• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.67-77
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• 2022

In South Korea, U-type girder development was attempted as a means to increase the length of I-type girder, but due to the large self-weight according to the post-tension method, the application of rail bridges of 30m or less is typical. There are not many examples of application of pre-tension type girder. This study does not limit the post-tension method, but applies the pre-tension method to induce a reduction in self-weight and materials used due to the reduction of the cross-section. In addition, we intend to apply the on-site pre-tensioning method using the internal reaction arm of the U-type girder. The prestressed concrete U-type girder bridge is composed of a concrete deck slab and a composite section. Compared to the PSC I-type, which is an open cross-section because the cross section is closed, structural performance such as resistance and rigidity is improved, the safety of construction is increased during the manufacturing and erection stage, and the height ratio is reduced due to the reduction of its own weight. Therefore, it is possible to secure the aesthetic scenery and economical of the bridge. As a result, it is expected that efficient construction will be possible with high-quality factory-manufactured members and cast-in-place members. In this paper, the introduction of the pre-tension method on-site and the analytical performance verification of the anchoring block for tension are included.

• 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 Information Security & Cryptology
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• v.33 no.2
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• pp.267-280
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• 2023

With development of computing and communications technologies, IoT environments based on high-speed networks have been extending rapidly. Especially, from home to an office or a factory, applications of IoT devices with sensing environment and performing computations are increasing. Unfortunately, IoT devices which have limited hardware resources can be vulnerable to cyber attacks. Hence, there is a concern that an IoT botnet can give rise to information leakage as a national cyber security crisis arising from abuse as a malicious waypoint or propagation through connected networks. In order to response in advance from unknown cyber threats in IoT networks, in this paper, We firstly define four types of We firstly define four types of characteristics by analyzing darknet traffic accessed from an IoT botnet. Using the characteristic, a suspicious IP address is filtered quickly. Secondly, the filtered address is identified by Cyber Threat Intelligence (CTI) or Open Source INTelligence (OSINT) in terms of an unknown suspicious host. The identified IP address is finally fingerprinted to determine whether the IP is a malicious host or not. To verify a validation of the proposed method, we apply to a Darknet on real-world SOC. As a result, about 1,000 hosts who are detected and blocked preemptively by the proposed method are confirmed as real IoT botnets.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.153-164
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• 2023

The objective of this study is to examine the removal rate of air pollutants, specifically sulfur oxides (SO_x) and nitrogen oxides(NO_x), using concrete permeable blocks containing zeolite beads coated with materials capable of eliminating these pollutants. Titanium dioxide(TiO₂) powder and coconut shell powder were utilized for the removal of SO_x and NO_x and were applied as coatings on the zeolite beads. Concrete permeable block specimens embedded with the coated zeolite beads were produced using an actual factory production line. Test results demonstrated that the concrete permeable block containing zeolite beads coated with coconut shell powder in the surface layer achieved SO_x and NO_x removal rates of 12.5% and 99%, respectively, exhibiting superior performance compared to other blocks. Additionally, the flexural strength and slip resistance were 5.3MPa and 65BPN or higher, respectively, satisfying the requirements specified in KS F 4419 and KS F 4561. Conversely, the permeability coefficient exhibited low permeability, with grades 2 and 3 before and after contaminant pollution, according to the standard for 'design, construction, and maintenance of pavement using permeable block'. In conclusion, incorporating zeolite beads coated with coconut shell powder in the surface layer enables simultaneous removal of SO_x and NO_x, irrespective of ultraviolet rays, while maintaining adequate flexural strength and slip resistance. However, the permeability is significantly reduced, necessitating further improvements.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.24-36
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• 2023

OSC is drawing attention to supplement limitations such as productivity problems and on-site safety and quality problems of existing on-site labor-oriented construction production methods. In order to activate the introduction and use of OSC in the domestic construction market, it is important to innovate the technology applied to each stage of OSC process (design and engineering, factory manufacturing, site assembly, and maintenance), but it is also necessary to develop a project management method suitable for OSC method. However, research related to OSC currently being conducted in Korea is mainly in terms of related technology development, and research on deriving project management measures for the success of OSC projects is insufficient. Therefore, it is time for research on deriving a project management plan based on the core success factors of the OSC project. Therefore, by conducting importance-performance analysis on 69 OSC critical success factors derived from the previous study, the study was conducted to derive key improvement factors for OSC introduction and utilization improvement and to provide implications for this. The results of this study are expected to have useful implications for the R&D planning and policy-making process for OSC activation in the future.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.286-293
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• 2022

Recently, the importance of elemental technologies constituting smart factories is increasing due to the 4th Industrial Revolution, and simulation is widely used as a tool to learn these technologies. In particular, PID control is an automatic control technique used in various fields, and most of them analyze mathematical models in certain situations or research on application development with built-in controllers. In actual educational environment requires PID simulator training as well as PID control principles. In this paper, we propose a model that enables education and practice of various PID controls through 3D simulation. The proposed model implemented virtual balls and Fan and implemented PID control by configuring a system so that the force can be lifted by the air pressure generated in the Fan. At this time, the height of the ball was expressed in a graph according to each gain value of the PID controller and then compared with the actual system, and through this, satisfactory results sufficiently applicable to the actual class were confirmed. Through the proposed model, it is expected that the rapidly increasing elemental technology of smart factories can be used in various ways in a remote classroom environment.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• 한국체육학회지인문사회과학편
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• v.55 no.1
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• pp.221-233
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• 2016

The purpose of this study was to prove a development and initial validation of the korean version of coach-athlete relationship maintenance scale that originated from the work of Rhind & Jowett(2012) in pro baseball. The items were then administered to 132 Participants(29 coaches and 103 athletes) completed the questionnaires of the coach-athlete relationship maintenance in First preliminary investigation. Maximum likelihood estimate was used to identify the latent underlying structure. In order to verify the validity of Korean version of coach-athlete relationship maintenance was administered to an independent sample of 273 coaches and athletes. Pro baseball coach-athlete relationship maintenance is consisted of six factors(25 items) with conflict management, motivational, preventative, openness/assurance, support, and social network. SPSS18.0 and AMOS16.0 were used to analyze the exploratory factor analysis, confirmatory factory analysis and internal consistency, test-retest with bootstrapping using of the data in this study. The results of the pro baseball coach-athlete relationship maintenance scale had six factors with 25 items, and each six factor was positively correlated. Overall, this study verified pro baseball coach-athlete relationship maintenance questionnaire. Thus, suggest that path of comparing the differences between the first division and farm team by using the test of the structural model invariance across the groups.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.