2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)最新文献_第2页

Clustering techniques for data network planning in Smart Grids 智能电网中数据网络规划的聚类技术

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000059

Ladislav Vrbsky, M. Silva, D. Cardoso, C. Francês

{"title":"Clustering techniques for data network planning in Smart Grids","authors":"Ladislav Vrbsky, M. Silva, D. Cardoso, C. Francês","doi":"10.1109/ICNSC.2017.8000059","DOIUrl":"https://doi.org/10.1109/ICNSC.2017.8000059","url":null,"abstract":"Smart Grid, a modern approach to electricity distribution, requires innovation on various fronts. Communication is a key component of Smart Grid applicability. To satisfy Quality of Service (QoS) needs when deciding on network structure and topology, especially in urban areas, artificial intelligence techniques may be applied. Techniques such as clustering methods or genetic algorithms are useful to resolve this optimization problem. Choice of network topology for a specific electrical grid is also important. This choice influences the resulting QoS and network implementation price. This paper uses graph theory formulation to create a model. This model is designed to optimize topology of data network while accounting for delay constrains. Since this problem belongs to class NP-hard, this paper indicates appropriate clustering methods that best suit a given Smart Grid scenario in terms of QoS. A typical wireless network planning scenario of electric energy distribution is used as a case study. Each resulting cluster will contain a base station to attend the needs of Smart Grid Intelligent electronic devices in its cluster area. The algorithms K-medoids and K-means had the best performance with K-medoids bringing financial benefits regarding base station deployment.","PeriodicalId":145129,"journal":{"name":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114795742","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

PI observer robust fault estimation for motorcycle lateral dynamics 摩托车横向动力学的PI观测器鲁棒故障估计

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000079

D. Ichalal, N. A. Oufroukh, P. Damon, Hichem Arioui, S. Mammar

引用次数: 2

Evaluating machine learning algorithms for applications with humans in the loop 评估有人类参与的应用的机器学习算法

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000136

A. K. Gopalakrishna, T. Ozcelebi, J. Lukkien, A. Liotta

{"title":"Evaluating machine learning algorithms for applications with humans in the loop","authors":"A. K. Gopalakrishna, T. Ozcelebi, J. Lukkien, A. Liotta","doi":"10.1109/ICNSC.2017.8000136","DOIUrl":"https://doi.org/10.1109/ICNSC.2017.8000136","url":null,"abstract":"Applications employing data classification such as smart lighting that involve human factors such as perception lead to non-deterministic input-output relationships where more than one output may be acceptable for a given input. For these so called non-deterministic multiple output classification (nDMOC) problems, the relationship between the input and output may change over time making it difficult for the machine learning (ML) algorithms in a batch setting to make predictions for a given context. In this paper, we describe the nature of nDMOC problems and discuss the Relevance Score (RS) that is suitable in this context as a performance metric. RS determines the extent by which a predicted output is relevant to the user's context and behaviors, taking into account the inconsistencies that come with human (perception) factors. We tailor the RS metric so that it can be used to evaluate ML algorithms in an online setting at run-time. We assess the performance of a number of ML algorithms, using a smart lighting dataset with non-deterministic one-to-many input-output relationships. The results indicate that using RS instead of classification accuracy (CA) is suitable to analyze the performance of conventional ML algorithms applied to the category of nDMOC problems. Instance-based online ML gives the best RS performance. An interesting finding is that the RS keeps increasing with increasing number of samples, even after the CA performance converges.","PeriodicalId":145129,"journal":{"name":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414377","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

A semantic IoT framework to support RESTful devices' API interoperability 支持RESTful设备API互操作性的语义物联网框架

