• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Safety and Health at Work
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• v.12 no.3
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• pp.416-420
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• 2021

Hazardous area classification design is required to reduce the explosion risk in process plants. Among the international design guidelines, only IEC 60079-10-1 proposes a new type of zone, namely zone 2 NE, to prevent explosion hazards. We studied how to meet the zone 2 NE grade for a facility handling hydrogen gas, which is considered as most dangerous among explosive gases. Zone 2 NE can be achieved considering the grade of release, as well as the availability and effectiveness of ventilation, which are factors indicative of the facility condition and its surroundings. In the present study, we demonstrate that zone 2 NE can be achieved when the degree of ventilation is high by accessing temperature, pressure, and size of leak hole. The release characteristic can be derived by substituting the process condition of the hydrogen gas facility. The equations are summarized considering relation of the operating temperature, operating pressure, and size of leak hole. Through this relationship, the non-hazardous condition can be realized from the perspective of inherent safety by the combination of each parameter before the initial design of the hydrogen gas facility.

• Convergence Security Journal
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• v.12 no.1
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• pp.49-54
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• 2012

Special applications designed for smart phone, so called "Apps" are rapidly emerging as unique and effective sources of environmental monitoring tools. Using the advantages of Information and Communication Technology (ICT), this paper propose an application that provides Indoor Gas Monitoring System. In this paper, use four wireless gas sensor modules to acquire sensors data wirelessly coupled with the advantages of existing portable smart device based on Android platform to display the real-time data from the sensor modules. Additionally, this paper adapts a simple gas classification algorithm to inform in-door Gas for users real-time based.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Journal of Powder Materials
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• v.24 no.5
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• pp.389-394
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• 2017

Ni wires with a diameter and length of 0.4 and 100 mm, respectively, and a purity of 99.9% are electrically exploded at 25 cycles per minute. The Ni nanopowders are successfully synthesized by a pulsed wire evaporation (PWE) method, in which Ar gas is used as the ambient gas. The characterization of the nanopowders is carried out using X-ray diffraction (XRD) and a high-resolution transmission electronmicroscope (HRTEM). The Ni nanopowders are classified for a multilayer ceramic condenser (MLCC) application using a type two Air-Centrifugal classifier (model: CNI, MP-250). The characterization of the classified Ni nanopowders are carried out using a scanning electron microscope (SEM) and particle size analysis (PSA) to observe the distribution and minimum classification point (minimum cutting point) of the nanopowders.

• Journal of the Korea Safety Management & Science
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• v.19 no.3
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• pp.1-9
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• 2017

In the industrial field, various type of fuel have been used for product processing facilities. Recent for 10 years, the usage of natural gas (NG) was gradually increased. Because it has many merits; clean fuel, no transportation, storage facility and so on. There are common safety concept that strict explosion protection approaches are needed for facilities where explosive materials such as flammable liquid, vapor and gases exist. But some has an optimistic point of view that the lighter than air gases such as NG disperse rapidly, hence do not form explosion environment upon release into the atmosphere, many parts has a conventional safety point of view that those gases are also inflammable gases, hence can form explosion environment although the extent is limited and present. In this paper, the heating equipments (Hot Oil Heater) was reviewed and some risk management measures were proposed. These measures include hazardous area classification and explosion-proof provisions of electric apparatus, an early gas leak detection and isolation, ventilation system reliability, emergency response plan and training and so on. This study calculates Hazardous Area Classification using the hypothetical volume in the KS C IEC code.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.21-29
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• 2018

In many companies handling flammable liquids, explosion-proof electrical equipment have been installed according to the Korean Industrial Standards (KS C IEC 60079-10-1). In these standards, hazardous area for explosive gas atmospheres has to be classified by the evaluation of the evaporation rate of flammable liquid leakage. The evaporation rate is an important factor to determine the zones classification and hazardous area distance. However, there is no systematic method or rule for the estimation of evaporation rate in these standards and the first principle equations of a evaporation rate are very difficult. Thus, it is really hard for industrial workplaces to employ these equations. Thus, this problem can trigger inaccurate results for evaluating evaporation range. In this study, empirical models for estimating an evaporation rate of flammable liquid have been developed to tackle this problem. Throughout the sensitivity analysis of the first principle equations, it can be found that main factors for the evaporation rate are wind speed and temperature and empirical models have to be nonlinear. Polynomial regression is employed to build empirical models. Methanol, benzene, para-xylene and toluene are selected as case studies to verify the accuracy of empirical models.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.56-65
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• 2020

Forecasting of coming fire accidents is quite a challenging problem cause normally fire accidents occur for a variety of reasons and seem randomness. However, if fire accidents that cause critical losses can be forecasted, it can expect to minimize losses through preemptive action. Classifications using machine learning were determined as appropriate classification criteria for the forecasting cause it classified as a constant damage scale and proportion. In addition, the analysis of the periodicity of a critical fire accident showed a certain pattern, but showed a high deviation. So it seems possible to forecast critical fire accidents using advanced prediction techniques rather than simple prediction techniques.

• Smart Structures and Systems
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• v.24 no.6
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• pp.803-812
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• 2019

Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

• Journal of Energy Engineering
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• v.25 no.3
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• pp.149-156
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• 2016

This paper surveyed and analyzed cases of DB development overseas to set the range of DB to be developed for analyzing energy-saving policies in the domestic transportation sector. The foregoing prerequisites were used to establish system for classification in the broad scale under which system for classification in detail indicators that suit one in the broader indicators was set based on analysis of domestic / overseas cases to determine DB development range in the transportation sector required to analysis domestic energy-saving policies. Accordingly, six items subject to the broadest classification were determined, i.e. energy consumption, energy basic unit, emissions of greenhouse gas, economic indicators, transportation volume / transportation records and basic automobile data. Large classification and sub-items determined by surveying expert opinions were set and proposed as DB classification indicators.