• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.117-124
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• 2023

Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.

• Journal of Service Research and Studies
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• v.12 no.4
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• pp.50-71
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• 2022

After the declaration of the Covid-19 pandemic impacted most of the industries resulting economic fallout. Firms sought for solutions of governments regulations to prevent spread of infectious diseases. This led to demand rise of digital layer and spectrums of virtual reality. Replacing the reality in to virtual and interactions with the digital contents by augmented reality, the consequences were decrement of human-to-human contact. Concerns of Covid-19 and public interests of digital solutions has led to significant amounts of research and developments of Virtual/Augment Reality resulted to driven up new terms of extended reality. However, the uses in industries and the characteristics of the extended reality are currently not defined. In this paper the goal is to define and classify the uses and characteristics of extended reality based on previous researches suggested by research institute. By developing a new classification models of extended realities core technology, uses of industries and utility to analyze trends of extended reality. Two separate classification models of uses of industries and utility will be used as a tool by creating a linkage matrix. The x-axis is divided by utiliy classification model of extended reality. The y-axis are divided into classification model of uses in industries. This matrix will be used as a tool to present a guideline for industry-utility development where extended reality can be served as a service

• Journal of Korean Society of Industrial and Systems Engineering
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• v.1 no.1
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• pp.45-48
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• 1978

If two inspectors classify items in a lot into m classes, it is possible that each of them makes wrong classification in some cases, thus causing bias. Expressions have been obtained for the limits of this bias in estimating the proportion of the different classes. From the results of the classification they obtained limit for the estimates of Proportions have been worked out, based on assumption regarding the magnitudes of probabilities of misclassification. Now we suppose that $P_{ti}{\;}(t=1.2)$ is the probability that t the inspector classifies correctly an item in class $A_i$ and $q_{tji}$ is the probability that he misclassifies in $A_j$ an item actually belonging to $A_i$, therefor, $P_{ti}+ \sum\limits_{j{\neq}i}q_{tji}=1$ An estimate for the proportion $P_k$ of the class $A_k$ in the lot would be $\hat{P}_k=r_{kk}+(\frac{1}{2})\sum\limits_{j{\neq}k}r_{kj}+r_{jk}$ The % Bias in proportion $\hat{P}_k$ is $\frac{E(\hat{P}_k)-P_k}{P_k}{\times}100$

• Journal of Information Technology Services
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• v.19 no.1
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• pp.55-69
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• 2020

The Internet cyber space has become more important as it enters the intelligent information society of the 4th Industrial Revolution beyond the information age through the development of ICT, the expansion of personalized services through mobile and SNS, the development of IoT, big data, and artificial intelligence. The Internet has formed a new paradigm in human civilization, but it has focused only on the efficiency of its functions. Therefore, various side effects such as information divide, cyber terrorism, cyber violence, hacking, and personal information leakage are emerging. In this situation, facing the intelligent information society can lead to an uncontrollable chaos. Therefore, this study classifies the cyber dysfunction of intelligent information society and analyzes social cognition, suggests cyber dysfunction standard of intelligent information society, and examines the seriousness of dysfunction, and suggests technical research directions for future technologies and services. The dysfunctional classification of the intelligent information society was classified into five areas of cyber crime and terrorism, infringement of rights, intelligent information usage culture, intelligent information reliability, and social problems by FGI methodology. Based on the classification, the social perception of current and future cyber dysfunction severity was surveyed and it showed female is more sensitive than male about the dysfunction. A GAP analysis confirmed social awareness that the future society would be more serious about AI and cyber crime

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.1-10
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• 2016

In this paper, a car model recognition system using image processing and machine learning is proposed and it's performance is also evaluated. The system recognizes the front of car because the front of car is different for every car model and manufacturer, and difficult to remodel. The proposed method extracts HOG features from training data set, then builds classification model by the HOG features. If user takes photo of the front of car, then HOG features are extracted from the photo image and are used to determine the model of car based on the trained classification model. Experimental results show a high average recognition rate of 98%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1475-1482
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• 2010

This paper aims at finding the policy implications of mobile government services based on the comparison of domestic and foreign cases. We developed a framework for the classification of mobile government services and examined the domestic and foreign mobile government services to identify policy implications and dynamic trends of the mobile government. In the policy perspective, we suggest customer-centric service redesign, extensive adoption of mobile service solutions, and new service development reflecting new mobile trends.

• Ocean Systems Engineering
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• v.4 no.1
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• pp.53-62
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• 2014

The detection of ship wakes image can demonstrate substantial information regarding on a ship, such as its tonnage, type, direction, and speed of movement. Consequently, the wake target recognition is a favorable way for ship identification. This paper proposes a Local Binary Pattern (LBP) approach to extract image features (wakes) for training an Improved Back Propagation Artificial Neural Network (IBPANN) to identify ship speed. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over 80%. Specifically, the lower ship's speed, the better accurate rate, sometimes it's accuracy could be close to 100%. In addition, one significant feature of this method is that it can receive a higher recognition rate than the nearest neighbor classification method.

• 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 Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.41-54
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• 2007

To make most out of Order Review/Release(ORR) models, they are to be analyzed and classified according to their prospective users' requirements. To this end, we discuss ORR functions and so-called "ORR paradox", and propose an ORR model classification scheme named "COMPACT(COMplexity-imPACT) Matrix". Under the scheme, the complexity and impact levels of each ORR model are rated one after another in order to position it across the matrix. We explore the process and present the results, insisting that a DSS should suggest ORR models to its users on the complexity-impact basis.

• 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.