after massive failures along multiple stages, by identifying the optimal sequence of repair actions to maximize carried live traffic. Motivated by the initial deployments of quantum-key-distribution (QKD) over optical networks appearing in several locations worldwide, in this work we model and solve the progressive QKD network recovery (PQNR) problem in QKD networks to accelerate the recovery after failures. We formulate an integer linear programming (ILP) model to optimize the achievable accumulative key rates during recovery for four different QKD network architectures, considering different capabilities of using trusted relay and optical bypass. Due to the computational limitations of the ILP model, we propose a deep reinforcement learning (DRL) algorithm based on a twin delayed deep deterministic policy gradients (TD3) framework to solve the PQNR problem for large-scale topologies. Simulation results show that our proposed algorithm approaches well compared to the optimal solution and outperforms several baseline algorithms. Moreover, using optical bypass jointly with trusted relay can improve the performance in terms of the key rate by 14% and 18% compared to the cases where only optical bypass and only trusted relay are applied, respectively.

Optimization of truss structures using multi-objective cheetah optimizer

In this study, a multi-objective version of the recently proposed cheetah optimizer called multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration from the targeted hunting strategy employed by cheetahs, which involves a sequence of actions: searching for prey, patiently waiting for the right moment to attack, swiftly launching the attack, and then retreating from the prey and returning to their habitat. MOCO is the result of modification and enhancement from its single-objective counterpart, utilizing a Pareto dominance-based approach. This adaptation allows MOCO to efficiently handle multiple objectives, explores and exploits promising areas in the optimization landscape, and identifies non-dominated solutions, offering valuable tradeoff choices for decision-makers. To demonstrate its practical applications, the MOCO method has been employed to address five intricate structural design problems. These problems involve a pair of competing objectives: the minimization of structural weight and the reduction of maximum nodal displacement. To gauge the efficacy and efficiency of the proposed algorithm, a comparative analysis is conducted against three alternative state-of-the-art multi-objective algorithms. Furthermore, a rigorous evaluation is carried out utilizing hypervolume testing. The findings reveal that the MOCO algorithm surpasses the performance of the other algorithms, underscored by its capacity to uncover a diverse array of non-dominated solutions. To delve deeper into the experimental results, statistical analysis employing Friedman’s rank test is employed. The solutions generated and the convergence patterns exhibited by the MOCO approach underscore its exceptional proficiency in resolving intricate design problems.

Physicochemical, structural and functional properties of pomelo peel pectin extracted by combination of pulsed electric field and cellulase hydrolysis

In this study, pectin extracted from pomelo peel was investigated using three different combination methods of pulsed electric field (PEF) and cellulase. Three action sequences were performed, including PEF treatment followed by enzymatic hydrolysis, enzymatic hydrolysis followed by PEF treatment, and enzymatic hydrolysis simultaneously treated by PEF. The three corresponding pectins were namely PEP, EPP and SP. The physiochemical, molecular structural and functional properties of the three pectins were determined. The results showed that PEP had excellent physiochemical properties, with the highest yield (12.08 %), total sugar (80.17 %) and total phenol content (38.20 %). The monosaccharide composition and FT-IR analysis indicated that the three pectins were similar. The molecular weights of PEP, EPP and SP were 51.13, 88.51 and 40.00 kDa, respectively. PEP showed the best gel properties, emulsification stability and antioxidant capacity among the three products, due to its high galacturonic acid and total phenol content, appropriate protein and low molecular weight. The mechanism of PEF-assisted cellulase hydrolysis of pomelo peel was also revealed by SEM analysis. These results suggested that PEF pretreatment was the best method, which not only improved the efficiency of enzymatic extraction, but also reduced resource waste and increased financial benefits.

Variation-aware directed graph convolutional networks for skeleton-based action recognition

Directed Graph convolutional networks (DGCNs) have been indeed gaining attention and being applied in skeleton-based action recognition tasks to capture the hierarchical relationships of skeleton via directed graph topology. However, they typically pay the same attention to regions with variations and regions with relative statics of the skeleton, which leads to low accuracy when recognizing fine-grained actions. To this end, we design an innovative variation-aware directed graph convolutional network (VA-DGCN) to focus on the regions where the variation takes place. VA-DGCN comprises a variation-aware directed spatial convolution (VDSC) module and a multi-scale contrastive temporal convolution (MCTC) module. Specifically, in order to capture subtle variations in fine-grained actions, VDSC introduce the average posture of the action sequence as the static anchor. In VDSC, the channel-specific topology branch is designed to model the different kinematic properties of different channels to extract global features, to which global-attention graph convolution is added for unnaturally connected joints. Subsequently, we identify the regions with variations by comparing global features acquired from action sequence and average posture. In temporal, MCTC comprises multiple branches for extracting temporal features at different scales. Moreover, to maximize the mutual information between branches, we introduce contrastive learning to drive the module to learn more meaningful action representations. We conduct extensive experiments on three public datasets to validate the feasibility and efficacy of our proposed VA-DGCN.

