• Journal of the Korean earth science society
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• v.35 no.5
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• pp.375-384
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• 2014

The number of students choosing science, technology, engineering, and mathematics (STEM) related careers are declining. Thus it became a worldwide challenge in the $21^{st}$ century. As public images of the engineers are unfavorable and inaccurate, misconceptions and stereotypes about engineers are prevailing. The purpose of this study was to investigate elementary school students' perceived mental and pictorial images of engineers and the nature of engineering work. This study involved 512 fifth and sixth grade students (Boys: 287 and Girls: 225) from four elementary schools at one of metropolitans in South Korea. The Draw An Engineer-Korean version (DAE-K) was developed based on Draw an Engineer (DAE) and Draw a Scientist (DAS), and Song and Kim (1999)'s instruments. A pilot-tested was conducted with 33 elementary students prior to the main study. The students were asked to answer how they think the engineers would be, to draw an engineer at work, and to write the engineer's personal information and the job description. Engineers were perceived as a person fixing, building, manufacturing, working outdoors in labors' clothes such as a robe. Engineers were shown with building tools, robots, airplanes, machines, conveyor belt, etc. Moreover, compared to the scientists, engineers were perceived as less intelligent, less imaginative, and less accurate. The results of this study revealed that elementary school students had a lack of accurate images of engineers. Students' current perceived images of engineers could help educators find the baselines for the future engineering education in elementary schools. In addition, the findings of this study could also contribute to the development of engineering education in terms of gender issues, STEM career choice, and even cultural diversity.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.364-370
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• 2008

In this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode and implemented real time monitoring system in real test-bed environment. The proposed scheme offers beacon based real-time scheduling for reliable association process with parent nodes and dynamically assigns network address by using NAA (Next Address Assignment) mechanism. For the large scale multi-sensor processing, our real-time monitoring system accomplished the intelligent database processing, which can generate not only the alert messages to the civilians but also process various sensing data such as fire, air, temperature and etc. Moreover, we also developed mobile robot which can support network mobility. Though the performance evaluation by using real test-bed system, we illustrate that our proposed system demonstrates promising performance for emergence monitoring systems.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.1-9
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• 2021

With the convergence of the information and communication technologies, a new age of technological civilization has arrived. This is the age of intelligent revolution, known as the 4th industrial revolution. The 4th industrial revolution is based on technological innovations, such as robots, big data analysis, artificial intelligence, and unmanned transportation facilities. This revolution would interconnect all the people, things, and economy, and hence will lead to the expansion of the industry. A high-density, high-capacity energy technology is required to maintain this interconnection. As a next-generation energy source, lithium-ion batteries are in the spotlight today. However, lithium-ion batteries can cause thermal runaway and fire because of electrical, thermal, and mechanical abuse. In this study, thermal runaway was induced in 72.5 Ah NCM pouch-type lithium-ion batteries because of thermal abuse. The surface of the pouch-type lithium-ion batteries was heated by the hot plate heating method, and the effect of the rate of increase in the surface temperature on the thermal runaway trigger time was analyzed using Minitab 19, a statistical analysis program. The correlation analysis results confirmed that there existed a strong negative relationship between each variable, while the regression analysis demonstrated that the thermal runaway trigger time of lithium-ion batteries can be predicted from the rate of increase in their surface temperature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1013-1018
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• 2021

Due to the recent accident caused by the automated vehicle, discussions on the ethical aspects of AI have been actively underway. This paper confirms that AI is inevitably connected to ethical components through the concepts and techniques related to robots-AI, and argues that ethical aspects are built-in, not post facto. Furthermore, this devises a solution to the trolley dilemma that can serve as a clue to ethical problems associated with automated vehicles. Preferentially, that process contains writing Bayesian networks. Next, only important and influential data are left after the pre-processing stage, and crowd-sourcing & extrapolation is used to calculate the exact figures of the networks. Through this process, this argues that humans' subjects are certainly included in implementing algorithms and models and discusses the necessity and direction of engineering liberal arts, especially education of ethics that distinguished from major education to prevent distortions and biases abouts AI systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1317-1322
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• 2021

