• 지능정보연구
• /
• 제7권2호
• /
• pp.83-93
• /
• 2001

기업부실예측은 과거로부터 많은 연구가 이루어진 분야로, 주로 통계기법에 의한 분류예측문제로 다루어져 왔다. 최근에는 인공신경망, 의사결정나무 등 비선형성을 반영할 수 있는 인공지능 기법을 적용한 연구가 많이 수행되고 있다. 본 연구에서는 최적화에 주로 활용하는 인공지능 기법인 유전자 알고리즘을 규칙추출을 통한 기업부실예측 모형의 개발에 적용하고, 활용가능성을 검증하였다.

• Safety and Health at Work
• /
• 제12권3호
• /
• pp.416-420
• /
• 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.

• 융합보안논문지
• /
• 제12권1호
• /
• pp.49-54
• /
• 2012

최근 스마트폰의 등장과 무선 통신의 발전으로 원격지의 정보를 활용하여 사용자에게 편리한 서비스를 제공하는 연구가 활발하게 진행되고 있으나, 각 서비스 응용 및 센서 노드들의 특징에 따라 별도 모니터링 시스템을 설계 및 구축이 필요하다. 따라서 본 논문은 이러한 비효율성을 해결하기 위해 제안된 모니터링 시스템이 타 센서 네트워크 시스템에서 연동이 가능하도록 설계하였으며, 가스센서의 정보를 센서 네트워크를 통해 실내 가스 상태를 판단하여 위험 수준v및 상황을 스마트폰 사용자에게 알려주는 실내 가스 모니터링 시스템 제안한다.

• 산업경영시스템학회지
• /
• 제39권1호
• /
• pp.105-115
• /
• 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.

• 한국분말재료학회지
• /
• 제24권5호
• /
• pp.389-394
• /
• 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.

• 대한안전경영과학회지
• /
• 제19권3호
• /
• pp.1-9
• /
• 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.

• 한국안전학회지
• /
• 제33권4호
• /
• pp.21-29
• /
• 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.

• 한국가스학회지
• /
• 제24권1호
• /
• pp.56-65
• /
• 2020

일반적으로 화재는 다양한 원인으로 발생하며 무작위로 보이기에 화재의 발생을 예측한다는 것은 매우 도전적인 문제이다. 하지만 모든 화재가 아닌 큰 피해를 주는 초대형 화재사고의 예측이 가능하다면, 선제적 대응을 통한 손실 최소화를 기대할 수 있다. 본 연구에서는 국가 전체를 대상으로 초대형 화재사고를 예측하기 위해 기계학습 기법인 k-평균 클러스터링을 이용하여 화재사고를 분류하고, 이를 인위적인 설정이 강한 비전문가 기준, 전문가 기준 분류 결과와 비교하여 예측에 적절한 분류 기준을 제안하였다. 비교 결과 기계학습을 이용한 분류가 일정한 피해규모와 비율로 분류되어, 예측에 적절한 분류 기준이라 판단하였다. 또한 초대형 화재사고의 주기성을 분석한 결과 일정한 패턴을 보였지만 높은 편차를 보였다. 따라서 단순 예측기법이 아닌 고급 예측기법을 사용하였을 때 초대형 화재사고의 발생 예측이 가능하다고 판단되었다.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.803-812
• /
• 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.

• 에너지공학
• /
• 제25권3호
• /
• pp.149-156
• /
• 2016

본고의 목적은 국내 수송부문 에너지절약 및 에너지효율 향상 정책효과 분석 및 평가를 위한 기초 DB의 구축범위를 도출하는 것이다. 국내외 사례분석에 기초하여 도출한 대분류 항목은 에너지소비, 에너지원단위, 이산화탄소 또는 온실가스 배출량, 경제지표, 수송량/수송실적, 자동차 관련 기초자료 등이다. 전문가 의견조사에 의해 에너지 소비, 수송량/수송실적, 에너지효율/에너지원단위, 자동차, 에너지경제, 에너지환경 등 대분류 도출 하에, 하위 항목으로 세분하여, 각 구성항목에 대한 세부 분류에 대한 정보를 반영할 수 있는 분류지표로 설정하였다.