• Journal of the Korean Society of Safety
• /
• v.33 no.4
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.2
• /
• pp.33-38
• /
• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• KEPCO Journal on Electric Power and Energy
• /
• v.2 no.1
• /
• pp.61-69
• /
• 2016

Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

• Journal of the Korean Society of Safety
• /
• v.22 no.6
• /
• pp.81-86
• /
• 2007

The purpose of this study is to provide foot classification on 30 generation young men and women by factor analysis and cluster analysis. The sample for this work was chosen from data which were collected and measured by Size Korea during two years($2003{\sim}2004$). In order to analyze and compare features of the foot of men and women, analysis was performed about 871 subjects(male: 422, female: 449) on 24 body parts including height, width, thickness, circumference, length and angle. According to the result of factor analysis about measured data, there were seven factors and six factors for men and women respectively. After cluster analysis, data for men and women were commonly divided by three types for utilization of research results. Type 1 and type 3 had wide distribution about men. Type 2 had wide distribution about women. The results of this study can be applied in manufacturing and design of comfortable shoes and socks.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.7
• /
• pp.1668-1672
• /
• 2009

In this study, the method by Robertson which has been most commonly used for classifying soils, using piezocone test results, was compared with that by Schneider which was most recently proposed. Both methods were applied to the soils in Gyeonggi province and the classifying results were investigated. It has been found that the difference between the results according to the methods was not so large and Schneider's method showed slightly better results for clay region and vice versa. Such factors as large field database, normalized tip resistance, pore water pressure, and drain condition were found to need further research for more reliable soil classification.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.1388-1389
• /
• 2015

Electropulsegraphy is a medical device that was invented by an orient medical physician and a few engineers to help the physicians to diagnose patients in more systematic way by analyzing waveforms generated from the device. Data generated form the device has been collected for over several decades, and undergoes functional upgrades today. The device generates 33 waveforms that reflect the states of patients. As one of those upgrading efforts, we strive to develop an intelligent algorithm that makes the diagnostic process automatically, which was previously done manually for a long period of time. The logistic regression algorithm is used for our classification problems, which is one of those well-known algorithms for various classification problems such as character recognition systems. Out of the 33 waveforms, we only use 5 waveform data (Type1 toType5) as training data sets to estimate the parameters of the logistic regression. And the parameters are used to classify waveform inputs chosen at random.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1179-1191
• /
• 2019

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.6_2
• /
• pp.1055-1062
• /
• 2023

Globally, the need for remanufacturing or reusing ships and various mechanical parts continues to increase due to environmental problems including global warming. Research on remanufacturing is being carried out in many areas. However, research on inspection and classification to identify the performance or degree of wear of mechanical parts is insufficient. In particular, studies on the inspection-ability and classification-ability of mechanical parts equipped with various materials and complex forms are highly required. Remanufacturing must be considered from the stage of design to extend the life cycle of mechanical parts. Particularly, it is very important to perform research for evaluating the degree of ease to inspect and classify various sorts of wear or deterioration of parts caused by long-term use easily. In this study, the degree of ease in inspecting or classifying mechanical parts for remanufacturing is defined as inspection-ability and classification-ability. In fact, to remanufacture old parts, inspection-ability and classification-ability should be reflected from the stage of design. The purpose of this study is to evaluate the inspection-ability and classification-ability of ships and various mechanical parts. This researcher has presented the quantitative evaluation procedure of inspection-ability and classification-ability, derived the factors and ranges that influence each of the details of easiness, assigned scores according to the ranges of the factors, and calculated weights. Lastly, this study presents the procedure of scoring to evaluate the overall weights of inspection-ability and classification-ability and also inspection-ability and classification-ability quantitatively.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.2
• /
• pp.25-31
• /
• 2013

This paper analyzed the management quality activities and performance based on industrial types. We divided the business into four industrial types, manufacturing industry, service industry, medical institution and public enterprise. We analyzed the differences of the elements of management quality in industrial types. The results show that leadership, measurement, analysis and knowledge management and workforce focus categories are not significant and strategic planning, customer and market focus, process management and performance categories are significant. This paper proposes the directions of management quality activities and performance according to industrial types based on these results.

• The Journal of the Korea Contents Association
• /
• v.20 no.6
• /
• pp.256-266
• /
• 2020

Spatial distribution analysis of industrial locations plays a very important role in the establishment of relevant spatial policies and plans. The first thing to consider in this analysis is what analysis indicators and spatial units are used, because the interpretation of the analysis results may vary depending on the analysis indicators and the spatial units. Therefore, this study first examines various industrial integration indicators considering spatial autocorrelation and suggests the classification of regional types of industrial aggregation through the combination of related indicators. And then, this paper aims to empirically analyze the root industry by presenting a methodology for analyzing industrial integration by various spatial units such as individual locations, grids, and administrative districts. The results of the empirical analysis show that the grid in the spatial unit can be analyzed in more detail than the administrative unit. In addition, it is expected to overcome the limitations such as differences in interpretation that may occur due to the setting of spatial units. In the classification of regional types, the south-eastern region of Ulsan, Busan, and Changwon, and the western region of the SMA of Incheon, Hwaseong, and Ansan were analyzed as the industrial cluster type.