Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control最新文献

The Synchronized Monitoring System for Operation Capability of Diver 潜水员作业能力同步监测系统

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388221

Fan Wei, Lili Meng, Li Qiang, Li Leilei

{"title":"The Synchronized Monitoring System for Operation Capability of Diver","authors":"Fan Wei, Lili Meng, Li Qiang, Li Leilei","doi":"10.1145/3388218.3388221","DOIUrl":"https://doi.org/10.1145/3388218.3388221","url":null,"abstract":"Objective:Divers underwater operation capacity will gradually weakened as underwater operation time, until completely lost his ability to do homework, but the degree of weakened at different time after operation can't test.This study aimed at laboratory trained divers in the complete test ability of homework problems at work, a kind of design can be in real-time test platform diving underwater operation in testing the ability, to monitor the divers underwater operation ability during the test.Methods:The underwater operation ability test was divided into two aspects: physical test and reaction ability test.[1]. In this paper, we can carry out the physical test without decompression by using the method of the transition cabin of the pressurized cabin, and carry out the real-time test of the underwater reaction capability using the self-developed underwater operation capability real-time test device. Results: Using the laboratory pressurized chamber combined with real-time power bicycle do physical test, using the mature ability to respond to test software with waterproof, resistance to high pressure processing technology for underwater real-time response ability test, achieve real-time monitoring of underwater diving personnel when testing the effect of the operation ability[2].Conclusion: the test method can solve the problem that the operation ability cannot track and monitor in real time, which provides a convenient basis for the study of the ergonomic study of the divers.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972965","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: 0

An Energy Optimal Guidance Law for Non-linear Systems Considering Impact Angle Constraints 考虑冲击角约束的非线性系统能量最优制导律

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388227

Yue Zhu, Junyan Xu

引用次数: 2

An Extended Approach to Estimating Closeness to Singularity in Parallel Manipulators based on Actuating Efforts Values 基于作动力值估计并联机构奇异接近度的扩展方法

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388232

K. Erastova, P. Laryushkin

引用次数: 0

Medical Data Classification Using Binary Brain Storm Optimization Algorithm 基于二元头脑风暴优化算法的医疗数据分类

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388224

Ogwo E. Ogwo, H. Turabieh, A. Sheta, Scott A. King

{"title":"Medical Data Classification Using Binary Brain Storm Optimization Algorithm","authors":"Ogwo E. Ogwo, H. Turabieh, A. Sheta, Scott A. King","doi":"10.1145/3388218.3388224","DOIUrl":"https://doi.org/10.1145/3388218.3388224","url":null,"abstract":"With the growing access to technology in the medical domain, an increased volume of medical data is recorded. The size and complexity of these data make the process of analysis of meaningful discoveries of beneficial patterns more challenging. This problem has attracted numerous researchers around the world. Statistical methods have been employed to handle medical data for diagnosis purposes. Unfortunately, these methods were less capable of dealing with these massive and complex datasets. To solve this problem, we suggest a process to classify medical data which includes feature selection and classification using a number of supervised learning techniques. Binary Brain Storm Optimization (BBSO) is used for feature selection, which is a population search approach that simulates the process of electing the best idea (solution), among others. We simulated six different classifiers: Naive-Bayes, K-Nearest Neighbor, Support Vector Machine, Linear Discriminant Analysis, Decision Tree and Random Forest. Five datasets adopted from the UCI Machine Learning Repository, (Breast Cancer, Diabetes, Heart Disease, Chronic Kidney, and SPECT), are employed as a benchmark test data. The performance of BBSO is evaluated using accuracy on the datasets using the various classifiers. Experimental results show that the proposed approach improves the classification performance for better medical diagnosis.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130461806","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

Assessment of Vocational Types and EEG Analysis Using Holland Test Questionnaire 职业类型评估及荷兰测验问卷的脑电图分析

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388230

Saim Rasheed, Hassanin M. Al-Barhamtoshy, H. Saifaddin, W. Shalash

{"title":"Assessment of Vocational Types and EEG Analysis Using Holland Test Questionnaire","authors":"Saim Rasheed, Hassanin M. Al-Barhamtoshy, H. Saifaddin, W. Shalash","doi":"10.1145/3388218.3388230","DOIUrl":"https://doi.org/10.1145/3388218.3388230","url":null,"abstract":"This paper aims to evaluate EEG as an application predictor of Holland test with career interest. Consequently, it is essential to develop and analyze the brain wave of clinical changes of the EEG signals during Holland answering questions. The experimental test begins by answering Holland test with 34 participants of staff members at KAU (College of Computing and Information Technology and College of Engineering). Accordingly, the test is applied twice, one without using EEG and the second with EEG recording. The proposed solution uses 34 answering of participants of Holland career test dataset after getting the content data from the answering of Holland using the EEG and initial career assessment. Consequently, the Holland answering test (without EEG) is analyzed along with EEG analysis of the Holland answering in different frequency bands such as delta, theta, alpha and beta. Therefore, we aim to provide researchers, a methodology, if we could identify career interest using EEG signals or to determine the level of contribution of EEG signals would support identifying career types.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125103808","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

A Convolutional Neural Networks-based Model for Sales Prediction 基于卷积神经网络的销售预测模型

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388228

Vitaliy Buyar, A. Abdel-raouf

引用次数: 5

Slippage Estimation of Two Wheeled Mobile Robot Using Recurrent Deep Neural Network 基于递归深度神经网络的两轮移动机器人滑移估计

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388233

İsmail Özçil, A. Koku, E. I. Konukseven

{"title":"Slippage Estimation of Two Wheeled Mobile Robot Using Recurrent Deep Neural Network","authors":"İsmail Özçil, A. Koku, E. I. Konukseven","doi":"10.1145/3388218.3388233","DOIUrl":"https://doi.org/10.1145/3388218.3388233","url":null,"abstract":"Position, velocity and acceleration information are important for mobile robots. Due to the wheel slippages, encoder data may not be reliable and IMU data also contains a cumulative error. Errors of inertial measurements are accumulated over velocity and position estimates and as time increases, these errors grow higher. Due to robot hardware and the operating surface, ground truth may not be available. In this work recurrent deep neural network is proposed in order to reduce the error in speed and yaw angle estimates coming from encoder and IMU data. Neural networks are commonly used to capture the behavior of linear and nonlinear systems. Since ground-wheel interaction forces are modeled with non-linear models such as the Magic formula and determining parameters of those models require time and test setups, there is a need for simpler methods to model the behavior of simple robots. Neural networks could be used to model non-linear systems. In this work, a recurrent deep neural network is proposed to estimate the speed and yaw angle of a two-wheeled differentially driven mobile robot. Using the information coming from the camera positioned above the test area as ground truth, the network is trained. After that, the output of the network is recorded in the absence of ground truth information in the network. Finally, the performance of the network is evaluated using network output, sensor data calculation, and ground truth.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240602","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

Machine Learning Algorithms for Diabetes Prediction: A Review Paper 糖尿病预测的机器学习算法:综述论文

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388231

Abir Al-Sideiri, Z. C. Cob, S. M. Drus

引用次数: 6

Early Detection of Hepatocellular Carcinoma in PET/CT Images using Improved K-Means Techniques based on Pixel Density 基于像素密度的改进K-Means技术在PET/CT图像中早期检测肝细胞癌

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388519

Gamal G. N. Geweid, Mahmoud Abdallah, Ayman M. Hassan

{"title":"Early Detection of Hepatocellular Carcinoma in PET/CT Images using Improved K-Means Techniques based on Pixel Density","authors":"Gamal G. N. Geweid, Mahmoud Abdallah, Ayman M. Hassan","doi":"10.1145/3388218.3388519","DOIUrl":"https://doi.org/10.1145/3388218.3388519","url":null,"abstract":"Hepatocellular carcinoma leads to more human deaths currently. Patient survival rates can be increased by early detection of the tumor which is the main problem. In many cases, the task of early detection in liver grayscale images is very complicated since the intensity values between healthy and abnormal tissues may be very similar. In this paper, a pre-processing step of pixel colors is introduced to determine the pathology that is being observed, then, followed by a robust detection technique for liver PET/CT images using a k-means clustering algorithm based on pixel intensity optimization and evaluation of probability distribution functions. In this method, k cluster centers are changed with the distance between each pixel to each cluster center. This includes three main stages: pre-processing, segmentation, and measuring the percentage of the region having carcinoma. The unwanted regions can be removed from the segmented image by using the median filter. This work consisted of a comparative study of certain segments of medical image techniques in order to determine as accurately as possible when estimating quality segmentation from performance measures, such as Peak Signal-to-Noise Ratio, percentage of tumor detection, segmentation error, and coefficient similarity dice. The algorithm is applied to 60 sets of different real data in the form of liver PET/CT images with and without tumor tissues. The simulation results showed better detection was obtained using the proposed method.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133923470","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: 0

A Bearing Fault Diagnosis Method using Transfer Learning and Dempster-Shafer Evidence Theory 基于迁移学习和Dempster-Shafer证据理论的轴承故障诊断方法

Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control Pub Date : 2019-12-14 DOI: 10.1145/3388218.3388220

Duy-Tang Hoang, Hee-Jun Kang

{"title":"A Bearing Fault Diagnosis Method using Transfer Learning and Dempster-Shafer Evidence Theory","authors":"Duy-Tang Hoang, Hee-Jun Kang","doi":"10.1145/3388218.3388220","DOIUrl":"https://doi.org/10.1145/3388218.3388220","url":null,"abstract":"Rolling element bearings are among the most important components in rotary machines. The reliable operation of rotary machines highly depends on the performance of bearing. Therefore, bearing fault diagnosis is a critical task in the industry. Signal-based fault diagnosis for bearings has applied extensively deep learning algorithms because of their ability to automatically extract features from fault signals measured from rotary machines. However, designing a deep learning model for any fault diagnosis problem is not a trivial task since each deep model has a complex structure and a huge number of hyper-parameters and trainable parameters. Each hyper-parameter of a deep learning model has a profound impact on the performance of that model. The selection of appropriate hyper-parameters is often conducted manually based on the Trial & Error method and experiences of the designer. Transfer learning is a technique that adopts already existing machine learning models into new domains. This technique helps to save the designing and training time of machine learning models, especially deep neural networks. In this paper, transfer learning technique is exploited to the problem of bearing fault diagnosis. A pre-trained deep neural network in the domain of image classification is adopted and modified to extract features from vibration signals measured by multiple sensors. The effectiveness of the proposed method is verified by experiments conducted with actual bearing data set supplied by Case Western Reverse University Bearing Data Center.","PeriodicalId":345276,"journal":{"name":"Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133895337","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: 5