• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.118-125
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• 2018

An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lavender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reacted remarkably to the aroma fragrances and the good correlation of r=0.58-0.88 with the aromatic odor intensities by olfaction was confirmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aroma oils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognition rate was 57.1%-71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all confusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.161-172
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• 2016

The use of a chemical sensor array can help discriminate between chemicals when comparing one sample with another. The ability to classify pattern characteristics from relatively small pieces of information has led to growing interest in methods of sensor recognition. A variety of pattern recognition algorithms, including the adaptive radial basis function network (RBFN), may be applicable to gas and/ or odor classification. In this paper, we provide a broad review of approaches for various types of gas and/or odor identification techniques based on RBFN and drift compensation techniques caused by sensor poisoning and aging.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.689-701
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• 2017

In recent offshore industries, various ambiguous terms have been used without clear definition or classification, causing difficulties in legal, technical, and educational understanding and usage. For an example, the commonly used term of 'Offshore Plant' in Korea is not an universal word technically. There has been no clear technical or legal definition about the 'Offshore Plant' and its classification is also very ambiguous; sometimes it is used to refer offshore oil and gas production platform or it is used to mean offshore renewable power generation plant in some cases. To build a conceptual framework, therefore, this paper suggests a classification of offshore units (1) using internationally agreed terms, (2) agreed with the technical classification used by the ship classification society and (3) being able to include not only the current but also future concepts of offshore units.

• Journal of the Korean Institute of Gas
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• v.13 no.3
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• pp.7-14
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• 2009

Many countries are managing the transportation of hazardous materials under the specific provisions especially, as well as use, storage and management, because of their high risks. For the purpose of the revision of rail safety law for the safe transportation of hazardous materials, amount and kind of hazardous materials transported by rail in Korea are analysed and the standards of classification of hazardous materials are compared in domestic and abroad. There are lots of benefits for national rail safety law to implement an international law because our country's geographic location is convenient to connect the continent and to across the border. It is suggested that implementing a classification and test methods of hazardous materials enable to use internationally for the preparation of rail transportation to be increased.

• Journal of the Korean Institute of Gas
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• v.2 no.1
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• pp.90-98
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• 1998

In order to establish detailed plans for fire protection and reduce the possible fire accidents in the future, collection of domestic and foreign fire accident cases and fundamental analysis are very important. In this study the classification model for fire accidents was developed and the direction to a new model was suggested by comparison ours with the accidents classification model of NFPA of United States of America and Japan. A new developed PC-based database program for fire accidents (FADBS) was used to analyse fire accidents easily and efficiently.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2011.06a
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• pp.3-6
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• 2011

The utilization of gas as ship fuel requires a new set of regulations by IMO and society of classification. Maritime Safety Committee(MSC) and the subcommittee Bulk-Liquids and Gases(BLG) in IMO developed "Interim Guidelines on Safety for Natural Gas-fueled Engine Installation in Ships(Res.MSC.285(86))" for the use of natural gas in internal combustion engine. According to the requirement of Res.MSC.285(86) for natural gas-fueled engine installations in ships, several parts of ships should follow safety criteria in terms of Fuel bunkering, Gas safe Machinery spaces, Gas Fuel Storage and etc. In this thesis, details of the IGF code shall be described and development of the IGF code in IMO shall be illustrated.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.493-497
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• 2020

Hazardous area classification is designed to prevent chemical plant explosions in advance. Generally, the duration of the explosive atmosphere is used for zone type classification. Herein, IEC code, a quantitative zone type classification methodology, was used to achieve Zone 2 NE, which indicates a practical non-explosion condition. This study analyzed the operating pressure of a vessel handling propane to achieve Zone 2 NE by applying the IEC code via MATLAB. The resulting zone type and hazardous area grades were compared with the results from other design standards, namely API and EI codes. According to the IEC code, the operating pressure of vessels handling propane should be between 101325-116560.59 Pa. In contrast, the zone type classification criteria used by API and EI codes are abstract. Therefore, since these codes could interpret excessively explosive atmospheres, care is required while using them for hazardous area classification design.