Load restoration of electricity distribution systems using a novel two‐stage method

AbstractThis paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method.

Unit coordination knowledge enhanced autonomous decision-making approach of heterogeneous UAV formation

Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.

Attitudes and Skills in Basic Life Support after Two Types of Training: Traditional vs. Gamification, of Compulsory Secondary Education Students: A Simulation Study.

It is recommended to implement the teaching of Basic Life Support (BLS) in schools; however, studies on the best training method are limited and have been a priority in recent years. The objective of this study was to analyze the attitudes and practical skills learned during BLS training using a gamified proposal. A comparative study was carried out, consisting of Compulsory Secondary Education students [control group (CG; classical teaching) and experimental group (EG; gamified proposal)]. The instruments used were the CPR and AED action sequence observation sheet, data from the Laerdal Resusci Anne manikin and AED and Attitude Questionnaire towards Basic Life Support and the Use of the Automated External Defibrillator. Sixty-eight students (33 girls) with a mean age of 13.91 ± 0.70 years were recruited. Results were significantly better in the EG (n = 37) [i.e., breathing control (p = 0.037); call to emergency services (p = 0.049); mean compression depth (p = 0.001); self-confidence (p = 0.006); intention to perform BLS and AED (p = 0.002)]; and significantly better in the CG (n = 31) [Total percentage of CPR (p < 0.001); percentage of correct compression (p < 0.001); time to apply effective shock with AED (p < 0.001); demotivation (p = 0.005). We can conclude that the group that was trained with the training method through the gamified proposal presents better intentions and attitudes to act in the event of cardiac arrest than those of the classic method. This training method allows for similar results in terms of CPR and AED skills to classical teaching, so it should be taken into account as a method for teaching BLS to secondary education students.

Effect of phosphorus impurities on sulfoaluminate cement-modified gypsum-based self-leveling mortar and improvement method

The preparation of gypsum-based self-leveling mortar (GSLM) from solid waste-derived sulfoaluminate cement-modified hemihydrate gypsum is an effective path to enhance its mechanical properties. However, the phosphorus impurities in phosphogypsum (PG) still pose a challenge to the hemihydrate gypsum & sulfoaluminate cement composite system. To evaluate the impact of phosphorus impurities and develop targeted removal strategies, this study first assessed the effects of different types of phosphorus impurities on the mechanical properties and hydration characteristics of the GSLM. Then, a method of pretreating PG with carbide slag (CS) was proposed, and the removal efficiency and mechanism of phosphorus were examined by ICP-OES and XPS testing. The results indicated that the soluble phosphorus impurities severely inhibit the hydration of both CŜH0.5 and C4A3Ŝ, leading to a most notable reduced mechanical performance. The sequence of inhibitory action was as follows: H3PO4 >> Ca(H2PO4)2·H2O > CaHPO4·2H2O > Ca3(PO4)2. CS could effectively immobilize the soluble phosphorus in PG. The pretreatment by CS notably enhanced the hydration degree of the GSLM by converting soluble phosphorus into Ca3(PO4)2 beforehand. In addition, the alkaline environment provided by CS promoted the ettringite formation. When the CS content approaches 3%, a PG-based GSLM with good performances meeting the G20 standard requirements of GSLM can be prepared.

Sequential action-induced invariant representation for reinforcement learning

How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforcement learning. Recently, unsupervised representation learning methods based on bisimulation metrics, contrast, prediction, and reconstruction have shown the ability for task-relevant information extraction. However, due to the lack of appropriate mechanisms for the extraction of task information in the prediction, contrast, and reconstruction-related approaches and the limitations of bisimulation-related methods in domains with sparse rewards, it is still difficult for these methods to be effectively extended to environments with distractions. To alleviate these problems, in the paper, the action sequences, which contain task-intensive signals, are incorporated into representation learning. Specifically, we propose a Sequential Action–induced invariant Representation (SAR) method, which decouples the controlled part (i.e., task-relevant information) and the uncontrolled part (i.e., task-irrelevant information) in noisy observations through sequential actions, thereby extracting effective representations related to decision tasks. To achieve it, the characteristic function of the action sequence’s probability distribution is modeled to specifically optimize the state encoder. We conduct extensive experiments on the distracting DeepMind Control suite while achieving the best performance over strong baselines. We also demonstrate the effectiveness of our method at disregarding task-irrelevant information by applying SAR to real-world CARLA-based autonomous driving with natural distractions. Finally, we provide the analysis results of generalization drawn from the generalization decay and t-SNE visualization. Code and demo videos are available at https://github.com/DMU-XMU/SAR.git.

