Naur Form Research Articles

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.

Differential evolution ensemble designer

A meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, DEED evolves effective and robust DE ensemble designs. The design components of DE ensemble algorithms include population management, constituent algorithms in the ensemble, information mixing amongst the sub-populations in the ensemble and the numerical parameters associated with various aspects of the ensemble. DEED employs Dynamic Structured Grammatical Evolution (DSGE) as the meta-evolutionary algorithm. A Backus–Naur form (BNF) grammar has been developed in this paper to represent the design space of DE ensembles and used by DSGE to evolve DE ensemble designs. DEED has been employed to evolve DE ensemble designs for solving 30-dimensional CEC’17 benchmark functions. The evolved designs (both the best design as well as all the final evolved designs) have been validated on CEC’14 and CEC’17 functions at 10, 30 and 50 dimensions and on real-world numerical optimization problems in CEC’11 benchmark suite. The DEED evolved designs have also been tested against the state-of-the-art algorithm configurator - irace. The performance of DEED evolved ensemble designs have been observed to be very competitive against that of manually designed and tuned state-of-the-art DE ensemble algorithms in the literature. DEED has also been demonstrated to evolve both co-operative and competitive style DE ensembles. The simulation experiments demonstrate the effectiveness as well as robustness of the evolved ensemble designs and the reliability of DEED framework in consistently evolving effective DE ensemble designs.

Building Greibach Normal Form Grammars Using Genetic Algorithms

Grammatical inference of context-free grammars using positive and negative language examples is among the most challenging task in modern artificial and natural language technology. Recently, several implementations combining various techniques, usually including the Backus–Naur form, have been proposed. In this paper, we explore a new implementation of grammatical inference using evolution methods focused on the Greibach normal form and exploiting its properties, and also propose new solutions both in the evolutionary processes and in the corresponding fitness estimation.

A Language & an Approach for the Development of IoT Solutions

IoT can contribute to the resolution of problems inherent in the control of our environment and to the automation of decisions based on data extracted from that environment. However, the opportunity to contribute to these activities is not offered to everyone. Indeed, according to the majority of decision makers in different fields, IoT solution development is limited to only IoT experts in these fields. According to our opinion this truth is not absolute. In order to demystify IoT and trivialize participation in the IoT solution development effort a language specific to IoT domain has been defined. This language is based on a meta-model of IoT and is made of a textual notation conforming to Backus Naur Form. It allows each participant in a development activity to express and discuss his or her idea of a solution to an IoT problem. An approach build on this language proposes a demarche that starts from the IoT meta-model to arrive at a solution code. Each developer can derive the meta-model to obtain a solution model. Next, she (or he) uses our development environment to concretize this model. Our environment is build using Eclipse/Xtext. It offers technological tools to support our approach. The case study that provides a proof of concept of the approach falls within the domain of smart agriculture.

Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm)

Ant colony systems have been widely employed in optimization issues primarily focused on path finding optimization, such as travelling salesman problem. The main advantage lies in the choice of the edge to be explored, defined using pheromone trails. This paper proposes the use of ant colony systems to explore a Backus–Naur form grammar whose elements are solutions to a given problem. Similar models, without using ant colonies, have been used to solve optimization problems or to automatically generate programs such as grammatical swarm (based on particle swarm optimization) and grammatical evolution (based on genetic algorithms). This work presents the application of proposed ant colony rule derivation algorithm and benchmarks this novel approach in a well-known deceptive problem, the Santa Fe Trail. Proposed algorithm opens the way to a new branch of research in swarm intelligence, which until now has been almost nonexistent, using ant colony algorithms to generate solutions of a given problem described by a BNF grammar with the advantage of genotype/phenotype mapping, described in grammatical evolution. In this case, such mapping is performed based on the pheromone concentration for each production rule. The experimental results demonstrate proposed algorithm outperforms grammatical evolution algorithm in the Santa Fe Trail problem with higher success rates and better solutions in terms of the required steps.

