Inference Rules Research Articles

Method Provides Real-Time Anomaly Detection for Drilling-Fluid Properties

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212443, “Real-Time Anomaly-Detection Methodology for Drilling-Fluid Properties,” by Moacyr N.B. Filho, Thalles P. Mello, and Cláudia M. Scheid, Federal Rural University of Rio de Janeiro, et al. The paper has not been peer reviewed. _ Online drilling-fluid-measurement technologies have become an essential tool for drilling automation. While online density measurements are widespread, the availability of rheology measurements is increasing quickly, and additional properties are central to ongoing field trials. The complete paper presents the concept of supervisory and advisory systems dedicated to support the detection of abnormal events and to provide guidelines for fluid treatment. Introduction The Laboratory of Fluid Flow of the Federal Rural University of Rio de Janeiro and Petrobras developed a flow loop for the proposal and validation of online sensors for the measurement of drilling-fluid properties. In previous work, the authors described the development of a set of online sensors to measure fluid properties with control of operational conditions of temperature, pressure, and fluid flow rate. Following previous research, this study proposes a new method using a technique based purely on data to diagnose and detect deviations in drilling-fluid properties. The method proposed applies principal component analysis (PCA) for detecting anomalies in drilling fluids. Method PCA-Based Anomaly-Detection Technique. This method consists of the linear transformation of the space of monitored variables, which generates a new space of variables called principal components, in which each principal component is a linear combination of the process variable. This allows the new space of variables to be linearly independent—that is, the principal components are linearly independent, allowing the calculation of process monitoring statistics. The ability to detect anomalies in drilling-fluid properties is necessary to train the system in the normal state of operation. The training based on the normal state of operation was performed through the calculation of the principal-component matrix and the selection of the reduced principal-component matrix. Once the process model was trained, the monitoring of new samples was performed with the help of two statistical tools that described the health of the process operation: T² and Q statistics. The T² statistic measures variations in the main space. T² only detects variation in the subspace of the first principal components larger than those attributed to the dynamics of the process. The Q statistic denotes the change of events not explained by the principal-component model. It is a measure of the difference, or residual, between a sample and its projection on the model. The failure-detection procedure based on the use of PCA consisted of continuous assessments of whether process statistics were within or outside an acceptable range. A method previously proposed in the literature used control systems based on fuzzy logic to monitor properties of drilling fluids and propose corrections, which are performed automatically by an experimental unit containing actuators for additions of drilling agents as thickeners and viscosifiers. The application of fuzzy logic depends on inference rules, which must be provided to the system by the programmer. This is a limitation of corrective systems based on fuzzy logic.

Bicategorical type theory: semantics and syntax

AbstractWe develop semantics and syntax for bicategorical type theory. Bicategorical type theory features contexts, types, terms, and directed reductions between terms. This type theory is naturally interpreted in a class of structured bicategories. We start by developing the semantics, in the form of comprehension bicategories. Examples of comprehension bicategories are plentiful; we study both specific examples as well as classes of examples constructed from other data. From the notion of comprehension bicategory, we extract the syntax of bicategorical type theory, that is, judgment forms and structural inference rules. We prove soundness of the rules by giving an interpretation in any comprehension bicategory. The semantic aspects of our work are fully checked in the Coq proof assistant, based on the UniMath library.

Optimal length cutting plane refutations of integer programs

In this paper, we discuss the computational complexities of determining optimal length refutations of infeasible integer programs (IPs). We focus on three different types of refutations, namely read-once refutations, tree-like refutations, and dag-like refutations. For each refutation type, we are interested in finding the length of the shortest possible refutation of that type. In the case of this paper, the length of a refutation is equal to the number of inferences in that refutation. The refutations in this paper are also defined by the types of inferences that can be used to derive new constraints. We are interested in refutations with two inference rules. The first rule corresponds to the summation of two constraints and is called the ADD rule. The second rule is the DIV rule which divides a constraint by a positive integer. For integer programs, we study the complexity of approximating the length of the shortest refutation of each type (read-once, tree-like, and dag-like). In this paper, we show that the problem of finding the shortest read-once refutation is NPO PB-complete. Additionally, we show that the problem of finding the shortest tree-like refutation is NPO-hard for IPs. We also show that the problem of finding the shortest dag-like refutation is NPO-hard for IPs. Finally, we show that the problems of finding the shortest tree-like and dag-like refutations are in FPSPACE.