Anticipation (second-order motor planning) is stored in memory - processing of grasp postures in a priming paradigm.

The end-state comfort effect (ESC) describes the tendency to grasp an object with an initial uncomfortable grasp posture in order to achieve a comfortable end posture. The ESC is an example for anticipative processes in manual action. ESC planning is investigated in many studies where this effect is measured in the context of motor observation and motion capture. However, there is little evidence if the anticipative link between different action states, especially between initial grasp postures and comfortable end postures, is represented in memory. The aim of the present study was to investigate whether the perception of a grasp posture holding a bar leads to the activation of action-related representations of grasping actions. For this purpose, a priming paradigm was used in which prime images were shown depicting either a comfortable (overhand grip) or uncomfortable (underhand grip) grasp posture holding a two-colored bar. The subsequently shown target images represented either a comfortable (thumb-up) or uncomfortable (thumb-down) final grasp posture of this grasping action. Due to the different grasp postures in the prime and target, prime-target pairs represented different types of action sequences. Furthermore, physically possible, and physically impossible actions were presented. Participants were asked to react to the top color of the bar shown in the target-picture, whereby the shown grasp posture was irrelevant for this decision. Results showed that reaction times did not differ after presentation of an overhand grip to target pictures showing comfortable or uncomfortable final grasp postures. In contrast, after presentation of an underhand grip in the prime, reactions to target pictures with final comfortable grasp postures were faster compared to target pictures with uncomfortable grasp postures. The effect was only found for the physically possible action. The findings suggest that the perception of the underhand grip leads to cognitive pre-activation of a final action state. The present study suggests that the association between an initial uncomfortable underhand grip and its action effect, in form of a final action state that is consistent with the ESC, is represented in memory. Such motor representation might be important for the anticipation and control of goal-directed grasping.

Perseverance reduces whereas impulsivity increases the risk of reoffending

By law, every criminal verdict describes the norm violation in question. To date, the use of this information for prognostic purposes has been standardized only for sex offenses. This study presents a content-analytic approach to the material. A theory-based coding system allows for systematic tracing of the offender’s affective and regulatory processing during norm violation, targeting the entire action sequence: initial activation affect (positive vs. negative), impulsivity vs. intentionality, affect regulation, and coping with intention-implementation difficulties. In a sample of N = 304 males convicted of violent and/or sexual offenses, the processing variables were investigated with respect to (a) objective measurability, (b) relationships with established reoffending risk markers, and (c) their (incremental) performance in predicting reoffending. With respect to objectivity (a), the inter-rater agreement (n = 111) varied widely among the variables (intraclass correlation range 0.03–0.62). Regarding risk markers (b), significant associations were found with the Level of Service Inventory–Revised (LSI-R) subscales criminal history, alcohol/drugs, emotional/personal, and attitudes/orientation. The Crime Scene Behavior Risk Measure for sexual offenses (n = 106) correlated with psychological processing variables for the items multiple juvenile offenders and male victim. Two variables emerged as relevant for predicting recidivism (c): negatively motivated impulsivity predicted violent reoffending, while persistence when coping with implementation difficulties negatively predicted sexual and general reoffending. These relationships statistically mediated the predictive contribution of actuarial risk tools (LSI-R/crime scene behavior risk). Psychological processes during norm violation as reported in verdicts appear to represent underexploited information within risk assessment and beyond.

Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences.

To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities. EHR-based audit logs of attending physicians and advanced practice providers (APPs) from four surgical intensive care units in 2019 were included. Neural language models (LMs) were trained and validated separately for attendings' and APPs' action sequences. Action entropy was calculated as the cross-entropy associated with the predicted probability of the next action, based on prior actions. To validate the measure, a matched pairs study was conducted to assess the difference in action entropy during known high cognitive effort scenarios, namely, attention switching between patients and to or from the EHR inbox. Sixty-five clinicians performing 5904429 EHR-based audit log actions on 8956 unique patients were included. All attention switching scenarios were associated with a higher action entropy compared to non-switching scenarios (P < .001), except for the from-inbox switching scenario among APPs. The highest difference among attendings was for the from-inbox attention switching: Action entropy was 1.288 (95% CI, 1.256-1.320) standard deviations (SDs) higher for switching compared to non-switching scenarios. For APPs, the highest difference was for the to-inbox switching, where action entropy was 2.354 (95% CI, 2.311-2.397) SDs higher for switching compared to non-switching scenarios. We developed a LM-based metric, action entropy, for assessing cognitive burden associated with EHR-based actions. The metric showed discriminant validity and statistical significance when evaluated against known situations of high cognitive effort (ie, attention switching). With additional validation, this metric can potentially be used as a screening tool for assessing behavioral action phenotypes that are associated with higher cognitive burden. An LM-based action entropy metric-relying on sequences of EHR actions-offers opportunities for assessing cognitive effort in EHR-based workflows.