Implementation of Tamias to Check Production Rules for Parsing Expression Grammar

Parsing Expression Grammar (PEG) proposed by Ford has the higher expressive ability than traditional Backus–Naur form, but it also has problems such as prefix capture. “Prefix capture” is a problem of hiding the language to be accepted according to the order of choice. To support checking syntax files including such mistakes, this paper proposes Tamias: a production rules checker to support checking the PEG syntax files. Tamias has PEG interpreter which can check production rules of PEG. It can verify the behavior of production rules and measure the reach rate of choices.

ConfVD: System Reactions Analysis and Evaluation Through Misconfiguration Injection

In recent years, misconfigurations have become one of the major causes of software system failures, resulting in numerous service outages. What is worse, misconfigurations are also costly to diagnose and troubleshoot. This remains a great challenge for sysadmins (system administrators) to detect, diagnose, or troubleshoot these misconfigurations. Unlike software bugs, misconfigurations are more vulnerable to sysadmins’ mistakes. Developers and researchers are attempting to improve system reactions to misconfigurations to ease the burden of sysadmins’ diagnoses. Such efforts would greatly benefit from the techniques that can comprehensively detect bad system reactions through injected misconfigurations. Unfortunately, few such studies have achieved the above goal in the past, primarily because they only relied on generic alterations and failed to find a way to systematically generate misconfigurations. In this paper, we study eight mature open-source and commercial software packages and summarize a fine-grained classification of option types. Based on this classification, we use Augmented Backus–Naur Form to summarize and extract syntactic and semantic constraints of each type. In order to generate comprehensive misconfigurations in the test systems, we propose misconfiguration generation methods for our constraints. We implement a tool named Configuration Vulnerability Detector (ConfVD) to conduct misconfiguration injection and further analyze the systems’ reaction abilities to various misconfigurations. We carried out comprehensive analyses upon Apache Httpd, MySQL, PostgreSQL, and Yum. The results of our analysis show that our option classification covers 96% of 1582 options from the above-mentioned systems. Our constraints are more fine grained than previous works and their accuracy was found to be 91% (ascertained by manual verification). Our technique could improve generic alteration approaches without constraints, and we found that ConfVD could find nearly three times the bad reactions that were found by ConfErr. In total, we found 65 bad reactions from the systems being tested and our fine-grained constraints contributed 27.7% more bad reactions than techniques only using coarse-grained constraints.

A Language for Financial Chart Patterns

In stock markets around the world, financial analysts continuously monitor and screen chart patterns (technical patterns) to predict future price trends. Although a plethora of methods have been proposed for classification of these patterns, there is no uniform standard in defining their shapes. To facilitate the classification and discovery of chart patterns in financial time series, we propose a novel domain-specific language called “Financial Chart Pattern Language” (FCPL). The proposed language is formally described in Extended Backus–Naur Form (EBNF). FCPL allows incremental composition of complex shapes from simple basic units called primitive shapes. Hence, patterns defined in FCPL can be reused for composing new chart patterns. FCPL separates the specification of a chart pattern from the mechanism of its implementation. Due to its simplicity, FCPL can be used by stock market experts and end users to describe the patterns without programming expertise. To highlight its capabilities, several representative financial chart patterns are defined in FCPL for illustration. In the experiments, we classify several representative chart patterns from the datasets of HANG SENG INDEX (HSI), NYSE AMEX COMPOSITE INDEX (NYSE), and Dow Jones Industrial Average (DJI).

An automated safety risk recognition mechanism for underground construction at the pre-construction stage based on BIM

Abstract A highly challenging problem that has been plaguing regulators is the inaccurate collection and sharing of safety risks for underground construction. In order to establish a mechanism that could achieve timely and accurate recognition at the pre-construction stage, Building information model (BIM) is adopted as the risk recognition platform. This paper provides the automated safety risk recognition process based on BIM which is generally composed of three parts. The first part is to build the risk database. By means of knowledge structuralizing, questionnaires, depth interviews and group decision-making, explicit and tacit knowledge source are acquired. The safety risk knowledge source is divided into three categories. SQL database is used to express the safety risk knowledge, and all safety risks are stored in the BIM-cloud. The second part is to analyse the relation between engineering information and safety risks. Risk-related engineering information is extracted from BIM models. Backus–Naur form is used to describe the syntax of languages used in computing. A mapping table of engineering information and safety risks is established. The third part concerns the automated safety risk recognition mechanism in the BIM platform. The safety risk recognition mechanism is expressed as “If e, then h (CF (h, e), λ)”. The confidence level as the link is adopted to reveal the mechanism. Finally, a case about flowing sand risk at the foundation pit bottom is conducted. The reasoning process and recognition results are demonstrated. The paper concludes by summarizing the main scientific contribution and giving direction to future research in this field.

