- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Integrating Grey Data Preprocessor and Deep Belief Network for Day-ahead PV Power Output Forecast
摘要： Generation output forecasting is a crucial task for planning and sizing of a photovoltaic (PV) power plant. The purpose of this paper is to present an effective model for day-ahead forecasting PV power output of a plant based on deep belief network (DBN) combined with grey theory-based data preprocessor (GT-DBN), where the DBN attempts to learn high-level abstractions in historical PV output data by utilizing hierarchical architectures. Test results obtained by the proposed model are compared with those obtained by other five forecasting methods including autoregressive integrated moving average model (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and DBN alone. It shows that the proposed model is superior to other models in forecasting accuracy and is suitable for day-ahead PV power output prediction.
关键词： supervised learning,time series analysis,power generation planning,neural networks,Renewable energy
更新于2025-09-04 15:30:14
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An entanglement-based wavelength-multiplexed quantum communication network
摘要： Quantum key distribution has reached the level of maturity required for deployment in real-world scenarios. It has previously been shown to operate alongside classical communication in the same telecommunication fibre and over long distances in fibre and in free-space links. Despite these advances, the practical applicability of quantum key distribution is curtailed by the fact that most implementations and protocols are limited to two communicating parties. Quantum networks scale the advantages of quantum key distribution protocols to more than two distant users. Here we present a fully connected quantum network architecture in which a single entangled photon source distributes quantum states to many users while minimizing the resources required for each. Further, it does so without sacrificing security or functionality relative to two-party communication schemes. We demonstrate the feasibility of our approach using a single source of bipartite polarization entanglement, which is multiplexed into 12 wavelength channels. Six states are then distributed between four users in a fully connected graph using only one fibre and one polarization analysis module per user. Because no adaptations of the entanglement source are required to add users, the network can readily be scaled to a large number of users, without requiring trust in the provider of the source. Unlike previous attempts at multi-user networks, which have been based on active optical switches and therefore limited to some duty cycle, our implementation is fully passive and thus has the potential for unprecedented quantum communication speeds.
关键词： Entanglement,Quantum key distribution,Quantum communication,Quantum networks,Wavelength-multiplexed
更新于2025-09-04 15:30:14
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Low Photon Count Phase Retrieval Using Deep Learning
摘要： Imaging systems’ performance at low light intensity is affected by shot noise, which becomes increasingly strong as the power of the light source decreases. In this Letter, we experimentally demonstrate the use of deep neural networks to recover objects illuminated with weak light and demonstrate better performance than with the classical Gerchberg-Saxton phase retrieval algorithm for equivalent signal over noise ratio. The prior contained in the training image set can be leveraged by the deep neural network to detect features with a signal over noise ratio close to one. We apply this principle to a phase retrieval problem and show successful recovery of the object’s most salient features with as little as one photon per detector pixel on average in the illumination beam. We also show that the phase reconstruction is significantly improved by training the neural network with an initial estimate of the object, as opposed to training it with the raw intensity measurement.
关键词： low light,phase retrieval,shot noise,Gerchberg-Saxton algorithm,deep neural networks
更新于2025-09-04 15:30:14
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Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
摘要： IMPORTANCE Convolutional neural networks have recently been applied to ophthalmic diseases; however, the rationale for the outputs generated by these systems is inscrutable to clinicians. A visualization tool is needed that would enable clinicians to understand important exposure variables in real time. OBJECTIVE To systematically visualize the convolutional neural networks of 2 validated deep learning models for the detection of referable diabetic retinopathy (DR) and glaucomatous optic neuropathy (GON). DESIGN, SETTING, AND PARTICIPANTS The GON and referable DR algorithms were previously developed and validated (holdout method) using 48 116 and 66 790 retinal photographs, respectively, derived from a third-party database (LabelMe) of deidentified photographs from various clinical settings in China. In the present cross-sectional study, a random sample of 100 true-positive photographs and all false-positive cases from each of the GON and DR validation data sets were selected. All data were collected from March to June 2017. The original color fundus images were processed using an adaptive kernel visualization technique. The images were preprocessed by applying a sliding window with a size of 28 × 28 pixels and a stride of 3 pixels to crop images into smaller subimages to produce a feature map. Threshold scales were adjusted to optimal levels for each model to generate heat maps highlighting localized landmarks on the input image. A single optometrist allocated each image to predefined categories based on the generated heat map. MAIN OUTCOMES AND MEASURES Visualization regions of the fundus. RESULTS In the GON data set, 90 of 100 true-positive cases (90%; 95% CI, 82%-95%) and 15 of 22 false-positive cases (68%; 95% CI, 45%-86%) displayed heat map visualization within regions of the optic nerve head only. Lesions typically seen in cases of referable DR (exudate, hemorrhage, or vessel abnormality) were identified as the most important prognostic regions in 96 of 100 true-positive DR cases (96%; 95% CI, 90%-99%). In 39 of 46 false-positive DR cases (85%; 95% CI, 71%-94%), the heat map displayed visualization of nontraditional fundus regions with or without retinal venules. CONCLUSIONS AND RELEVANCE These findings suggest that this visualization method can highlight traditional regions in disease diagnosis, substantiating the validity of the deep learning models investigated. This visualization technique may promote the clinical adoption of these models.
关键词： visualization,convolutional neural networks,deep learning,glaucoma,diabetic retinopathy
更新于2025-09-04 15:30:14
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Deep Learning in Image Cytometry: A Review
摘要： Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data.
关键词： image cytometry,machine learning,biomedical image analysis,convolutional neural networks,deep learning,cell analysis,microscopy
更新于2025-09-04 15:30:14
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[Systems & Control: Foundations & Applications] Uncertainty in Complex Networked Systems (In Honor of Roberto Tempo) || Networked Quantum Systems
摘要： This chapter presents a survey of results in the area of networked quantum systems. The chapter includes background material on quantum linear system models and finite level quantum system models. Different forms of these models are discussed and the issue of physical realizability is addressed. Also, the Kalman decomposition for linear quantum systems is described. The use of optical linear quantum networks in the physical realization of quantum systems is discussed for both the passive and non-passive case.
关键词： networked quantum systems,finite level quantum system models,quantum linear system models,Kalman decomposition,physical realizability,optical linear quantum networks
更新于2025-09-04 15:30:14
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Entanglement between artificial atoms and photons of lossless cavities
摘要： We investigated the dynamics of atom-field entanglement for two natural or artificial two-level atoms interacting with a one-mode quantum electromagnetic field by means of multiphoton transitions in a lossless cavity. Tavis-Cummings model is used to describe the interaction of the atoms and real microwave coplanar cavity field. We carried out the mathematical modeling of the dynamics of the system under consideration for various initial states of the atomic subsystem and an intensive coherent field of the cavity. We showed that for small multiplicities, the atoms and the field, which were initially in a pure separable state, can return to this state during the evolution. We also found that for large multiplicities the atoms and the field are in the entangled atom-field state in the process of the system evolution with the exception of the initial time instant. These results can be used in the theory of quantum networks.
关键词： Tavis-Cummings model,multiphoton transitions,atom-field entanglement,quantum networks
更新于2025-09-04 15:30:14
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - Automatic Guidewire Tip Segmentation in 2D X-ray Fluoroscopy Using Convolution Neural Networks
摘要： Guidewire tip detection in the percutaneous coronary intervention is important. It assists physicians in navigating and is a prerequisite for clinic applications such as surgical skill assessment and robot assisted surgery. Nevertheless, accurate detection is not a trivial task due to the noisy background of the 2D X-ray image and the thin, deformable structure of the tip. In this paper, an automatic method based on cascaded convolution neural networks is proposed to segment the tip in the 2D X-ray image. The main contribution of the method is to use a cascade detection-segmentation structure to overcome the noisy background and the large deformation of the tip, achieve robust, high-precision segmentation. On the other hand, sufficient annotated training samples are necessary for convolution neural network models, while pixel-level annotating is tedious and time-consuming. Accordingly, a novel data augmentation algorithm is introduced to improve the model generalization and performance, reduce the cost of data annotation. Evaluations were conducted on a dataset consisting of 22 different sequences of 2D X-ray images, 15 sequences for training and 7 sequences for evaluation. The proposed approach obtained tip precision of 0.532 pixels, F1 score of 0.939, false tracking rate of 0.800%, and missing tracking rate of 9.900% on the test set. And the running speed is 4-5 frames per second.
关键词： Guidewire tip detection,2D X-ray fluoroscopy,Convolution Neural Networks,Data augmentation,Segmentation
更新于2025-09-04 15:30:14
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[IEEE 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - Helsinki (2018.6.3-2018.6.5)] 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - CHANNEL-MISMATCH DETECTION ALGORITHM FOR STEREOSCOPIC VIDEO USING CONVOLUTIONAL NEURAL NETWORK
摘要： Channel mismatch (the result of swapping left and right views) is a 3D-video artifact that can cause major viewer discomfort. This work presents a novel high-accuracy method of channel-mismatch detection. In addition to the features described in our previous work, we introduce a new feature based on a convolutional neural network; it predicts channel-mismatch probability on the basis of the stereoscopic views and corresponding disparity maps. A logistic-regression model trained on the described features makes the ?nal prediction. We tested this model on a set of 900 stereoscopic-video scenes, and it outperformed existing channel-mismatch detection methods that previously served in analyses of full-length stereoscopic movies.
关键词： machine learning,channel mismatch,quality assessment,convolutional neural networks,Stereoscopic video
更新于2025-09-04 15:30:14
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[IEEE 2018 3rd International Conference for Convergence in Technology (I2CT) - Pune (2018.4.6-2018.4.8)] 2018 3rd International Conference for Convergence in Technology (I2CT) - Towards Designing an Adaptive Framework for Facial Image Quality Estimation at Edge
摘要： This paper proposes a framework for facial image quality estimation in order to address the limitation of real-time applicability of facial recognition. This framework determines whether an image is suitable for facial recognition. We ?rst exploit machine learning algorithms to map the relationship between image quality features and performance of facial recog- nition. We extract a variety of features (like focus measure, brightness, obscured face) and study their in?uence on the accuracy of face recognition. After examining the results of this approach, we then used deep learning to build a binary classi?er which accepts or rejects images before sending them for actual facial recognition. This decision is taken based on the probability of the facial recognition framework correctly matching a face from the image. We used images from the Chokepoint dataset, and OpenFace- an open source facial recognition software, for building our framework.
关键词： Classi?cation,Deep Learning,Edge Computing,Convolutional Neural Networks,Machine Learning
更新于2025-09-04 15:30:14