start-ver=1.4
cd-journal=joma
no-vol=28
cd-vols=
no-issue=1
article-no=
start-page=76
end-page=88
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20221109
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Growing neural gas based navigation system in unknown terrain environment for an autonomous mobile robot
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Recently, various types of autonomous robots have been expected in many fields such as a disaster site, forest, and so on. The autonomous robots are assumed to be utilized in unknown environments. In such environments, a path planning to a target point set in the unknown area is a fundamental capability for efficiently executing tasks. To realize the 3D space perception, GNG with Different Topologies (GNG-DT) proposed in our previous work can learn the multiple topological structures with in the framework of learning algorithm. This paper proposes a GNG-DT based 3D perception method by utilizing the multiple topological structures for perceiving the 3D unknown terrain environment and a path planning method to the target point set in the unknown area. Especially, a traversability property of the robot is added to GNG-DT as a new property of the topological structures for clustering the 3D terrain environment from the 3D point cloud measured by 3D Lidar. Furthermore, this paper proposes a path planning method utilizing the multiple topological structures. Next, this paper shows several experimental results of the proposed method using simulation terrain environments for verifying the effectiveness of our proposed method. Finally, we summarize our proposed method and discuss the future direction on this research.
en-copyright=
kn-copyright=

en-aut-name=TodaYuichiro
en-aut-sei=Toda
en-aut-mei=Yuichiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=OzasaKoki
en-aut-sei=Ozasa
en-aut-mei=Koki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=MatsunoTakayuki
en-aut-sei=Matsuno
en-aut-mei=Takayuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=Graduate school of natural science and technology, Okayama University
kn-affil=

affil-num=2
en-affil=Graduate school of natural science and technology, Okayama University
kn-affil=

affil-num=3
en-affil=Graduate school of natural science and technology, Okayama University
kn-affil=
en-keyword=Growing neural gas
kn-keyword=Growing neural gas
en-keyword=3D perception
kn-keyword=3D perception
en-keyword=Navigation system
kn-keyword=Navigation system
END

start-ver=1.4
cd-journal=joma
no-vol=12
cd-vols=
no-issue=3
article-no=
start-page=1705
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20220207
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Growing Neural Gas with Different Topologies for 3D Space Perception
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research.
en-copyright=
kn-copyright=

en-aut-name=TodaYuichiro
en-aut-sei=Toda
en-aut-mei=Yuichiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=WadaAkimasa
en-aut-sei=Wada
en-aut-mei=Akimasa
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=MiyaseHikari
en-aut-sei=Miyase
en-aut-mei=Hikari
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=OzasaKoki
en-aut-sei=Ozasa
en-aut-mei=Koki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=MatsunoTakayuki
en-aut-sei=Matsuno
en-aut-mei=Takayuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=MinamiMamoru
en-aut-sei=Minami
en-aut-mei=Mamoru
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

affil-num=1
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=2
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=3
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=4
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=5
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=6
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=
en-keyword=3D space perception
kn-keyword=3D space perception
en-keyword=growing neural gas
kn-keyword=growing neural gas
en-keyword=topological structure learning method
kn-keyword=topological structure learning method
END

start-ver=1.4
cd-journal=joma
no-vol=
cd-vols=
no-issue=
article-no=
start-page=82
end-page=91
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2019
dt-pub=20190802
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Region of Interest Growing Neural Gas for Real-Time Point Cloud Processing
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.
en-copyright=
kn-copyright=

en-aut-name=TodaYuichiro
en-aut-sei=Toda
en-aut-mei=Yuichiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=LiXiang
en-aut-sei=Li
en-aut-mei=Xiang
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=MatsunoTakayuki
en-aut-sei=Matsuno
en-aut-mei=Takayuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=MinamiMamoru
en-aut-sei=Minami
en-aut-mei=Mamoru
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