Fact-checking Vietnamese Information Using Knowledge Graph, Datalog, and KG-BERT

In the era of digital information, ensuring the accuracy and reliability of information is crucial, making fact-checking a vital process. Currently, English fact-checking has thrived due to various language processing tools and ample datasets. However, the same cannot be said for Vietnamese fact-checking, which faces significant challenges due to the lack of such resources. To address these challenges, we propose a model for checking Vietnamese facts by synthesizing three popular technologies: Knowledge Graph (KG), Datalog, and KG-BERT. The KG serves as the foundation for the fact-checking process, containing a dataset of Vietnamese information. Datalog, a logical programming language, is used with inference rules to complete the knowledge within the Vietnamese KG. KG-BERT, a Deep Learning (DL) model, is then trained on this KG to rapidly and accurately classify information that needs fact-checking. Furthermore, to put Vietnamese complex sentences into the fact-checking model, we present a solution for extracting triples from these sentences. This approach also contributes significantly to the ease of constructing foundational datasets for the Vietnamese KG. To evaluate the model's performance, we create a Vietnamese dataset comprising 130,190 samples to populate the KG. Using Datalog, we enrich this graph with additional knowledge. The KG is then utilized to train the KG-BERT model, achieving an impressive accuracy of 95%. Our proposed solution shows great promise for fact-checking Vietnamese information and has the potential to contribute to the development of fact-checking tools and techniques for other languages. Overall, this research makes a significant contribution to the field of data science by providing an accurate solution for fact-checking information in Vietnamese language contexts.

Semantic rule checking of cross-domain building data in information containers for linked document delivery using the shapes constraint language

Information exchange is a crucial process throughout the lifecycle of built assets. Building Information Modeling (BIM) and standardized information delivery enable exchanging, storing, and linking cross-domain information bundled in containers such as the standardized Information Container for Linked Document Delivery (ICDD). While assessing the quality of the interlinking and metadata is essential to allow all parties to interpret an information container correctly, most research efforts have focused on code compliance of domain-specific models. In order to address this gap, this paper proposes an approach for validating ICDD information containers using the Shapes Constraint Language (SHACL), the W3C recommendation for Resource Description Framework (RDF) data validation. For optimized querying and validation, a case study applies SHACL inference rules to the linking structure of ICDD to infer predicates between linked entities in four ICDD containers in varying dataset sizes and different linking methods. Afterward, the validation of these containers is analyzed using three types of validation rules with increasing complexity for another SHACL implementation. Results show the feasibility of cross-domain building information validation in ICDD with SHACL and how performance enhancements can be obtained on an inferred dataset compared to the original dataset.

Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival

Aquaculture involves cultivating various marine and freshwater aquatic creatures within regulated environments. Monitoring the aquatic environmental conditions in real-time is crucial for successful fish farming. The Internet of Things (IoT) offers significant potential for real-time monitoring, and this paper introduces an IoT framework designed for efficient monitoring and effective control of various water-related aquatic environmental parameters. The proposed system is implemented as an embedded system utilizing sensors and an Arduino microcontroller. In cultivating pond water, diverse sensors such as pH, temperature, and turbidity sensors are deployed, with each sensor connected to an Arduino Uno-based microcontroller board. These sensors collect data from the water, which is then stored as a CSV file in an IoT cloud platform called ThingSpeak through the Arduino microcontroller. To gather data for analysis, we conducted measurements across five ponds, varying in size and environmental conditions. After getting the real-time data, we compared our experimental results with the standard reference values. As a result, we could take the decision of whether a pond is suitable for cultivating fish or not. After that, we labeled the data with 11 fish categories: Katla, sing, prawn, shrimp, rui, tilapia, pangas, karpio, magur, silver carp, and koi. The data was analyzed using 10 machine learning (ML) algorithms, including J48, Random Forest, K Nearest Neighbors (K-NN), K*, Logistic Model Tree (LMT), Reduced Error Pruning Tree (REPTree), Jumping Rule Inference with Pruned Search (JRIP), Partial Decision Trees (PART), Decision Table, and Logit boost. After experimental analyses, it was discovered that only three of the five ponds were ideal for fish farming, and those three ponds only met the required standards for pH, Temperature, Turbidity, and Conductivity. Among the state-of-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO) for one scenario. This study includes prototype hardware details of the proposed IoT system.

