• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.488-497
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• 2004

This paper describes the hybrid document summarization using the indicative summarization and the query-based summarization. The learning models are built from teaming documents in order to extract topic phrases. We use Naive Bayesian, Decision Tree and Supported Vector Machine as the machine learning algorithm. The system extracts topic phrases automatically from new document based on these models and outputs the summary of the document using query-based summarization which considers the extracted topic phrases as queries and calculates the locality-based similarity of each topic phrase. We examine how the topic phrases affect the summarization and how many phrases are proper to summarization. Then, we evaluate the extracted summary by comparing with manual summary, and we also compare our summarization system with summarization mettled from MS-Word.

• International Commerce and Information Review
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• v.13 no.4
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• pp.81-100
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• 2011

With the smart device deployment, the interest in NFC technology is increasing. In this study, to be successful in NFC based business commercialization, we present main factors affecting success of NFC based e-commerce business model. To this end, we conduct NFC and business models, case study methodology through literature review. And then, we suggest representative NFC e-commerce business model cases, and practices that affect the success or failure of the six factors are derived Derived factors are based on inductive learning to apply the technology to create a case study table, and decision trees to bring it, NFC-based commerce business models need to be successful at the strategic implications are present.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.419-424
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• 2001

This paper deals with the development of a new method that can obtain heat transfer coefficient and reference tree stream temperature simultaneously. The method is based on transient heat transfer experiments using two narrow-band TLCs. The method is validated through error analysis in terms of the random uncertainties in the measured temperatures. It is shown how the uncertainties in heat transfer coefficient and tree stream temperature can be reduced. The general method described in this paper is applicable to many heat transfer models with unknown free stream temperature.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.89-96
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• 2016

In this paper, we study the relationship between vacancy duration and information items of a job posting by using generalized linear models and a decision tree analysis w.r.t. the three factors such as company characteristics, employment conditions, and constraints. The results indicate that the employment conditions rather than company characteristics are more influential to the vacancy duration. These effects are presumed to be based on the complex relations between the decisions of the employers and the job seekers. And in this paper we suggest the need to provide personalized and profiled labor market information tailored for a quick decision to job seekers and employers. Policy implication is that since employer's decision affects the vacation duration, employers may had better to provide a comprehensive labour market information including supply and demand of the required skills in order to reduce the time for judgment on the cost-effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.37-43
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• 2007

We use influence diagrams to describe event trees used in safety analyses of low-probability high-risk incidents. This paper shows how the branch parameters used in the event tree models can be updated by a bayesian method based on the observed counts of certain well-defined subsets of accident sequences. We focus on the analysis of the shared branch parameters, which may frequently often in the real accident initiation and propagation to more severe accident. We also suggest the way to utilize different levels of accident data to forecast low-probability high-risk accidents.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.241-247
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• 2001

Network reconfiguration in distribution systems is realized by changing the status of sectionalizing switches, and is usually done for loss reduction of load balancing in the system. This paper presents an effective heuristic based switching scheme to solve the distribution feeder loss reduction problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to apply a proposed technique called cyclic best first search. the proposed algorithm identify the most effective the set of switch status configuration of distribution system for loss reduction. To demonstrate the validity of the proposed algorithm, numerical calculations are carried out the 32, 69 bus system models.

• Journal of the Society of Disaster Information
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• v.9 no.1
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• pp.30-49
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• 2013

In this research we developed a new traffic accident forecasting model on the basis of land use. A new traffic accident forecasting model by type was developed based on market segmentation and further introduction of variables that may reflect characteristics of various regions using Classification and Regression Tree Method. From the results of analysis, activities variables such as the registered population, commuters as well as road size, traffic accidents causing facilities being the subjects of activities were derived as variables explaining traffic accidents.

• Annual Conference of KIPS
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• 2019.10a
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• pp.473-474
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• 2019

Recently, data security and convenience are critical requirements to the interaction and collaboration of tourism service systems for the tourism industry. However, there are still many challenges for current tourism service systems to fulfill these requirements since they have inconsistent structures with different access control models and security policies. Blockchain has the potential to evolve the conventional tourism industry benefiting by its unique features such as data privacy and transparency. This paper mainly aims the interworking mechanism of heterogenous blockchain platforms for secure tourism service. We propose interworking scheme to connect multi-blockchain platforms for enhancing data integrity in the tourism industry. A proof of concept design and implement based on Hyperledger Fabric and Winding Tree.

• Smart Media Journal
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• v.13 no.2
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.