Software in the era of the Fourth Industrial Revolution is becoming a key foundation in an intelligent information society. Therefore, it is necessary to study the new direction of manpower training and education that can cope with the times. To this end, the Ministry of Education reorganized the curriculum and is implementing software education based on a logical problem-solving process based on computing thinking skills rather than acquiring general ICT knowledge. However, there is a lack of securing high-quality educational content for software education, and there is also a lack of teaching aids that can be taught in connection with advanced IT technologies. To overcome this, this paper proposes the development of external expansion devices to expand educational content and functions capable of convergent software education such as artificial intelligence using coding robots for software education. Through this, effective software education is possible by improving the curriculum of the existing simple problem-solving method and developing various learning materials.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.221-228
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• 2022

With the application of artificial intelligence technology, robots can provide efficient services in real life. Unlike industrial manipulators that do simple repetitive work, this study presented design methods of 6 degree of freedom robot arm and intelligent object search and movement methods for use alone or in collaboration with no place restrictions in the service robot field and verified performance. Using a depth camera and deep learning in the ROS environment of the embedded board included in the robot arm, the robot arm detects objects and moves to the object area through inverse kinematics analysis. In addition, when contacting an object, it was possible to accurately hold and move the object through the analysis of the force sensor value. To verify the performance of the manufactured robot arm, experiments were conducted on accurate positioning of objects through deep learning and image processing, motor control, and object separation, and finally robot arm was tested to separate various cups commonly used in cafes to check whether they actually operate.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• Journal of Korea Trade
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• v.26 no.1
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• pp.57-80
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• 2022

Purpose - This study aims to examine the relationships between the acceptance of the 4th industrial revolution logistics technology, logistics safety behavior, and logistics safety performance, as well as the moderated mediating effects of logistics safety behavior through safety culture in Korea. Design/methodology - Research models and hypotheses were established based on prior research related to the 4th industrial revolution logistics technology, logistics safety, and logistics performance. The survey was conducted on the employees of logistics companies, and reliability analysis, confirmatory factor analysis, discriminant validity analysis, structural equation model analysis, and mediating effect analysis were performed. In addition, the moderated mediating effect analysis applying SPSS Process Model No. 7 was conducted. Findings - Usefulness and sociality of the acceptance of the 4th industrial revolution logistics technology had a significant effect on logistics safety behavior. Ease of use, sociality, and efficiency had meaningful effect on logistics safety performance. And in the relationships between the acceptance of logistics technology and logistics safety performance, logistics safety behavior had a significant mediating effect. But the moderated mediating effect of safety behavior through safety culture was not significant. Logistics companies can improve logistics safety performance through the utilization of new logistics technologies such as intelligent logistics robots, autonomous driving technology, and artificial intelligence, etc. Originality/value - This is the first study to analyze the relationships between the acceptance of logistics technology in the 4th industrial revolution and logistics safety. In addition, previous studies analyzed mediating effects or moderating effects, but this is the first study to identify the moderated mediating effects of safety behavior through safety culture. In other words, it has originality in terms of research methodology.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.49-58
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• 2009

Top-down approach in the intelligent robot research has focused on the single object intelligence however, it has two weaknesses. One is that has a high cost and a long spending time of sensing, calculating and communications. The other is the difficulty of responding to react changes in the unpredictable environment. we propose the collective intelligence algorithm based on Bottom-up approach for improving these weaknesses and the applied agent model and verify by simulation. The Modified Flocking Algorithm proposed in this research is the algorithm which is modified version of the concept of the Flocking (Craig Reynolds) which is used to model the flocks, herds, and schools in the graphics or games, and simplified the operation of conventional Flocking algorithm to make it easy to apply for the number of group robots. We modeled the Boid agent and verified possibility collectivization of the Modified Flocking Algorithm by simulation. And We validated by the actual multiple mobile robot experiment.