A Differential Datalog Interpreter

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization; that is, to adjust the computation to new data instead of restarting from scratch. One of the major caveats is that deleting data is notoriously more involved than adding since one has to take into account all possible data that has been entailed from what is being deleted. Differential dataflow is a computational model that provides efficient incremental maintenance, notoriously with equal performance between additions and deletions, and work distribution of iterative dataflows. In this paper, we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm with the same optimizations. Experimental results suggest that monotonic aggregation is more powerful than ascenting merely the powerset lattice.

A robust twin support vector machine based on fuzzy systems

PurposeTwin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).Design/methodology/approachThis study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.FindingsThe experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.Originality/valueThis study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

Logic for reasoning about bugs in loops over data sequences (IFIL)

Classic deductive verification is not focused on reasoning about program incorrectness. Reasoning about program incorrectness using formal methods is an important problem nowadays. Special logics such as Incorrectness Logic, Adversarial Logic, Local Completeness Logic, Exact Separation Logic and Outcome Logic have recently been proposed to address it. However, these logics have two disadvantages. One is that they are based on under-approximation approaches, while classic deductive verification is based on the over-approximation approach. One the other hand, the use of the classic approach requires defining loop invariants in a general case. The second disadvantage is that the use of generalized inference rules from these logics results in having to prove too complex formulas in simple cases. Our contribution is a new logic for solving these problems in the case of loops over data sequences. These loops are referred to as finite iterations. We call the proposed logic the Incorrectness Finite Iteration Logic (IFIL). We avoid defining invariants of finite iterations using a symbolic replacement of these loops with recursive functions. Our logic is based on special inference rules for finite iterations. These rules allow generating formulas with recursive functions corresponding to finite iterations. The validity of these formulas may indicate the presence of bugs in the finite iterations. This logic has been implemented in a new version of the C-lightVer system for deductive verification of C programs.

Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.

Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma

Lymph node metastasis (LNM) constitutes one of the main prognostic factors for long-term survival in endometrial carcinoma (EC). However, the previous studies on LNM diagnosis failed to consider both model interpretability and class imbalance. In this study, the extended belief rule base (EBRB) expert system is introduced to develop a novel EBRB-based LNM diagnosis model. First, the interpretability of the EBRB expert system is investigated to demonstrate the feasibility on LNM diagnosis; Second, imbalanced learning is introduced to improve rule generation scheme for constructing base EBRBs; Third, by considering the trust of base EBRBs and base diagnoses, ensemble learning is introduced to improve rule inference scheme for diagnosing final LNM. In the case study, real EC patient data collected from Fujian Provincial Maternity and Children's Hospital are used to verify the effectiveness of the proposed EBRB-based model by comparing with the variants of rule generation schemes and rule inference schemes, as well as some machine learning-based LNM diagnosis models. The comparative results showed that the proposed EBRB-based model has better sensitivity, specificity, and geometric mean in diagnosing LNM for EC patients.

