• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.485-492
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• 2021

The wind turbines with a rated capacity of 50kW or less are generally considered as small class. Small wind turbines are an attractive alternative for off-grid power system and electric home appliances, both as stand-alone application and in combination with other energy technologies such as energy storage system, photovoltaic, small hydro or diesel engines. The research objective is to develop the 50kW scale wind turbine blades in ways that resemble as closely as possible with the construction and methods of utility scale turbine blade manufacturing. The mold process based on wooden form is employed to create a hollow, multi-piece, lightweight design using carbon fiber and fiberglass with an epoxy based resin. A hand layup prototyping method is developed using high density foam molds that allows short cycle time between design iterations of aerodynamic platforms. A production process of five blades is manufactured and key components of the blade are tested by IEC 61400-23 to verify the appropriateness of the design. Also, wind system with developed blades is tested by IEC 61400-12 to verify the performance characteristics. The results of blade and turbine system test showed the available design conditions for commercial operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.268-278
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• 2021

Technologies that are leading the fourth industrial revolution, such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cyber-physical systems (CPS) are developing and generalizing. The demand to improve productivity, economy, safety, etc., is spreading in various industrial fields by applying these technologies. Digital twins are attracting attention as an important technology trend to meet demands and is one of the top 10 tasks of the Korean version of the New Deal. In this study, papers, magazines, reports, and other literature were searched using Google. In order to investigate the contribution or role of geospatial information in the digital twin application, the definition of a digital twin, we investigated technology trends of domestic and foreign companies; the components of digital twins required in manufacturing, plants, and smart cities; and the core techniques for driving a digital twin. In addition, the contributing contents of geospatial information were summarized by searching for a sentence or word linked between geospatial-related keywords (i.e., Geospatial Information, Geospatial data, Location, Map, and Geodata and Digital Twin). As a result of the survey, Geospatial information is not only providing a role as a medium connecting objects, things, people, processes, data, and products, but also providing reliable decision-making support, linkage fusion, location information provision, and frameworks. It was found that it can contribute to maximizing the value of utilization of digital twins.

• 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.

• Korean Journal of Food Science and Technology
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• v.54 no.3
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• pp.264-271
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• 2022

In this study, the nutritional properties of sterilized and non-heat-pressed raw perilla oil (SRPO) were studied and its potential as a functional food was evaluated. The copper, cobalt, and calcium levels were high in sterilized and SRPO. The total polyphenol content and ABTS radical scavenging activity were the highest in SRPO, whereas nitrite scavenging activity was the highest in 45℃ cold pressed perilla oil (CPPO). The above results confirmed that sterilized and non-heat-pressed perilla oil had high mineral and total polyphenol contents, as well as ABTS radical scavenging activity and nitrite scavenging ability. The peroxide value of SRPO decreased as the storage period increased, and the acid value of low-temperature pressed perilla oil over 65℃ (LPPO) significantly increased. This work also provided an opportunity to develop a new method for manufacturing perilla oil, and it is hoped that these experiments will form a basis for the commercialization of perilla oil.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.77-90
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• 2022

This study seeks to develop a stretch sensor for measuring the wrist movements of people using fishing lures. In order to confirm wrist movement, a stretch sensor was attached to the wrist band, and measurements of the dorsiflexion, plantar flexion, and fishing landing motion were measured using a scale to gauge factor, tensile strength, and elongation recovery rate. A conductive sensor using CNT dispersion was developed and applied to the E-band under the same conditions. A total of 15 sensors of the same size and five types of impregnation once, twice, and three times each were used to measure the gauge factor using UTM. The sensor that was impregnated twice had the best gauge rate, and the prototypes were manufactured with three sensors with high gauge rates and tensile strength. The results of the operation test conducted by connecting to the Arduino showed that Sample 1, which had the highest tensile strength and gauge factor, had a stable graph wavelength in three operations. Samples 2 and 3 showed stable wavelengths in the dorsiflexion and the plantar flexion; however, signal noise appeared in the fishing landing motion. This showed stable wavelengths in the two motions, but the wavelengths of the graphs differ depending on the tensile strength and gauge factor in the fishing landing motion. As a result, it was possible to identify the conditions necessary for manufacturing a stretch sensor for measuring wrist movement. This study will contribute to the development of smart wearable products for lure fishing.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.19-36
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• 2020

The bidding phase for logistics outsourcing services is critical for both shippers and logistics companies. According to the logistics bidding phase, the shipper should provide logistics operation information to logistics companies to resolve uncertainty. In addition, the logistics company can win the contract volume that was placed in the bid by expressing their experience and know-how, and proposing to share the risks and benefits of the shipper's logistics operation. Therefore, it is necessary to examine the factors that can be identified during the bidding phase for logistics outsourcing and how these factors affect the satisfaction of logistics outsourcing services. Based on the factors identified in the preceding studies on logistics outsourcing partnership factors and those on logistics outsourcing determinants, a survey was conducted on experts engaged in logistics companies, performing logistics for domestic shippers and analyzed using Smart-PLS. This study presents the following implications. First, in the logistics bidding phase, the shipper should provide its logistics operation information to logistics firms to resolve uncertainties. Details An in-depth explanation of the operation details will be presented via the bidding presentation, and on-site tours of manufacturing plants and logistics centers should also be carried out if necessary. Second, in the bidding phase, logistics companies should appeal through proposals to their competitiveness, such as experience and knowledge of the logistics of the shipper, and also consider alliances with other logistics companies to supplement their insufficient logistics services. Third, logistics companies should make proposals to share profits and risks through logistics outsourcing during the bidding phase, propose accepting risks from environmental uncertainties of the shipper within its capacity to an acceptable extent, and share the benefits of carrying out the shipper's logistics.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.