• International conference on construction engineering and project management
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• 2015.10a
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• pp.625-626
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• 2015

The purpose of this study is to present a cost classification system that can be used in gas plant construction projects. Towards this end, it examined the detailed statements of the construction companies which had experience in carrying out plant construction projects. Based on the above, it also presented a life-cycle (i.e., EPCC) cost classification system for the gas plant construction projects by conducting the Delphi analysis through the expert opinions.

• Journal of IKEEE
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• v.22 no.3
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• pp.700-705
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• 2018

In this paper, we propose the gas classifier based on restricted column energy neural network (RCE-NN) and present its hardware implementation results for real-time learning and classification. Since RCE-NN has a flexible network architecture with real-time learning process, it is suitable for gas classification applications. The proposed gas classifier showed 99.2% classification accuracy for the UCI gas dataset and was implemented with 26,702 logic elements with Intel-Altera cyclone IV FPGA. In addition, it was verified with FPGA test system at an operating frequency of 63MHz.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.309-312
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• 2001

A primitive gas classification system which can classify limited species of gas was designed and simulated. The 'electronic nose' consists of an array of 4 metal oxide gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision Part in PLD(programmable logic device) chip. Sensor array consists of four commercial, tin oxide based, semiconductor type gas sensors. BP(back propagation) neutral networks with MLP(Multilayer Perceptron) structure was designed and implemented on CPLD of fifty thousand gate level chip by VHDL language for processing the input signals from 4 gas sensors and qualification of gases in air. The network contained four input units, one hidden layer with 4 neurons and output with 4 regular neurons. The 'electronic nose' system was successfully classified 4 kinds of industrial gases in computer simulation.

• Applied Chemistry for Engineering
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• v.32 no.1
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• pp.102-109
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• 2021

Recently, an interest in risk calculation methods has been increasing in Korea due to the establishment of classification code for explosive hazardous area on gas facility (KGS CODE GC101), which is based on the international standard of classification of areas - explosive gas atmospheres (IEC 60079-10-1). However, experiments to check for leaks of combustible or toxic gases are very difficult. These experiments can lead to fire, explosion, and toxic poisoning. Therefore, even if someone tries to provide a laboratory for this experiment, it is difficult to install a gas leakage equipment. In this study we find out differences among actual experiments, CFD by using FLACS and calculation based on classification code for explosive hazardous area on gas facility (KGS CODE GC101) by comparing to each other. We develpoed KGS HAC (hazardous area classification) program which based on KGS GC101 for convenience and popularization. As a result, actual gas leak, CFD and KGS HAC are showing slightly different results. The results of dispersion of 1.8 to 2.7 m were shown in the actual experiment, and the CFD and KGS HAC showed a linear increase of about 0.4 to 1 m depending on the increase in a flow rate. In the actual experiment, the application of 3/8" tubes and orifice to take into account the momentum drop resulted in an increase in the hazardous distance of about 1.95 m. Comparing three methods was able to identify similarities between real and CFD, and also similarities and limitations of CFD and KGS HAC. We hope these results will provide a good basis for future experiments and risk calculations.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.716-721
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• 2010

In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.563-570
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• 2018

This paper proposes an efficient dissolved gas analysis(DGA) and classification method of an oil-filled transformer using machine learning algorithms to solve problems inherent in IEC 60599. In IEC 60599, a certain diagnosis criteria do not exist, and duplication area is existed. Thus, it is difficult to make a decision without any experts since the IEC 60599 standard can not support analysis and classification of gas date of a power transformer in that criteria. To address these issue. we propose a dissolved gas analysis(DGA) and classification method using a machine learning algorithm. We evaluate the performance of the proposed method using support vector machines with dissolved gas dataset extracted from a power transformer in the real industry. To validate the performance of the proposed method, we compares the proposed method with the IEC 60599 standard. Experimental results show that the proposed method outperforms the IEC 60599 in the classification accuracy.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.11-18
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• 2016

In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.46-64
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• 2019

Technical practice code, KGS GC101 2018, for explosion hazard area selection and distance calculation of gas facility was enacted and implemented from July 12, 2018. This code includes whole contents of IEC60079-10-1 2015 (Explosive atmospheres Part 10-1: Classification of areas - Explosive gas atmospheres), and clarifies the interpretation of ambiguous standards or adds guidelines for standards. KGS GC101 is a method for classifying explosion hazard place types: (1) Determination of leak grade (2) Determination of leakage hole size (3) Determination of leakage flow (4) Determination of dilution class (5) Determination of ventilation effectiveness, finally (6) Determination of danger place (7) Explosion The range of dangerous places can be estimated. In order to easily calculate this process, the program (KGS-HAC v1.14, C-2018-020632) composed by Visual Basic for Application (Excel) language was produced by Korea Gas Safety Corporation. We will discuss how to use codes and programs to select and set up explosion hazard zones for field users.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.45-52
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• 2017

In this paper, we investigated the following items for the development of gas safety work mobile app. In this study, which is a follow-up study after the completion of the scenario design and the first, second image extraction of the mobile app based on the initial research that has been studied, 1) Suggested classification of gas works by type classification and risk classification 2) The research and proposal of interaction method for effective interworking of mobile app and worker in many industrial fields of two-hand work have been made. In particular, the development of a mobile app that interacts with the main system that manages not only the gas work but also the field of each industrial field is the first attempt in Korea and has helped the worker to work freely and safely through various interaction methods.