• Plant Journal
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• v.16 no.1
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• pp.28-33
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• 2020

This study analyzes the requirements for international standardization by studying the approach to international standardization of equipment and bulk materials in the offshore oil & gas industry, and describes the prioritization of standardized equipment and the result of standardization in the first stage. Currently, international standardization activities can be classified into three activities. First, international standardization activities of bulk materials led by three major domestic shipbuilders, second, JIP33 equipment standardization activities led by IOGP, and finally, And standardization activities of UEJIP equipment centered on classification society. This study classifies various international standards applied in the offshore oil & gas industry, and introduces the activities of standardization activities of class-leading equipment and actual application examples.

• Journal of the Korean Society of Safety
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• v.29 no.2
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• pp.24-30
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• 2014

There have been controversies over whether explosion hazardous area(EHA) should be classified for facilities which use lighter-than-air gases such as city gas, hydrogen and ammonia. Two view points are confronting each other: an economic piont of view that these gases are lighter than air and disperse rapidly, hence do not form EHA upon release into the atmosphere, and a safety point of view that they are also inflammable gases, hence can form EHA although the extent is limited compared to heavy gases. But various standards such as KS, IEC, API, NFPA do not exclude light gases when classifying EHA and present examples of EHA for light gas facilities. This study calculates EHA using the hypothetical volume in the IEC code where the hole sizes required for the calculation were selected according to various nominal pipe sizes in such a way to conform to the EHA data in the API code and HSL. Then, 25 leakage scenarios were suggested for 5 different pipe sizes and 5 operating pressures that cover typical operating conditions of light gas facilities. The EHA for the minimum leakage scenario(25 mm pipe, 0.01MPa pressure) was found to correspond to a hypothetical volume larger than 0.1 $m^3$(medium-level ventilation). This confirms the validity of classifying EHA for facilities using lighter-than-air gases. Finally, a computer program called HACPL was developed for easy use by light gas facilities that classifies EHA according to operating pressures and pipe sizes.

• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.461-468
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• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.39-42
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• 2004

We have designed a portable electronic nose(e-nose) system using an array of commercial gas sensors for recognition and analyzing the various odours. In this paper, we have implemented a portable e-nose system using an array gas sensors and personal digital assistants(PDA) for recognizing and analyzing volatile organic compounds(VOCs) in the field. Field screening for pollutants has been a target of instrumental development for number of year. A portable e-nose system can be substantial benefit to rapidly localize the spacial extent of a pollution or to find pollutants source. And, by using PDA, E-nose have a better function such as the easy user-interface and data transfer by internet from on- site to remote computer. We adapted the Levenberg-Marquardt algorithm based on the back-propagation and proposed the method that could be predicted concentration levels of VOCs gases after classification by separating neural network into two parts.

• 한국약용작물학회:학술대회논문집
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• 2008.11a
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• pp.479-480
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• 2008

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.5
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• pp.567-571
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• 2004

Recently, GIS(Gas Insulated Switchgear) has been recognizing of importance on development of diagnosis technique which is happened problem on confidence for a long time use. Therefore, the measurement and analysis of PD with prior phenomenon of insulation breakdown is used many method of diagnosis for GIS. In this paper, we simulate trouble condition in DS and analysis trouble signal to use electrical and mechanical methods, interpretation of detected signal has analysed with to use ø-q-n pattern and neural network. For this analysis, we have used the induction and AE(acoustic emission) sensors. For the simulation experiment, we make DS for 170 KV GIS and analyze the classification and characteristics of detected signals with the application of neural network algorithm.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.128-134
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• 2012

This paper gives an overview of the requirements for electrical equipment in potentially explosive atmospheres and describes how these are applied to electrical machines and drives in hazardous areas. The method by which equipment can be shown to be safe in a whole range of gases, by testing in a single test gas, is covered. It is shown how the more recently introduced methods of protection for hazardous areas, increased safety and nonsparking, are ideally suited to AC machines and drives. A novel method of measuring the fullload temperature rise of electrical machines for hazardous, and other areas, without the need to connect a mechanical load to the machine's drive shaft is explained.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.266-268
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• 1998

The linear MHD(magnetohydro-dynamic) machine obtains the linear motion by replacing the solid conducting secondary of LIM with the ionized gas, plasma or the liquid metal. The electromagnetic pump which is a kind of MHD machine is divided into induction pump and conduction pump. This paper shows the classification and development trends of MHD machines and the characteristics of double sided flat linear induction pump (DFLIP).

• Journal of IKEEE
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• v.23 no.4
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• pp.1400-1407
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• 2019

In this paper, we propose a support vector machine (SVM) based gas classifier that can support real-time self-learning. The modified sequential minimal optimization (MSMO) algorithm is employed to train the proposed SVM. By using a shared structure for learning and classification, the proposed SVM reduced the hardware area by 35% compared to the existing architecture. Our system was implemented with 3,337 CLB (configurable logic block) LUTs (look-up table) with Xilinx Zynq UltraScale+ FPGA (field programmable gate array) and verified that it can operate at the clock frequency of 108MHz.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).