• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.337-346
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• 2022

This paper addresses the problem of vision-based ear loops ansd attachment inspection in mask production lines. This paper focuses on connections with ear loops and mask filter by an efficient combined approach. The proposed method used a template matching, shape detection and summation of histogram with preprocessing. We had a parameter for detecting defects heuristically. If the shape vertices are lower than the parameters our proposed method will find defective mask automatically. After finding normal masks in mask ear loops attachment status inspection algorithm our proposed method conducts attachment amount inspection. Our experimental results showed that the precision is 1 and the recall is 0.99 in the mask attachment status inspection and attachment amount inspection.

• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.903-911
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• 2022

A shrimp quality detection model using YOLOv4 deep learning algorithm is designed, which is superior in terms of network architecture, data processing and feature extraction. The shrimp images were taken and data expanded on their own, the LableImage platform was used for data annotation, and the network model was trained under the Darknet framework. Through comparison, the final performance of the model was all higher than other common target detection models, and its detection accuracy reached 93.7% with an average detection time of 47 ms, indicating that the method can effectively detect the quality of shrimp in the production process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.953-955
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• 1993

In this paper we propose a new stability theorem and a robust stability condition for linguistic fuzzy model systems in state space. First we define a stability in linear sense. After representing the fuzzy model by a system with disturbances, A necessary and sufficient condition for the stability is derived. This condition is proved to be a sufficient condition of the fuzzy model. The Q in the Lyapunov equation is iteratively adjusted by an gradient-based algorithm to improve its stability test. Finally, stability robustness bounds of a system having modeling error is derived. An example is also included to show that the stability test is powerful.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.523-524
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• 2021

COVID-19 확산으로 인해 우리나라는 공공장소 출입 시 마스크 착용이 의무화되었고, 체온이 37.5℃ 이상일 경우 발열로 간주하여 출입을 금지함에 따라 이를 효율적으로 검사할 수 있는 자동화 시스템을 개발하고자 한다. 이를 위해 다양한 각도, 마스크의 착용 위치에 따른 자료를 수집하여 모델에 적용하였고, 실시간 영상은 96.5%의 높은 정확도를 보였고, 영상 처리 추론 속도는 28fps임을 확인했다. 본 논문은 딥러닝 알고리즘을 활용한 마스크 착용 판별 및 체온 측정 시스템을 제시한다.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.418-425
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• 2022

For a commercial building, property managers play an important role in maximizing the benefit by reducing cost and increasing revenue in the operation and maintenance phase of the building. However, most of property managers are spending their time in monitoring facility managers who have little impact on cost reduction and maximization of operating profit. The industry practitioners have difficutlty in increasing the efficiency of thier work due to this work environment. In addition, both property managers(PMr) and facility managers(FMr) are dependent on the paper drawings and manuals, which can worsen the inefficiency and human errors are inevitable. This study aims to contribute to improvement of the current practice by developing a novel algorithm that autmatically links the facility information with 3D model, which can provide an efficient property management for commercial buildings.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.237-238
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• 2023

Urbanization and the increase in building scale have amplified the complexity of M.E.P design. Traditional design methods face limitations when considering intricate pathways and variables, leading to an emergent need for research in automated design. Initial algorithmic approaches encountered challenges in addressing complex architectural structures and the diversity of M.E.P types. However, with the launch of OpenAI's ChatGPT-3.5 beta version in 2022, new opportunities in the automated design sector were unlocked. ChatGPT, based on the Large Language Model (LLM), has the capability to deeply comprehend the logical structures and meanings within training data. This study analyzed the potential application and latent value of LLMs in M.E.P design. Ultimately, the implementation of LLM in M.E.P design will make genuine automated design feasible, which is anticipated to drive advancements across designs in the construction sector.

• Annual Conference of KIPS
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• 2024.05a
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• pp.444-446
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• 2024

한국의 육류 소비량이 늘어감에 따라 한우의 수요 및 공급도 점차 늘어가고 있다. 한우는 육질 등급(QG)과 육량 등급(YG)으로 나누어 판별되며 근내지방도, 고기 색, 지방색, 조직감, 성숙도, 도체 중량, 배최장근 단면적, 등지방두께 등 여러 항목을 고려한다. 현재는 주로 등배근을 맨눈으로 확인하는 수동 판별 방식이 사용된다. 하지만 평가사가 정확하게 판단하기 어렵고, 작업자의 부주의로 인한 육류의 오염 등 시간과 비용의 문제점이 있다. 본 연구에서는 이러한 문제점들을 한우 등급 판별 자동화로 해결하기 위해 한우의 등심 단면 이미지를 활용하여 등배근의 근내지방도를 산출하고 한우 등급을 자동 판별하는 알고리즘을 구현하였으며 평균 정확도는 79.2%를 달성하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.181-187
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• 2020

In this paper, we proposed a register allocation algorithm and resource allocation algorithm in the high level synthesis process for the SoC design synthesis system of high performance vehicles We have analyzed to the operator characteristics and structure of datapath in the most important high-level synthesis. We also introduced the concept of virtual operator for the scheduling of multi-cycle operations. Thus, we demonstrated the complexity to implement a multi-cycle operation of the operator, regardless of the type of operation that can be applied for commonly use in the resources allocation algorithm. The algorithm assigns the functional operators so that the number of connecting signal lines which are repeatedly used between the operators would be minimum. This algorithm provides regional graphs with priority depending on connected structure when the registers are allocated. The registers with connecting structure are allocated to the maximum cluster which is generated by the minimum cluster partition algorithm. Also, it minimize the connecting structure by removing the duplicate inputs for the multiplexor in connecting structure and arranging the inputs for the multiplexor which is connected to the operators. In order to evaluate the scheduling performance of the described algorithm, we demonstrate the utility of the proposed algorithm by executing scheduling on the fifth digital wave filter, a standard bench mark model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1845-1848
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• 2005

The automatic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters.