• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.5
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• pp.383-393
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• 1991

Lodging of rice plant is the most important damage by unfavourable weather conditions in rice cultivation. High levels of nitrogen application and growing of Japonica rice variety is one factor to increase the lodging damage. Lodging of rice plant decreasing grain yield; 34% at milky. 21% at dough. 20% at yellow stage. decreasing grain Quality. increasing green rice. and increasing labor cost to harvest. To decrease lodging damage. the lodging resistant varieties will be selected and cultural practices such as amount and time of nitrogen application. planting density. water management. and disease and pest control methods have to be study for the short and strong culm. and good rooting system. Also. application methods such amount and time of plant growth regulators and new types of chemicals will be develop for the reducing lodging of rice plant. To decrease the lodging damage in direct seeding cultivation. first identifying the differences of lodging mechanisms between hand transplanting and direct seeding, second establish the suitable direct seeding methods such as seedling establishement. fertilization. and water menagement.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.6
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• pp.706-711
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• 1997

Low supply of water is generally the most serious factor limiting rice production. The experiment was conducted to identify the reasonable method for minimum irrigation under ridge direct seeding on dry paddy, at National Honam Agricultural Experiment Station, RDA in 1996. The results showed that the reduction ratio of irrigation water was high in order of furrow irrigation at 15-day>furrow irrigation at 10-day>flooding irrigation at 10-day>, and furrow irrigation at 5-day intervals. However, milled rice yield was high in the furrow irrigation at 5-day intervals and in flooding irrigation at 10-day intervals due to high ripened grain as compared with other treatments indicating two treatments were the most reasonable irrigation methods in terms of saving the labor cost and water supply as well as the admittable yield performance.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.489-498
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• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.

• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.49-55
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• 2022

The role of drones has been expanded to various fields such as agriculture, construction, and logistics. In particular, agriculture drones are emerging as an effective alternative to solve the problem of labor shortage and reduce the input cost. In this study therefore, we proposed the fruit recognition algorithm and harvesting mechanism for fruit harvesting drone system that can safely harvest fruits at high positions. In the fruit recognition algorithm, we employ "You-Only-Look-Once" which is a deep learning-based object detection algorithm and verify its feasibility by establishing a virtual simulation environment. In addition, we propose the fruit harvesting mechanism which can be operated by a single driving motor. The rotational motion of the motor is converted into a linear motion by the scotch yoke, and the opened gripper moves forward, grips a fruit and rotates it for harvesting. The feasibility of the proposed mechanism is verified by performing Multi-body dynamics analysis.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.31-41
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• 2016

This study was conducted to develop a handy earth auger for use in sloppy and rugged forest terrains in order to reduce labor cost which comprises a major part of the production costs in forest afforestation projects. The first prototype is developed consist of two parts, the soil-digging screw and the battery power source. The specifications of the first prototype screw are: length of 170mm, a top diameter of 60mm, bottom diameter of 47mm, 23° angle for each helix, and a 50mm awl-head tip. The use of a single line of screw was selected for reduced weight. In addition, a power source of rotary DC Motor(WD-6G2425, WONILL, Korea) with a maximum torque of 30kgf-cm, rotation of 20-30rpm, K6G30C decelerator with a reduction ratio of 30:1 which could be used with no load for 48 was operated. In consideration of its weight, a lithium battery was utilized in line with the goal of developing a lightweight auger. In order to evaluate the performance of the first prototype, test sites were selected as 6 areas. The rotational force was found to be highest in area A(Solid area), followed by areas F(Mounted slope 40° area) and E(Mounted slope 30° area). It was also observed that in general, the rotational force increased along with the increase in soil depth with the maximum rotational force recorded at 10cm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.363-369
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• 2008

Change is inevitable and is a reality of construction projects. Change adjustment includes the cost associated with materials, labor, etc. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative impact of the changes. What is needed is a reliable method to identify and quantify the loss of productivity caused by cumulative impact of change orders. This study survey the change orders data in domestic area for public construction and analyze to quantify change order impact. This study developed concepts of "%CO", "%Delta", "%T" to capture change order effect on project and search the relationships between them. Finally, this study find strong relationship between change order and loss.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.635-647
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• 2006

Korean construction companies first advanced to the international markets in 1960's and so far have brought more than 4,900 projects which account for 193 billion dollars approximately. With the large increase of national employment and income being followed by the achievement, Korea's construction industry has made an enormous contribution to the improvement of domestic economy for the last 40 years. However, recently the increased risk in international markets as well as the sharpening competition with foreign companies promising in terms of advanced technologies and low labor cost have been driving Korean construction away from the market shares. According to ENR (Engineering News Record, 1994~2003), it is revealed that 15.1% of top 225 global contractors are suffering from loss in international construction markets. This phenomenon is largely due to the highly uncertain characteristics of international projects, which are inherently exposed to various and complicated risky situations. Furthermore, especially for Korean construction companies, it is often the case that the failure in an international construction project cannot be offset by even a sufficient number of successful domestic achievements. Therefore, not only the selective screening among the nominated projects which have strong possibility of collapse but the systematic strategies for controlling potential risk factors are also considered indispensable in international construction portfolio management. The purpose of this study is to first analyze the causal relationships of the profit-influencing variables and the project success, and develop the profitability forecasting model in international construction projects.