• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.127-134
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• 2019

With the advancement of IT technology, the amount of data generated has been growing exponentially every year. As an alternative to this, research on distributed systems and in-memory based big data processing schemes has been actively underway. The processing power of traditional big data processing schemes enables big data to be processed as fast as the number of nodes and memory capacity increases. However, the increase in the number of nodes inevitably raises the frequency of failures in a big data infrastructure environment, and infrastructure management points and infrastructure operating costs also increase accordingly. In addition, the increase in memory capacity raises infrastructure costs for a node configuration. Therefore, this paper proposes an in-memory-based hybrid big data processing scheme for improve the big data processing rate. The proposed scheme reduces the number of nodes compared to traditional big data processing schemes based on distributed systems by adding a combiner step to a distributed system processing scheme and applying an in-memory based processing technology at that step. It decreases the big data processing time by approximately 22%. In the future, realistic performance evaluation in a big data infrastructure environment consisting of more nodes will be required for practical verification of the proposed scheme.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.843-862
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• 2018

Purpose: The purpose of this study was to develop quality innovation techniques specialized for the small and medium-sized businesses. which account for the majority of Korean companies, were having a hard time utilizing the widely recognized quality innovation techniques due to resource constraints. Methods: First, we do review the existing Single PPM and 6 Sigma. And investigate the utilization of these methods including Toyota Production System. Second, we devised a four-step problem-solving methodology based on recent trends in quality innovation such as Simple, Speedy, and Smart. Third, we do survey on frequently used tools for quality innovation. Many opinion leaders including quality consultants and professors answered and gave us valuable comments about our selected quality tools. Finally, we do specify and map tools to each step of PASS. Results: In 2017, 167 companies participated in the quality innovation support business for small businesses according to the Korea Chamber of Commerce & Industry. We conducted performance checks on 167 companies that had completed the "PASS" projects. For the purpose of evaluating improvement performance, the survey was carried out using a structured questionnaire during the field visit of these companies mentioned above. For the reference, 165 out of 167 companies (98.8 % response rate) responded to the questionnaire and conducted performance analysis based on it. According to the survey, 97.6 percent of the respondents were very satisfied with their overall satisfaction with the quality innovation support projects for small and medium sized enterprises in 2017. Also, 93.3 % of the respondents were satisfied with the results of level of the target achievement. As a result, 160 companies (97.0 % of the participating companies) hope to partic ipate in the quality improvement project using "PASS" once again. Conclusion: In this paper, we introduce the new quality innovation methodology, which is named as 'PASS', It could support the long-range business plan of the small and medium-sized businesses to achieve total customer satisfaction resulting in increased market share and improved profit margin. The most small companies can use this "PASS" technique more easily, quickly and most efficiently than their existing known quality innovation techniques such as Six Sigma and Single PPM, etc.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.206-217
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• 2021

Purpose: Recently, a series of large social disasters have led to a lot of research to prevent social disasters as well as natural disasters and reduce damage. However, this paper aims to select the types of social disasters that local governments should focus on and create basic data for effective countermeasures and mitigation efforts. Method: Among 43 types of disasters announced by the Ministry of Public Administration and Security, 11 types of disasters were selected and collected to select the main types of disasters, and risk types were derived by region with risk maps. In order to derive the risk map, each detailed index was rescheduled to be 0-1 and weights were determined through entropy technique. Result: As a result, about 41% of the major disasters announced by the Ministry of Public Administration and Security were consistent, and the rest of the major types were disasters that could not be obtained or have not occurred in the past 20 years. Conclusion: Therefore, in order to establish an effective prevention and recovery plan for social disasters through this study, it was intended to present social disaster-focused disasters for each local government.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Korean Chemical Engineering Research
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• v.59 no.1
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• pp.68-76
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• 2021

As hazardous chemicals are releasing in process industries such as chemical & petro-chemical plants, the importance of initial responses has been always emphasized. However, little attention of quantitative analysis of the consequence by different initial responses during releasing of the chemicals has been done. The main objective of current paper is to investigate the effects of initial responses for the release accidents of hydrofluoric acid. For this, a simplified equation that can easily calculate the effect distance by varying concentrations of hydrofluoric acid was firstly deduced. In addition, a causal loops for the initial response steps using the system dynamics technique was constructed during release of 50% hydrofluoric acid. The effect distances according to different scenarios of the initial actions were also quantitatively analyzed by applying the simplified equation to the causal map. As a result, the highest reduction rate on the maximum effect distance was obtained with 'start time of action after leak detection' being about 87% while the lowest was 'arrival time of professional response team' being about 50%, as expected. It is expected that the results gained from the current study can be helpful as of basics of the initial response to the workplace, dealing with the hydrofluoric acid.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.671-678
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• 2021

In a previous study, technology protection levels were set on a qualitative basis. That study lacked quantitative standards, so here, we conduct a study to complement the previous study and to present an objective standard. This paper provides a method of setting a technical protection level that applies statistical analysis. To set the technology protection level, statistical analysis of six technical survey items is performed first. Second, the technical survey items are analyzed by AHP to quantify the opinions of experts in order to derive weights for each technical survey item. Finally, by using the normal distribution scatter map and median calculation method, the technology protection level is selected in three stages using the final detailed factor technology score reflecting the weight. The technology protection level methodology developed through this study is the first methodology with objectivity that can evaluate defense technology by level. If this methodology is applied in practice, it is believed that it will provide a scientific and quantitative technology value judgment criterion when setting the technology protection level in the future.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1025-1034
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• 2022

In this paper, we propose a post-processing method through interpolation of hole regions that occur when extracting point clouds. When image matching is performed on stereo image data, holes occur due to occlusion and building façade area. This area may become an obstacle to the creation of additional products based on the point cloud in the future, so an effective processing technique is required. First, an initial point cloud is extracted based on the disparity map generated by applying stereo image matching. We transform the point cloud into a grid. Then a hole area is extracted due to occlusion and building façade area. By repeating the process of creating Triangulated Irregular Network (TIN) triangle in the hall area and processing the inner value of the triangle as the minimum height value of the area, it is possible to perform interpolation without awkwardness between the building and the ground surface around the building. A new point cloud is created by adding the location information corresponding to the interpolated area from the grid data as a point. To minimize the addition of unnecessary points during the interpolation process, the interpolated data to an area outside the initial point cloud area was not processed. The RGB brightness value applied to the interpolated point cloud was processed by setting the image with the closest pixel distance to the shooting center among the stereo images used for matching. It was confirmed that the shielded area generated after generating the point cloud of the target area was effectively processed through the proposed technique.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.55-61
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• 2022

Currently police and fire departments use a Network/Wifi/GPS based emergency location positioning system established by mobile carriers to directly link with the device of the people who request the rescue to accurately position the expected location in the call area. However in the case of mountain rescue it is difficult to rescue the victim in golden time because the location of the search area cannot be limited when the victim is located in a radio shadow area of the mountain or the device power is off and this situation become worse if victim fail to report 911 by himself due to the injury. In this paper, we are expected to solve the previous problem by propose the mobile telecommunication forensic simulator consist of time series of cell information, human mobility model which include some general and specific features (age, gender, behavioral characteristics of victim, etc.) and intelligent infer system. The results of analysis appear in heatmap of polygons on the map based on the probability of the expected location information of the victim. With this technology we are expected to contribute to rapid and accurate lifesaving by reducing the search area of rescue team.