• Journal of IKEEE
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• v.25 no.1
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• pp.95-100
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• 2021

If we know the location of the hull block and the welding feeder in the shipyard, we can easily obtain the location information of the worker. That data is very useful for implementing a workplace safety monitoring system. However, it is difficult to apply a fixed communication network to the workplace due to the specificity of the hull structure and welding process. In this study, inductive power line communication, which can replace dedicated communication line, was reviewed. A ferrite core was used as an inductive coupler to be installed on the power cable of the welding machine, and a nano-crystalline core was applied as a coupler to be fastened to the support rod of the metal block. In order to visualize the operating principle of the proposed couplers, 3D modeling and finite element analysis were performed with the COMSOL AC/DC module. In the communication performance test using an aluminum profile, when the communication channel was formed by the contact of the welding electrode, the bandwidth was kept above 6 Mbps.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.2
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• pp.43-48
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• 2021

Precast concrete can be used to reduce construction period and enhance construct ability. However structural problems could be occurred due to the wrong application of boundary condition and misunderstanding of structural behavior in the process of segmentation of original structure system. I experienced a serious deflections and cracks due to the increase of bending moment and creep after the construction of precast concrete slab, and we learned that this is from the misunderstanding of support conditions and structure behaviors of precast slab panel. Two support columns under the precast slab are inserted to reduce the bending moment, and the camber according to jacking force should be estimated for the structural safety during the reinforcing work. A proper support condition and the flexural stiffness of precast concrete slab were applied to check the deflection and crack for existing structure by inverse analysis, and we can estimate the camber according to jacking force of the precast concrete slab, and suggest a method to make safe structure.

• Journal of Engineering Education Research
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• v.23 no.6
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• pp.3-16
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• 2020

This study aims to introduce the ENACT model, which is a systematic teaching-learning model for cultivating social responsibility of science and engineering college students, and to discuss its educational implications. For the development of the ENACT model, we conducted extensive literature reviews on RRI, STEM education, and science and technology studies (STS). In addition, we examined exemplary overseas education programs emphasizing social responsibility of scientists/engineers and citizens. The ENACT model consists of five steps; 1) Engage in SSIs, 2) Navigate SSIs, 3) Anticipate consequences, 4) Conduct scientific and engineering practice, and 5) Take action. This model links Socioscientific Issues (SSI) education with engineering education, dividing the major elements of social responsibility education for scientists and engineers into the dimensions of epistemology and praxis, and reflected them in the model. This effort enables science and engineering college students to pursue more responsible and sustainable development by carrying out the responsible problem-solving process based on an understanding of the nature of science and technology. We plan to implement ENACT model based programs for science and engineering college students and to examine the effects.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.1-7
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• 2021

This study attempted to detect possible collapse and fire accidents in facilities for disaster monitoring of large facilities, and to develop a customized beacon to recognize the internal situation of an IoT-based facility when a disaster occurs. In the case of data measurement using the existing strain gauge sensor, the strain gauge sensor was connected by wire to measure it, but this study changed it to wireless so that the presence and absence of structural deformation can be monitored in real time. In this process, in order to use the Wheatstone bridge, a strain sensor module that can be connected to a customized beacon was manufactured, and a system configuration was conducted to remotely check the measurement data. To verify measurement data, 10 customized beacons and 2 gateways were installed on the 15th floor of the Advanced Institue of Convergence Technology, and as a result of analysis of measurement data, it was confirmed that the strain data values were distributed between 7 and 8.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.411-434
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• 2022

In deep geological disposal concept for spent nuclear fuel, it is well-known that rock mass at near-field experiences the thermal-hydraulic-mechanical (THM) coupled behavior. The mechanical properties of rock changes during the coupled process, and it is important to consider the changes into the analysis of numerical simulation and in-situ tests for long-term stability evaluation of nuclear waste disposal repository. This report collected the previous studies on indirect coupled behaviors of rock. The effects of water saturation and temperature on some mechanical properties of rock was considered, while the change in hydraulic conductivity of rock due to stress was included in the indirect coupled behavior.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.61-69
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• 2022

As the delivery industry grew around the restaurant industry along with the COVID-19 situation, the number of delivery workers increased significantly. Along with that, new forms of delivery using personal mobility (PM) also emerged and two-wheeled or PM-related accidents are steadily increasing. This study manufactures a PM's driving analysis device to establish a safe delivery monitoring environment. This system was constructed to process data collected from the driving analysis device and through a cloud server, which would recognize and record special situations (acceleration/deceleration, speed bump) that could occur during the PM's driving situation. As a result, the angular speed, acceleration, and geomagnetic values collected from the IMU in the device were able to determine whether to drive, drive on the sidewalk, and drive on the speed bump. This technology was able to achieve approximately 1600 times more driving information storage efficiency than conventional image-based recording devices.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.66-83
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• 2022

Artificial Intelligence (AI) is to assumed to be one of next generation technology which determine technological competitiveness and strategic advantage of a certain country. By using the patent data, this study aims to have a comparative analysis of the technological competitiveness of evolving artificial intelligence at different stages of development among the five largest intellectual property offices in the world (IP5). For the analysis data, all AI technology patent data from 1956 to 2019 were utilized according to the classification system presented in the "WIPO 2019 Technology Trend: Artificial Intelligence" report published by the World Intellectual Property Organization (WIPO) in 2019. The results shows that China has already surpassed the United States in terms of the number of patent applications in the field of artificial intelligence technology. However, in the domains of the United States, Europe, Japan, and Korea, the technology competitiveness of the United States is far ahead of China. Interestingly, the rate of increase of Korea's technology competitiveness is also very fast, and it has been shown that the technology strength is ahead of China in non-Chinese domains. The significance of this study can be found in the fact that the temporal and spatial change process of technological competitiveness of significant countries in the field of artificial intelligence technology artificial intelligence was viewed as a macro-framework using the technology index (TS) the differences were compared.