• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.95-105
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• 2001

Out-door learning activity in environmental education has been emphasized as an effective method in environmental education since the aims of environmental education emphasize students'value, attitude, actions as well as knowledge. In order to implement successfully out-door learning activity in environmental education classrooms, teachers'perceptions to environmental problems and experiences at fields are essential. An environmental education network among the metropolitan city and provincial office of education, nongovernmental organization of environmental movement and education and university was established and a teachers'in-service training program of out-door learning centered environmental education was implemented. The program was developed in order to 1) connect environmental education with the regional environmental situations, 2) provide teachers with opportunities to participate in an out-door learning program, and 3) train teachers to be environmental education leaders of out-door learning. For evaluation of the program, responses of participants to questionnaire were analyzed. Most of teachers responded that their perception of environment was changed positively after the participation in the program. This study suggested that a future planning of a teachers'in-service training program of out-door learning centered environmental education should be developed in considerations of arranging enough hours for out-door learning at regional environmental sites, applying performance assessment, providing teachers with multiple opportunities with programs in different levels including enriched programs, and establishing an environmental education network among nongovernmental organization of environment movement and education, university, and local offices and department of education.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.57-60
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• 2008

Maglev is a system that a train runs levitated above a rail. Therefore it is very important to maintain a constant levitation gap for achieving serviceability and ride comfort. This study is a cooperation research subject of the 3-1 subject, performance improvement of maglev track structures, of the Center for Urban Maglev Program in Korea, started in 2006. The aim of this study is development of rapid constructions of bridge superstructure for maglev. At present, precast deck is widely used because of its superiority to cast-in-place concrete on quality and the term of works. The research group suggested basic systems of maglev guideway with PSC-U type and trapezoidal open steel box type girder, and precast deck, cooperating with Korea Railroad Research Institute, the managing institute of the 3-1 subject. In this study, a mock-up consisted of girders, decks and rail was fabricated and test was performed for constructability, serviceability and maintenance evaluation of PSC U-type girder, precast deck, and new guide rail system.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.312-318
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• 2012

Recently, although the need of marine robots being raised in extreme areas, the basis is very deficient. Fortunately, as the robot competition is vitalizing and the need of the robot education is increasing, it is desirable to establish the basis of the R&D and industrialization of marine robots and to train professionals through the development and diffusion of marine robot kits. However, in conventional case, there is no underwater-type autonomous marine robot kit for the marine robot competition, which has the abilities of the underwater locomotion and target detection and avoidance. To solve this problem, a marine robot kit which has the abilities of the underwater locomotion, the waterproof and the weight adjustment, is developed. To verify the performance of the developed kit, test and evaluation such as surge, pitch, yaw, obstacle avoidance is performed. The test and evaluation results show that the possibility of the real applications of the developed kit.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.356-364
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• 2017

The process of inspecting electric railway vehicles is complicated and these vehicles accompany a risk of safety accidents. This developed system will be a great help in simplifying the shunting process and be very useful in terms of ensuring safety and providing user convenience. In this paper, the results of performance evaluation tests are studied on a movable catenary system for railway vehicles that secured mechanical durability, convenience, and operator safety by applying a specific rigid bar catenary of an existing mobile train line. We presented an analysis of the basic characteristics for site installation including sorting. In conclusion, this developed system was obtained in good results through durability test, durable mechanical load test and safety test in require specifications.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.845-849
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• 2024

Currently, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of an engineer and with simple measuring tools. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the characteristics of cracks inside the rail due to rail surface damage were studied. In field measurements, rail surface damage was selected, old rail samples were collected in the acceleration and braking sections, and a scanning electron microscope (SEM) was used to evaluate the rail surface damage was used to analyze the crack characteristics. As a result of the analysis, the crack mechanism caused by the running train and the crack characteristics of the acceleration section where cracks occur at an angle rising toward the rail surface were experimentally proven.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.56-66
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• 1998

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed with two conflict objectives. The neuro-controller is trained off-line with evolutionary programming(EP) in such a way that it becomes optimal by minimizing the given cost function through global evaluation based on desired control performance during the whole training time interval. However, it is not easy to find an optimal solution which satisfies individual objective simultaneously. With the concept of Pareto optimality and EP, we train the proposed controller more effectively and obtain a valuable set of optimal solutions. Simulation results show the efficacy of the proposed controller in a viewpoint of improvement of performance of a step response like fast settling time and small undershoot or overshoot compared with that of a conventional linear controller.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.209-220
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• 2011

Self-tapping screws have some operational peculiarities. In spite of their economical advantage that requires no prior tapping operation, a weakness of self-tapping screw-tightening operations is that screws can easily be tightened at a non-right angle, thus resulting in an improper tightening strength. Increases in outsourced workers have reduced labor costs, but the accompanying high worker fluidity means that new workers are more frequently introduced into factories. It is necessary to train new workers for self-tapping screw-tightening operations, which occupies a considerable portion of ordinary assembly works. The purpose of this study is to develop and implement a skill transfer system for the operation. This study (1) proposes a set of characteristic values for evaluating the quality of the operation and develops a device that can measure these values; (2) proposes criteria for evaluating the resultant quality of the tightening; and (3) develops a skill training system for better work performance. Firstly, sets of characteristic values for evaluating the quality of the operation, namely, torque, vertical pressure forces and horizontal vibration forces, are proposed. A device that can measure these values is developed. Secondly, criteria for evaluating the resultant quality of the tightening are identified, involving tightening torque, maximum vertical pressure and timing, vibration area during the processing and tightening period, and work angle. By using such parameters, workers with the proper aptitude can be identified. Thirdly, a skill training system for the operation is developed. It consists of screwdriver operation training and screw-tightening training with feedback information about the results of the operation. Finally, the validity of the training system is experimentally verified using new operators and actual workers.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.11-18
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• 2020

This study proposes a technique for the determination of outlet blockage using deep learning in a conveyor system. The proposed method aims to apply the best model to the actual process, where we train various CNN models for the determination of outlet blockage using images collected by CCTV in an industrial scene. We used the well-known CNN model such as VGGNet, ResNet, DenseNet and NASNet, and used 18,000 images collected by CCTV for model training and performance evaluation. As a experiment result with various models, VGGNet showed the best performance with 99.03% accuracy and 29.05ms processing time, and we confirmed that VGGNet is suitable for the determination of outlet blockage.