affil-num=1
en-affil=Okayama University
kn-affil=

affil-num=2
en-affil=Okayama University
kn-affil=

affil-num=3
en-affil=Okayama University
kn-affil=

affil-num=4
en-affil=Okayama University
kn-affil=
en-keyword=Growing Neural Gas
kn-keyword=Growing Neural Gas
en-keyword=Point cloud processing
kn-keyword=Point cloud processing
en-keyword=Topological structure learning
kn-keyword=Topological structure learning
END

start-ver=1.4
cd-journal=joma
no-vol=55
cd-vols=
no-issue=4
article-no=
start-page=245
end-page=252
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2001
dt-pub=200108
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Significance of adrenomedullin under cardiopulmonary bypass in children during surgery for congenital heart disease.
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=<p>To elucidate the effect of adrenomedullin (AM) on fluid homeostasis under cardiopulmonary bypass (CPB), we investigated the serial changes in plasma AM and other parameters related to fluid homeostasis in 13 children (average age, 28.2 months) with congenital heart disease during cardiac surgery under CPB. Arterial blood and urine samples were collected just after initiation of anesthesia, just before commencement of CPB, 10 min before the end of CPB, 60 min after CPB, and 24 h after operation. Plasma AM levels increased significantly 10 min before the end of CPB and decreased 24 h after operation. Urine volume increased transiently during CPB, which paralleled changes in AM. Simple regression analysis showed that plasma AM level correlated significantly with urinary vasopressin, urine volume, urinary sodium excretion, and plasma osmolarity. Stepwise regression analysis indicated that urine volume was the most significant determinant of plasma AM levels. Percent rise in AM during CPB relative to control period correlated with that of plasma brain natriuretic peptide (r = 0.57, P &#60; 0.01). Our results suggest that AM plays an important role in fluid homeostasis under CPB in cooperation with other hormones involved in fluid homeostasis.</p>

en-copyright=
kn-copyright=

en-aut-name=TakeuchiMamoru
en-aut-sei=Takeuchi
en-aut-mei=Mamoru
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=MoritaKiyoshi
en-aut-sei=Morita
en-aut-mei=Kiyoshi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=IwasakiTatsuo
en-aut-sei=Iwasaki
en-aut-mei=Tatsuo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=TodaYuichiro
en-aut-sei=Toda
en-aut-mei=Yuichiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=OeKatsunori
en-aut-sei=Oe
en-aut-mei=Katsunori
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=TagaNaoyuki
en-aut-sei=Taga
en-aut-mei=Naoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=HirakawaMasahisa
en-aut-sei=Hirakawa
en-aut-mei=Masahisa
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

affil-num=1
en-affil=
kn-affil=Okayama University

affil-num=2
en-affil=
kn-affil=Okayama University

affil-num=3
en-affil=
kn-affil=Okayama University

affil-num=4
en-affil=
kn-affil=Okayama University

affil-num=5
en-affil=
kn-affil=Okayama University

affil-num=6
en-affil=
kn-affil=Okayama University

affil-num=7
en-affil=
kn-affil=Okayama University
en-keyword=adrenomedullin
kn-keyword=adrenomedullin
en-keyword=cardiopulmonary bypass
kn-keyword=cardiopulmonary bypass
en-keyword=vasopressin
kn-keyword=vasopressin
en-keyword=pediatric cardiac surgery
kn-keyword=pediatric cardiac surgery
en-keyword=brain natriuretic peptide
kn-keyword=brain natriuretic peptide
END

start-ver=1.4
cd-journal=joma
no-vol=
cd-vols=
no-issue=
article-no=
start-page=
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2007
dt-pub=20070630
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=好中球エラスターゼ阻害薬，シベレスタットはラット出血性ショック後の肺傷害を改善する
kn-title=A neutrophil elastase inhibitor, sivelestat, ameliorates lung injury after hemorrhagic shock in rats
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=
en-copyright=
kn-copyright=

en-aut-name=TodaYuichiro
en-aut-sei=Toda
en-aut-mei=Yuichiro
kn-aut-name=戸田雄一郎
kn-aut-sei=戸田
kn-aut-mei=雄一郎
aut-affil-num=1
ORCID=

affil-num=1
en-affil=
kn-affil=岡山大学
END