• Smart Media Journal
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• v.12 no.9
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• pp.28-37
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• 2023

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.83-101
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• 2005

Customer retention is one of the major issues in life insurance industry, in which competition is increasingly fierce. There are many things for the life insurers to do many things to retain the customers. One of those things is to make sure to keep in touch with all customers. When an insurance-planner resigned, his/her customers must be taken care of by some planner-assistants. This article outlines the design of Contact Scheduling System (CSS) that supports planner-assistants for contacting the customers. Planner-assistants are unable to share the resigned insurance-planner's experience and knowledge regarding the customer relationship management. The CSS developed by employing both Classification And Regression Tree (CART) technique and Sequential Pattern Mining (SPM) technique has a two-stage process. In the first stage, it segments the customers into eight groups by CART model. Then it generates contact scheduling information consisting of contact-purpose, contact-interval and contact-channel, according to the segment's typical contact pattern. Contact-purpose is derived by schedule-driven, event-driven, or business-rule-driven. Schedule-driven contact is determined by SPM model. In the operation of CSS in a realistic situation, it shows a practicality in supporting planner-assistants to keep in touch with the customers efficiently and effectively.

• Journal of the Korea Computer Graphics Society
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• v.20 no.2
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• pp.1-11
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• 2014

The quality of 2D-to-3D conversion depends on the accuracy of the assigned depth to scene objects. Manual depth painting for given objects is labor intensive as each frame is painted. Specifically, a human is one of the most challenging objects for a high-quality conversion, as a human body is an articulated figure and has many degrees of freedom (DOF). In addition, various styles of clothes, accessories, and hair create a very complex silhouette around the 2D human object. We propose an efficient method to estimate visually pleasing depths of a human at every frame in a monocular video. First, a 3D template model is matched to a person in a monocular video with a small number of specified user correspondences. Our pose estimation with sequential joint angular constraints reproduces a various range of human motions (i.e., spine bending) by allowing the utilization of a fully skinned 3D model with a large number of joints and DOFs. The initial depth of the 2D object in the video is assigned from the matched results, and then propagated toward areas where the depth is missing to produce a complete depth map. For the effective handling of the complex silhouettes and appearances, we introduce a partial depth propagation method based on color segmentation to ensure the detail of the results. We compared the result and depth maps painted by experienced artists. The comparison shows that our method produces viable depth maps of humans in monocular videos efficiently.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.416-421
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• 2010

The KISTI (Korea Institute of Science and Technology Information) began to produce the Korean human information called Visible Korean and Digital Korean since 2000 because there was no human information in Korea which could represent the physical characteristics of Korean human body. The Visible Korean consists of CT, MR, sectioned and segmented images of Korean human body. We obtained the serially sectioned images by grinding the Korean cadaver in horizontal direction and segmented these images by outlining the inner organs of human. We have produced the sectioned images of Korean male whole body, male head, and female pelvis in2008. The segmentation and 3D reconstruction of these images are now in proceeding. The Digital Korean consists of CT images of about 100 Korean cadavers. These CT images were segmented by individual bone, reconstructed to produce the 3D bone models and the skin surface model was also added. The mechanical properties of individual bones were obtained by measuring the property of individual bone sample. We have distributed these Korean human informations to users in domestic and abroad. About 70 institutes in domestic, and 20 institutes in abroad have used our data in research use and nearly 160 proceedings and articles were published since 2001. We think these human informations have a role of medical information infrastructure that could be used in the field of medical education, biomechanics, virtual reality etc.

• Journal of Service Research and Studies
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• v.8 no.1
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• pp.89-102
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• 2018

The Industrial Classification is a systematic taxonomy of industrial activities and the Standard Industrial Classification is used in all country by their own a consistent classification method. Therefore, it is employed to analyze current status of industry affairs using statistical investigations in terms industrial activities for making industrial policies and to compare industrial activity among countries. Since the Second Industrial Revolution, the need for the homogenous standard of industrial classification among countries emerged as the economic and industrial exchanges between the countries have became more active. In 1940, Colin Clark who british economist divided the industry into the first (primitive), second (processed), and third (service) industries. Based on this, the United Nations Office for Statistics (UNSD) established International Standard Industry Classification (ISIC) in 1948, which most countries invoke it. ISIC(International Standard Industry Classification) and the standard industry classifications of countries have reached the present after several revisions since the enactment of the Act. In the 2000s, the standard industry classification is amended to reflect the emergence of new industries and changes in industrial structure, mainly featuring the creation and segmentation of sections in the tertiary industry domains. It also shows that primary and secondary sectors are shifting to tertiary industry. In this study, the causes of these common phenomena are systematically identified and the problems present classification systems have been analyzed. Also proposed is the direction of formation of the industrial classification system from a service economy point of view and the conceptual model of the new classification system. In the future, it is necessary to validate the proposed model through this study and to carry out various new classification system studies.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.151-162
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• 2015

A theme park is a complex space, in which held is a performance & exhibition event created to make visitors feel fun and a deep impression through a certain theme. Recently, local governments are increasingly planning for theme parks with the aim of local economy revitalization. Accordingly, this paper set up a development model for a Korean culture theme park after deducting success factors for creating a successful theme park through the case of Korean culture theme park. To be concrete, this paper deducted the success factors through literature research and case research as follows: Setting of themes having identity and consistency; programs that can give fun and a deep impression; convenient support facilities reflecting customers' needs; differentiated marketing for progress in customer attraction power; location linked with cultural resources around a theme park and a space for communicating a message; privately-initiated project promotion and the main body of operation; target setting consequent on market segmentation, and demand forecast, etc. A theme park cannot be successfully operated to contribute to local economy revitalization until such success factors should be closely linked and harmonized with each other. In addition, in order for a theme park to make a continued growth, it's required that problems occurring at the operational stage should be endlessly improved, and contents strengthening should be continuously done in line with circumstances of the times and viewers' eye level.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.163-173
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• 2014

When evaluating the spatial resolution images and evaluation of low contrast resolution using CT standard phantom, and might present a automated quantitative evaluation method for minimizing errors by subjective judgment of the evaluator be, and try to evaluate the usefulness. 120kVp and 250mAs, 10mm collimation, SFOV(scan field of view) of 25cm or more than, exposure conditions DFOV(display field of view) of 25cm, and were evaluated the 24 passing images and 20 failing images taken using a standard reconstruction algorithm by using the Nuclear Associates, Inc. AAPM CT Performance Phantom(Model 76-410). Quantitative evaluation of low contrast resolution and spatial resolution was using an evaluation program that was self-developed using the company Mathwork Matlab(Ver. 7.6. (R2008a)) software. In this study, the results were evaluated using the evaluation program that was self-developed in the evaluation of images using CT standard phantom, it was possible to evaluate an objective numerical qualitative evaluation item. First, if the contrast resolution, if EI is 0.50, 0.51, 0.52, 0.53, as a result of evaluating quantitatively the results were evaluated qualitatively match. Second, if CNR is -0.0018~-0.0010, as a result of evaluating quantitatively the results were evaluated qualitatively match. Third, if the spatial resolution, as a result of using a image segmentation technique, and automatically extract the contour boundary of the hole, as a result of evaluating quantitatively the results were evaluated qualitatively match.