• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.10
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• pp.901-908
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• 2012

In an intelligent automated manufacturing environment, an administrator may use M2M (Machine-to-Machine) communication to recognize machine movement and the environment, as well as to respond to any potential dangers. However, commonly used wireless protocols for this purpose such WLAN (Wireless Local Area Network), ZigBee, and Bluetooth use the same ISM (Industrial Science Medical) band, and this may cause frequency interference among different devices. Moreover, an administrator is frequently exposed to dangerous conditions as a result of being surrounded by densely distributed moving machines. To address this issue, we propose in this paper to employ a location-based frequency interference management using fingerprinting scheme in industrial environments and its advanced localization schemes based on k-NN (Nearest Neighbor) algorithms. Simulation results indicate that the proposed schemes reduce distance error, frequency interference, and any potential danger may be responded immediately by continuous tracing of the locations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.829-832
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• 2009

Recently, IT-centered convergence between different industries has undergone rapid expansion, accompanied by major advances in u-IT development and digital convergence. Notably, in the automotive-IT convergence sector, automotive sensors and electronic devices interact closely and intelligently with each other, thereby increasing driver safety and convenience and creating the optimal driving environment. This has led to the generation of value-added for the future-oriented automotive industry. Sensing technologies - which are used to monitor traffic situations and transmit correct information (or warnings) on the road traffic situation to car drivers, and to provide accurate information to road traffic controllers - represent both the birth of high-safety, intelligent automotive technologies and the key to automotive manufacturing. In view of these developments, this paper examines the characteristics and structure of the automotive sensor industry, and outlines the policy implications for the automotive sensor industry with regard to the development of the automotive-IT convergence industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.89-94
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• 2021

Recently, manufacturing companies are introducing many intelligent production processes that apply IIoT/ICT to improve competitiveness, and a system that maintains availability, improves productivity, and optimizes management costs is needed as a preventive measure using environmental data generated from air ejectors. Therefore, in this study, a dedicated control board was developed and LoRa communication module was applied to remotely control it to collect and manage information about compressors from cloud servers and to ensure that all operators and administrators utilize common data in real time. This dramatically reduced M/S steps, increased system operational availability, and reduced local server operational burden. It dramatically reduced maintenance latency by sharing system failure conditions and dramatically improved cost and space problems by providing real-time status detection through wired and mobile utilization by maintenance personnel.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.99-110
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• 2019

As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

• Korean Chemical Engineering Research
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• v.57 no.3
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• pp.425-431
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• 2019

As rechargeable metal-air batteries will be ideal energy storage devices in the future, an active cathode electrocatalyst is required with bi-functionality on both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) during discharge and charge, respectively. Here, a class of perovskite cathode catalyst with a micro-tubular structure has been developed by controlling bi-functionality from different Ru and Ni dopant ratios. A micro-tubular structure is achieved by the activated carbon fiber (ACF) templating method, which provides uniform size and shape. At the perovskite formula of $LaCrO_3$, the dual dopant system is successfully synthesized with a perfect incorporation into the single perovskite structure. The chemical oxidation states for each Ni and Ru also confirm the partial substitution to B-site of Cr without any changes in the major perovskite structure. From the electrochemical measurements, the micro-tubular feature reveals much more efficient catalytic activity on ORR and OER, comparing to the grain catalyst with same perovskite composition. By changing the Ru and Ni ratio, the $LaCr_{0.8}Ru_{0.1}Ni_{0.1}O_3$ micro-tubular catalyst exhibits great bi-functionality, especially on ORR, with low metal loading, which is comparable to the commercial catalyst of Pt and Ir. This advanced catalytic property on the micro-tubular structure and Ru/Ni synergy effect at the perovskite material may provide a new direction for the next-generation cathode catalyst in metal-air battery system.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.49-58
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• 2019

In this paper, we propose an edge cloud platform architecture for implementing smart factory. The edge cloud platform is one of edge computing architecture which is mainly focusing on the efficient computing between IoT devices and central cloud. So far, edge computing has put emphasis on reducing latency, bandwidth and computing cost in areas like smart homes and self-driving cars. On the other hand, in this paper, we suggest not only common functional architecture of edge system but also light weight cloud based architecture to apply to the specialized requirements of smart factory. Cloud based edge architecture has many advantages in terms of scalability and reliability of resources and operation of various independent edge functions compare to typical edge system architecture. To make sure the availability of edge cloud platform in smart factory, we also analyze requirements of smart factory edge. We redefine requirements from a 4M1E(man, machine, material, method, element) perspective which are essentially needed to be digitalized and intelligent for physical operation of smart factory. Based on these requirements, we suggest layered(IoT Gateway, Edge Cloud, Central Cloud) application and data architecture. we also propose edge cloud platform architecture using lightweight container virtualization technology. Finally, we validate its implementation effects with case study. we apply proposed edge cloud architecture to the real manufacturing process and compare to existing equipment engineering system. As a result, we prove that the response performance of the proposed approach was improved by 84 to 92% better than existing method.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.37-47
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• 2021

In this paper, we propose a new deep learning model for tongue segmentation with improved accuracy compared to the existing model by diversifying the receptive field in the U-net. Methods such as parallel convolution, dilated convolution, and constant channel increase were used to diversify the receptive field. For the proposed deep learning model, a tongue region segmentation experiment was performed on two test datasets. The training image and the test image are similar in TestSet1 and they are not in TestSet2. Experimental results show that segmentation performance improved as the receptive field was diversified. The mIoU value of the proposed method was 98.14% for TestSet1 and 91.90% for TestSet2 which was higher than the result of existing models such as U-net, DeepTongue, and TongueNet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.792-800
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• 2021

This paper presents a new structure for a linear active magnetic bearing using a Halbach magnet array. The proposed magnetic bearing consisted of a Halbach magnet array, center magnet, and single coil. The proposed linear active magnetic bearing has a high dynamic force compared to the previous study. The high dynamic force could be obtained by varying the thickness of a horizontally magnetized magnet. The new structure of Halbach linear active magnetic bearing has a high dynamic force. Therefore, the proposed linear active magnetic bearing increased the bandwidth of the system. Magnetic modeling and optimal design of the new structure of the Halbach linear active magnetic bearing were performed. The optimal design was executed on the geometric parameters of the proposed linear active magnetic bearing using Sequential Quadratic Programming. The proposed linear active magnetic bearing had a static force of 45.06 N and a Lorentz force constant of 19.54 N/A, which is higher than previous research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.