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

Sellars on modality: possible worlds and rules of inference

ABSTRACT This paper discusses the account of alethic modality as presented by Wilfrid Sellars in his earlier work from 1947 to 1958. Its aim is twofold. First, I discuss Sellars' analysis by exploring its historical relationship to Carnap's account of modality. I argue that Carnap's early syntactic treatment of modality profoundly influenced Sellars' own so-called ‘regulist' account of modality in terms of rules of inference. Furthermore, it is suggested that Sellars' lesser-known possible worlds analysis was influenced by Carnap's later semantic account of modality. The second aim of the paper is a critical one. I raise a number of objections to Sellars’ account of modality and argue that his account confronts some serious limitations. I also argue that Sellars does not provide a satisfying explanation of the relationship between his regulist account and his possible worlds analysis. I offer such an explanation by arguing that his regulist account should be understood as a pragmatic account of what is conveyed by the use of modal sentences, whereas his possible worlds analysis is a semantic account of the asserted content of modal statements. That is, the regulist account and the possible worlds analysis have different explanatory targets.

Some Fundamental Research Tools in Mathematical Sciences

Research in mathematical sciences either builds up the insight or breaks the boundary of the literature of the pertaining mathematical research area. A mathematical research technique is an attentive, persistent and systematic approach based on the logical rules of inference and mathematical rules of inference to find something new. Mathematical modeling, construction of theorems with the proofs, design of algorithms, data with simulation could be considered as the fundamental tools in mathematical research. In this paper, we discuss some fundamental research tools which are useful to do research in mathematical sciences.

Developed ABCLASS-Miner Classification Algorithm Based Rule Extraction for Denim Fabrics

Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture.

Research on the problem of aggregation of multiple rules in fuzzy inference systems

In this paper, we establish the matching relation between implication operator and aggregation operator, which provides a new solution for the design and construction of multi-rule fuzzy inference system. Firstly, according to the definition and monotonicity of implication operator, a new classification method of implication operator is proposed, and then the fuzzy inference process using different implication operators is classified. Then, dynamic maximum aggregation operator and dynamic minimum aggregation operator are proposed. Based on the compositional rule of inference (CRI) method, a matching method and basis of implication operator and aggregation operator for fuzzy inference systems is given and illustrated with examples. Finally, the applicability of the proposed method in this paper is further illustrated by comparing the method with existing methods in the literature and using the nearness degree as an evaluation index.

Formal Security Analysis of ECC-Based RFID in Logic of Events Theory

Radio frequency identification (RFID) is a crucial component of the Internet of Things (IoT), and RFID using elliptic curve Cryptography (ECC) is a public key cryptosystem authentication approach that tackles the problem of electronic tag data encryption in RFID systems. The commercialisation and large-scale deployment of RFID systems has raised a number of security-related issues that suggest the need for security protocols. Logic of events theory (LoET) is a formal method for constructing and reasoning about distributed systems and protocols involving security concepts. This paper proposes three event classes, Compute, Retrieve, and Generate, and related axioms and inference rules to formally abstract the ECC session key generation function and formally institute the authentication process of both parties, and the ex-tended LoET is used to analyse the security properties of ECC-based RFID security protocols. Under reasonable assumptions, an ECC-based RFID mutual authentication scheme is shown to satisfy the strong mutual authentication feature. It is shown that extended logic of events theory may be used to prove the security properties of this class of ECC-based RFID protocols.

Interval multi-objective grey wolf optimization algorithm based on fuzzy system

PurposeThe interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.Design/methodology/approachFirst, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.FindingsThe experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.Originality/valueThis study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.

The New Collapse Argument against Quantifier Variance

AbstractQuantifier variantists accept multiple alternative ontological languages in which quantifiers obey the usual inference rules despite having different meanings. But collapse arguments seem to show that these quantifiers would be provably equivalent to one another. Cian Dorr has pushed this discussion forward by formulating the collapse argument in terms of an algebra of meanings that are common amongst the languages. I attempt to show that quantifier variantists can respond. But an important distinction between types of quantifier variance emerges, between those in which quantifier meanings draw on a single objective backbone of “portions of reality,” and those (such as the type that is arguably associated with neo-Fregeanism) in which they do not.

Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

AbstractSome types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets. Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results.