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- 2018
- Conditional Random Fields (CRF)
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- green tide
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[IEEE 2018 IEEE Power & Energy Society General Meeting (PESGM) - Portland, OR, USA (2018.8.5-2018.8.10)] 2018 IEEE Power & Energy Society General Meeting (PESGM) - Study of Impact of Cloud Distribution on Multiple Interconnected Solar PV Plants Generation and System Strength
摘要： Dependence of solar power generation on solar irradiance results in sudden and dramatic variations in power generation following significant changes in cloud distribution over a solar PV plant. Currently, this phenomenon is being one of the most challenging issues in resource planning and maintaining the reliability of modern power grids with high penetration of solar power. The dramatic variation of solar power generation has a direct impact on system strength at the Points of Interconnection (POIs). Hence, the power quality of the system is compromised, especially because solar PV plants are usually interconnected to distribution systems and near load zones. In this paper, an Artificial Neural Network (ANN) based approach is developed to forecast the clouds distribution for the estimation of sudden and dramatic variations in the solar irradiance. This estimate is used to evaluate the system strength in terms of voltage stability at each POI. We apply newly developed methodology to measure the system strength known as Site-Dependent Short Circuit Ratio (SDSCR), which provides more accurate results of system strength evaluation. The validity and effectiveness of the developed approach is confirmed through comparing its results versus the cloud distribution data provided by weather satellites.
关键词： Artificial neural network,renewable energy,system strength,voltage stability,short circuit ratio
更新于2025-09-23 15:22:29
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Metric Learning for Patch-Based 3-D Image Registration
摘要： Patch-based image registration is a challenging problem in visual geometry, the crucial component of which is the selection of an appropriate similarity measure. The similarity measure participates in the objective calculation of the pose optimization, which determines the optimization convergence performance. In this paper, we propose learning a similarity metric of patches from reference and target images such that the pairwise patches with a small projection error receive high similarity scores. To achieve this objective, we designed and trained the classification, regression, and rank networks separately based on self-collected data sets. The network can directly output the projection error according to the patches, which is sensitive to the deviation of the pose transformation. We also designed evaluation criteria and validated the superior performance of the network's outputs compared with the performance of traditional methods, such as the sum of absolute difference and the sum of squared differences.
关键词： neural network,Image registration,pose optimization,metric learning
更新于2025-09-23 15:22:29
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Design and Evaluation of Optical Circuit Switches for Intra-Datacenter Networking
摘要： With the rapid growth in intra-datacenter traffic, the high power consumption stemming from the huge number of electrical switches is becoming a critical issue. Hence, high-port-count optical circuit switches are urgently needed. In this paper, we overview recently developed optical circuit switch architecture based on two-dimensional switches, i.e., space switches and wavelength-routing switches. The attainable maximum switch port counts and hardware requirements are quantitatively evaluated through extensive computer simulations that consider 10-Gbps intensity modulation with direct detection systems, 43-Gbps differential quadrature phase shift keying with self-coherent detection systems, and 128-Gbps dual-polarization quadrature phase shift keying with coherent detection systems. The simulation results confirm the existence of switching architecture that can accommodate more than 1,000 ports with relatively light hardware requirements.
关键词： wavelength routing,optical circuit switch,Datacenter network
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fully Convolutional Semi-Supervised Gan for Polsar Classification
摘要： We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other “single-player” classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.
关键词： terrain classification,fully convolutional network,generative adversarial network,semi-supervised learning
更新于2025-09-23 15:21:21
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Sky Image-Based Solar Irradiance Prediction Methodologies Using Artificial Neural Networks
摘要： In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.
关键词： Artificial Neural Network,Photovoltaic Power Generation,Solar Energy,Global Horizontal Irradiance Prediction,Sky Image
更新于2025-09-23 15:21:21
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A novel method on the edge detection of infrared image
摘要： Infrared image processing is important for fault identification of high-voltage equipment. This paper studies the problem on the edge detection of infrared image. First a kind of spiking neural network is constructed, and by using the characteristics of the spiking neuron, a novel method is designed to achieve the edge detection of infrared image. Finally, some typical examples are included and corresponding experimental results show the effectiveness and advantage of the proposed method.
