Knowledge-based Simulation Research Articles

94 Phygital Simulation for a Large Group of Learners in a Regional Teaching

Over the years, knowledge-based simulation has been a valuable way of delivering certain content of the syllabus in medical education. There have been challenges to undertake any form of educational activities during the COVID-19 pandemic due to a global shutdown The aims of the study were to deliver an inclusive and effective simulation session to a large cohort of trainees in emergency medicine and to provide an immersive learning environment to learners by enhancing their feeling of being present in the simulation hub.We delivered a phygital simulation with the aid of digital technology in a way that encouraged participation from all attendees. We sought to overcome known challenges of simulation teaching during the peak period of COVID-19 and identified newer challenges unique to the situation and suggestions for future improvements or simulation.During the simulation, we utilized a standardized patient (SP) (who did not require further training for our method) and various equipment/components of the room including cameras for the different views required. The room had a limit of five persons (as per COVID-19 restrictions) so we designed a teaching method that enabled us to cater efficiently for the remaining 33 participants of the scheduled regional teaching session. A volunteer candidate was chosen from the online audience to lead in the simulation. Prior to selection, all candidates were shown the brief which explained the expectation of leading and the different camera views available to aid their performance. They viewed the whole room with separate focuses on the SP, the device required to perform a procedure, and their appointed physical presence (PP) who they will instruct to perform the procedure. We identified advantages, challenges and areas of future development and believe that this type of teaching can be applied to a wide range of simulations that required demonstration.

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.

A computational study of hedgehog signalling involved in basal cell carcinoma reveals the potential and limitation of combination therapy

BackgroundThe smoothened (SMO) receptor is an essential component of the Sonic hedgehog (SHH) signalling, which is associated with the development of skin basal cell carcinoma (BCC). SMO inhibitors are indicated for BCC patients when surgical treatment or radiation therapy are not possible. Unfortunately, SMO inhibitors are not always well tolerated due to severe side effects, and their therapeutical success is limited by resistance mutations.MethodsWe investigated how common are resistance-causing mutations in two genomic databases which are not linked to BCC or other cancers, namely 1000 Genomes and ExAC. To examine the potential for combination therapy or other treatments, we further performed knowledge-based simulations of SHH signalling, in the presence or absence of SMO and PI3K/Akt inhibitors.ResultsThe database analysis revealed that of 18 known mutations associated with Vismodegib-resistance, three were identified in the databases. Treatment of individuals carrying such mutations is thus liable to fail a priori. Analysis of the simulations suggested that a combined inhibition of SMO and the PI3K/Akt signalling pathway may provide an effective reduction in tumour proliferation. However, the inhibition dosage of SMO and PI3K/Akt depended on the activity of phosphodiesterases (PDEs). Under high PDEs activities, SMO became the most important control node of the network. By applying PDEs inhibition, the control potential of SMO decreased and PI3K appeared as a significant factor in controlling tumour proliferation.ConclusionsOur systems biology approach employs knowledge-based computer simulations to help interpret the large amount of data available in public databases, and provides application-oriented solutions for improved cancer resistance treatments.

Discussion of “Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining” by Reza Akhavian and Amir H. Behzadan

Discussion of “Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining” by Reza Akhavian and Amir H. Behzadan

Closure to “Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining” by Reza Akhavian and Amir H. Behzadan

Reza Akhavian, S.M.ASCE; and Amir H. Behzadan, A.M.ASCE Ph.D. Candidate, Dept. of Civil, Environmental, and Construction Engineering, Univ. of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816-2540. E-mail: reza@knights.ucf.edu Assistant Professor, Dept. of Civil, Environmental, and Construction Engineering, Univ. of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816-2540 (corresponding author). E-mail: amir .behzadan@ucf.edu

Evaluation of queuing systems for knowledge-based simulation of construction processes

Abstract During the course of a construction project, there are many situations in which formation of waiting lines or queues is inevitable. The effect of resource delays in queues on the overall project completion time and cost has motivated researchers to employ simulation for analysis of queuing systems in order to identify the best operational strategies to reduce the time wasted in queues. Providing proper and timely input data with high spatial and temporal accuracy for queuing systems simulation enhances the reliability of decisions made based upon the simulation output. Hence, the presented paper describes a methodology for collecting and mining of spatio-temporal data corresponding to the interactions of queue entities to extract computer interpretable knowledge for simulation input modeling. The developed framework was validated using empirical datasets collected from a series of experiments. The extracted relevant knowledge from the queuing system entities was used to update corresponding simulation models.

Erratum for “Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining” by Reza Akhavian and Amir H. Behzadan

Erratum for “Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining” by Reza Akhavian and Amir H. Behzadan

Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining

AbstractIn order to develop a realistic simulation model, it is critical to provide the model with factual input data based on the interactions and events that take place between real entities. However, the existing trend in simulation of construction fleet activities is based on estimating input parameters such as activity durations using expert judgments and assumptions. Not only may such estimations not be precise, but project dynamics can influence model parameters beyond expectation. Therefore, the simulation model may not be a proper and reliable representation of the real engineering system. In order to alleviate these issues and improve the current practice of construction simulation, a thorough approach is needed that enables the integration of field data into simulation modeling and systematic refinement of the resulting models. This paper describes the latest efforts by authors to design and test a novel methodology for multimodal-process data capturing, fusion, and mining that provides a solid...

Engineering Knowledge-Based Variance-Reduction Simulation and G-Dominance for Structural Frame Robust Optimization

This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance-reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncertainties into consideration in the design variables. The simultaneous minimization of the constrained weight (adding structural weight and average distribution of constraint violations) on the one hand and the standard deviation of the distribution of constraint violation on the other are handled with multiobjective optimization-based evolutionary computation in two different multiobjective algorithms. The optimum design values of the deterministic structural problem in question are proposed as a reference point (the aspiration level) in reference-point-based evolutionary multiobjective algorithms (here g-dominance is used). Results including S-metric statistics in a structural frame test case with uncertain loads show considerable reductions in computational costs without harming the nondominated front quality, obtaining a design set that makes it possible to select minimum weight and maximum robustness optimum designs.

Knowledge-based simulation model generation for control law design applied to a quadrotor UAV

Like for all mechatronic systems, the role of control software in unmanned aerial vehicle (UAV) design is becoming more important. As part of an automated control software development framework, this article discusses the development of a simulation model generation method. As a basis, the application of knowledge-based engineering (KBE) is suggested, requiring the definition of an ontology to capture the various domain concepts and relationships. The initial knowledge base represents concepts and relations to create models with Modelica, the object-oriented modelling language used to construct the simulation model. The need for physics-based, high-fidelity simulation models using the latest design parameters is illustrated by investigating the model of a quadrotor UAV. The results show that the obtained model can form the basis for control design and that the approach provides means to integrate the dynamics analysis and control design into a modelling framework using a combination of object-oriented component modelling and KBE principles.

An object-oriented knowledge-based traffic light simulator

An object-oriented knowledge-based traffic light simulator

A Study of Knowledge-Based Simulation for Enterprise Resources Planning

The conventional way of using the Enterprise Resources Planning (ERP) applications is inadequate for the highly dynamic, complex and demanding business environment. The efficient use of the ERP system relies heavily on the know-how, experience and quality of the users. Since the knowledge of operating ERP system is difficult to be acquired, shared and diffused among the users, it takes time and money to train well-experienced users. As a result, this paper presents a framework of Knowledge-based Simulation System for Enterprise Resources Planning named KBSimERP. The KBSimERP provides an integrated learning environment for achieving three perspectives, which includes the knowledge acquisition, knowledge diffusion and performance measurement perspectives respectively. Hence, a prototype of the KBSimERP is built for production planning and control, and the performance of the system is preliminarily evaluated. Satisfactory results are obtained.

Robust optimization based on knowledge discovery from metal forming simulation

Abstract Knowledge-based engineering (KBE) and numerical simulation technology has been used widely in metal forming area. However, the knowledge discovery keeps bottleneck yet in building knowledge base in KBE. To properly understand the results of numerical simulation and obtain robust optimization parameter, a lot of knowledge and experience is also needed. The paper presented a new robust optimization method based on knowledge discovery from numerical simulation. Firstly, a framework of robust optimization based on knowledge discovery from simulation is proposed. Secondly, a data-mining algorithm is developed to deal with the data of numerical simulation. Finally, based on the result of knowledge discovery, robust optimization model is established and solved to obtain the robust optimization parameter.

Knowledge-based simulation of bunkering services in the port of Kaohsiung

Bunkering services are complex and require experienced engineers to make bunker barge allocation decisions. We have developed a knowledge-based simulation model to evaluate overall system performance. This knowledge-based simulation integrates artificial intelligence and system simulation. The rationale behind using such a technique is that it is not feasible to use real humans in a simulation due to the availability of human resources and lack of flexibility in scenario analysis. Knowledge-based systems can compensate for human decision-making processes that are absent in simulation models. Thus, the human role can be replaced by an expert system, permitting the synergistic effects of the two modeling approaches to accurately represent the entire system being modeled. The model is capable of quantifying the service quality under various bunker barge allocation scenarios and fleet expansion plans. The more efficient bunkering service plans were acquired and presented using the case study in the Port of Kaohsiung.

Knowledge-based simulation and analysis of supply chain performance

Although various supply chain strategies such as vendor managed inventory (VMI) are used for keeping low inventory and reducing cost in supply chain management, most enterprises lack an integrated simulation system to mimic the behaviour and performance of supply chain partners so as to select the appropriate strategy for implementation. This paper presents a knowledge-based simulation approach for simulating the effect of downstream and upstream operations in supply chain. Based on the simulation result, the performance of supply chain partners can be analysed, evaluated and predicted. With the use of artificial intelligent (AI) techniques, the knowledge-based simulation platform (KBSP) can accumulate the successful experience of the enterprises in formulating and implementing supply chain strategies. The potential benefits of KBSP include the provision of guidance, solution and simulation environment for enterprises to reduce risks for their supply chain operations.

A knowledge-based collaborative simulation system for supply chain integration

The importance of adopting and implementing a relevant and appropriate supply chain strategy has often been undermined to bring effective changes into an organisation. The customisation of appropriate supply chain strategy is often not easy and business workflow procedures may not map into current practices of a company. This paper presents a Knowledge-Based Collaborative Simulation System (KBCSS) for supply chain integration. It is proposed for supply chain parties to select a suitable strategy and make a right decision at the right time to maximise profits and minimise risks. Through this system, successful experience of supply chain parties can be assimilated for formulating and implementing supply chain strategies so as to increase total profits of a whole supply chain. The potential benefit is to provide guidance and solutions for the organisations to reduce risks and improve collaboration with their business partners. A prototype system has been built and its performance is preliminarily evaluated through a trial implementation at a selected reference site.

Tackling uncertainty in ERP-controlled manufacturing environment: A knowledge management approach

This paper proposes a knowledge management approach to tackle uncertainty in ERP-controlled manufacturing environment. The development of a knowledge-based ERP planning model and a knowledge-based ERP-controlled manufacturing simulation model within which knowledge of tackling underlying causes of uncertainty can be stored for future reference are discussed. A combination of explicit and tacit knowledge are created and applied to update the planned parameters and to decide for the appropriate buffering or dampening techniques to tackle the underlying causes of uncertainty. Results from interviews and simulation experiments of a real case company showed significant reduction in tardy delivery after the use of the knowledge management approach. Some underlying causes of uncertainty are found to be more likely to result in tardy delivery than the others, and some are found to be more receptive to the effects of the knowledge management approach, but it is shown that interactions are generally difficult to manage due to their unstable effects.

MODELING OF MATERIAL HANDLING HOIST OPERATIONS IN A PCB MANUFACTURING FACILITY

This paper outlines a framework for flexible hoist scheduling that utilizes a knowledge-based simulation system to emulate multi-hoist operations in an electroplating line and derives hoist schedule to meet production demands. The selected hoist schedule is then used by the knowledge-based controller to control the material handling hoist operations in real-time. The paper describes the overall design of the knowledge-based simulator for material handling hoist operations that forms the fundamental part of the proposed framework. Most hoist schedules in industry are experimented on-line, which are costly and time consuming. The use of knowledge-based simulation will save both cost and time. The simulator aims at improving current manual approaches so that better schedules can be identified. We demonstrate the application of the simulator to bi-directional multi-hoist line configuration that is commonly found in industry today, where loading and unloading stations are on the same end of the line, and consider other more realistic system parameters than those considered in previous studies. Three new hoist scheduling heuristics are proposed and their performances are compared with four previously studied rules. To this end, we also study the effect of several system variables such as hoist speed and inter-hoist distance on productivity. The simulation models developed are based on data from actual multi-hoist electroplating lines within several PCB manufacturing facilities.

A working prototype of a dynamic geographical information system

The objective of this work is to develop a dynamic geographical information system (DGIS) applicable for the simulation of spatio-temporal phenomena. DGIS is designed and implemented as a dynamic raster GIS in a fully integrated systems strategy using theory of modelling and simulation, discrete event systems specifications, and knowledge-based simulation methodologies. The approach accommodates the representations and operators of current GIS in a broader systems representation scheme that includes the constructs for handling continuous time, model base, multiple resolution, hierarchy, taxonomy, variable structure, and intelligent agents. DGIS is defined as an endomorphic system that can be transformed by the user into different, application-specific, spatial dynamic simulation environments depending on the models selected to fit the various aspects of its structure. In this paper we describe the theory, methods, and technologies upon which the system is designed and implemented and discuss how it can support complex spatial simulations. To probe operational applicability, an instance of DGIS is generated: a fire spread simulation environment, which is tested in the simulation of 15 real fire events.

Fuzzy rule learning during simulation of manufacturing resources

Knowledge-based simulation is an effective way to deal with a wide variety of uncertainty and complexity issues. In this context, knowledge acquisition plays a central role and addresses what has been a bottleneck in most simulation models. In this paper, a new knowledge auto-acquisition technique for simulation of manufacturing resources using genetic algorithm (GA) to learn fuzzy knowledge is proposed. Given the fuzzy variables and the number of fuzzy sets, the fuzzy rules will be automatically deduced via learning. In the proposed approach, all potential fuzzy relations among fuzzy variables are enumerated into a form of matrix function, manipulated by means of GA operating on the simulation model. This novel matrix equation can be de-numerated into symbolic rules, put into dynamic simulation resources, and simulated interactively. The paper concludes with an application in a final assembly environment of a hard disk manufacturing company.