Modularization Techniques Research Articles

A digital twin-based modularized design approach for smart warehouses

ABSTRACT The rapid emergence and widespread adoption of next-generation information technologies have led to a growing recognition of the need for digitalized warehouse designs. However, creating a digital replica of a physical warehouse in a virtual environment is a complex task. This research introduces a framework for designing smart warehouses based on digital twins, consisting of four key steps: (1) defining the dimensions of the digital twin, (2) establishing a digital twin framework that encompasses the physical warehouse, digital twin, and design processes, (3) implementing modularization techniques for the digital twin, and (4) operating the smart warehouse based on the digital twin. To validate the proposed framework, we present a detailed case study involving a semiconductor manufacturing plant. The results demonstrate the effectiveness of the digital twin-based smart warehouse design and its operational processes.

CHALLENGES OF PRODUCT MODULARIZATION METHODS IN SMES: LESSONS LEARNED FROM A MANUFACTURER OF RIGID INFLATABLE BOATS

AbstractNowadays, Western rigid inflatable boat (RIB) manufacturers are facing increased competition from Asian and Middle East producers and the need to push the modularization of their product architecture. Many modularization techniques have been developed to support this effort, being the Modular Function Deployment a well-established multi-stage modularization technique with applications in several industries. Despite the reported literature, the challenges and learnings from the application of modularization techniques have focused on large organizations and complex product systems. In this paper, a case study is presented with the objective of analysing the MFD process implementation in the context of an SME manufacturer of RIB. A learning framework focused on the project and process perspectives is used to facilitate the systematic extraction of lessons from the experience of the MFD implementation. This paper contributes to the modularization techniques literature by observing a modularization method as a learning process and makes it evident the call for more investigation on the implementation process of modularization methods.

A sustainable model based on genetic algorithm for garment redesign process

PurposeThis study proposes a garment modularization model based on an interactive genetic algorithm. The suggested model consists of extraction and identification of parts and the determination and implementation of connections. Rules and corresponding mathematical equations have been formulated for the part's extractions from the discarded products and connections for the redesigned products.Design/methodology/approachSustainability entices scholars and practitioners while referring to reducing waste to control environmental degradation. One of the ways to safeguard natural resources is to increase the reuse of old or discarded products. The current study focuses on the redesign process to improve the reuse of products.FindingsThe intelligent system proposed based on the modularization techniques is expected to simplify and quantify the redesign process. The model can further help in the minimization of wastage and environmental degradation.Originality/valuePresently, manual decisions are taken by the designers based on their memory, experience and intuition to extract and join the parts.

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

An Enhanced Multifactor Multiobjective Approach for Software Modularization

Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective

In the past two decades, the use of ontologies has grown accompanied by a diversity in ontological representations and applications to more comprehensive domains. Knowledge engineers have found it expeditious to break down large (monolithic) ontologies to work with smaller fragments. Ontology modularization is the process of extracting a fragment, or "module", from an ontology, based on predefined requirements. Due to both the diversity in ontological representations and motivations for modularizing, the body of research on ontology modularization techniques has become extremely large and may be intimidating to the novice ontology researcher. The objective of the paper is to present a comprehensive, albeit high-level, review of ontology modularization techniques. A systematic literature review covering January 1st 2000 to July 31st 2020 was performed to find and classify papers on ontology modularization techniques. The techniques exhibiting certain properties with respect to several features were assessed, and the modularization techniques were classified with a multi-dimensional perspective. The classifications are intended to guide one to a suitable modularization process in accordance with the requirements. The limitations of ontology modularization techniques are highlighted in the conclusion, and characteristics of a desirable framework for an ontology representation that would be best-suited for modularization are presented.

Using termination points and 3D visualization for dimensional control in prefabrication

To increase productivity, quality, and safety, heavy industrial construction projects are increasingly adopting prefabrication and modularization techniques. This shift, in turn, has resulted in fabrication shops fabricating more complex assemblies with tighter tolerances. However, most measurement in these shops is conducted using manual hand measurement tools, which can be costly and is known to cause significant rework due to geometric noncompliance of termination points in particular. Termination points are defined as the coordinate system points where assemblies connect or are constrained. Automated, 3D-scanning measurement and visualization systems can potentially be accurate, repeatable, and objective sources of termination point data. In this paper, a new framework for classification and calculation of termination points is presented that is based on automated, 3D-scanning measurement and visualization. The utilization of the framework enables fabrication shops and project owners to adopt effective 3D measurement solutions. To investigate the usefulness of the defined framework, a termination-point-based scan-vs-BIM method is developed for objects with circular cross sections, such as pipe spool assemblies. The method was validated in an industrial-scale experimental study. The study demonstrated that the new framework can be used to develop applications that are more accurate and provide superior visualization to craft workers during fabrication, and thus potentially improve productivity and reduce rework.

A graph-based clustering algorithm for software systems modularization

Clustering algorithms , as a modularization technique, are used to modularize a program aiming to understand large software systems as well as software refactoring. These algorithms partition the source code of the software system into smaller and easy-to-manage modules (clusters). The resulting decomposition is called the software system structure (or software architecture). Due to the NP-hardness of the modularization problem, evolutionary clustering approaches such as the genetic algorithm have been used to solve this problem. These methods do not make much use of the information and knowledge available in the artifact dependency graph which is extracted from the source code. To overcome the limitations of the existing modularization techniques, this paper presents a new modularization technique named GMA (Graph-based Modularization Algorithm). In this paper, a new graph-based clustering algorithm is presented for software modularization. To this end, the depth of relationships is used to compute the similarity between artifacts, as well as seven new criteria are proposed to evaluate the quality of a modularization. The similarity presented in this paper enables the algorithm to use graph-theoretic information. To demonstrate the applicability of the proposed algorithm, ten folders of Mozilla Firefox with different domains and functions, along with four other applications, are selected. The experimental results demonstrate that the proposed algorithm produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software designer in the software reverse engineering process to extract easy-to-manage and understandable modules from source code.

Special Issue “Progress in Thermal Process Engineering”

The Special Issue “Progress in Thermal Process Engineering” contains a total of eight articles, seven research papers and a review article. The topics of the individual articles reflect the variety of current research in the field of thermal process engineering. The contributions address important issues such as modularization, digitization, new equipment and simulation techniques. It becomes clear that efficiency efforts are an essential feature of current research in the mentioned field. Efficiency in the sense of energy efficiency as well as in the sense of more efficient, i.e., more flexible, production. The authors of the articles originate from the USA, Russia, Switzerland and Germany.

Current Research Status of Skutterudite-based Thermoelectric Materials and Modularization Techniques for Mid-high Temperature Applications

Current Research Status of Skutterudite-based Thermoelectric Materials and Modularization Techniques for Mid-high Temperature Applications

Scaling-up domain-specific modelling languages through modularity services

Abstract Context Model-driven engineering (MDE) promotes the active use of models in all phases of software development. Even though models are at a high level of abstraction, large or complex systems still require building monolithic models that prove to be too big for their processing by existing tools, and too difficult to comprehend by users. While modularization techniques are well-known in programming languages, they are not the norm in MDE. Objective Our goal is to ease the modularization of models to allow their efficient processing by tools and facilitate their management by users. Method We propose five patterns that can be used to extend a modelling language with services related to modularization and scalability. Specifically, the patterns allow defining model fragmentation strategies, scoping and visibility rules, model indexing services, and scoped constraints. Once the patterns have been applied to the meta-model of a modelling language, we synthesize a customized modelling environment enriched with the defined services, which become applicable to both existing monolithic legacy models and new models. Results Our proposal is supported by a tool called EMF-Splitter, combined with the Hawk model indexer. Our experiments show that this tool improves the validation performance of large models. Moreover, the analysis of 224 meta-models from OMG standards, and a public repository with more than 300 meta-models, demonstrates the applicability of our patterns in practice. Conclusions Modularity mechanisms typically employed in programming IDEs can be successfully transferred to MDE, leading to more scalable and structured domain-specific modelling languages and environments.

A review of modularization techniques in artificial neural networks

Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed. Modular neural networks (MNNs) are neural networks that embody the concepts and principles of modularity. MNNs adopt a large number of different techniques for achieving modularization. Previous surveys of modularization techniques are relatively scarce in their systematic analysis of MNNs, focusing mostly on empirical comparisons and lacking an extensive taxonomical framework. In this review, we aim to establish a solid taxonomy that captures the essential properties and relationships of the different variants of MNNs. Based on an investigation of the different levels at which modularization techniques act, we attempt to provide a universal and systematic framework for theorists studying MNNs, also trying along the way to emphasise the strengths and weaknesses of different modularization approaches in order to highlight good practices for neural network practitioners.

Multi-objective search-based software modularization: structural and non-structural features

Software modularization techniques are employed to understand a software system. The purpose of modularization is to decompose a software system from a source code into meaningful and understandable subsystems (modules). Since modularization of a software system is an NP-hard problem, the modularization quality obtained using evolutionary algorithms is more reasonable than greedy algorithms. All evolutionary algorithms presented for software modularization only consider structural features that are dependent on the syntax of programming languages. For most programming languages, does not exist a tool to extract structural features, so it is not possible to modularize them. To overcome this problem, this paper presents a new multi-objective fitness function, named MOF, which exploits the structural (such as calling dependency and inheritance dependency) and non-structural features (such as semantic contained in the code comments and identifier names), aiming to automatically guide optimization algorithms to find a good decomposition of software systems. To evaluate the performance of this objective function, three optimization strategies, namely global-based search, combining global and local search, and Estimation of Distribution (EoD), are adapted to optimize it. The results on Mozilla Firefox indicate that the proposed algorithm which is based on EoD along with the new MOF function outperforms the algorithms that use structural-based objective functions in guiding the optimization process, in finding more understandable modules.

Penerapan Composite Logic dalam Mengkolaborasikan Framework Terkait Multimedia Forensik

Like the digital forensics in general, multimedia forensics requires an integrated and flexible framework that can guide the process of proof is procedurally in order to maintain the validity of a digital evidence so that it can be justified in court. But this time, the digital forensics investigation framework that develops more emphasis on computer forensic investigations in general and did not give a specific stage of multimedia forensics. Often in investigating multimedia forensics investigator uses a framework that vary depending on the type of multimedia content to be analyzed, of course it becomes inflexible and inefficient, whereas multimedia content share characteristics that allow it to be integrated into a single unit. This study develops a forensic multimedia framework by implementing the Composite Logic to collaborate on several multimedia framework and related documents. Logic Composite method is a method of modeling the distribution of structured logical modularization techniques with explicit interface in which a model consists of a set of components that are interconnected with the principle of decomposition. In other words, the application of this method will allow researchers to extract and merge several frameworks into a single unit does not eliminate the function and the basic structure of the frameworks.

Using internal domain-specific languages to inherit tool support and modularity for model transformations

Model-driven engineering (MDE) has proved to be a useful approach to cope with today’s ever-growing complexity in the development of software systems; nevertheless, it is not widely applied in industry. As suggested by multiple studies, tool support is a major factor for this lack of adoption. In particular, the development of model transformations lacks good tool support. Additionally, modularization techniques are inevitable for the development of larger model transformations to keep them maintainable. Existing tools for MDE, in particular model transformation approaches, are often developed by small teams and cannot keep up with advanced tool support for mainstream general-purpose programming languages, such as IntelliJ or Visual Studio. Internal DSLs are a promising solution to these problems. In this paper, we investigate the impact of design decisions of an internal DSL to the reuse of tool support and modularization concepts from the host language. We validate our findings in terms of understandability, applicability, tool support, and extensibility using three case studies from academia, a model-driven engineering platform, and the industrial automation domain where we apply an implementation of an internal model transformation language on the .NET platform. The results confirm the value of inherited modularity and tool support while conciseness and understandability are still competitive.

Interactive Refactoring of Web Service Interfaces Using Computational Search

Successful Web services evolve through a process of continuous change due to several reasons such as improving the quality, fixing bugs and adding new features. However, this evolution process may weaken the design of the Web service's interface by aggregating many non-cohesive and semantically unrelated operations. Thus, the service interface becomes unnecessarily complex for users to find relevant operations to be used by their services-based systems. In this paper, we propose an interactive recommendation approach, based on evolutionary algorithms, that dynamically adapts and suggests a possible remodularization of the Web services interface design to users/developers and takes their feedback into consideration. Our approach uses an interactive multi-criteria decision-making algorithm, based on interactive Non-dominated Sorting Genetic Algorithm (NSGA-II), to find a set of good design interface modularization solutions. These solutions provide a trade-off between improving several interface design quality metrics (e.g., coupling, cohesion, number of port types, and number of antipatterns) and fix Web services design antipatterns, maximizing the satisfaction of the interaction constraints learnt from the user feedback during the execution of the algorithm while minimizing the deviation from the initial design. We evaluated our approach on a set of 22 real world Web services, provided by Amazon and Yahoo. Statistical analysis of our experiments shows that our dynamic interactive Web services interface modularization approach performed significantly better than the state-of-the-art modularization techniques in terms of generating well-designed Web services interface for users.

Knowledge-Based Network Management System for Movable and Deployable ICT Resource Unit

When a disaster occurs, the demand for information and communication technology (ICT) services drastically increases. To meet such demands, a national project was undertaken in Japan to develop the Movable and Deployable ICT Resource Unit (MDRU). One challenge regarding the MDRU is securing operators to work the units in emergency situations. As ICT service users have diverse and frequently changing demands, strong technical skills and practical knowledge are required for the administration of MDRUs. In this paper, we propose a knowledge-based network management system to alleviate the burden on administrators. To deal with the structural changes to network systems that frequently occur with changes in ICT service demand, we introduce modularization techniques into our previous research. The proposed system can be easily reconfigured by join/disjoin modules corresponding to changes in the system configuration of the MDRU. The results of our experiments using the implemented experimental system confirm that the proposed system can be applied to MDRU operation and effectively supports administrators.

Can SNOMED CT be squeezed without losing its shape?

BackgroundIn biomedical applications where the size and complexity of SNOMED CT become problematic, using a smaller subset that can act as a reasonable substitute is usually preferred. In a special class of use cases—like ontology-based quality assurance, or when performing scaling experiments for real-time performance—it is essential that modules show a similar shape than SNOMED CT in terms of concept distribution per sub-hierarchy. Exactly how to extract such balanced modules remains unclear, as most previous work on ontology modularization has focused on other problems. In this study, we investigate to what extent extracting balanced modules that preserve the original shape of SNOMED CT is possible, by presenting and evaluating an iterative algorithm.MethodsWe used a graph-traversal modularization approach based on an input signature. To conform to our definition of a balanced module, we implemented an iterative algorithm that carefully bootstraped and dynamically adjusted the signature at each step. We measured the error for each sub-hierarchy and defined convergence as a residual sum of squares <1.ResultsUsing 2000 concepts as an initial signature, our algorithm converged after seven iterations and extracted a module 4.7 % the size of SNOMED CT. Seven sub-hierarhies were either over or under-represented within a range of 1–8 %.ConclusionsOur study shows that balanced modules from large terminologies can be extracted using ontology graph-traversal modularization techniques under certain conditions: that the process is repeated a number of times, the input signature is dynamically adjusted in each iteration, and a moderate under/over-representation of some hierarchies is tolerated. In the case of SNOMED CT, our results conclusively show that it can be squeezed to less than 5 % of its size without any sub-hierarchy losing its shape more than 8 %, which is likely sufficient in most use cases.

Decomposing Ontologies for the Construction of Distributed Knowledge Bases: The mONTul Method

Ontology modularization methods aim either to extract modules for a given sets of terms, or partition ontologies into sets of modules. Each module should cover a specific subject matter of the theory, while the modularization task must preserve additional properties depending on its objectives: It may aim at reusability of knowledge, reduction of complexity, efficient reasoning, and tooling support, e.g. for efficient ontology maintenance and evolution. This article proposes the mONTul method for the modularization of SHIQ ontologies, towards the construction of distributed E–SHIQ knowledge bases. The aim is to compute decompositions for efficient distributed reasoning. The proposed framework combines locality-based rules with graph-based modularization techniques using a generic constraint problem solving framework. The article presents experimental results concerning the modularization task.

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q.