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Slide-bending Formation of Thin Metal Sheet by Using Force Control
Hikaru Nishimura,Hiroshi Harada,Yasuo Marumo,Teruo Yamaguchi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper describes slide-bending formation of thin metal sheet by using an industrial robot. The formation of parts made of very thin sheets has become increasingly important following the miniaturization of industrial products, including electrical and mechanical devices. A new process called a slide-bending formation was proposed. The authors have made an automatic slide-bending formation system which consists of an industrial robot, z-axis stage, a force sensor and a CCD camera. The bending formation of metal sheet made by stainless steel was investigated system atically. From the results of the experiment, it is shown that the trajectory of the z-stage was controlled so that the reactive force is kept constant. The bending angle of the thin metal sheet can be controlled by the applied load.
Automatic Segmentation of Finger Bone Regions from CR Images Using Improved DeepLabv3+
Hikaru Ono,Seiichi Murakami,Tohru Kamiya,Takatoshi Aoki 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and treatment of these bone diseases are important because they significantly interfere with the quality of life (QOL) as the symptoms progress. Visual screening based on CR is used as a diagnosing tool for bone diseases. However, imaging diagnosis is subjective and lacks objectivity, and there is a possibility that lesions may be overlooked. In addition, it is difficult to find out subtle changes from images, increasing the workload for doctors. To solve these problems, there is a need to develop a computer aided diagnosis (CAD) system that can quantitatively diagnose bone diseases. We propose a method for automatic extraction of phalange regions for the CAD system to diagnose these diseases. The proposed method can extract the phalanges with high accuracy by using the improved DeepLabv3+. In this paper, we apply the proposed method to 101 cases of CR images and mIoU of 0.949 was obtained.