关键词： spiking neural network,Edge detection,infrared image
更新于2025-09-23 15:21:21
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Star sensor installation error calibration in stellar-inertial navigation system with a regularized backpropagation neural network
摘要： The star sensor is the attitude reference in a stellar-inertial navigation system. It is essential to acquire the star sensor installation error, which has a great influence on the system navigation performance. However, traditional methods have a poor tolerance for a large range of installation errors, especially when the system works under a separate installation mode. In this paper a novel calibration method, using a regularized backpropagation (BP) neural network, is proposed. With a specially designed calibration procedure, the neural network is structured with BP and the regularization is improved. The network training is conducted for parameter solidification. The calibration can be achieved without formula derivation and numerical calculation under both small and large installation errors. In the experiment, the calibration accuracy is about 5 arcsec under small installation errors and about 20 arcsec under large installation errors, which is much better than a Kalman filter. The proposed method has the potential to be a universal star sensor calibration method under integrative installation mode or separated installation mode with large installation error.
关键词： neural network,installation error calibration,star sensor
更新于2025-09-23 15:21:21
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Deep Residual Network with Subclass Discriminant Analysis for Crowd Behavior Recognition
摘要： In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior attributes (classes). Features from these subclasses are then regularized using an eigenmodeling scheme. This enables to model the variance appearing from the intra-subclass information. Low dimensional discriminative features are extracted after using the total subclass scatter information. Dynamic time warping is used on the cosine distance measure to find the similarity measure between videos. A 1-nearest neighbor (NN) classifier is used to find the respective crowd behavior attribute classes from the normal videos. Experimental results on large crowd behavior video database show the superior performance of our proposed framework as compared to the baseline and current state-of-the-art methodologies for the crowd behavior recognition task.
关键词： Crowd behavior recognition,discriminant analysis,residual network,feature extraction
更新于2025-09-23 15:21:21
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Hardware-efficient Signal Processing Technologies for Coherent PON Systems
摘要： Future passive optical network (PON) systems supporting more than 50 Gb/s/λ present a challenge for the use of intensity modulation direct detection (IM-DD). Since coherent technology improves the receiver sensitivity over that of IM-DD, it is a promising candidate for 100 Gb/s or higher PON systems. Introducing hardware-efficient signal processing technologies tailored to PON systems will help render coherent technology suitable for PON systems. We here review hardware-efficient signal processing technologies suitable for PON systems. We introduce two types of simplified adaptive equalization (AEQ), one which sacrifices differential group delay compensation (DGDC), and another which sacrifices some chromatic dispersion compensation but does provide DGDC. Transmission experiments on a 100 Gb/s/λ-based coherent wavelength division multiplexing (WDM) PON system showed that simplified AEQ without DGDC and with DGDC exhibited only 0.2 dB and 1.4 dB penalty respectively, compared with conventional DSP. The additional penalty due to the maximum possible cumulative DGD was evaluated by numerical simulation. Conventional AEQ and the simplified AEQ with DGDC showed negligible penalty, but the simplified AEQ without DGDC showed a 1.4 dB penalty. We also introduce simplified carrier phase recovery (CPR) with inter-polarization phase offset estimation, and this showed the same performance as the conventional DSP, in both experiment and simulation. Taking these results into account, 100 Gb/s/λ-based coherent WDM PON systems with the simplified AEQs in combination with the simplified CPR were shown to be able to support the loss budget required for 8 ONU splits over an 80 km span of single mode fiber.
关键词： adaptive equalization (AEQ),5G mobile front haul (MFH),coherent communication,carrier phase recovery (CPR),100 Gb/s-class passive optical network (PON)
更新于2025-09-23 15:21:21
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IEEE Access Special Section Editorial: Optical Wireless Technologies for 5G Communications and Beyond
摘要： Wide bandwidth and dense spatial reuse are of extreme importance for future wireless communication networks, including 5G and beyond. In particular, these properties are important to enable future wireless networks to cope with the explosive increase in the demand for high data-rate communications. Optical wireless communications (OWC) is a promising technology for achieving this goal due to the abundant reusable license-free optical spectrum. This potential of OWC attracted significant global attention both from communications and optoelectronics viewpoints, and continues to do so.
关键词： 5G,beam-steering,mixed FSO/fiber backhauling,OFDM,MIMO,Optical wireless communications,vehicle-to-vehicle communications,free-space optics,vehicle-to-infrastructure communications,network-layer aspects,visible-light communications
更新于2025-09-23 15:21:21