• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.383-388
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• 2008

The safe and efficient operation of the chemical processes has become one of the primary concerns of chemical companies, and a variety of fault diagnosis methods have been developed to diagnose faults when abnormal situations arise. Recently, many research efforts have focused on fault diagnosis methods based on quantitative history data-based methods such as statistical models. However, when the history data-based models trained with the data obtained on an operation mode are applied to another operating condition, the models can make continuous wrong diagnosis, and have limits to be applied to real chemical processes with various operation modes. In order to classify operation modes of chemical processes, this study considers three multivariate models of Euclidean distance, FDA (Fisher's Discriminant Analysis), and PCA (principal component analysis), and integrates them with process dynamics to lead dynamic Euclidean distance, dynamic FDA, and dynamic PCA. A case study of the TE (Tennessee Eastman) process having six operation modes illustrates the conclusion that dynamic PCA model shows the best classification performance.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.579-587
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• 2008

Promotion of the line no.2 in Seoul Metro was changing from the existing signaling facilities for ATS(Automatic Train Stop) vehicles to the up-to-date signaling facilities for ATO(Automatic Train Operation). But, in consequence of conducting a trial run after being equipped with the ATO signaling facilities, the matter related to mix-operation with the existing ATS signaling facilities appeared. The operation of the existing ATS signaling system in combination with the ATO signaling system has made improper working related to frequency recognition of the ATS On-board Computerized Equipment. This obstructs operation of a working ATS vehicle. That is, as barring operation of an ATS vehicle that should proceed, it may make the proceeding ATS vehicle stop suddenly and after all, it will cause safety concerns. In this paper, we designed a wayside track occupancy detector that previously prevents improper working related to frequency recognition of the ATS On-board Computerized Equipment by gripping classification and working processes of operating trains throughout transmission of local signaling information from the existing facilities, which does not need to change or replace the existing signaling facilities. Furthermore, we described general characteristics of the wayside track occupancy detector and modeled the IFC(InterFace Contrivance) device and the logical circuit recognizing signal information. Then, we made an application program of PLC(programmable Logic Computer) based on the stated model. We, in relation to data transfer method, used the frame in TCP/IP transfer mode as the standard, and we demonstrated that ATO transmission frequency is intercepted.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.10-17
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• 2016

The purpose of this paper is to deduct components that are in the group of highest risk(top 10%). the group is conducted for classification into groups by values according to risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet. Top 10% of failure mode among total potential failure modes(72 failure modes) of ESS included 5 BMS(battery included) failure modes, 1 invert failure mode, and 1 cable connectors failure mode in which BMS was highest. This is because ESS is connected to module, try, and lack in the battery part as an assembly of electronic information communication and is managed. BMS is mainly composed of the battery module and communication module. There is a junction box and numerous connectors that connect these two in which failure occurs most in the connector part and module itself. Finally, this paper proposes RPN by each step from the starting step of ESS design to installation and operation. Blackouts and electrical disasters can be prevented beforehand by managing and removing the deducted risk factors in prior.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.93-110
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• 2017

This study aims at classifying and analyzing the business models of reverse overseas direct purchasing through online shopping-malls. After analyzing the current status of the reverse overseas direct purchasing online markets, this study identifies relevant critical business factors and synthesizes prior studies to construct and analyse the business models. This study proposes that the business models can be classified into five types according to the operation modes and three types according to the delivery methods. So theoretically 15 business models can be identified. For each business model this study analyzes the characteristics and the pros & cons. It also suggests deployment strategies for companies by considering cost reduction, brand establishment, customer management, customer aquisition, and easiness from the business perspective. From the customers perspective, cost reduction, reliability, royalty, ease to purchase, and accessibility can be considered according to the types of operation mode. The main contribution of this study is to provide the basic classifications and structures of reverse overseas direct purchasing business models systematically. As the result, our study evaluates the business models that which one is better than others in a situation in terms of company and customer. Lastly, we talk about limits and future prospects of the study.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.33-45
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• 2022

This paper relates to the improvement application of operation requirements documents(ORD) on which empirical researches are not sufficient. The application of ORD was drawn up and operated in the acquisition phase, and recently it was decided to apply it in the requirements planning phase. However, the reality is that there are deficiencies in the performance of works, such as the classification of works among related organizations. Therefore, this study analyzed the cases of writing ORD that have been promoted so far, and Its status was enhanced by reflecting it in laws and regulations. In addition, the concept was redefined, and improvement measures such as the level of writing ORD were suggested for each acquisition stages. Above all, it is desirable to incrementally supplement and develop from preliminary conceptual research in the planning stage and drafting at the military level. In addition, the relevant legal system should be supplemented so that such ORD application can be flexibly established in defence acquisition.

• Journal of Power System Engineering
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• v.13 no.1
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• pp.59-64
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• 2009

Electronic control system of the engine is composed of various sensors and actuators, This paper is concerned with fault analysis for the stable operation of it. We suggest the technology that can systematically and reliably analyze fault causes of sensors and actuators by using the fault generating program. In results, we can acquire the systematic road map of occurring faults as well as the valuable information related to the operations of sensors and actuators. These results should be very useful to get the classification of fault causes, develop an electronic control system of engine, and review control strategies of it.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.253-270
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• 2021

In January 2013, the National Institute of Standards and Technology (NIST) announced the CAESAR (Competition for Authenticated Encryption: Security, Applicability, and Robustness) contest to identify authenticated ciphers that are suitable for a wide range of applications. A total of 57 submissions made it into the first round of the competition out of which 6 were announced as winners in March 2019. In the process of the competition, NIST realized that most of the authenticated ciphers submitted were not suitable for resource-constrained devices used as end nodes in the Internet-of-Things (IoT) platform. For that matter, the NIST Lightweight Cryptography Standardization Process was set up to identify authenticated encryption and hashing algorithms for IoT devices. The call for submissions was initiated in 2018 and in April 2019, 56 submissions made it into the first round of the competition. In August 2019, 32 out of the 56 submissions were selected for the second round which is due to end in the year 2021. This work surveys the 32 authenticated encryption schemes that made it into the second round of the NIST lightweight cryptography standardization process. The paper presents an easy-to-understand comparative overview of the recommended parameters, primitives, mode of operation, features, security parameter, and hardware/software performance of the 32 candidate algorithms. The paper goes further by discussing the challenges of the Lightweight Cryptography Standardization Process and provides some suitable recommendations.