• Journal of Korea Technology Innovation Society
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• v.10 no.1
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• pp.22-46
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• 2007

This article introduces a recent innovation in Korea's human resources development policy in the SW sector. Facing serious problems in cultivating SW engineers such as a mismatch in supply and demand of SW workers, shortage of globally competitive SW professionals, and insufficient education and training of university graduates, the Korean government has decided to adopt a new paradigm in national SW engineering education, based on supply chain management (SCM) in manufacturing. SCM has been a major component of the corporate competitive strategy, enhancing organizational productiveness and responsiveness in a highly competitive environment. It weighs improving competitiveness of the supply chain as a whole via long-term commitment to supply chain relationships and a cooperative, integrated approach to business processes. These characteristics of SCM are believed to provide insight into a more effective IT education and university-industry collaboration. On the basis of the SCM literature, a framework for industry-oriented SW human resources development is designed, and then applied in the case of nurturing computer-software engineers in Korea. This approach is expected to fumish valuable implications not only to Korean policy makers, but also to other countries making similar efforts to enhance the effectiveness and flexibility in human resources development. The construction of SCM-based SW HRD model is first trial to apply SCM into SW HRD field. The model is divided into three kinds of primary activities and two kinds of supportive activities in the field of value chain such as SW HRD Council, SW demand and supply plan establishment and the integration of SW engineering capabilities that contribute the reduction of the skill and job matching through SW HR demand and supply collaboration.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Archives of design research
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• v.2 no.1
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• pp.13-41
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• 1989

The raw materials for plant-stalk-folkcrafts are cultivated in the whole country. Most Korean farmers, to increase their income, have produced mats and containers with various plants as a side line from very long ago. At first, they began from the instruments of life and then made folkart and at last get to manu\ulcornerfacturing of industrial folk craft. The folkcrafts, made of plant-stalk, which could nor conform to changing society, are partly declined and partly handed down as the traditions. The social change today, however, makes living conditions more speedy and multisided, accordingly the characteristics of demand also become in\ulcornerdividual and various. While the demend is various like this, suppliers cannot answer demendants' requirements, and consequently, the demand and profit cannot be increased. According to this, the purpose of this study is set up to give an answer to the situation that is at the traditional standstill, through an examination of the motives. I. The crafts of plant-stalk are made only in an organized relationship between agriculture, industry and art as it is compounded art of gathering raw material, manufacturing, producing, improv\ulcornering design and production conditions. It may be possible that a farmer gathers material and weaves it manually but in others, it is im\ulcornerpossible to refine, bleach and dye because the process requires a professional industrial treatment. It is impossible to make art works to a farmer as every farner does not always have aesthetic sense. Though a farmer or producer has these all abilities, it is not desirable to him from economical view. 2. The development or improvement is essential in many sides but the most important thing seems to he in design. According to reports, it is, howevt!r, fact that the crafts improved in design of existing works have more expanded the sales than newly developed works. Therefore, ir appears advisable to improve designs of existing things positively as they have merit of occupying a position already, but on the other hand, new crafts have to be also developed and the producer should grasp the proper time. 3. Building up an industrial complex to improve design with collecting the producing districts for this works scattered allover the country is very desirable for speedy communication, intensive educa\ulcorner tion or training, and effective guidance. 4. In producing for export abroad, before everything, must know the life environment, custom! and manners, main thought of the country, and then produce according to these. S. The crafts of plant-stalk are the fIrst industry in present but in the futher it should change intc second or third industry. 6. A synethetic organization for supporting side line should be established for effectiveness, and experts have to be secured and also the educational-industrial complex and activation of study should be preceded.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.892-902
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• 2015

