Boolean Variables Research Articles

Online Monitoring Data Processing Method of Transformer Oil Chromatogram Based on Association Rules

AbstractThe oil chromatography online monitoring system can monitor the insulation status of power transformers continuously and real‐time. The accuracy of its detection data directly affects the stable operation of the transformers. Aiming at the problem of abnormal data in the current transformer online monitoring data set, this paper proposes an online oil chromatographic data processing method based on association rules and support vector regression (SVR) analysis. First, using piecewise linearization to convert numerical variables into Boolean variables, and then exploring gas indicators with strong correlations in oil chromatogram. Then, using the improved box plot method to detect the abnormal value points in it, and distinguish the data of different abnormal patterns based on the association rules. Finally, the advanced particle swarm optimization support vector regression (APSO‐SVR) algorithm is used to repair the data to improve the credibility of the data set. Using actual transformer oil chromatographic monitoring data for testing, the results show that the proposed data processing method can effectively improve the accuracy of oil chromatographic online monitoring data. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Fuzzy logical conclusions and conclusions in expert systems of medical diagnostics

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used.

A Model for the Assessment of Management Competencies of Masters in Management

The article presents the authors’ model for the assessment of management competencies (MC) of masters in management, based on expert evaluation of the MC level by 28 criteria, broken into three groups of management functions: effectiveness of management decisions, organization and monitoring of work, and motivation and development of personnel. Six MC levels are set depending on the intensity of manifestation of each group of management functions: strong, medium, and weak. A binary matrix is constructed for each level by the method of paired comparisons. The distances between the measured MC level of a master in management and the benchmark matrices are proposed to be calculated as the Hamming metric, because it allows to employ Boolean values and accounts for the sequence of the measurements. 
 The model application is demonstrated by field experiments with master students of the specialty “Management” in the second academic year. To confirm the adequacy of the model and results of its testing, the consistency of expert opinions is estimated by the concordance factor; the statistical significance of the latter is confirmed by use of Pearson statistics; the input data for an assumed student with a high level of MC formation are simulated, with demonstrating through calculations that the minimal distance will be the one between the matrix of this student’s data and the benchmark matrix for the high MC level. 
 The calculations were made using MathCad software. The consistency of expert opinions was evaluated by use of Statistica software package. 
 The proposed model can be applied to assess any kind of competencies, for students of various specialties. It will require only changed assessment criteria.

Intelligent information system for supporting design solutions in the conditions of multivariate selection

The modeling of multivariate choice problems in the design of products within the framework of an information and software system is considered. The typical elements and functions of the information and software system of multivariate choice are given. Models of optimization problems of forming acceptable sets of alternative solutions when choosing analogs of parts and components are proposed. The formation of the initial set of alternative solutions is carried out on the basis of an assessment of the design, technological, electrical and operational characteristics of the alternatives of choice. The formalization of the problem of the final selection of the composition of parts and components according to technical and economic indicators is given. The proposed models are based on the use of Boolean variables describing the alternatives of choice. The structure of a specialized database of bath rooms for storing the characteristics of parts and components and the results of intermediate and final selection of design solutions is also given.

Modal Functional (Dialectica) Interpretation

We adapt our light Dialectica interpretation to usual and light modal formulas (with universal quantification on boolean and natural variables) and prove it sound for a non-standard modal arithmetic based on Goedel's T and classical S4. The range of this light modal Dialectica is the usual (non-modal) classical Arithmetic in all finite types (with booleans); the propositional kernel of its domain is Boolean and not S4. The `heavy' modal Dialectica interpretation is a new technique, as it cannot be simulated within our previous light Dialectica. The synthesized functionals are at least as good as before, while the translation process is improved. Through our modal Dialectica, the existence of a realizer for the defining axiom of classical S5 reduces to the Drinking Principle (cf. Smullyan).

The Effects of Flexibility and Relative Muscle Strength on Injury in Running

When exercising, physical injury is almost inevitable. Although there is a multitude of practices to avoid injury, a large portion of luck is required to minimize injury proneness. In this paper, with the aid of a public dataset gait kinetics and kinematics, flexibility and strength are tested against the Boolean value of injury to conduct a linear binary regression model.

OPTIMIZATION OF TIMETABLE AT THE UNIVERSITY

Context. In this paper, we consider a well-known university scheduling problem. Such tasks are solved several times a year in every educational institution. The problem of constructing an optimal schedule remains open despite numerous studies in this area. This is due to the complexity of the corresponding optimization problem, in particular, its significant dimension. This complicates its numerical solution with existing optimization methods. Scheduling models also need improvement. Thus, schedule optimization is a complex computational problem and requires the development of new methods for solving it. 
 Objective. Improvement of optimization models for timetabling at the university and the use of new effective methods to solve them.
 Method. We use the exact quadratic regularization method to solve timetabling optimization problems. Exact quadratic regularization allows transforming complex optimization models with Boolean variables into the problem of maximizing the vector norm on a convex set. We use the efficient direct dual interior point method and dichotomy method to solve this problem. This method has shown significantly better results in solving many complex multimodal problems. This is confirmed by many comparative computational experiments. The exact quadratic regularization method is even more effective in solving timetabling problems. This optimization method is used for the first time for this class of problems, so it required the development of adequate algorithmic support.
 Results. We propose a new, simpler timetabling optimization model that can be easily implemented software in Excel with the OpenSolver, RoskSolver, and others. We give a small example of building a schedule and describe step-by-step instructions for obtaining the optimal solution. 
 Conclusions. An efficient new technology developed for university timetable, which is simple to implement and does not require the development of special software. The efficiency of the technology is ensured by the use of a new method of exact quadratic regularization.

Using evolutionary algorithms to determine the environmental projects effectiveness

Environmental projects have a high degree of uncertainty and risk of their implementation. In the process of assessing their effectiveness, it is necessary to take into account the interests of all the participants in the environmental project. The environmental projects effectiveness evaluation is formalized in the form of a mathematical problem of nonlinear programming with Boolean variables with constraints such as equality and inequality. To solve it, the interactive genetic algorithm for the environmental projects implementation trajectory formation was developed.

Decision Making Models on the Market of Cloud Services

Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages. The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals. Results. To run a broker?s business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria. Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues. Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.

Stochastic Local Search over Minterms on Structured SAT Instances

We observed that Conjunctive Normal Form (CNF) encodings of structured SAT instances often have a set of consecutive clauses defined over a small number of Boolean variables. To exploit the pattern, we propose a transformation of CNF to an alternative representation, Conjunctive Minterm Canonical Form (CMCF). The transformation is a two-step process: CNF clauses are first partitioned into disjoint subsets such that each subset contains CNF clauses with shared Boolean variables. CNF clauses in each subset are then replaced by Minterm Canonical Form (i.e., partial solutions), which is found by enumeration. We show empirically that a simple Stochastic Local Search (SLS) solver based on CMCF can consistently achieve a higher success rate using fewer evaluations than the SLS solver WalkSAT on two representative classes of structured SAT problems.

Industry Communication Based on TCP/IP Protocol

Abstract The design of control systems needs to ensure communication between multiple PLCs or external IoT devices. Furthermore, there are several ways of communication between them. This article dealt with the PLC communication based on TCP/IP protocol, and compared several communication options between two PLCs of the S7-300 and S7-1200 series connected using Profinet with a solution built in C# connected to the PLC via an OPC server. We used MODBUS TCP, TCON, and PUT/GET program blocks for comparison. We transmitted a digital Boolean data type and an analogue value of the integer data type via Profinet to read data from the S7-300 PLC and send data to the S7-1200. We compared the programming instructions standardly used as a client/server PLC programming with a Windows Forms application, and evaluated the advantages and disadvantages of this solution even when using external IoT devices. The solution was applied and successfully tested for communication between PLC S7-1200 and Nvidia Jetson Nano. We wrote program instructions for PLC in Siemens Tia Portal V15.

On the parameterized complexity of the synthesis of Boolean nets with restricted place environments

Though constructing a sequential system model may be technically uncomplicated and rather ‘straightforward’, its result is often cumbersome and difficult to analyze since all possible interleavings of system's actions have to be present. This naturally raises the interest to the possibility of automatic synthesis of models that are able to grasp behavioral parallelism of the system. Boolean Petri nets are a classical formalism that enjoys generic concepts of concurrency, synchronization and conflict, and allows to obtain a concise yet expressive system representation. Let τ be a Boolean type of nets. The problem of τ-synthesis consists in deciding whether a given transition system A is isomorphic to the reachability graph of a Boolean Petri net N of type τ (a τ-net, for short). In case of a positive decision, N should be constructed. Another instance of the synthesis problem takes the dependencies of places into account: A place p of a τ-net depends on a transition t if p can influence t, that is, a marking of p can inhibit the firing of t, or if p can be influenced by t, that is, the firing of t can change a marking of p. The problem τ-synthesis of Boolean nets with restricted dependencies (τ-SOBNWRD) consists in deciding for a given transition system A and natural number d whether there exists a Boolean Petri net N of type τ such that, firstly, the reachability graph of N and A are isomorphic and, secondly, every place p of N depends on at most d transitions. For many Boolean types that allow independence, the (unrestricted) τ-synthesis problem is NP-complete, and for all of them τ-SOBNWRD inherits the NP-completeness. In this paper, we enhance our understanding of τ-SOBNWRD from a parameterized point of view: We show for 27 of the 128 thinkable Boolean types that allow independence that τ-SOBNWRD parameterized by d is W[1]-hard, and for 75 of them that the problem is W[2]-hard.

FKT is Not Universal \u2014 A Planar Holant Dichotomyfor Symmetric Constraints

We prove a complexity classification for Holant problems defined by an arbitrary set of complex-valued symmetric constraint functions on Boolean variables. This is to specifically answer the question: Is the Fisher-Kasteleyn-Temperley (FKT) algorithm under a holographic transformation (Valiant, SIAM J. Comput. 37(5), 1565–1594 2008) a universal strategy to obtain polynomial-time algorithms for problems over planar graphs that are intractable on general graphs? There are problems that are #P-hard on general graphs but polynomial-time solvable on planar graphs. For spin systems (Kowalczyk 2010) and counting constraint satisfaction problems (#CSP) (Guo and Williams, J. Comput. Syst. Sci. 107, 1–27 2020), a recurring theme has emerged that a holographic reduction to FKT precisely captures these problems. Surprisingly, for Holant, we discover new planar tractable problems that are not expressible by a holographic reduction to FKT. In particular, a straightforward formulation of a dichotomy for planar Holant problems along the above recurring theme is false. A dichotomy theorem for #CSPd, which denotes #CSP where every variable appears a multiple of d times, has been an important tool in previous work. However the proof for the #CSPd dichotomy violates planarity, and it does not generalize to the planar case easily. In fact, due to our newly discovered tractable problems, the putative form of a planar #CSPd dichotomy is false when d ≥ 5. Nevertheless, we prove a dichotomy for planar #CSP2. In this case, the putative form of the dichotomy is true. (This is presented in Part II of the paper.) We manage to prove the planar Holant dichotomy relying only on this planar #CSP2 dichotomy, without resorting to a more general planar #CSPd dichotomy for d ≥ 3. A special case of the new polynomial-time computable problems is counting perfect matchings (#PM) over k-uniform hypergraphs when the incidence graph is planar and k ≥ 5. The same problem is #P-hard when k = 3 or k = 4, which is also a consequence of our dichotomy. When k = 2, it becomes #PM over planar graphs and is tractable again. More generally, over hypergraphs with specified hyperedge sizes and the same planarity assumption, #PM is polynomial-time computable if the greatest common divisor (gcd) of all hyperedge sizes is at least 5. It is worth noting that it is the gcd, and not a bound on hyperedge sizes, that is the criterion for tractability.

Conditions for logical contextuality and nonlocality

Contextuality and nonlocality are nonclassical properties exhibited by quantum statistics whose implications profoundly impact both the foundations and applications of quantum theory. In this paper we provide some insights into logical contextuality and inequality-free proofs. The former can be understood as the possibility version of contextuality, while the latter refers to proofs of quantum contextuality and nonlocality that are not based on violations of some noncontextuality (or Bell) inequality. By ``possibilistic'' we mean a description in terms of possibilities for the outcomes, which are Boolean variables assuming value one when the corresponding probability is strictly larger than zero and zero otherwise. The present work aims to build a bridge between these two concepts from what we call possibilistic paradoxes, which are sets of possibilistic conditions whose occurrence implies contextuality and nonlocality. As the main result, we demonstrate the existence of possibilistic paradoxes whose occurrence is a necessary and sufficient condition for logical contextuality in a very important class of scenarios. Finally, we discuss some interesting consequences arising from the completeness of these possibilistic paradoxes.

Two SAT solvers for solving quantified Boolean formulas with an arbitrary number of quantifier alternations

In recent years, expansion-based techniques have been shown to be very powerful in theory and practice for solving quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over Boolean variables. Such approaches partially expand one type of variable (either existential or universal) for obtaining a propositional abstraction of the QBF. If this formula is false, the truth value of the QBF is decided, otherwise further refinement steps are necessary. Classically, expansion-based solvers process the given formula quantifier-block wise and use one SAT solver per quantifier block. In this paper, we present a novel algorithm for expansion-based QBF solving that deals with the whole quantifier prefix at once. Hence recursive applications of the expansion principle are avoided and only two incremental SAT solvers are required. While our algorithm is naturally based on the forall Exp+Res calculus that is the formal foundation of expansion-based solving, it is conceptually simpler than present recursive approaches. Experiments indicate that the performance of our simple approach is comparable with the state of the art of QBF solving, especially in combination with other solving techniques.