Застосування CAD/CAE-системи Solidworks у задачах аналізу міцності деталей верстатних пристосувань

The strength of parts is a critically important characteristic of the design of Clamping Fixtures. Calculating the strength of parts of Clamping Fixtures is necessary both when designing new structures and when checking existing ones. Traditional calculation methods, such as nominal stress calculation, although easy to use, have low accuracy. This creates a need for more accurate and efficient strength analysis methods. Modern CAD/CAE systems, in particular SolidWorks with the Simulation application, provide powerful tools for engineering calculations and finite element modeling. The algorithm of static calculation presented in the article on the example of the "Grip" part demonstrates the sequence of actions during strength analysis in the SolidWorks environment. This algorithm includes creation of a 3D model, generation of a finite-element mesh, material selection, determination of anchoring and loading conditions, calculation and analysis of results. An important stage of the analysis is the choice of the material of the part. At the same time, it should be taken into account that the databases of foreign CAD/CAE systems may not contain some domestic materials, which requires the selection of analogues with similar characteristics. The system allows you to visualize the results of calculations in the form of colored diagrams of stress and strain distribution. This greatly facilitates the analysis and interpretation of the results, allowing the critical areas of the structure to be quickly identified. The use of CAD/CAE systems for strength analysis of machine tool parts has a number of advantages compared to traditional methods. It increases the accuracy of calculations, reduces the time for their implementation, allows you to easily make changes to the design and instantly assess their impact on the strength of the part. The proposed method allows you to carry out both design and verification calculations of details of Clamping Fixtures. This makes it a versatile tool for structural engineers. The implementation of such strength analysis methods is particularly relevant both for the educational process and for real production. In terms of training, it allows students to gain practical skills in working with modern engineering systems. In production, it helps to improve the quality of design, reduce the number of errors and optimize structures.

Morphology of Pornography: Magical and Fairy-Tale Roots of Modern Porn Discourse

In this article, the author analyzes visual discourses of physicality in modern porn discourse through the prism of structuralist theory proposed by Russian researcher Vladimir Propp. The article is one of the first attempts in modern historiography to transplant the methods and principles proposed by Vladimir Propp to analyze the current state of porn in popular culture. The purpose of the article is to analyze the relationships and interdependencies between modern porn culture, presented in visual forms, and its historical predecessors. It is assumed that the origins of pornographic discourse, which operates in visual forms, can be localized in folk traditional culture. Forms and dimensions of physicality and actions of the heroes of modern pornographic discourse are perceived as structural elements of mass culture. The results of the study can be summarized in the following way: 1) the visual structure of modern porn has lost its uniqueness and originality in the consumer society; 2) the plots and sequence of actions of the heroes of modern pornographic discourse can be compared with similar plots and behavioral strategies of folk discourse; 3) porn in modern society plays roles that are similar to ones played by fairy tales in the traditional culture of pre-modern societies. The author believes that modern porn discourse imitates reality, although initially the behavior of the characters and the plots are fantastic and unrealistic in their nature. It is shown that the development of porn became a reaction to the demand of consumer society for visualized narrative constructions based on the archetypes of traditional folk culture.

Assessment of the industrial policy to the military-industrial complex effectiveness based on neural networks based on fuzzy logic

The relevance of the topic under consideration on the development of universal applied methods for assessing the effectiveness and efficiency of industrial policy in the military-industrial complex (hereinafter - MIC) is due to its high practical significance in the context of the challenges of technological development associated with digitalization and informatization. It is also due to the peculiarities of the current stage of Russia’s economic development in the conditions of financial and technological sanctions from Western states. The purpose of the study is to develop the main provisions of a methodological approach to assessing the effectiveness of industrial policy in the defense industry based on the use of economic-mathematical modeling apparatus, which involves the construction of neural networks based on fuzzy logic. In the course of the research the authors have solved the following problems: mathematically formalized the object of analysis; developed an algorithm for determining the effectiveness of industrial policy in the MIC using neural networks; formalized the model for assessing the effectiveness of such industrial policy based on fuzzy sets; proposed a system of indicators for assessing industrial policy in the MIC; determined the sequence of actions in the construction the fuzzy model for assessing the effectiveness of industrial policy in the MIC by means of Fuzzy Logic on the software platform MatLab.