• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.45-64
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• 2022

Due to the recent and rapid globalization, logistics outsourcing has expanded globally and is seen as a means of creating a robust logistics system. However, many businesses continue to have difficulties with their logistics outsourcing contracts, which compels them to reinstate the logistics function for internal management. This study aims to investigate how organizational capabilities of logistics service providers (LSPs), notably flexibility, integration, innovation, and technological capabilities, impact on the logistics outsourcing success in Ugandan food processing firms. Using a structured questionnaire survey, cross-sectional data collected from 211 food processing firms in Kampala - Uganda were analyzed by partial least squares-structural equation modeling (PLS-SEM) using SmartPLS 3.3.7 software to examine the theorized relationships. The study findings revealed that whereas the technological and innovation capabilities positively and significantly influence logistics outsourcing success, the effects of flexibility and integration capabilities were insignificant. Additionally, the importance-performance map analysis (IPMA) reveals that the technological capability is a priority capability, followed by the innovation capability if logistics outsourcing success is to be achieved. Conversely, flexibility and integration capabilities are of low priority.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.381-386
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• 2023

Many existing test case generation researchers extract test cases from models. However, research on generating test cases from natural language requirements is required in practice. For this purpose, the combination of natural language analysis and requirements engineering is very necessary. However, Requirements analysis written in Korean is difficult due to the diverse meaning of sentence expressions. We research test case generation through natural language requirement definition analysis, C3Tree model, cause-effect graph, and decision table steps as one of the test case generation methods from Korean natural requirements. As an intermediate step, this paper generates test cases from C3Tree model-based decision tables using meta-modeling. This method has the advantage of being able to easily maintain the model-to-model and model-to-text transformation processes by modifying only the transformation rules. If an existing model is modified or a new model is added, only the model transformation rules can be maintained without changing the program algorithm. As a result of the evaluation, all combinations for the decision table were automatically generated as test cases.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.52-62
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• 2023

The objective of this study is to propose an automated algorithm for precise cutting length of slab rebar complying with regulations such as anchorage length, standard hooks, and lapping length. This algorithm aims to improve the traditional manual quantity take-off process typically outsourced by external contractors. By providing accurate rebar quantity data at BBS(Bar Bending Schedule) level from the bidding phase, uncertainty in quantity take-off can be eliminated and reliance on out-sourcing reduced. In addition, the algorithm allows for early determination of precise quantities, enabling construction firms to preapre competitive and optimized bids, leading to increased profit margins during contract negotiations. The proposed algorithm not only streamlines redundant tasks across various processes, including estimating, budgeting, and BBS generation but also offers flexibility in handling post-contract structural drawing changes. In particular, the proposed algorithm, when combined with BIM, can solve the technical problems of using BIM in the early phases of construction, and the algorithm's formulas and shape codes that built as REVIT-based family files, can help saving time and manpower.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.288-290
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• 2022

As part of the SOC digitization for smart water management and flood prevention, the government reported that automatic and remote control system for drainage facilities (180 billion won) to 57% of national rivers and established a real-time monitoring system (30 billion won). In addition, they were also planning to establish a smart dam safety management system (15 billion won) based on big data at 11 regions. Therefore, research is needed for smart water management and flood prevention system that can accurately calculate the flow rate through real-time flow rate measurement of rivers. In particular, the most important thing to improve the system implementation and accuracy is to ensure the accuracy of real-time flow rate measurements. To this end, radar sensors for measuring the flow rate of electromagnetic waves in the United States and Europe have been introduced and applied to the system in Korea, but demand for improvement of the system continues due to high price range and performance. Consequently, we would like to propose an improved flow rate measurement and flood forecast system by developing a radar sensor for measuring the electromagnetic surface current meter for real-time flow rate measurement.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.13-23
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• 2023

Unlike building construction projects, where the activity is repeatedly carried out in a limited area, civil engineering projects such as roads and railroads are carried out in a linear type in a horizontal working space over several tens of kilometers. Each activity is managed with a station number that has a unit of distance from the starting point to the end point. For this reason, since the work location information of the activity is a major management factor, the Gantt chart system that expresses only schedule information may have limitations. In this study, authors propose a method for constructing a linear schedule chart that can simultaneously express schedule information indicating the start and finish dates and location information indicating the start and end positions of each activity, and develop a system for generating a linear schedule chart. In the study, the coordinate axes of the linear schedule chart consisted of distance and date values on the X and Y axes, respectively, and each activity was expressed as a symbol that can infer the type of work to increase the visibility of the linear schedule chart compared to the simple bar chart method. The linear schedule chart generation system was reviewed for practical applicability by utilizing the actual schedule data of bridge structures in a railroad project.

• Journal of Environmental Impact Assessment
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• v.32 no.2
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• pp.61-71
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• 2023

As differences in the impact of each species on a spatial scale occur, analysis at the landscape scale is necessary to evaluate the impact of a development project. In previous studies, the Incidence Function Model (IFM) based on meta population theory was used to analyze the impact of species on the environment that changes according to urban development. However, since the model was required at least 10 occupied areas, it is difficult to use it for species that are difficult to monitor such as endangered species. Therefore, we proposed the Incidence Function Model (IFM) using species distribution model to fill the species data. In addition, we reviewed whether the developed model can be used in environmental impact assessment. As a result of the analysis, the minimum occupancy of Prionailurus bengalensis on urban development decreased to 56.5% and the possibility of survival to 28.7%. We confirmed that It rapidly decreased from the reference points of 230 and 70habitats through analysis of the meta-population capacity according to the decrease in the number of habitats. These results can be assessing the environment impact of each species on habitat loss. And it can support decision-making on the minimum number and area of habitat for species protection. This study is expected to be used as basic data for environment impact assessment on before and after development projects and mitigation measures plans, thereby increasing the effectiveness of reduction plans.