• Title/Summary/Keyword: Joint development

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A Study on the Development Type of Component Joint Design for Environment Friendly Multi Housing Remodeling (공동주택의 친환경 리모델링을 위한 부품접합부 개선방안의 유형화에 관한 연구 II;욕실 및 주방을 중심으로)

  • Lim, Seok-Ho;Kim, Soo-Am;Hwang, Eun-Kyoung;Yun, Mae-Han
    • Proceeding of Spring/Autumn Annual Conference of KHA
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    • 2006.11a
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    • pp.370-373
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    • 2006
  • Lastly, the problem of filling occurs in the process of concealed joint of adjacent components and was seen in joints between the structure and an electrical or mechanical device. Therefore, the dry processing which replaces wet joint was seen in joints between the structure and a door, ones between the structure and a finishing materials, ones between a gypsum board and electrical device, and ones between a water pipe and a tile finishing. And a process of separating the area of jointed parts in order to eliminate the problem of overlap was seen in joints between a door and a finishing material and ones between a gypsum board on the ceiling and a light. Lastly, an analysis of a process of exposing concealed parts indicated that the problem of filling could be applied to joints between the structure and an electrical or mechanical device and ones between a finishing material and a mechanical device. This study sought out methods to apply Environment Friendly Multi Housing Remodeling that allow easy remodeling as part of environment- preserving policies. It also contains basic data useful to implement long-life houses in the future by presenting a comprehensive design standard for them

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Development of a 2D Posture Measurement System to Evaluate Musculoskeletal Workload (근골격계 부하 평가를 위한 2차원 자세 측정 시스템 개발)

  • Park, Sung-Joon;Park, Jae-Kyu;Choe, Jae-Ho
    • Journal of the Ergonomics Society of Korea
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    • v.24 no.3
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    • pp.43-52
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    • 2005
  • A two-dimensional posture measurement system was developed to evaluate the risks of work-related musculoskeletal disorders(MSDs) easily on various conditions of work. The posture measurement system is an essential tool to analyze the workload for preventing work-related musculoskeletal disorders. Although several posture measurement systems have been developed for workload assessment, some restrictions in industry still exist because of its difficulty on measuring work postures. In this study, an image recognition algorithm was developed based on a neural network method to measure work posture. Each joint angle of human body was automatically measured from the recognized images through the algorithm, and the measurement system makes it possible to evaluate the risks of work-related musculoskeletal disorders easily on various working conditions. The validation test on upper body postures was carried out to examine the accuracy of the measured joint angle data from the system, and the results showed good measuring performance for each joint angle. The differences between the joint angles measured directly and the angles measured by posture measurement software were not statistically significant. It is expected that the result help to properly estimate physical workload and can be used as a postural analysis system to evaluate the risk of work-related musculoskeletal disorders in industry.

Arthroscopic Treatment of Mixed Pyogenic and Tuberculous Arthritis of Shoulder Joint (견관절의 화농성 및 결핵성 복합 관절염의 관절경적 치료 - 증례보고 -)

  • Ko, Sang-Bong;Kim, Shin-Keun;Choi, Won-Kee;Park, Jae-Hyun;Choi, Chang-Hyuk
    • Journal of the Korean Arthroscopy Society
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    • v.12 no.2
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    • pp.129-133
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    • 2008
  • Although the incidence of tuberculosis has been decreased due to new anti-tuberculous agents, improved socioeconomic status and development of multimodal preventive methods, in recent that is increased due to low vaccination rate and appearance of multidrug resistence organism. And the incidence of pyogenic shoulder joint arthritis is increasing due to frequent injection therapy as primary treatment. We have managed the mixed shoulder joint arthritis-pyogenic and tuberculous-with arthroscopic debridement and antituberculous medication successfully and then we report this case with relevant literatures.

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QoE-aware Energy Efficiency Maximization Based Joint User Access Selection and Power Allocation for Heterogeneous Network

  • Ji, Shiyu;Tang, Liangrui;Xu, Chen;Du, Shimo;Zhu, Jiajia;Hu, Hailin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.10
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    • pp.4680-4697
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    • 2017
  • In future, since the user experience plays a more and more important role in the development of today's communication systems, quality of experience (QoE) becomes a widely used metric, which reflects the subjective experience of end users for wireless service. In addition, the energy efficiency is an increasingly important problem with the explosive growth in the amount of wireless terminals and nodes. Hence, a QoE-aware energy efficiency maximization based joint user access selection and power allocation approach is proposed to solve the problem. We transform the joint allocation process to an optimization of energy efficiency by establishing an energy efficiency model, and then the optimization problem is solved by chaotic clone immune algorithm (CCIA). Numerical simulation results indicate that the proposed algorithm can efficiently and reliably improve the QoE and ensure high energy efficiency of networks.

Robust 2D human upper-body pose estimation with fully convolutional network

  • Lee, Seunghee;Koo, Jungmo;Kim, Jinki;Myung, Hyun
    • Advances in robotics research
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    • v.2 no.2
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    • pp.129-140
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    • 2018
  • With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models

  • Uddin, Md. Zia;Kim, Jaehyoun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.6
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    • pp.2767-2780
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    • 2016
  • Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

로보트 아크용접에서 시각인식장치를 이용한 용접선의 추적

  • 손영탁;김재선;조형석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1993.10a
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    • pp.550-555
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    • 1993
  • The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

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