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000071

B. D. Martino, A. Esposito, Salvatore Augusto Maisto, Stefania Nacchia

{"title":"A semantic IoT framework to support RESTful devices' API interoperability","authors":"B. D. Martino, A. Esposito, Salvatore Augusto Maisto, Stefania Nacchia","doi":"10.1109/ICNSC.2017.8000071","DOIUrl":"https://doi.org/10.1109/ICNSC.2017.8000071","url":null,"abstract":"With the diffusion of sensors and smart devices, and the advances in connection technologies, the Internet of Things (IoT) has become a very popular topic. Because of the creation and expansion of new and existing sensor networks, the need to define a common standard for sensors' interfaces representation has arisen. Currently it is difficult to make different sensors and sensors' networks interoperate seamlessly, since their interfaces are not always well specified or are not ready to be adapted immediately to one another. In order to overcome the current lack of a shared standard, in this paper we propose an IoT framework which, by analysing sensors' APIs RESTful descriptions and the interfaces exposed by smart-sensors, tries to integrate different sensors' interfaces into a common Aggregator. Such an Aggregator relies on wrappers and adapters, either automatically built or provided by experts, to make programming and using sensors from different providers completely opaque to the users, who only sees a set of general and abstract functions available. Semantic technologies and matching algorithms are used to support the creation of such wrappers and to easily discover and categorize new sensor types.","PeriodicalId":145129,"journal":{"name":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115875960","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: 10

Feature selection for flow-based intrusion detection using Rough Set Theory 基于粗糙集理论的流入侵检测特征选择

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000162

Frank Beer, Ulrich Bühler

引用次数: 14

Smart office lighting control using occupancy sensors 使用占用传感器的智能办公室照明控制

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000135

Xin Wang, T. Tjalkens, J. Linnartz

引用次数: 11

Automatic cataract detection and grading using Deep Convolutional Neural Network 基于深度卷积神经网络的白内障自动检测与分级

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000068

Linglin Zhang, Jianqiang Li, Yi Zhang, Hern-soo Han, Bo Liu, Jijiang Yang, Qing Wang

{"title":"Automatic cataract detection and grading using Deep Convolutional Neural Network","authors":"Linglin Zhang, Jianqiang Li, Yi Zhang, Hern-soo Han, Bo Liu, Jijiang Yang, Qing Wang","doi":"10.1109/ICNSC.2017.8000068","DOIUrl":"https://doi.org/10.1109/ICNSC.2017.8000068","url":null,"abstract":"Cataract is one of the most prevalent causes of blindness in the industrialized world, accounting for more than 50% of blindness. Early detection and treatment can reduce the suffering of cataract patients and prevent visual impairment from turning into blindness. But the expertise of trained eye specialists is necessary for clinical cataract detection and grading, which may cause difficulties to everybody's early intervention due to the underlying costs. Existing studies on automatic cataract detection and grading based on fundus images utilize a predefined set of image features that may provide an incomplete, redundant, or even noisy representation. This paper aims to investigate the performance and efficiency by using Depp Convolutional Neural Network (DCNN) to detect and grad cataract automatically, it also visualize some of the feature maps at pool5 layer with their high-order empirical semantic meaning, providing a explanation to the feature representation extracted by DCNN. The proposed DCNN classification system is cross validated on different number of population-based clinical retinal fundus images collected from hospital, up to 5620 images. There are two conclusions suggested in this paper: The first one is, the interference of local uneven illumination and the reflection of eyes were overcome by using the retinal fundus images after G-filter, which makes an significant contribution to DCNN classification. The second one is, with the increase of the amount of available samples, the DCNN classification accuracies are increasing, and the fluctuation range of accuracies are more stable. The best accuracy, our method achieved, is 93.52% and 86.69% in cataract detection and grading tasks separately. It is demonstrated in this paper that the DCNN classifier outperforms state-of-the-art in the performance. Further more, The proposed method has the potential to be applied to other eye diseases in future.","PeriodicalId":145129,"journal":{"name":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127303063","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: 74

Percentile performance analysis of infrastructure-as-a-service clouds with task retrials 具有任务重试的基础设施即服务云的百分位性能分析

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000103

Li Zhu, Yuandou Wang, Wanbo Zheng, Lei Wu, Ye Yuan, Peng Chen, Yunni Xia

引用次数: 0

Input variables selection criteria for data-driven Soft Sensors design 数据驱动软传感器设计的输入变量选择准则

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000119

M. Xibilia, N. Gemelli, G. Consolo

引用次数: 6

Retrofit of air conditioning systems through an Wireless Sensor and Actuator Network: An IoT-based application for smart buildings 通过无线传感器和执行器网络改造空调系统:基于物联网的智能建筑应用

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2017-05-16 DOI: 10.1109/ICNSC.2017.8000066

Bruno Eduardo Medina, Leandro Tiago Manera

引用次数: 19