A high-level domain-specific language for SIEM (design, development and formal verification)

Organizations deploy security information and event management (SIEM) systems for centralized management of security events. The real-time security monitoring capability of the SIEM depends on the correlation process where events data are matched against the security rules. Most SIEM systems use general purpose languages to define security rules. Creating new rules in general purpose languages require excellent programming skills in the proprietary language and intimate knowledge of events. This paper introduces a high-level domain-specific language (HDSL) which simplifies rule creation for the SIEM system. We formally specify the HDSL with extended Backus–Naur form grammar in another tool for language recognition according to the model driven engineering approach. In our implementation framework, the rules defined in the HDSL are converted in the standard event processing language. For evaluation purpose, the converted security rules are tested on the service real-time data security analytics. The results indicate that the rules are converted accurately and generate alarms when specific attacks are detected. For checking correctness of the HDSL, formal verification is carried out using satisfiability modulo theory and Z3 solver. The results are evaluated under diverse attack scenarios, which reveal that HDSL is functioning correctly. The HDSL enhances the SIEM correlation capabilities by providing a tranquil approach for writing the correlation rules.

Grammatical Evolution for Neural Network Optimization in the Control System Synthesis Problem

Grammatical evolution is a perspective branch of the genetic programming. It uses evolutionary algorithm based search engine and Backus – Naur form of domain-specific language grammar specifications to find symbolic expressions. This paper describes an application of this method to the control function synthesis problem. Feed-forward neural network was used as an approximation of the control function, that depends on the object state variables. Two-stage algorithm is presented: grammatical evolution optimizes neural network structure and genetic algorithm tunes weights. Computational experiments were performed on the simple kinematic model of a two-wheel driving mobile robot. Training was performed on a set of initial conditions. Results show that the proposed algorithm is able to successfully synthesize a control function.

An Ontology-based Approach for Knowledge Integration in Product Collaborative Development

AbstractKnowledge integrated model (KIM) is regarded as an effective and critical way to enhance efficiency of product collaborative development (PCD). However, it is much knotty to establish a unified description rule as there exists diverse and heterogeneous designer knowledge. In this paper, an approach based on ontology for KIM is proposed to straightforwardly descript heterogeneous knowledge and achieve a matching of knowledge and design mission objectives easily. First, a KIM of PCD is presented in accordance with the characteristics of product design activities. Hereafter, a knowledge transformation method based on “problem domain – functional domain” model is developed to accurately reflect customer expectations for new products, wherein, knowledge ontology tree including “customer creativity, emotional knowledge, customer needs, and basic knowledge” is constructed to standardize customer needs. Then, a knowledge matching function (KMF) and a Backus–Naur form (BNF) are introduced to measure similarity of request and candidate. Thereafter, a service engine (SE) including KMF and BNF is generated by the knowledge integrated system (KIS). Finally, simulations and experiments illustrate the effectiveness of the proposed methods and models.

Metalanguage of Normal Forms of Knowledge

A metalanguage of normal forms of knowledge and its text and graphic descriptions are presented. Numerous linguistic examples including the syntax of a subset of simple English sentences and a metalanguage of Extended Backus---Naur Form are formally described in text and graphic forms. The conclusion is made that the metalanguage presented can be compared favorably with human capabilities of knowledge representation and use.

A uniform approach for access control and business models with explicit rule realization

Access control is an important part of security in software, such as business applications, since it determines the access of users to objects and operations and the constraints of this access. Business and access control models are expressed using different representations. In addition, access control rules are not generally defined explicitly from access control models. Even though the business model and access control model are two separate modeling abstractions, they are inter-connected as access control is part of any business model. Therefore, the first goal is to add access control models to business models using the same fundamental building blocks. The second goal is to use these models and define general access control rules explicitly from these models so that the connection between models and their realizations are also present. This paper describes a new common representation for business models and classes of access control models based on the Resource---Event---Agent (REA) modeling approach to business models. In addition, the connection between models and their represented rules is clearly defined. We present a uniform approach to business and access control models. First, access control primitives are mapped onto REA-based access control patterns. Then, REA-based access control patterns are combined to define access control models. Based on these models, general access control rules are expressed in Extended Backus---Naur Form.