Learning from survey propagation: a neural network for MAX-E-3-SAT

Many natural optimization problems are NP-hard, which implies that they are probably hard to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for all (or even most) instances in practice. This paper presents a new algorithm for computing approximate solutions in Θ(N) for the maximum exact 3-satisfiability (MAX-E-3-SAT) problem by using supervised learning methodology. This methodology allows us to create a learning algorithm able to fix Boolean variables by using local information obtained by the Survey Propagation algorithm. By performing an accurate analysis, on random conjunctive normal form instances of the MAX-E-3-SAT with several Boolean variables, we show that this new algorithm, avoiding any decimation strategy, can build assignments better than a random one, even if the convergence of the messages is not found. Although this algorithm is not competitive with state-of-the-art maximum satisfiability solvers, it can solve substantially larger and more complicated problems than it ever saw during training.

MATHEMATICAL MODELS FOR DIAGNOSTICS OF A BOLTED CONDUCTIVE JOINT UNDER CONDITIONS OF CHANGING MODE PARAMETERS

The reliability of the transmission and distribution of electricity in ports, ships and energy enterprises depends on the condition of the bolted conductive joints. Operational practice shows that 2 % of cases of emergency shutdowns of technological lines and fires on electrical equipment occur due to the weakening of bolted conductive joints. The main reasons for the bolted joint weakening are dynamic changes in operating current loads and changes in climatic parameters. It is possible to increase the reliability of bolted conductive joints using modern diagnostic methods that allow identifying the appearance of bolted joint weakening. The existing methods for diagnosing the state of bolted joints, which are based on monitoring the temperature of the bolted joint, are analysed. It has been established that the use of such methods to identify the initial moment of the joint weakening is impractical, since they react to the emergency state of the bolted joint, that is, to the critical temperature value. To identify the initial moment of the bolted joint weakening, it is proposed to simultaneously monitor the values of the load current and the parameters of the ambient air, which makes it possible to increase the reliability of detecting pre-emergency situations. To diagnose the initial moment of the bolted joint weakening, it is proposed to use the estimated Boolean functions. Arguments of Boolean variables are obtained for a stationary and continuous process of changing current and ambient temperatures, a stationary and deterministic process as well as a random and deterministic process. The minimized Boolean functions are given for the normal operation of the bolted joint, when the initial moment of weakening appears and when the emergency mode occurs.

A Wasserstein distance based multiobjective evolutionary algorithm for the risk aware optimization of sensor placement

• A Multiobjective Evolutionary Algorithm for the sensor placement (SP) in a network. • A novel data structure for the spatio-temporal contamination dynamics. • The fitness of a SP is represented as the histogram of a discrete random variable. • The Wasserstein distance between SP's enables new genetic operators. In this paper we propose a new algorithm for the identification of optimal “sensing spots”, within a network, for monitoring the spread of “effects” triggered by “events”. This problem is referred to as “Optimal Sensor Placement” and many real-world problems fit into this general framework. In this paper sensor placement (SP) (i.e., location of sensors at some nodes) for the early detection of contaminants in water distribution networks (WDNs) will be used as a running example. Usually, we have to manage a trade-off between different objective functions, so that we are faced with a multi objective optimization problem. (MOP). The best trade-off between the objectives can be defined in terms of Pareto optimality. In this paper we model the sensor placement problem as a multi objective optimization problem with boolean decision variables and propose a Multi Objective Evolutionary Algorithm (MOEA) for approximating and analyzing the Pareto set. The evaluation of the objective functions requires the execution of a simulation model: to organize the simulation results in a computationally efficient way we propose a data structure collecting simulation outcomes for every SP which is particularly suitable for visualization of the dynamics of contaminant concentration and evolutionary optimization. This data structure enables the definition of information spaces, in which a candidate placement can be represented as a matrix or, in probabilistic terms as a histogram. The introduction of a distance between histograms, namely the Wasserstein (WST) distance, enables to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm MOEA/WST has been tested on two benchmark water distribution networks and a real world network. Preliminary results are compared with NSGA-II and show a better performance, in terms of hypervolume and coverage, in particular for relatively large networks and low generation counts.

Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks.

Deep Reinforcement Learning techniques demonstrate advances in the domain of robotics. One of the limiting factors is a large number of interaction samples usually required for training in simulated and real-world environments. In this work, we demonstrate for a set of simulated dexterous in-hand object manipulation tasks that tactile information can substantially increase sample efficiency for training (by up to more than threefold). We also observe an improvement in performance (up to 46%) after adding tactile information. To examine the role of tactile-sensor parameters in these improvements, we included experiments with varied sensor-measurement accuracy (ground truth continuous values, noisy continuous values, Boolean values), and varied spatial resolution of the tactile sensors (927 sensors, 92 sensors, and 16 pooled sensor areas in the hand). To facilitate further studies and comparisons, we make these touch-sensor extensions available as a part of the OpenAI Gym Shadow-Dexterous-Hand robotics environments.

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimization