• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Korean Journal of Computational Design and Engineering
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• v.13 no.1
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• pp.27-35
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• 2008

Digital Virtual Manufacturing is a technology facilitating effective product developments and agile productions via digital models representing the physical and logical schema and the behavior of real manufacturing systems. A digital virtual factory as a well-designed and integrated environment is essential for successful applications of this technology. In this research, we construct a sophisticated digital virtual factory for the section steel shop in a Korean shipbuilding company by 3-D CAD and virtual manufacturing simulation. The NIST-AMRF CIM hierarchical model and workflow analysis using IDEF methodology are also applied. This digital virtual factory can be applied for diverse engineering activities in design, manufacturing and control of the real factory, and improvements in quality of engineering and savings in time from design to production in shipbuilding are possible.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.172-175
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• 1998

Currently, many companies are trying to be more competitive by implementing the most appropriate software package, called ERP (Enterprise Resource Planning). ERP can support the company's business process. A repository, which can story and retrieve enterprise-wide information, is a key component of such software package. This paper implements an enterprise repository for the ERP. A wellknown repository standard, IRDS (Information Resource Dictionary System), is employed for the repository architecture. A meta schema that can help develop database, business applications, models, and workflow is proposed. To illustrate the practical usefulness, a real-life ERP prototype is built within the framework of the proposed repository.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.460-469
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• 2008

Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.750-761
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• 2006

As CBD technologies and researches have been matured, component framework as a larger reuse unit than component is being introduced. Especially issues related with adaptation and integration of components in CBD are being raised as a new research topic. The component framework is given as a solution to resolve these issues. However, current approaches don't suggest a sound and comprehensive reference model and development process applying reference model. In order to develop practical and stable component framework, reference model and concrete guidelines are essential elements. In this paper, we propose a generic reference model integrating existing reference models and a design technique of component framework based on it. Especially, we propose concrete and pragmatic guidelines such as how to design component framework architecture's view and style, how to design commonality and variability of component framework, how to design macro workflows among components, and so on. We believe that the proposed reference model becomes basis for component framework development, and the proposed design technique will support reliable and effective development of the component framework.

• Asia pacific journal of information systems
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• v.9 no.1
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• pp.59-75
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• 1999

Currently, many companies are trying to be more competitive by implementing the most appropriate software package, called ERP (Enterprise Resource Planning). ERP can support the company's business process. A repository, which can store and retrieve enterprise-wide information, is a key component of such software package. This paper implements an enterprise repository for the ERP. A well-known repository standard, IRDS (Information Resource Dictionary System), is employed for the repository architecture. The proposed metadata schema can help develop database, business applications, models, and workflow. To illustrate the practical usefulness, a real-life ERP prototype is built within the framework of the proposed repository.

• Journal of KIBIM
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• v.9 no.1
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• pp.1-10
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• 2019

In process of creating BIM model for tunnel, many user use still CAD, so the workflow for BIM design is not perfect. Therefore, in this paper, we proposes a method to automatically create BIM model without converting 2D drawings in tunnel design. It can allow engineers to design BIM-based tunnel with maximum use of linear information and modify the BIM model whenever there is changed linear information. To do this, we use Dynamo, which can reduce the time required for creating and modifying BIM models during design changes, saving time wasted for BIM conversion design.

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.87-106
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• 2002

The behavioral and dynamic implications of an ERP implementation/installation are, to say the least, not well understood. Getting the switches set to enable the ERP software to go live is becoming straightforward. The really difficult part is understanding all of the dynamic interactions that accrue as a consequence. Dynamic causal and connectionist models are employed to facilitate an understanding of the dynamics and to enable control of the information-enhanced processes to take place. The connectionist model ran be analyzing (behind the scenes) the information accesses and transfers and coming If some conclusions about strong linkages that are getting established and what the behavioral implications of those new linkages and information accesses we. Ultimately, the connectionist model will come to an understanding of the dynamic, behavioral implications of the larger ERP implementation/installation per se. The underlying connectionist model will determine information transfers and workflow. Once a map of these two infrastructures is determined by the model, it becomes a relatively easy job for an analyst to suggest improvements in both. Connectionist models start with analog object structures and then use learning to produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Based on earlier work performed and published by the author[10][11], a Connectionist ReasOning and LEarning System(CROLES) is developed that mimics the real-world reasoning infrastructure. Coupled with an explanation subsystem, this system can provide explanations as to why a particular reasoning structure behaved the way it did. Such a system operates in the backgmund, observing what is happening as every information access, every information response coming from each and every intelligent node (whether natural or artificial) operating within the ERP infrastructure is recorded and encoded. The CROLES is also able to transfer all workflows and map these onto the decision-making nodes of the organization.

• Genomics & Informatics
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• v.11 no.1
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• pp.46-51
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• 2013

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.49-55
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• 2019

Organizations design and operate business process models to achieve their goals efficiently and systematically. With the advancement of IT technology, the number of items that computer systems can participate in and the process becomes huge and complicated. This phenomenon created a more complex and subdivide flow of business process.The process instances that contain workcase and events are larger and have more data. This is an essential resource for process mining and is used directly in model discovery, analysis, and improvement of processes. This event log is getting bigger and broader, which leads to problems such as capacity management and I / O load in management of existing row level program or management through a relational database. In this paper, as the event log becomes big data, we have found the problem of management limit based on the existing original file or relational database. Design and apply schemes to archive and analyze large event logs through Hadoop, an open source distributed file system, and HBase, a NoSQL database system.