2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)最新文献

Decision Behavioral Approach for Self Organizing Multi Agent Robots Based on Deep Neural Network 基于深度神经网络的自组织多智能体机器人决策行为方法

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935961

Abdulrahman I. Ahmed, S. Maged, F. Tolbah

引用次数: 1

A Moving Object Detection Algorithm of Jitter Video 一种抖动视频的运动目标检测算法

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935975

Wei Zhang, Xiaofeng Shi, Tao Jin, Shengke Chen, Yunjiong Xu, Wei Sun, Yang Xue, Zhicheng Yu

引用次数: 2

Path Planning for Autonomous Underwater Vehicle in Presence of Moving Obstacle Based on Three Inputs Fuzzy Logic 基于三输入模糊逻辑的移动障碍物下自主水下航行器路径规划

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8936029

Xiangjun Li, Wei Wang, Jinyan Song, Dan Liu

引用次数: 9

Dual Band Quadrature VCO Using Switched-transformer Coupling for Wireless Robot Applications 基于开关变压器耦合的双频正交压控振荡器在无线机器人中的应用

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935963

W. Lai, S. Jang, Jyun-Jhih Wang

引用次数: 1

Simulation of Tibialis Anterior Muscle Transfer for Congenital Clubfoot Based on OpenSim 基于OpenSim的先天性畸形足胫骨前肌移植模拟

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935979

Sujiao Li, Xueqin Luo, Yuanmin Tang, He Lan, Hongliu Yu

{"title":"Simulation of Tibialis Anterior Muscle Transfer for Congenital Clubfoot Based on OpenSim","authors":"Sujiao Li, Xueqin Luo, Yuanmin Tang, He Lan, Hongliu Yu","doi":"10.1109/ACIRS.2019.8935979","DOIUrl":"https://doi.org/10.1109/ACIRS.2019.8935979","url":null,"abstract":"Congenital clubfoot is a common deformity in children with cerebral palsy. It results in child's foot inversion with horseshoe shape, which seriously affects the basic function of normal walking. For the evaluation of the effect of treatment, in addition to clinical testing, the previous study used the three-dimensional gait analysis system to verify the effect of the treatment. These evaluations can only evaluate the results after the treatment but not predict the effect of the operation. Therefore, this paper adopts OpenSim modeling software to establish a personalized human body model by manually scaling and importing the joint angle. Changes in tendon moment and moment arm before and after transfer are examined in this experiment. The maximum dorsal flexion moment of the tibialis anterior muscle is changed from 15 N · m (before transfer) to 18 N · m (after transfer), and the average moment arm is changed from 24 mm to 29 mm. The maximum inversion moment of the tibialis anterior muscle is changed from 18 N · m (before transfer) to 1 N · m (after transfer), and the average moment arm is changed from 19 mm to -3 mm, which becomes eversion moment. It can be seen the tibialis anterior muscle transfer surgery increases the dorsal flexion moment, reduces the inversion moment, and also increases the eversion moment, which has significant effects in the treatment of clubfoot. By taking these effect into consideration, we can help the doctor to determine a better surgical plan.","PeriodicalId":338050,"journal":{"name":"2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115456813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Comparison of MARG Sensor Results for Different Mounting Positions and Physiques for Accurate Knee Joint Motion Measurement 膝关节运动精确测量中不同安装位置和体态MARG传感器结果的比较

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8936032

Y. Igarashi, Akira Komatsu, T. Iwami, Hiroaki Tsukamoto, Y. Shimada

引用次数: 2

Application of Acceleration Tracking Control for a Small-Scale Earthquake Simulator 加速度跟踪控制在小型地震模拟器中的应用

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935977

K. A. R. Carranza, R. Bedruz, R. Baldovino, R. R. Vicerra, John Anthony C. Jose, E. Dadios, Lessandro Estelito, E. Sybingco

引用次数: 0

Deep Learning and Classic Machine Learning Approach for Automatic Bone Age Assessment 骨龄自动评估的深度学习和经典机器学习方法

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935965

A. Wibisono, M. Saputri, P. Mursanto, Joachim Rachmad, Alberto, Ari Tri Wibowo Yudasubrata, Fadzil Rizki, Ernest Anderson

{"title":"Deep Learning and Classic Machine Learning Approach for Automatic Bone Age Assessment","authors":"A. Wibisono, M. Saputri, P. Mursanto, Joachim Rachmad, Alberto, Ari Tri Wibowo Yudasubrata, Fadzil Rizki, Ernest Anderson","doi":"10.1109/ACIRS.2019.8935965","DOIUrl":"https://doi.org/10.1109/ACIRS.2019.8935965","url":null,"abstract":"The rapid growth of technology has initiated the development of automated system in various fields, including medical. One of the application is an automatic bone assessment from left-hand X-ray images which helps radiologist and pediatrician to take a decision regarding children’s growth status. However, one of the major issues in developing this automated system is determining the appropriate technique which can produce effective and reliable prediction, especially when dealing with vast amount of data. The dataset used in this work is taken from RSNA bone age dataset which has 9 GB size consists of 12.611 images with various resolutions. To overcome this problem, we implemented and analyzed two different approaches for automatic bone assessment: deep learning and classic machine learning. For the deep learning approach, we utilized two different pre-trained Convolutional Neural Network (CNN) models, i.e. VGG16 and MobileNets. On the other hand, classic machine learning approach implemented Canny edge detection to extract image feature and several traditional regressor algorithms. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Symmetric Mean Absolute Percentage Error (SMAPE), and time execution are employed as evaluation metrics. The results of our experiments show that deep learning based VGG16 model performs better in predicting bone age values compared to classic machine learning. The MAE and RMSE achieved by VGG16 are 14.78 months and 18.93 months respectively. However, classic machine learning approach has better error percentage in general, marked by lower SMAPE, i.e 28.34%. In terms of time execution, classic machine learning approach performs 10 times faster than deep learning based approach.","PeriodicalId":338050,"journal":{"name":"2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122417826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Comparative Study of Deep Learning Based Features in SLAM 基于深度学习的SLAM特征比较研究

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8935995

Chengqi Deng, Kaitao Qiu, R. Xiong, Chunlin Zhou

引用次数: 10

MAP Aided Self-positioning Based on LIDAR Perception for Autonomous Vehicles 基于LIDAR感知的自动驾驶汽车MAP辅助自定位

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) Pub Date : 2019-07-01 DOI: 10.1109/ACIRS.2019.8936045

Rong-Terng Juang

引用次数: 2