• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.1-13
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• 2019

The advent of a fourth industrial revolution, built on advances in digital technology, has coincided with studies using various unmanned aerial vehicles (UAVs) being performed worldwide. However, the accuracy of different sensors and their suitability for particular research studies are factors that need to be carefully evaluated. In this study, we evaluated UAV photogrammetry using smart technology. To assess the performance of digital photogrammetry, the accuracy of common procedures for generating orthomosaic images and digital surface models (DSMs) using terrestrial laser scanning (TLS) techniques was measured. Two different type of non-surveying camera(Smartphone camera, fisheye camera) were attached to UAV platform. For fisheye camera, lens distortion was corrected by considering characteristics of lens. Accuracy of orthoimage and DSM generated were comparatively analyzed using aerial and TLS data. Accuracy comparison analysis proceeded as follows. First, we used Ortho mosaic image to compare the check point with a certain area. In addition, vertical errors of camera DSM were compared and analyzed based on TLS. In this study, we propose and evaluate the feasibility of UAV photogrammetry which can acquire 3 - D spatial information at low cost in a construction site.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

• International Journal of Highway Engineering
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• v.12 no.3
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• pp.17-26
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• 2010

Maintenance of the roads and bridges is a major issue for all road administrators around the world, and various initiatives are being implemented in each region for the purpose of controlling the ever increasing road maintenance cost while ensuring the safety of the vehicles driving. Efforts for such initiatives have also been made in Asia and initiatives for managing heavy-weight vehicles have recently gained momentum in Korea and Japan. We have developed a technology for unevenly installing bar-shaped sensors (piezo quartz sensors) to enable dynamic axle load measurement at a highly accurate level, and have estimated our measurement accuracy of axle load/gross weight, etc. on an actual road. The measurement accuracy of the axle load/gross weight varies significantly depending on the number of sensors installed. In our implementation, the target accuracy was set to below ${\pm}5%$ for gross weight measurement so that automatic regulation can be applied. We have achieved our target by installing 8-point measurement system. However, to have this technology widely accepted, it was necessary to reduce the system size so that it can be easily implemented. Therefore, we have estimated the relationship between the measurement accuracy and the system size (number of measurement points), and have come up with the proposal of 3-point measurement as an optimum number of measurement points, and have estimated its performance on an actual road. Additionally, we evaluated the relationship between the measurement accuracy and vehicle velocity.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.2
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• pp.242-252
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• 2008

The objective of this study was to suggest a new efficiency measurement indicator for evaluating the menu management efficiency of decision making units(DMUs) in contract-foodservice operations and to determine the relationship between the DEA(data envelopment analysis) menu efficiency score and menu factors. The results of applying DEA revealed relatively efficient types of service and frequency of meals. The efficient service was shown as a self-service type that operates Monday to Saturday. The considered menu factors included meal price, food cost per meal, meal counts, number of menu items, use of favorite menu use, forecasting error, accuracy of ordering, ratio of inventory, ratio of food loss, use of processed foods and use of prepared vegetables are considered. There were significant correlations between the DEA score and meal price, meal counts, number of menu items, ratio of food loss, accuracy of ordering and use of processed foods respectively. According to the regression results, menu price had a positive influence on the DEA menu efficiency score, and food cost per meal and the use of prepared foods had negative influences respectively.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.192-202
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• 2017

This study develops a text spam filtering system for Facebook based on two variable categories: keywords learned from Instagram and meta-information of Facebook posts. Since there is no explicit labels for spam/ham posts, we utilize hash tags in Instagram to train classification models. In addition, the filtering accuracy is enhanced by considering meta-information of Facebook posts. To verify the proposed filtering system, we conduct an empirical experiment based on a total of 1,795,067 and 761,861 Facebook and Instagram documents, respectively. Employing random forest as a base classification algorithm, experimental result shows that the proposed filtering system yield 99% and 98% in terms of filtering accuracy and F1-measure, respectively. We expect that the proposed filtering scheme can be applied other web services suffering from massive spam posts but no explicit spam labels are available.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.515-528
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• 2012

The aim of this article is to provide a study on the issue of inventory inaccuracy and to show the manner in which RFID technology can improve the inventory management performance. The objective of inventory control is to monitor the stock flow of merchandises in order to understand the operating profit and loss. A proper mechanism of inventory control could be made to help the profitability. As RFID is applied to inventory control, it can improve efficiency, enhance accuracy and achieve security. In this paper, we introduce the evolution of different mechanisms of inventory control with RFID system-counting method, collect-all method, and continuous monitoring method. As for improving the accuracy of inventory check during business hours, continuous monitoring is the solution. We introduce the infrastructure of the RFID inventory management system based on M2M architecture can make the inventory be efficiently monitored with instant warnings.

• Journal of the Korea Safety Management & Science
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• v.16 no.2
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• pp.147-155
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• 2014

This paper suggests a specific model that could efficiently improve the interaction and the interface between WMS(Warehouse Management System) terminal and PDA terminal through real time processing in manufacturing shop. The proposed model shows that the new method can more efficiently perform to reduce processing time for shipping and receiving, compared with the current approach. As a result of the certain test among the main server, WMS system, and PDA terminal, it is noted in case of the new proposed system that the effects of proposed model are as follows: (a) While the receiving lead time for carrying by the current method was 2 hours, the receiving lead time by the new method was 20 minutes. (b) While the shipping lead time for carrying by the current method was 1 hours, the shipping lead time by the new method was 15 minutes. (c) While the inventory rate of accuracy by the current method was 85%, the inventory rate of accuracy by the new method was 98%.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.167-174
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• 2023

Currently, the budget for vocational competency development training has been expanded, but the number of participants has decreased. As the budget for the Vocational Competency Development Project increases, the participation of a large number of people becomes necessary. This study aims to derive factors that affect satisfaction by selecting factors related to respondent characteristics, training institutions, training types, and job performance for satisfaction with vocational competency development training, and to study ways to improve satisfaction. Data were collected through focus group interviews (FGI), and logistic regression analysis was conducted through feasibility review and reliability analysis. As a result, in the case of the model, it was confirmed that the degree of agreement between the case actually measured and the case predicted by the model was low in the Hosmer and Lemeshow test, but the overall classification accuracy was classified as 96.0% in the classification accuracy table. As for the influence of the factors, the result was derived that the application of knowledge technology, training institution facility equipment, Business Collaboration, long-term work plan, and satisfaction with work performed have an influence in the order.