• Information Systems Review
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• v.17 no.1
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• pp.65-92
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• 2015

Driven by the development of information technologies, information system (IS) use has been common even in military organizations. In particular, field artillery is currently using the Battalion Tactical Commanding System-A1 (BTCS-A1) to improve fire support. The use of BTCS-A1 makes fire-commanding processes simple and autonomous, which leads to shorten time to support fire. Although BTCS-A1 has been considered as a helpful system, there still exists some dispute regarding its effectiveness and impact on task performance. By conceptualizing BTCS-A1 use as appropriation, this study investigates how BTCS-A1 appropriation promotes task performance. We also hypothesize that IS quality, organizational pressure (institutional pressure and supervisor influence), and team climate (team learning climate and team empowerment climate) increase the appropriation. Survey results show that organizational pressure and team climate promote BTCS-A1 appropriation, which improves users' task performance. However, effect of IS quality is not significant. Theoretical and practical implications are presented.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.7 no.4
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• pp.8-16
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• 2004

The industrialization of central Europe more than 100 ago marked the beginning of densely concentrated buildings in quickly growing cities. A cheap type of roofing material of that time was tar. But it was dangerous because it was high inflammable. Then some roofer had a splendid idea. They used sandy material as a final layer atop the impermeable tar layer. These roofs were much more fire resistant than the typical roofs. In this sandy layer some plant species began to grow spontaneously. This was the beginning of the green roof history of modern Europe. A number of these green roofs survived both world wars. In the early 80's in Berlin alone, 50 such buildings existed and they continued to be waterproof until the present day. Since the 1992 Earth Summit of 1992 in Rio de Janeiro(http://www.johannesburgsummit.org/html/basic_info/unced.html) the term "sustainable development" became of central interest of urban designers. In city regions green roofs had become synonymous with this term. With a small investment, long-lasting roofs can be created. Further back in history, more exciting examples of green roofs can be found. The hanging gardens of antiquity are well-known. There are also green roofs built as insulation against cold and heat all over the world. For over 20 years, roof greening in central Europe has been closely examined for various reasons. Roof greening touches several different disciplines. Of primary interest is the durability of the roofs. But ecologists are also interested in green roofs, for instance in biodiversity research. The beneficial effect of greening on water proofing was also proven. For some time, the issue of fire protection was investigated. According to tests, green roofs received a harsh careful rating. Their fire protective property is considered similar to that of tile roofs. Another recent impulse for the green roof movement in Germany has come from the evident improvement of storm water retention and the reduced burden on the sewer system. The question of whether and how much energy green roofs can save has become an urgent question. The state of the research and also various open questions from a central European point of view will be discussed in the context of international collaboration. Apart from academic considerations, those who involve themselves in this issue take a predominantly positive view of the numerous existing green roofs in Germany. In some cities, green roofs are the typical construction technique for new buildings. A few outstanding examples will conclude this review. In Germany, about 20 companies, some of which operate internationally, specialize in green roof consulting. Learning from each other in an open-ended way with respect to different construction techniques and applications in various climatic regions can only be accomplished through such international collaboration as is taking place here.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• Journal of environmental and Sanitary engineering
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• v.22 no.3
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• pp.65-76
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• 2007

Values and attitude towards the environment begin to form in elementary school. Thus, environmental education is effective to promote children's sensibility on the environment, to increase their interest and concern on it, and to make them have friendly attitudes towards it. As a measure of such education, experiential learning activities are being emphasized, where children can see, feel and experience for themselves in a familiar environment surrounding them. Based on the results of this research, the following proposals can be made for environmental education necessary for elementary school children. i) the contents of environmental education should be selected and organized according to grades. Also, schedule should be secured to provide environmental education in certain time. ii) program should be developed to fit into local characteristics and academic level, providing connective and consistent environmental education. iii) activities for environmental education in elementary school can be effective only if connective guidances are provided among school, home and local community. iv) the recycling and separate collection activity program used in this research was limited to 3rd graders in small-size rural schools. Additional research may be necessary to see how long their attitudes last according to different grades.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.151-160
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• 2013

Recently, children's deaths are found to be mostly caused by various accidents in Korea. But it is in reality that the safety training are very roughly conducted, and even contemporarily appropriate training materials are not sufficiently furnished contrary to the increasing significance of the safety training to reduce the children's accidental risks. This study is to compare and analyze the safety training courses of domestic and overseas elementary schools and various safety training materials. This study is purposed ultimately to reduce the accidental risks of elementary school students by suggesting the future development direction. It is concluded in this study that more appropriate safety training courses and materials should be provided to train the students to habituate their safe behaviors with a view to protect the elementary school students against the accidents. In addition, the safety training should be conducted consistently by reflecting the students' characters, and the pertinent training materials should be developed for the students' spontaneous learning and for more practical preventive training.

• The Korean Journal of Emergency Medical Services
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• v.24 no.3
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• pp.65-78
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• 2020

Purpose: The purpose of the study was to investigate the core competency and educational needs of paramedic students in disaster management. Methods: A self-reported questionnaire was completed by 207 paramedic students between June 1 and October 29, 2017. The study instrument included disaster preparedness (15 items), disaster management core competency (26 items), disaster education needs (26 items). Data were analyzed using t-test, ANOVA, and Duncan's multiple range test using IBM SPSS 24.0. Results: The students reported that only 13% had experienced or witnessed disasters; however, 95.2% would be willing to help in the event of a disaster. Their disaster preparedness was 1.84 points on a 3-point scale. We did see differences in disaster preparedness by background: hospital practice (F=5.352, p=.001); fire-fighting practice (F=8.994, p=.000). The students had a core competency of disaster management at 3.25 points on a 5-point scale with differences depending on major satisfaction (F=3.760, p=.006). The level of student demands for disaster education was 4.29 points. Conclusion: If variety of educational environments are provided for disaster-related learning and training, the core competency of disaster management for paramedic students will improve. The students will be available as disaster management experts in various fields, even after graduation.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.57-65
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• 2023

Emergency numbers, such as 112 and 119, are used in many countries to connect people in need with emergency services such as police, fire, and medical assistance. We describe development directions of intelligent response technology for emergency calls. The development of this technology refers to enhancing the efficiency and effectiveness of response systems by using advanced methods such as artificial intelligence, machine learning, and big data analytics. We focus on a system that assists the receptionist of an emergency call. In the future, the recognition rate and decision-making accuracy of intelligent response technologies should be improved considering characteristics of public safety and emergency domain data. Although the current technology remains at the level of assisting a receptionist, a fully autonomous response technology is expected to emerge in the future.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.