Platonic Investigations最新文献_第8页

Prototyping and Validating the CORA Ontology: Case Study on a Simulated Reconnaissance Mission CORA本体的原型设计与验证:以模拟侦察任务为例

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/LARS-SBR-WRE48964.2019.00066

Antonio Batista de Oliveira Neto, Jonatas Araújo Silva, Marcos E. Barreto

{"title":"Prototyping and Validating the CORA Ontology: Case Study on a Simulated Reconnaissance Mission","authors":"Antonio Batista de Oliveira Neto, Jonatas Araújo Silva, Marcos E. Barreto","doi":"10.1109/LARS-SBR-WRE48964.2019.00066","DOIUrl":"https://doi.org/10.1109/LARS-SBR-WRE48964.2019.00066","url":null,"abstract":"Ontologies play an important role across several domains as they represent and define categories, properties and relationships among concepts, data and entities existing in those domains. Specially in Robotics, ontologies can be used as a standard way to represent and share knowledge and reasoning among autonomous agents. CORA (core ontology or robotics and automation) is a standard ontology developed by the IEEE ORA working group mapping main concepts and axioms from the robotics and automation (R&A) domain. After CORA has been approved as an official IEEE standard, ORA was split in different sub-groups addressing, among other topics, task representation (RTR) and autonomous robots architectures (AuR). This paper presents a simulation study on a multirobot reconnaissance application intended to serve as proof-ofconcept on the feasibility of the CORA ontology and its related architecture. We show how robots and tasks are represented and dynamically mapped into the ontology, as well discuss some technical aspects of our simulation when dealing with the proposed architecture. Our results show the proposed ontology (CORA) and its associated reference architecture (ROA) are able to be correctly mapped during application runtime.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"27 1","pages":"341-345"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87609897","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: 2

Differential Evolution based TDF-IMC scheme for load frequency control of single-area power systems 基于差分演化的TDF-IMC单区电力系统负荷频率控制方法

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929572

Bheem Sonker, Deepak Kumar, P. Samuel

引用次数: 2

Online Sound Based Arc-Welding Defect Detection Using Artificial Neural Networks 基于人工神经网络的在线声音弧焊缺陷检测

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/LARS-SBR-WRE48964.2019.00053

Bryan Stefan Galani Pernambuco, C. Steffens, Jefferson R. Pereira, A. Werhli, R. Z. Azzolin, Emanuel da Silva Diaz Estrada

{"title":"Online Sound Based Arc-Welding Defect Detection Using Artificial Neural Networks","authors":"Bryan Stefan Galani Pernambuco, C. Steffens, Jefferson R. Pereira, A. Werhli, R. Z. Azzolin, Emanuel da Silva Diaz Estrada","doi":"10.1109/LARS-SBR-WRE48964.2019.00053","DOIUrl":"https://doi.org/10.1109/LARS-SBR-WRE48964.2019.00053","url":null,"abstract":"Heavy steel industries are constantly distressed with improving quality, reliability, and robustness of their product. Nevertheless, they are also concerned with reducing welding processes cost, men-power, and material waste. This leads to an interest in control techniques, automation, and robotization. Monitoring the welding process of continuously fed melting wire electrode is still an open problem, due to the instability caused by the electrical variables. We identify expressive gaps in the understanding of stability and quality of welding deposition. In this work, we introduce a low-cost system that monitors the stability of the weld bead and the transfer mode of the MIG/MAG welding process. We propose a non-intrusive solution that uses the sound signal produced by the electric arc to monitor and analyze the process in real-time. In this sense, we propose an Artificial Neural Network (ANN) based methodology to identify some types of discontinuities in the welding process. To support and foster the development of sound signal based neural network, we produce a sound signal dataset based on the experiments which recreate real situations in the welding industry. Processes with adequate conditions and welds with two types of discontinuities were executed to validate the methodology. The model performance is evaluated using both classification accuracy and confusion matrix. Results show to be possible to identify discontinuities in the welding process by looking only at the sound generated by the electric arc.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"33 1","pages":"263-268"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87943194","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

AyurLeaf: A Deep Learning Approach for Classification of Medicinal Plants AyurLeaf:一种药用植物分类的深度学习方法

