• Journal of Korean Society of Steel Construction
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• v.27 no.6
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• pp.503-511
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• 2015

In long span steel structure, the plate girder reinforced with stiffeners are commonly used. When choosing the cross section with deep depth of girder as well as narrow width, however, out of plane buckling can be a problem due to web slenderness. In an effort to solve this issue, current study determined the applicability of using corrugated web girder with deep depth as bending member, which is generally being utilized in both factory and warehouse nationwide. To accomplish this, we performed the loading test of H-shaped beam with sinusoidal corrugated web. Corrugated web CP-2.3 specimen exhibited 12% less maximal bending strength but CP-3.2 specimen exerted 24% increase in strength compared to plate web P-4.5. this result indicates that corrugated web provides enough strength even with unfavorable width-thickness ratio of plate. And bending as well as shear strength estimated by the Eurocode (EN 1993-1-5) were compared with both bending strength by loading test and shear strength estimated by KBC2009. In case of eurocode, increase in plate thickness did not help in bending performance improvement. moreover, shear performance was sensitive to the thickness of the web folds and the shape of the web plate.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.1-15
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• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.191-203
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• 2018

Identify the effects of ISO quality management system on the durability of the enterprise by applying it to early startups and small - and medium-sized enterprises. In particular, the quality management system of a technology start-up small and medium sized companies is important in applying the management system to the financial, management and quality control services rather than to research and development. The quality management system is applied to the medical equipment manufacturers, mobile phone brokers, foreign Chinese companies, and components manufacturers for diesel engines.Try it. Check the case where the quality management system has been applied to the company concerned and the nonconformities have been improved. In addition, the success of the project was determined by applying the quality control system to maintain the stability and perpetuation of the company through the case of the failed selection. The company explains that recently, it must appoint internal auditors to carry out continuous improvement activities and that those who want the development and continuity of the company must reflect the quality management system.

• Industry Promotion Research
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• v.6 no.4
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• pp.23-30
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• 2021

This study is a study on the satisfaction of the living conditions of the residents of Chungcheongnam-do, and the analysis data was based on the results of the Chungcheongnam-do social survey conducted in 2020 by Statistics Korea. The results of the analysis on the satisfaction of the living conditions of the residents of Chungnam Province are summarized as follows. The level of satisfaction with the quality of life of the living environment, which is a personal characteristic, was 5.92 out of 10 for the degree of satisfaction with one's life, and 6.28 out of 10 for the overall value of the work one is doing. The overall life satisfaction of the region (city and gun) was analyzed as 5.81 out of 10, indicating that the satisfaction of Chungnam residents was more than average. In addition, satisfaction with the residential housing environment was analyzed with the highest frequency and ratio of 43.5%, with 226 people being slightly satisfied. Satisfaction with facility use was also slightly higher in 231 people, showing 44.5% response rate, and slightly higher in women than in men. This study is meaningful in that it provides basic data such as policy implications for improving the quality of life by grasping the social interests related to the quality of life and the subjective consciousness of the people of Chungnam.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.59-66
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• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.175-185
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• 2023

Upon analyzing labor productivity in the construction industry across OECD countries, it was found that in 2019, labor productivity per employee in the South Korean construction industry was lower than that of major developed countries when adjusted for purchasing power parity(PPP). Specifically, when benchmarked against other countries at a base of 100, South Korea scored 76.9 in the United States, 88.4 in Japan, and 85.1 in the OECD average. Notably, South Korea ranked 25th in labor productivity per employee in the construction industry among 35 OECD countries in 2019, indicating a low standing. A comparative analysis of the construction market size and labor productivity in the construction industry across OECD countries revealed that larger construction markets did not necessarily correlate with higher labor productivity. To enhance labor productivity in the construction industry, this study proposed the active implementation of smart construction technology at construction sites and the promotion of on-site assembly work using off-site construction(OSC) technology, rather than traditional on-site labor. Moreover, it was recommended that the development of modular construction methods and technologies be expanded. In the future, if off-site production methods and modules are further developed through advanced robotics and factory automation, labor productivity is anticipated to increase due to the restructuring of production methods, such as manufacturing.