- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Tool for Unsupervised Flood Mapping Using Sentinel-1 Images
摘要： In this paper, we present a novel method for mapping flooded areas exploiting Sentinel-1 ground range detected products. The work introduces two novelties. As first, the input products. In fact, as far we know, no applications using these products has been so far presented in literature. Secondly, a new unsupervised methodology, based on the usage of opportune layers combined in a fuzzy decision system, is presented. Experimental results, obtained both on the single SAR image and on a couple of acquisitions in a change detection framework showed that our method is able to outperform the most popular classification techniques in terms of standard assessment parameters.
关键词： flooding,sentinel-1,classification,fuzzy systems,Synthetic aperture radar
更新于2025-09-23 15:23:52
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[IEEE 2019 IEEE 16th India Council International Conference (INDICON) - Rajkot, India (2019.12.13-2019.12.15)] 2019 IEEE 16th India Council International Conference (INDICON) - Performance Comparison Between Bipolar and Unipolar Switching Scheme for a Single-Phase Inverter Based Stand-alone Photovoltaic System
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-23 15:21:01
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[IEEE 2019 Far East NDT New Technology & Application Forum (FENDT) - Qingdao, Shandong province, China (2019.6.24-2019.6.27)] 2019 Far East NDT New Technology & Application Forum (FENDT) - Laser line generation for optimized interaction with hidden defects in active thermography
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： troubleshooting,self-healing,genetic algorithms,self-organizing networks (SONs),root cause analysis,Fuzzy systems,supervised learning
更新于2025-09-19 17:13:59
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Anisotropic Impedance Surface Enabled Low-Profile Broadband Dual-Circularly-Polarized Multi-Beam Reflect-Arrays for Ka-Band Applications
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-19 17:13:59
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[IEEE 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - Vellore, India (2019.3.22-2019.3.23)] 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - Ultra-high Negative Dispersion Compensating Index Guiding Single Mode Octagonal Photonic Crystal Fiber: Design and Analysis
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： troubleshooting,self-healing,genetic algorithms,self-organizing networks (SONs),root cause analysis,Fuzzy systems,supervised learning
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - High-Fidelity Few-Cycle Laser Pulses Generated Via Nonlinear Ellipse Rotation
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-19 17:13:59