• 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%.

• Journal of Practical Agriculture & Fisheries Research
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• v.19 no.1
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• pp.5-14
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• 2017

This study was conducted to investigate the effects of nutrient solution strength on growth and nutrient element concentrations in leaf lettuce (Lactuca sativa L. cv. 'Dduksum') by hydroponic culture under fluorescent lamp and LED. Leaf lettuce were grown in closed hydroponic cultivation systems supplied with 1/2, 1 and 2 strength of nutrient solution recommended by horticultural experiment station in Japan. The growth of 'Dduksum' was highest in the 2 strength of standard nutrient solution. The amount of nutrient element in the recycled nutrient solution was higher at 2, 1 and 1/2 strength of nutrient solution. The concentration of NO₃-N, Ca²⁺, Mg²⁺ in the recycled nutrient solutions increased in 1 and 2 strength of nutrient solution but that of NH₄-N decreased gradually in 1/2 and 1 strength of nutrient solution. The concentration of K, Ca, Mg in leaf lettuce was maintained in the normal range, whereas the concentration of phosphorous was 1.3 to 1.6%, which was higher than proper range. As the concentration of NH₄-N decreases gradually in all the treatments, it is necessary to raise the rate of NH₄-N or add it.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.601-608
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• 2022

This study examines the current status of smart manufacturing innovation policies in Germany and Korea, compares and analyzes the supply industry strategies of both countries, and suggests the direction for Korea's smart manufacturing innovation supply industry. Germany's supply industry strategy aims to strengthen the market dominance of domestic suppliers through high technology, compatibility, and high reliability based on reference for global demanding companies. On the other hand, the Korea's supply industry strategy remains at the level improvement of the demanding companies by stage, so it is time to take a long-term and consistent response with the goal of implementing smartization at the advanced level. By referring to Germany's supply industry strategy for the advancement of smart factories, it was intended to help in establishing government support policies and supplier strategies. In addition, based on the analysis results of the supply industry strategies of both countries, improvement measures for the advancement of Korea's smart factories were presented. Ultimately, the contents of this study can be used as basic data for policy establishment to strengthen the industrial competitiveness of Korea's small and medium-sized suppliers.

• 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 the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.223-237
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• 2023

The Seoul Onsu Industrial Complex, having been completed over 50 years ago, is an old industrial complex, with deteriorating infrastructure and factory buildings. Despite this, there's a current urban industrial agglomeration centered on the machinery industry in the Seoul Onsu Industrial Complex. This study aims to holistically analyze the physical deterioration of facilities in the aging industrial complex and the characteristics of industrial agglomeration to derive the identity of the Seoul Onsu Industrial Complex. Based on the research findings, the complex is seeing an enhanced urban industrial agglomeration due to the influx of small-scale businesses resulting from concentrated trade networks in the metropolitan area and plot subdivision, permission for noise-producing processes, and the ease of securing highly-skilled technicians. However, this agglomeration coexists with a weakening of the complex's production function, limited innovativeness of resident companies, and non-innovative features resulting from weakened competitiveness in the metropolitan machinery industry. In summary, the identity of the Seoul Onsu Industrial Complex is a 'Non-Innovative Urban Industry Agglomeration', an old industrial complex, witnessing non-innovative agglomeration based on a machinery industry network centered in the metropolitan area.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Journal of the Korea Institute of Construction Safety
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• v.6 no.1
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• pp.12-18
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• 2024

High-rise, large-scale, and diversification of buildings are possible, and the reduction of concrete cross-sections reduces the weight of the structure, thereby increasing or decreasing the height of the floor, securing a large number of floors at the same height, securing a large effective space, and reducing the amount of materials, rebar, and concrete used for designating the foundation floor. In terms of site construction and quality, a low water binder ratio can reduce the occurrence of dry shrinkage and minimize bleeding on the concrete surface. It has the advantage of securing self-fulfilling properties by improving fluidity by using high-performance sensitizers, making it easier to construct the site, and shortening the mold removal period by expressing early strength of concrete. In particular, with the rapid development of concrete-related construction technology in recent years, the application of ultra-high-strength concrete with a design standard strength of 100 MPa or higher is expanding in high-rise buildings. However, although high-rise buildings with more than 120 stories have recently been ordered or scheduled in Korea, the research results of developing ultra-high-strength concrete with more than 130 MPa class considering field applicability and testing and evaluating the actual applicability in the field are insufficient. In this study, in order to confirm the applicability of ultra-high-strength concrete in the field, a preliminary experiment for the member of a reduced simulation was conducted to find the optimal mixing ratio studied through various indoor basic experiments. After that, 130 MPa-class ultra-high-strength concrete was produced in a ready-mixed concrete factory in a mock member similar to the life size, and the flow characteristics, strength characteristics, and hydration heat of concrete were experimentally studied through on-site pump pressing.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.5-14
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• 2024

PHC piles demonstrate superior resistance to compression and bending moments, and their factory-based production enhances quality assurance and management processes. Despite these advantages that have resulted in widespread use in civil engineering and construction projects, the design process frequently relies on empirical formulas or N-values to estimate the soil-pile friction, which is crucial for bearing capacity, and this reliance underscores a significant lack of experimental validation. In addition, environmental factors, e.g., the pH levels in groundwater and the effects of seawater, are commonly not considered. Thus, this study investigates the influence of vibrating machine foundations on PHC pile models in consideration of the effects of varying pH conditions. Concrete model piles were subjected to a one-month conditioning period in different pH environments (acidic, neutral, and alkaline) and under the influence of seawater. Subsequent repeated direct shear tests were performed on the pile-soil interface, and the disturbed state concept was employed to derive parameters that effectively quantify the dynamic behavior of this interface. The results revealed a descending order of shear stress in neutral, acidic, and alkaline conditions, with the pH-influenced samples exhibiting a more pronounced reduction in shear stress than those affected by seawater.