Agri-food ICT(Information and Communications Technologies) convergence has been raised as an important issue for agricultural industry competence. In this situation, this study is to enhance agricultural competitiveness and seek to development plan for agricultural corporation by diagnosing informatization level. For this purpose, this study conducted survey on informatization level of 3,019 agricultural corporations and calculated level score. And result is compared with SMEs(Small and Medium Enterprise) informatization survey, including manufacturing and service industries, conducted by Korea Technology & Information Promotion Agency for SMEs in recent agricultural corporations' growing with automation of agricultural production and improving service to customer satisfaction. Evaluation system is established to calculate informatization level score and AHP(Analytic Hierarchy Process) method was used by the experts to investigate weighting of assessment area, assessment indicators, assessment items. As a result, agricultural corporation informatization level score was 40.16 points which is lower than the benefitted organization of agri-food IT convergence modeling(43.44 points). By assessment area, the informatization level of promotional environment area was low and investment and training items were analyzed low especially so need to improve urgently. In the analysis result by organization type, agricultural company corporation's informatization level was higher than the agricultural association corporation and 'Processing and distribution' was higher than others by business type. Informatization level of agricultural corporation is 80 percent of 2013 SMEs' level(50.18 points) and 59.4 percent of a large corporation(67.64 points). In particular, big difference is occurred in investment feasibility analysis, informatization investment and education which will be need to improve.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.40
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• pp.35.1-35.7
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• 2018

Background: After the resection at the mandibular site involving oral cancer, free vascularized fibular graft, a type of vascularized autograft, is often used for the mandibular reconstruction. Titanium mesh (T-mesh) and particulate cancellous bone and marrow (PCBM), however, a type of non-vascularized autograft, can also be used for the reconstruction. With the T-mesh applied even in the chin and angle areas, an aesthetic contour with adequate strength and stable fixation can be achieved, and the pores of the mesh will allow the rapid revascularization of the bone graft site. Especially, this technique does not require microvascular training; as such, the surgery time can be shortened. This advantage allows older patients to undergo the reconstructive surgery. Case presentation: Reported in this article are two cases of mandibular reconstruction using the ready-made type and custom-made type T-mesh, respectively, after mandibular resection. We had operated double blind peer-review process. A 79-year-old female patient visited the authors' clinic with gingival swelling and pain on the left mandibular region. After wide excision and segmental mandibulectomy, a pectoralis major myocutaneous flap was used to cover the intraoral defect. Fourteen months postoperatively, reconstruction using a ready-made type T-mesh (Striker-Leibinger, Freibrug, Germany) and iliac PCBM was done to repair the mandible left body defect. Another 62-year-old female patient visited the authors' clinic with pain on the right mandibular region. After wide excision and segmental mandibulectomy on the mandibular squamous cell carcinoma (SCC), reconstruction was done with a reconstruction plate and a right fibula free flap. Sixteen months postoperatively, reconstruction using a custom-made type T-mesh and iliac PCBM was done to repair the mandibular defect after the failure of the fibula free flap. The CAD-CAM T-mesh was made prior to the operation. Conclusions: In both cases, sufficient new-bone formation was observed in terms of volume and strength. In the CAD-CAM custom-made type T-mesh case, especially, it was much easier to fix screws onto the adjacent mandible, and after the removal of the mesh, the appearance of both patients improved, and the neo-mandibular body showed adequate bony volume for implant or prosthetic restoration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4924-4931
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• 2011

This research performed to evaluate a production processes reporting by the HACCP system of green vegetable juice products, containing lactic acid bacteria, stage of processing raw materials agricultural products and production facilities of general bacteria and pathogenic micro organism. General bacteria are found from four samples of storage of agricultural products at process stage and water was detected 8.67~14.67 CFU/ml. However, all samples were detected less than 105 CFU/ml as a legal standards after the process of UV sterilization. For the outcome of experiment of E.coli, E.coli O157:H7, B.cereus, L.moonocytogenes, Salmonella spp, Staph.aureus as the food poisoning bacterial, E.coli was detected until UV pre-step process in storage process and B.cereus was detected partly till 1st washing. Since all bacterial, Yeast and Mold are detected in main materials, pre-control method is a necessary to establish for decreasing with a number of initial bacteria of main materials and it is considered to establish the effective ways of washing and sterilization such as production facilities for cross contamination prevention of bacteria and Sthaphylococcus. Based on above results, the process of UV sterilization should be managed with CCP as an important process to reduce or eliminate the general and food poisoning bacterial of green vegetable juice products, including lactic acid bacteria. Therefore, it is considered to need an exhaustive HACCP plan such as control manual of UV sterilization, solution method, verification, education and training and record management.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Composites Research
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• v.36 no.1
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• pp.48-52
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• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.