Hybrid knowledge model of process planning and its green extension

The green process planning model was a necessary research field of the green manufacturing, which has drawn increasing attention from many scholars. This study proposes a multi-method [Backus---Naur Form (BNF) frame, binary tree,production rules, and objective-oriented methodology] hybrid frame model of process planning and reasoning mechanism. In this model, the hierarchical BNF frame was applied to modeling the structure of parts, the stages of process decisions and the existing green process indicators set. Then, two "procedure" programs were designed for the information exchange among the above models. This green-process planning model was proposed based on the traditional intelligent process planning model and was intended to introduce an overall (compared with the traditional partial green-process planning model) green-process decision mode. In the last section of this paper, a case study of the green-process planning for a stepped shaft is provided along with a number of essential knowledge models to illustrate the feasibility of this hybrid knowledge model.

Application of advanced Grammatical Evolution to function prediction problem

Grammatical Evolution (GE) is one of the evolutionary algorithms to find functions and programs, which can deal according to a syntax with tree structure by one-dimensional chromosome of Genetic Algorithm. An original GE starts from the definition of the syntax by means of Backus Naur Form (BNF). Chromosome in binary number is translated to that in decimal number. The BNF syntax selects according to the remainder of the decimal number with respect to the total number of candidate rules. In this study, we will introduce three schemes for improving the convergence property of the original GE. In numerical examples, the original GE is compared in function identification problem with the Genetic Programming (GP), which is one of the most popular evolutionary algorithm to find unknown functions or programs. Three algorithms are compared in Santa Fe trail problem and prediction problem of Nikkei stock average, which finds programs to control artificial ants collecting foods. The results show that the efficiency of schemes depends on the problem to be solved and that the schemes 1 and 2 are effective for Santa Fe trail problem and prediction problem of Nikkei stock average, respectively.

클로즈 아키텍처 기반의 비즈니스 프로세스 프레임워크

현대의 기업은 급변하는 비즈니스 환경에 맞는 응용 프로그램을 적시에 개발이 필요하다. 그러나 대부분의 기업들은 비즈니스 프로세스 개념 없이 애플리케이션을 개발하여 업무 변경 시, 필요한 응용프로그램을 적시에 개발하기 어렵다. 이를 해결하기 위해 클로즈 아키텍처 기반의 비즈니스 프로세스 프레임워크 제안한다. 이는 BPM(Business Process Model)과 SOA(Service Oriented Architecture), 그리고 CBD(Component Based Development) 접목을 통해 급변하는 비즈니스 프로세스 변경/개선이 용이하며, 이를 지원하는 응용프로그램을 재사용을 통해 적시에 개발하고 자 한다. 본 논문에서는 비즈니스 프로세스 프레임워크 내에 5-Layer 구조 및 그에 따른 BNF(Backus Naur Form)을 정의하였다. Modern enterprise may be in need of developing the right application programs under rapidly changing business environment in fast time-to-market. But Most of enterprise without business process mechanism are very hard to develop the application needed in time when it will be changing their business. To solve this problem, we propose the business process framework based on the closed architecture. This approach is to develop the right application with reusing software modules in time and easily under rapid business process changing/improving through mapping BPM(Business Process Model), SOA(Service Oriented Architecture), and CBD(Component Based Development). we also define 5-Layers of BPF(Business Process Framework), and BNF(Backus Naur Form) for structuring BPF.

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

Design and implementation of NC code compiler based on ANTLR

A design model of NC code complier based on ANother Tool for Language Recognition(ANTLR)was presented,which adopted the Extended Backus Naur Form(EBNF)to define the NC program syntax rule.The model was implemented with Java language,which can make it independent of operating system.The design and implementation of compiler were described from different aspects:lexical analysis,syntax and semantic analysis,information draws and so on.The practical results show that the model can construct the compiler prototype efficiently,compile the NC code faster and more correctly,and can be expanded easily.