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929394

M. Dileep, P. Pournami

{"title":"AyurLeaf: A Deep Learning Approach for Classification of Medicinal Plants","authors":"M. Dileep, P. Pournami","doi":"10.1109/TENCON.2019.8929394","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929394","url":null,"abstract":"Ayurvedic medicines have a vital role in preserving physical and mental health of human beings. Identification and classification of medicinal plants are essential for better treatment. Lack of experts in this field makes proper identification and classification of medicinal plants a tedious task. Hence, a fully automated system for medicinal plant classification is highly desirable. This work proposes AyurLeaf, a Deep Learning based Convolutional Neural Network (CNN) model, to classify medicinal plants using leaf features such as shape, size, color, texture etc. This research work also proposes a standard dataset for medicinal plants, commonly seen in various regions of Kerala, the state on southwestern coast of India. The proposed dataset contains leaf samples from 40 medicinal plants. A deep neural network inspired from Alexnet is utilised for the efficient feature extraction from the dataset. Finally, the classification is performed using Softmax and SVM classifiers. Our model, upon 5-cross validation, achieved a classification accuracy of 96.76% on AyurLeaf dataset. AyurLeaf helps us to preserve the traditional medicinal knowledge carried by our ancestors and provides an easy way to identify and classify medicinal plants.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"40 1","pages":"321-325"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86944296","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: 33

QRRP: A Query-driven Ring Routing Protocol for Mobile Sink based Wireless Sensor Networks QRRP:基于移动汇聚的无线传感器网络查询驱动环路由协议

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929714

Shubhra Jain, K. K. Pattanaik, R. Verma, A. Shukla

引用次数: 10

Design of a FSS Bandpass Filter for C-Band Application with Equivalent Circuit Model 基于等效电路模型的c波段FSS带通滤波器设计

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929244

Mohd Salman Khan, Somak Bhattacharyya

引用次数: 0

DICOM Depth Image Generation using GPGPU for Fast Position and Orientation Estimation on Liver Surgery Support System 基于GPGPU的DICOM深度图像生成用于肝脏手术支持系统的快速定位和方向估计

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929454

M. Koeda, Daiki Yano, Takumi Mori, Katsuhiko Onishi, H. Noborio

引用次数: 1

Performance analysis of deep learning CNN in classification of depression EEG signals 深度学习CNN在抑郁症脑电信号分类中的性能分析

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929254

P. Sandheep, S. Vineeth, Meljo Poulose, D. Subha

{"title":"Performance analysis of deep learning CNN in classification of depression EEG signals","authors":"P. Sandheep, S. Vineeth, Meljo Poulose, D. Subha","doi":"10.1109/TENCON.2019.8929254","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929254","url":null,"abstract":"With the advent of greater computing power each year, computer-based disease/condition diagnosis have been gaining significant importance recently. In this paper, an extensive analysis of the approach based on the classification of depression using electroencephalogram (EEG) signals is carried out. A computer-aided machine learning approach: Convolutional Neural Network (CNN), a deep learning method is used in this work. The deep CNN was trained using EEG signals from 30 normal and 30 depressed persons. The network attained the highest accuracy of 99.31% in classifying depression from EEG signals of normal controls recorded from the right hemisphere of the brain and 96.3% from the left hemisphere of the brain after ten-fold cross-validation. The performance of the CNN network was evaluated by evaluating the classification accuracy, varying different parameters such as the number of strides, learning rate parameter, number of epochs, and sample size. An extensive data learning approach is proposed to classify depression EEG signals from that of healthy controls. The key advantage of using deep learning is that they return state-of-the-art accuracy and do not require manual pre-processing or feature extraction from the signal.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"39 1","pages":"1339-1344"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82604626","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: 22

A Perception System for an Autonomous Pesticide Boom Sprayer 自主喷药臂感知系统研究

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/LARS-SBR-WRE48964.2019.00023

Gustavo Henrique do Nascimento, Felipe Weber, Gabrielle Almeida, F. Terra, Paulo L. J. Drews-Jr

引用次数: 6

Current Conveyor based Novel Gyrator filter for Biomedical Sensor Applications 基于电流传送带的新型生物医学传感器陀螺滤波器

Platonic Investigations Pub Date : 2019-10-01 DOI: 10.1109/TENCON.2019.8929308

P. S. Veerendranath, Vivek Sharma, Nithin Y. B. Kumar, M. H. Vasantha, E. Bonizzoni

引用次数: 1